diff --git a/10-Decision Tree/Project/Decision Tree Classifier Practical Implementation.ipynb b/10-Decision Tree/Project/Decision Tree Classifier Practical Implementation.ipynb
index a98d5662..2bb3725d 100644
--- a/10-Decision Tree/Project/Decision Tree Classifier Practical Implementation.ipynb	
+++ b/10-Decision Tree/Project/Decision Tree Classifier Practical Implementation.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -46,34 +46,34 @@
       "\n",
       "**Data Set Characteristics:**\n",
       "\n",
-      "    :Number of Instances: 150 (50 in each of three classes)\n",
-      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
-      "    :Attribute Information:\n",
-      "        - sepal length in cm\n",
-      "        - sepal width in cm\n",
-      "        - petal length in cm\n",
-      "        - petal width in cm\n",
-      "        - class:\n",
-      "                - Iris-Setosa\n",
-      "                - Iris-Versicolour\n",
-      "                - Iris-Virginica\n",
-      "                \n",
-      "    :Summary Statistics:\n",
+      ":Number of Instances: 150 (50 in each of three classes)\n",
+      ":Number of Attributes: 4 numeric, predictive attributes and the class\n",
+      ":Attribute Information:\n",
+      "    - sepal length in cm\n",
+      "    - sepal width in cm\n",
+      "    - petal length in cm\n",
+      "    - petal width in cm\n",
+      "    - class:\n",
+      "            - Iris-Setosa\n",
+      "            - Iris-Versicolour\n",
+      "            - Iris-Virginica\n",
       "\n",
-      "    ============== ==== ==== ======= ===== ====================\n",
-      "                    Min  Max   Mean    SD   Class Correlation\n",
-      "    ============== ==== ==== ======= ===== ====================\n",
-      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
-      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
-      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
-      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
-      "    ============== ==== ==== ======= ===== ====================\n",
+      ":Summary Statistics:\n",
       "\n",
-      "    :Missing Attribute Values: None\n",
-      "    :Class Distribution: 33.3% for each of 3 classes.\n",
-      "    :Creator: R.A. Fisher\n",
-      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
-      "    :Date: July, 1988\n",
+      "============== ==== ==== ======= ===== ====================\n",
+      "                Min  Max   Mean    SD   Class Correlation\n",
+      "============== ==== ==== ======= ===== ====================\n",
+      "sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
+      "sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
+      "petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
+      "petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
+      "============== ==== ==== ======= ===== ====================\n",
+      "\n",
+      ":Missing Attribute Values: None\n",
+      ":Class Distribution: 33.3% for each of 3 classes.\n",
+      ":Creator: R.A. Fisher\n",
+      ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
+      ":Date: July, 1988\n",
       "\n",
       "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
       "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
@@ -86,22 +86,23 @@
       "type of iris plant.  One class is linearly separable from the other 2; the\n",
       "latter are NOT linearly separable from each other.\n",
       "\n",
-      ".. topic:: References\n",
+      ".. dropdown:: References\n",
       "\n",
-      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
-      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
-      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
-      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
-      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
-      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
-      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
-      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
-      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
-      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
-      "     on Information Theory, May 1972, 431-433.\n",
-      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
-      "     conceptual clustering system finds 3 classes in the data.\n",
-      "   - Many, many more ...\n"
+      "  - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
+      "    Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
+      "    Mathematical Statistics\" (John Wiley, NY, 1950).\n",
+      "  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
+      "    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
+      "  - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
+      "    Structure and Classification Rule for Recognition in Partially Exposed\n",
+      "    Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
+      "    Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
+      "  - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
+      "    on Information Theory, May 1972, 431-433.\n",
+      "  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
+      "    conceptual clustering system finds 3 classes in the data.\n",
+      "  - Many, many more ...\n",
+      "\n"
      ]
     }
    ],
@@ -111,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -132,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -272,7 +273,7 @@
        "[150 rows x 4 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -283,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -304,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,16 +316,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "DecisionTreeClassifier()"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -335,47 +743,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "[Text(257.53846153846155, 504.7714285714286, 'X[3] <= 0.8\\ngini = 0.665\\nsamples = 120\\nvalue = [40, 37, 43]'),\n",
-       " Text(193.15384615384616, 427.11428571428576, 'gini = 0.0\\nsamples = 40\\nvalue = [40, 0, 0]'),\n",
-       " Text(321.9230769230769, 427.11428571428576, 'X[2] <= 4.75\\ngini = 0.497\\nsamples = 80\\nvalue = [0, 37, 43]'),\n",
-       " Text(128.76923076923077, 349.4571428571429, 'X[3] <= 1.65\\ngini = 0.057\\nsamples = 34\\nvalue = [0, 33, 1]'),\n",
-       " Text(64.38461538461539, 271.8, 'gini = 0.0\\nsamples = 33\\nvalue = [0, 33, 0]'),\n",
-       " Text(193.15384615384616, 271.8, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
-       " Text(515.0769230769231, 349.4571428571429, 'X[3] <= 1.75\\ngini = 0.159\\nsamples = 46\\nvalue = [0, 4, 42]'),\n",
-       " Text(321.9230769230769, 271.8, 'X[1] <= 2.65\\ngini = 0.49\\nsamples = 7\\nvalue = [0, 3, 4]'),\n",
-       " Text(257.53846153846155, 194.14285714285717, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
-       " Text(386.3076923076923, 194.14285714285717, 'X[2] <= 5.05\\ngini = 0.48\\nsamples = 5\\nvalue = [0, 3, 2]'),\n",
-       " Text(321.9230769230769, 116.48571428571432, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
-       " Text(450.69230769230774, 116.48571428571432, 'X[0] <= 6.15\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
-       " Text(386.3076923076923, 38.82857142857142, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
-       " Text(515.0769230769231, 38.82857142857142, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
-       " Text(708.2307692307693, 271.8, 'X[2] <= 4.85\\ngini = 0.05\\nsamples = 39\\nvalue = [0, 1, 38]'),\n",
-       " Text(643.8461538461538, 194.14285714285717, 'X[1] <= 3.1\\ngini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
-       " Text(579.4615384615385, 116.48571428571432, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
-       " Text(708.2307692307693, 116.48571428571432, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
-       " Text(772.6153846153846, 194.14285714285717, 'gini = 0.0\\nsamples = 37\\nvalue = [0, 0, 37]')]"
+       "<Figure size 1500x1000 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
@@ -383,12 +761,13 @@
     "##Visualize the Decision Tree\n",
     "from sklearn import tree\n",
     "plt.figure(figsize=(15,10))\n",
-    "tree.plot_tree(treeclassifier,filled=True)"
+    "tree.plot_tree(treeclassifier,filled=True)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -397,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -406,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -499,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -508,11 +887,428 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
+       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;, &#x27;log_loss&#x27;],\n",
+       "                         &#x27;max_depth&#x27;: [1, 2, 3, 4, 5],\n",
+       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
+       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
+       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;, &#x27;log_loss&#x27;],\n",
+       "                         &#x27;max_depth&#x27;: [1, 2, 3, 4, 5],\n",
+       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
+       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, max_features=&#x27;log2&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, max_features=&#x27;log2&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
       "text/plain": [
        "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
        "             param_grid={'criterion': ['gini', 'entropy', 'log_loss'],\n",
@@ -522,7 +1318,7 @@
        "             scoring='accuracy')"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -535,19 +1331,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'criterion': 'gini',\n",
-       " 'max_depth': 5,\n",
-       " 'max_features': 'sqrt',\n",
-       " 'splitter': 'random'}"
+       "{'criterion': 'entropy',\n",
+       " 'max_depth': 3,\n",
+       " 'max_features': 'log2',\n",
+       " 'splitter': 'best'}"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -558,16 +1354,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9583333333333333"
+       "0.95"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -578,7 +1374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -587,17 +1383,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 2, 0, 1, 0, 1, 2, 1, 0, 1, 2, 2, 1, 0, 0, 2, 2, 0, 0, 0, 2, 2,\n",
-       "       2, 0, 2, 0, 1, 1, 1, 2])"
+       "array([1, 2, 0, 1, 0, 1, 1, 1, 0, 1, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 2, 2,\n",
+       "       2, 0, 1, 0, 1, 1, 1, 2])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -608,7 +1404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -616,17 +1412,17 @@
      "output_type": "stream",
      "text": [
       "[[10  0  0]\n",
-      " [ 0  9  4]\n",
+      " [ 0 13  0]\n",
       " [ 0  0  7]]\n",
       "              precision    recall  f1-score   support\n",
       "\n",
       "           0       1.00      1.00      1.00        10\n",
-      "           1       1.00      0.69      0.82        13\n",
-      "           2       0.64      1.00      0.78         7\n",
+      "           1       1.00      1.00      1.00        13\n",
+      "           2       1.00      1.00      1.00         7\n",
       "\n",
-      "    accuracy                           0.87        30\n",
-      "   macro avg       0.88      0.90      0.87        30\n",
-      "weighted avg       0.92      0.87      0.87        30\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
       "\n"
      ]
     }
@@ -639,7 +1435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -649,16 +1445,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8666666666666667"
+       "1.0"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -666,67 +1462,11 @@
    "source": [
     "score"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -740,7 +1480,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/10-Decision Tree/Project/Diabetes Prediction Using Decision Tree Regressor.ipynb b/10-Decision Tree/Project/Diabetes Prediction Using Decision Tree Regressor.ipynb
index 7039e5fa..69d2f9fd 100644
--- a/10-Decision Tree/Project/Diabetes Prediction Using Decision Tree Regressor.ipynb	
+++ b/10-Decision Tree/Project/Diabetes Prediction Using Decision Tree Regressor.ipynb	
@@ -777,7 +777,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1080x720 with 2 Axes>"
       ]
@@ -1050,7 +1050,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1080x720 with 1 Axes>"
       ]
@@ -1079,7 +1079,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -1093,7 +1093,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/11-Random Forest/Projects/Classification/Random Forest Classification Implementation.ipynb b/11-Random Forest/Projects/Classification/Random Forest Classification Implementation.ipynb
index 3b197ea8..17aa2246 100644
--- a/11-Random Forest/Projects/Classification/Random Forest Classification Implementation.ipynb	
+++ b/11-Random Forest/Projects/Classification/Random Forest Classification Implementation.ipynb	
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
@@ -240,7 +240,7 @@
        "4   Executive        18468.0  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -293,7 +293,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -304,19 +304,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "Gender\n",
        "Male       2916\n",
        "Female     1817\n",
        "Fe Male     155\n",
-       "Name: Gender, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -328,20 +329,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "MaritalStatus\n",
        "Married      2340\n",
        "Divorced      950\n",
        "Single        916\n",
        "Unmarried     682\n",
-       "Name: MaritalStatus, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -352,18 +354,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "TypeofContact\n",
        "Self Enquiry       3444\n",
        "Company Invited    1419\n",
-       "Name: TypeofContact, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -374,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,18 +387,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "Gender\n",
        "Male      2916\n",
        "Female    1972\n",
-       "Name: Gender, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -407,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -610,7 +614,7 @@
        "4   Executive        18468.0  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -621,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
@@ -649,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -789,7 +793,7 @@
        "max        22.000000                  3.000000   98678.000000  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -816,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -847,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -876,7 +880,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -888,7 +892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -906,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
@@ -937,6 +941,7 @@
        "      <th>DurationOfPitch</th>\n",
        "      <th>Occupation</th>\n",
        "      <th>Gender</th>\n",
+       "      <th>NumberOfPersonVisiting</th>\n",
        "      <th>NumberOfFollowups</th>\n",
        "      <th>ProductPitched</th>\n",
        "      <th>PreferredPropertyStar</th>\n",
@@ -945,9 +950,9 @@
        "      <th>Passport</th>\n",
        "      <th>PitchSatisfactionScore</th>\n",
        "      <th>OwnCar</th>\n",
+       "      <th>NumberOfChildrenVisiting</th>\n",
        "      <th>Designation</th>\n",
        "      <th>MonthlyIncome</th>\n",
-       "      <th>TotalVisiting</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -960,6 +965,7 @@
        "      <td>6.0</td>\n",
        "      <td>Salaried</td>\n",
        "      <td>Female</td>\n",
+       "      <td>3</td>\n",
        "      <td>3.0</td>\n",
        "      <td>Deluxe</td>\n",
        "      <td>3.0</td>\n",
@@ -968,9 +974,9 @@
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>Manager</td>\n",
        "      <td>20993.0</td>\n",
-       "      <td>3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -981,6 +987,7 @@
        "      <td>14.0</td>\n",
        "      <td>Salaried</td>\n",
        "      <td>Male</td>\n",
+       "      <td>3</td>\n",
        "      <td>4.0</td>\n",
        "      <td>Deluxe</td>\n",
        "      <td>4.0</td>\n",
@@ -989,9 +996,9 @@
        "      <td>0</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>Manager</td>\n",
        "      <td>20130.0</td>\n",
-       "      <td>5.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1002,6 +1009,7 @@
        "      <td>8.0</td>\n",
        "      <td>Free Lancer</td>\n",
        "      <td>Male</td>\n",
+       "      <td>3</td>\n",
        "      <td>4.0</td>\n",
        "      <td>Basic</td>\n",
        "      <td>3.0</td>\n",
@@ -1010,9 +1018,9 @@
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>Executive</td>\n",
        "      <td>17090.0</td>\n",
-       "      <td>3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1023,6 +1031,7 @@
        "      <td>9.0</td>\n",
        "      <td>Salaried</td>\n",
        "      <td>Female</td>\n",
+       "      <td>2</td>\n",
        "      <td>3.0</td>\n",
        "      <td>Basic</td>\n",
        "      <td>3.0</td>\n",
@@ -1031,9 +1040,9 @@
        "      <td>1</td>\n",
        "      <td>5</td>\n",
        "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>Executive</td>\n",
        "      <td>17909.0</td>\n",
-       "      <td>3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1044,6 +1053,7 @@
        "      <td>8.0</td>\n",
        "      <td>Small Business</td>\n",
        "      <td>Male</td>\n",
+       "      <td>2</td>\n",
        "      <td>3.0</td>\n",
        "      <td>Basic</td>\n",
        "      <td>4.0</td>\n",
@@ -1052,9 +1062,9 @@
        "      <td>0</td>\n",
        "      <td>5</td>\n",
        "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>Executive</td>\n",
        "      <td>18468.0</td>\n",
-       "      <td>2.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1068,29 +1078,36 @@
        "3          0  33.0  Company Invited         1              9.0   \n",
        "4          0  36.0     Self Enquiry         1              8.0   \n",
        "\n",
-       "       Occupation  Gender  NumberOfFollowups ProductPitched  \\\n",
-       "0        Salaried  Female                3.0         Deluxe   \n",
-       "1        Salaried    Male                4.0         Deluxe   \n",
-       "2     Free Lancer    Male                4.0          Basic   \n",
-       "3        Salaried  Female                3.0          Basic   \n",
-       "4  Small Business    Male                3.0          Basic   \n",
+       "       Occupation  Gender  NumberOfPersonVisiting  NumberOfFollowups  \\\n",
+       "0        Salaried  Female                       3                3.0   \n",
+       "1        Salaried    Male                       3                4.0   \n",
+       "2     Free Lancer    Male                       3                4.0   \n",
+       "3        Salaried  Female                       2                3.0   \n",
+       "4  Small Business    Male                       2                3.0   \n",
        "\n",
-       "   PreferredPropertyStar MaritalStatus  NumberOfTrips  Passport  \\\n",
-       "0                    3.0     Unmarried            1.0         1   \n",
-       "1                    4.0      Divorced            2.0         0   \n",
-       "2                    3.0     Unmarried            7.0         1   \n",
-       "3                    3.0      Divorced            2.0         1   \n",
-       "4                    4.0      Divorced            1.0         0   \n",
+       "  ProductPitched  PreferredPropertyStar MaritalStatus  NumberOfTrips  \\\n",
+       "0         Deluxe                    3.0     Unmarried            1.0   \n",
+       "1         Deluxe                    4.0      Divorced            2.0   \n",
+       "2          Basic                    3.0     Unmarried            7.0   \n",
+       "3          Basic                    3.0      Divorced            2.0   \n",
+       "4          Basic                    4.0      Divorced            1.0   \n",
        "\n",
-       "   PitchSatisfactionScore  OwnCar Designation  MonthlyIncome  TotalVisiting  \n",
-       "0                       2       1     Manager        20993.0            3.0  \n",
-       "1                       3       1     Manager        20130.0            5.0  \n",
-       "2                       3       0   Executive        17090.0            3.0  \n",
-       "3                       5       1   Executive        17909.0            3.0  \n",
-       "4                       5       1   Executive        18468.0            2.0  "
+       "   Passport  PitchSatisfactionScore  OwnCar  NumberOfChildrenVisiting  \\\n",
+       "0         1                       2       1                       0.0   \n",
+       "1         0                       3       1                       2.0   \n",
+       "2         1                       3       0                       0.0   \n",
+       "3         1                       5       1                       1.0   \n",
+       "4         0                       5       1                       0.0   \n",
+       "\n",
+       "  Designation  MonthlyIncome  \n",
+       "0     Manager        20993.0  \n",
+       "1     Manager        20130.0  \n",
+       "2   Executive        17090.0  \n",
+       "3   Executive        17909.0  \n",
+       "4   Executive        18468.0  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1101,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1112,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
@@ -1131,7 +1148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -1150,7 +1167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -1169,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -1188,7 +1205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -1372,7 +1389,7 @@
        "4                       5       1   Executive        18468.0            2.0  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1390,7 +1407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1401,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -1579,7 +1596,7 @@
        "4        18468.0            2.0  "
       ]
      },
-     "execution_count": 53,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1590,18 +1607,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "ProdTaken\n",
        "0    3968\n",
        "1     920\n",
-       "Name: ProdTaken, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1612,7 +1630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1790,7 +1808,7 @@
        "4        18468.0            2.0  "
       ]
      },
-     "execution_count": 55,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1801,7 +1819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
@@ -1810,7 +1828,7 @@
        "((3910, 17), (978, 17))"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1823,7 +1841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -1863,7 +1881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1887,11 +1905,439 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;OneHotEncoder&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n",
+       "                                 Index([&#x27;TypeofContact&#x27;, &#x27;Occupation&#x27;, &#x27;Gender&#x27;, &#x27;ProductPitched&#x27;,\n",
+       "       &#x27;MaritalStatus&#x27;, &#x27;Designation&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;StandardScaler&#x27;, StandardScaler(),\n",
+       "                                 Index([&#x27;Age&#x27;, &#x27;CityTier&#x27;, &#x27;DurationOfPitch&#x27;, &#x27;NumberOfFollowups&#x27;,\n",
+       "       &#x27;PreferredPropertyStar&#x27;, &#x27;NumberOfTrips&#x27;, &#x27;Passport&#x27;,\n",
+       "       &#x27;PitchSatisfactionScore&#x27;, &#x27;OwnCar&#x27;, &#x27;MonthlyIncome&#x27;, &#x27;TotalVisiting&#x27;],\n",
+       "      dtype=&#x27;object&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;OneHotEncoder&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n",
+       "                                 Index([&#x27;TypeofContact&#x27;, &#x27;Occupation&#x27;, &#x27;Gender&#x27;, &#x27;ProductPitched&#x27;,\n",
+       "       &#x27;MaritalStatus&#x27;, &#x27;Designation&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;StandardScaler&#x27;, StandardScaler(),\n",
+       "                                 Index([&#x27;Age&#x27;, &#x27;CityTier&#x27;, &#x27;DurationOfPitch&#x27;, &#x27;NumberOfFollowups&#x27;,\n",
+       "       &#x27;PreferredPropertyStar&#x27;, &#x27;NumberOfTrips&#x27;, &#x27;Passport&#x27;,\n",
+       "       &#x27;PitchSatisfactionScore&#x27;, &#x27;OwnCar&#x27;, &#x27;MonthlyIncome&#x27;, &#x27;TotalVisiting&#x27;],\n",
+       "      dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">OneHotEncoder</label><div class=\"sk-toggleable__content \"><pre>Index([&#x27;TypeofContact&#x27;, &#x27;Occupation&#x27;, &#x27;Gender&#x27;, &#x27;ProductPitched&#x27;,\n",
+       "       &#x27;MaritalStatus&#x27;, &#x27;Designation&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">StandardScaler</label><div class=\"sk-toggleable__content \"><pre>Index([&#x27;Age&#x27;, &#x27;CityTier&#x27;, &#x27;DurationOfPitch&#x27;, &#x27;NumberOfFollowups&#x27;,\n",
+       "       &#x27;PreferredPropertyStar&#x27;, &#x27;NumberOfTrips&#x27;, &#x27;Passport&#x27;,\n",
+       "       &#x27;PitchSatisfactionScore&#x27;, &#x27;OwnCar&#x27;, &#x27;MonthlyIncome&#x27;, &#x27;TotalVisiting&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
       "text/plain": [
        "ColumnTransformer(transformers=[('OneHotEncoder', OneHotEncoder(drop='first'),\n",
        "                                 Index(['TypeofContact', 'Occupation', 'Gender', 'ProductPitched',\n",
@@ -1904,7 +2350,7 @@
        "      dtype='object'))])"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1915,7 +2361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1925,7 +2371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [
     {
@@ -2285,7 +2731,7 @@
        "[3910 rows x 26 columns]"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2296,7 +2742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2306,7 +2752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
@@ -2327,7 +2773,7 @@
        "        -0.44611323,  2.06138184]])"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2345,7 +2791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
@@ -2705,7 +3151,7 @@
        "[3910 rows x 26 columns]"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2716,7 +3162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
@@ -2736,7 +3182,7 @@
        "Name: ProdTaken, Length: 3910, dtype: int64"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2747,21 +3193,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
     "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import accuracy_score, classification_report,ConfusionMatrixDisplay, \\\n",
-    "                            precision_score, recall_score, f1_score, roc_auc_score,roc_curve "
+    "from sklearn.metrics import accuracy_score, classification_report,ConfusionMatrixDisplay,precision_score, recall_score, f1_score, roc_auc_score,roc_curve "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [
     {
@@ -2770,18 +3216,18 @@
      "text": [
       "Logisitic Regression\n",
       "Model performance for Training set\n",
-      "- Accuracy: 0.8458\n",
-      "- F1 score: 0.8200\n",
-      "- Precision: 0.6994\n",
+      "- Accuracy: 0.8460\n",
+      "- F1 score: 0.8202\n",
+      "- Precision: 0.7016\n",
       "- Recall: 0.3032\n",
-      "- Roc Auc Score: 0.6366\n",
+      "- Roc Auc Score: 0.6368\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Accuracy: 0.8354\n",
-      "- F1 score: 0.8078\n",
-      "- Precision: 0.6829\n",
+      "- Accuracy: 0.8364\n",
+      "- F1 score: 0.8087\n",
+      "- Precision: 0.6914\n",
       "- Recall: 0.2932\n",
-      "- Roc Auc Score: 0.6301\n",
+      "- Roc Auc Score: 0.6307\n",
       "===================================\n",
       "\n",
       "\n",
@@ -2794,11 +3240,11 @@
       "- Roc Auc Score: 1.0000\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Accuracy: 0.9254\n",
-      "- F1 score: 0.9247\n",
-      "- Precision: 0.8242\n",
-      "- Recall: 0.7853\n",
-      "- Roc Auc Score: 0.8723\n",
+      "- Accuracy: 0.9202\n",
+      "- F1 score: 0.9185\n",
+      "- Precision: 0.8304\n",
+      "- Recall: 0.7435\n",
+      "- Roc Auc Score: 0.8533\n",
       "===================================\n",
       "\n",
       "\n",
@@ -2811,11 +3257,28 @@
       "- Roc Auc Score: 1.0000\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Accuracy: 0.9305\n",
-      "- F1 score: 0.9253\n",
-      "- Precision: 0.9695\n",
-      "- Recall: 0.6649\n",
-      "- Roc Auc Score: 0.8299\n",
+      "- Accuracy: 0.9315\n",
+      "- F1 score: 0.9265\n",
+      "- Precision: 0.9697\n",
+      "- Recall: 0.6702\n",
+      "- Roc Auc Score: 0.8325\n",
+      "===================================\n",
+      "\n",
+      "\n",
+      "KNN classifier\n",
+      "Model performance for Training set\n",
+      "- Accuracy: 0.9471\n",
+      "- F1 score: 0.9448\n",
+      "- Precision: 0.9439\n",
+      "- Recall: 0.7613\n",
+      "- Roc Auc Score: 0.8755\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Accuracy: 0.8875\n",
+      "- F1 score: 0.8784\n",
+      "- Precision: 0.8189\n",
+      "- Recall: 0.5445\n",
+      "- Roc Auc Score: 0.7576\n",
       "===================================\n",
       "\n",
       "\n",
@@ -2844,6 +3307,7 @@
     "    \"Logisitic Regression\":LogisticRegression(),\n",
     "    \"Decision Tree\":DecisionTreeClassifier(),\n",
     "    \"Random Forest\":RandomForestClassifier(),\n",
+    "    \"KNN classifier\":KNeighborsClassifier(),\n",
     "    \"Gradient Boost\":GradientBoostingClassifier()\n",
     "}\n",
     "for i in range(len(list(models))):\n",
@@ -2898,7 +3362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2911,7 +3375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [
     {
@@ -2923,7 +3387,7 @@
        " 'n_estimators': [100, 200, 500, 1000]}"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2934,7 +3398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2947,7 +3411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [
     {
@@ -2961,7 +3425,7 @@
        "   'n_estimators': [100, 200, 500, 1000]})]"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2972,29 +3436,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  98 tasks      | elapsed:    9.5s\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:   20.4s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
       "---------------- Best Params for RF -------------------\n",
       "{'n_estimators': 1000, 'min_samples_split': 2, 'max_features': 7, 'max_depth': None}\n"
      ]
@@ -3021,7 +3470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [
     {
@@ -3037,11 +3486,11 @@
       "- Roc Auc Score: 1.0000\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Accuracy: 0.9315\n",
-      "- F1 score: 0.9265\n",
-      "- Precision: 0.9697\n",
-      "- Recall: 0.6702\n",
-      "- Roc Auc Score: 0.8325\n",
+      "- Accuracy: 0.9325\n",
+      "- F1 score: 0.9277\n",
+      "- Precision: 0.9699\n",
+      "- Recall: 0.6754\n",
+      "- Roc Auc Score: 0.8352\n",
       "===================================\n",
       "\n",
       "\n"
@@ -3106,19 +3555,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -3167,7 +3614,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -3181,7 +3628,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/11-Random Forest/Projects/Classification/auc.png b/11-Random Forest/Projects/Classification/auc.png
index 1cfdd261..9079134f 100644
Binary files a/11-Random Forest/Projects/Classification/auc.png and b/11-Random Forest/Projects/Classification/auc.png differ
diff --git a/11-Random Forest/Projects/Regression/Random Forest Regression Implementation.ipynb b/11-Random Forest/Projects/Regression/Random Forest Regression Implementation.ipynb
index df9d297a..82b89726 100644
--- a/11-Random Forest/Projects/Regression/Random Forest Regression Implementation.ipynb	
+++ b/11-Random Forest/Projects/Regression/Random Forest Regression Implementation.ipynb	
@@ -21,7 +21,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ERROR! Session/line number was not unique in database. History logging moved to new session 492\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -626,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -635,7 +643,7 @@
        "120"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -646,27 +654,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "i20             906\n",
-       "Swift Dzire     890\n",
-       "Swift           781\n",
-       "Alto            778\n",
-       "City            757\n",
-       "               ... \n",
-       "Quattroporte      1\n",
-       "GTC4Lusso         1\n",
-       "C                 1\n",
-       "Gurkha            1\n",
-       "Altroz            1\n",
-       "Name: model, Length: 120, dtype: int64"
+       "model\n",
+       "i20            906\n",
+       "Swift Dzire    890\n",
+       "Swift          781\n",
+       "Alto           778\n",
+       "City           757\n",
+       "              ... \n",
+       "Ghibli           1\n",
+       "Altroz           1\n",
+       "GTC4Lusso        1\n",
+       "Aura             1\n",
+       "Gurkha           1\n",
+       "Name: count, Length: 120, dtype: int64"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -677,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -688,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -810,7 +819,7 @@
        "4    22.77    1498      98.59      5  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -821,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -830,7 +839,7 @@
        "(3, 5, 2)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -841,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -867,7 +876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -876,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1139,7 +1148,7 @@
        "[15411 rows x 14 columns]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1150,7 +1159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1159,7 +1168,7 @@
        "((12328, 14), (3083, 14))"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1179,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1200,7 +1209,7 @@
        "         0.06194201, -0.40302241]])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1218,7 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1231,7 +1240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1246,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1294,14 +1303,14 @@
       "\n",
       "K-Neighbors Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 325886.8736\n",
-      "- Mean Absolute Error: 91467.6671\n",
+      "- Root Mean Squared Error: 325873.0088\n",
+      "- Mean Absolute Error: 91425.4705\n",
       "- R2 Score: 0.8691\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 253118.4156\n",
-      "- Mean Absolute Error: 112704.3545\n",
-      "- R2 Score: 0.9149\n",
+      "- Root Mean Squared Error: 253024.3951\n",
+      "- Mean Absolute Error: 112526.3461\n",
+      "- R2 Score: 0.9150\n",
       "===================================\n",
       "\n",
       "\n",
@@ -1312,22 +1321,22 @@
       "- R2 Score: 0.9995\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 309775.5497\n",
-      "- Mean Absolute Error: 125501.4245\n",
-      "- R2 Score: 0.8725\n",
+      "- Root Mean Squared Error: 303114.1531\n",
+      "- Mean Absolute Error: 124485.6390\n",
+      "- R2 Score: 0.8779\n",
       "===================================\n",
       "\n",
       "\n",
       "Random Forest Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 139138.1663\n",
-      "- Mean Absolute Error: 39895.9839\n",
-      "- R2 Score: 0.9761\n",
+      "- Root Mean Squared Error: 121206.4793\n",
+      "- Mean Absolute Error: 40179.2223\n",
+      "- R2 Score: 0.9819\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 221936.2665\n",
-      "- Mean Absolute Error: 100966.5873\n",
-      "- R2 Score: 0.9346\n",
+      "- Root Mean Squared Error: 222496.2705\n",
+      "- Mean Absolute Error: 101503.6968\n",
+      "- R2 Score: 0.9342\n",
       "===================================\n",
       "\n",
       "\n"
@@ -1380,7 +1389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1394,7 +1403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1407,50 +1416,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done   5 out of  18 | elapsed:    2.8s remaining:    7.3s\n",
-      "[Parallel(n_jobs=-1)]: Done  15 out of  18 | elapsed:    3.0s remaining:    0.5s\n",
-      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    3.0s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  98 tasks      | elapsed:   15.9s\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:   53.9s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
+      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
       "---------------- Best Params for KNN -------------------\n",
       "{'n_neighbors': 10}\n",
       "---------------- Best Params for RF -------------------\n",
-      "{'n_estimators': 100, 'min_samples_split': 2, 'max_features': 'auto', 'max_depth': None}\n"
+      "{'n_estimators': 500, 'min_samples_split': 2, 'max_features': 8, 'max_depth': None}\n"
      ]
     }
    ],
@@ -1476,39 +1454,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Random Forest Regressor\n",
-      "Model performance for Training set\n",
-      "- Root Mean Squared Error: 129998.4877\n",
-      "- Mean Absolute Error: 39738.5156\n",
-      "- R2 Score: 0.9792\n",
-      "----------------------------------\n",
-      "Model performance for Test set\n",
-      "- Root Mean Squared Error: 228415.2018\n",
-      "- Mean Absolute Error: 102398.2134\n",
-      "- R2 Score: 0.9307\n",
-      "===================================\n",
-      "\n",
-      "\n",
-      "K-Neighbors Regressor\n",
-      "Model performance for Training set\n",
-      "- Root Mean Squared Error: 363464.0671\n",
-      "- Mean Absolute Error: 103451.3465\n",
-      "- R2 Score: 0.8371\n",
-      "----------------------------------\n",
-      "Model performance for Test set\n",
-      "- Root Mean Squared Error: 263872.0571\n",
-      "- Mean Absolute Error: 117483.0441\n",
-      "- R2 Score: 0.9075\n",
-      "===================================\n",
-      "\n",
-      "\n"
+     "ename": "InvalidParameterError",
+     "evalue": "The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[27], line 10\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mlist\u001b[39m(models))):\n\u001b[0;32m      9\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(models\u001b[38;5;241m.\u001b[39mvalues())[i]\n\u001b[1;32m---> 10\u001b[0m     model\u001b[38;5;241m.\u001b[39mfit(X_train, y_train) \u001b[38;5;66;03m# Train model\u001b[39;00m\n\u001b[0;32m     12\u001b[0m     \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[0;32m     13\u001b[0m     y_train_pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_train)\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:1466\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1461\u001b[0m partial_fit_and_fitted \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1462\u001b[0m     fit_method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpartial_fit\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m _is_fitted(estimator)\n\u001b[0;32m   1463\u001b[0m )\n\u001b[0;32m   1465\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m global_skip_validation \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m partial_fit_and_fitted:\n\u001b[1;32m-> 1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1471\u001b[0m     )\n\u001b[0;32m   1472\u001b[0m ):\n\u001b[0;32m   1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:666\u001b[0m, in \u001b[0;36mBaseEstimator._validate_params\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    659\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate types and values of constructor parameters\u001b[39;00m\n\u001b[0;32m    660\u001b[0m \n\u001b[0;32m    661\u001b[0m \u001b[38;5;124;03m    The expected type and values must be defined in the `_parameter_constraints`\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    664\u001b[0m \u001b[38;5;124;03m    accepted constraints.\u001b[39;00m\n\u001b[0;32m    665\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 666\u001b[0m     validate_parameter_constraints(\n\u001b[0;32m    667\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parameter_constraints,\n\u001b[0;32m    668\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_params(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m    669\u001b[0m         caller_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m,\n\u001b[0;32m    670\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:95\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[1;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     90\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     91\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     92\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     93\u001b[0m     )\n\u001b[1;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     98\u001b[0m )\n",
+      "\u001b[1;31mInvalidParameterError\u001b[0m: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead."
      ]
     }
    ],
@@ -1560,7 +1520,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -1574,7 +1534,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/12-Adaboost/Projects/Projects/Adaboost Regression Implementation.ipynb b/12-Adaboost/Projects/Projects/Adaboost Regression Implementation.ipynb
index c075d08e..d966fff7 100644
--- a/12-Adaboost/Projects/Projects/Adaboost Regression Implementation.ipynb	
+++ b/12-Adaboost/Projects/Projects/Adaboost Regression Implementation.ipynb	
@@ -19,7 +19,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: matplotlib in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (3.7.5)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (1.1.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (4.57.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (1.4.7)\n",
+      "Requirement already satisfied: numpy<2,>=1.20 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (1.24.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (24.1)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (10.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (3.1.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
+      "Requirement already satisfied: importlib-resources>=3.2.0 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from matplotlib) (6.4.0)\n",
+      "Requirement already satisfied: zipp>=3.1.0 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from importlib-resources>=3.2.0->matplotlib) (3.20.2)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\mudas\\onedrive\\desktop\\ml project\\env\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -193,7 +229,7 @@
        "4         570000  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -222,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -244,7 +280,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -257,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -396,7 +432,7 @@
        "4    22.77    1498      98.59      5         570000  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -407,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -433,7 +469,7 @@
        "       'Gurkha'], dtype=object)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -444,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -472,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -606,7 +642,7 @@
        "4    22.77    1498      98.59      5  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -635,7 +671,7 @@
        "120"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -646,27 +682,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "i20             906\n",
-       "Swift Dzire     890\n",
-       "Swift           781\n",
-       "Alto            778\n",
-       "City            757\n",
-       "               ... \n",
-       "Ghost             1\n",
-       "Quattroporte      1\n",
-       "Aura              1\n",
-       "Ghibli            1\n",
-       "Altroz            1\n",
-       "Name: model, Length: 120, dtype: int64"
+       "model\n",
+       "i20            906\n",
+       "Swift Dzire    890\n",
+       "Swift          781\n",
+       "Alto           778\n",
+       "City           757\n",
+       "              ... \n",
+       "Ghibli           1\n",
+       "Altroz           1\n",
+       "GTC4Lusso        1\n",
+       "Aura             1\n",
+       "Gurkha           1\n",
+       "Name: count, Length: 120, dtype: int64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -677,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -688,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -810,7 +847,7 @@
        "4    22.77    1498      98.59      5  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -821,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -830,7 +867,7 @@
        "(3, 5, 2)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -841,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -867,7 +904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -876,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1139,7 +1176,7 @@
        "[15411 rows x 14 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1150,7 +1187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1159,7 +1196,7 @@
        "((12328, 14), (3083, 14))"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1179,7 +1216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1200,7 +1237,7 @@
        "         0.06194201, -0.40302241]])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1218,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1232,7 +1269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1247,7 +1284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1295,14 +1332,14 @@
       "\n",
       "K-Neighbors Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 325886.8736\n",
-      "- Mean Absolute Error: 91467.6671\n",
+      "- Root Mean Squared Error: 325873.0088\n",
+      "- Mean Absolute Error: 91425.4705\n",
       "- R2 Score: 0.8691\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 253118.4156\n",
-      "- Mean Absolute Error: 112704.3545\n",
-      "- R2 Score: 0.9149\n",
+      "- Root Mean Squared Error: 253024.3951\n",
+      "- Mean Absolute Error: 112526.3461\n",
+      "- R2 Score: 0.9150\n",
       "===================================\n",
       "\n",
       "\n",
@@ -1313,35 +1350,35 @@
       "- R2 Score: 0.9995\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 305593.0563\n",
-      "- Mean Absolute Error: 124819.3507\n",
-      "- R2 Score: 0.8759\n",
+      "- Root Mean Squared Error: 307607.9850\n",
+      "- Mean Absolute Error: 125358.5442\n",
+      "- R2 Score: 0.8743\n",
       "===================================\n",
       "\n",
       "\n",
       "Random Forest Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 129769.2686\n",
-      "- Mean Absolute Error: 39653.0860\n",
-      "- R2 Score: 0.9792\n",
+      "- Root Mean Squared Error: 134089.1920\n",
+      "- Mean Absolute Error: 40182.1676\n",
+      "- R2 Score: 0.9778\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 227094.9323\n",
-      "- Mean Absolute Error: 102202.3818\n",
-      "- R2 Score: 0.9315\n",
+      "- Root Mean Squared Error: 228086.3106\n",
+      "- Mean Absolute Error: 101935.1724\n",
+      "- R2 Score: 0.9309\n",
       "===================================\n",
       "\n",
       "\n",
       "Adaboost Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 416905.3901\n",
-      "- Mean Absolute Error: 277555.1749\n",
-      "- R2 Score: 0.7857\n",
+      "- Root Mean Squared Error: 414753.8503\n",
+      "- Mean Absolute Error: 285280.0959\n",
+      "- R2 Score: 0.7879\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 452194.9658\n",
-      "- Mean Absolute Error: 297953.0288\n",
-      "- R2 Score: 0.7284\n",
+      "- Root Mean Squared Error: 449145.3138\n",
+      "- Mean Absolute Error: 306095.3307\n",
+      "- R2 Score: 0.7320\n",
       "===================================\n",
       "\n",
       "\n"
@@ -1395,7 +1432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1414,7 +1451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1428,69 +1465,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done   5 out of  18 | elapsed:    2.7s remaining:    7.3s\n",
-      "[Parallel(n_jobs=-1)]: Done  15 out of  18 | elapsed:    3.0s remaining:    0.5s\n",
-      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    3.0s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  98 tasks      | elapsed:   14.3s\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  1.1min finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  11 out of  36 | elapsed:    0.5s remaining:    1.3s\n",
-      "[Parallel(n_jobs=-1)]: Done  30 out of  36 | elapsed:    0.8s remaining:    0.1s\n",
-      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    1.1s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
+      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
+      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n",
       "---------------- Best Params for KNN -------------------\n",
       "{'n_neighbors': 10}\n",
       "---------------- Best Params for RF -------------------\n",
-      "{'n_estimators': 500, 'min_samples_split': 2, 'max_features': 8, 'max_depth': None}\n",
+      "{'n_estimators': 200, 'min_samples_split': 2, 'max_features': 7, 'max_depth': 15}\n",
       "---------------- Best Params for Adaboost -------------------\n",
-      "{'n_estimators': 60, 'loss': 'linear'}\n"
+      "{'n_estimators': 50, 'loss': 'linear'}\n"
      ]
     }
    ],
@@ -1516,52 +1506,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Random Forest Regressor\n",
-      "Model performance for Training set\n",
-      "- Root Mean Squared Error: 128802.0989\n",
-      "- Mean Absolute Error: 39966.8815\n",
-      "- R2 Score: 0.9795\n",
-      "----------------------------------\n",
-      "Model performance for Test set\n",
-      "- Root Mean Squared Error: 230608.2502\n",
-      "- Mean Absolute Error: 102247.4156\n",
-      "- R2 Score: 0.9294\n",
-      "===================================\n",
-      "\n",
-      "\n",
-      "K-Neighbors Regressor\n",
-      "Model performance for Training set\n",
-      "- Root Mean Squared Error: 363464.0671\n",
-      "- Mean Absolute Error: 103451.3465\n",
-      "- R2 Score: 0.8371\n",
-      "----------------------------------\n",
-      "Model performance for Test set\n",
-      "- Root Mean Squared Error: 263872.0571\n",
-      "- Mean Absolute Error: 117483.0441\n",
-      "- R2 Score: 0.9075\n",
-      "===================================\n",
-      "\n",
-      "\n",
-      "Adaboost\n",
-      "Model performance for Training set\n",
-      "- Root Mean Squared Error: 453454.8975\n",
-      "- Mean Absolute Error: 337930.1634\n",
-      "- R2 Score: 0.7465\n",
-      "----------------------------------\n",
-      "Model performance for Test set\n",
-      "- Root Mean Squared Error: 530557.9592\n",
-      "- Mean Absolute Error: 361436.4758\n",
-      "- R2 Score: 0.6261\n",
-      "===================================\n",
-      "\n",
-      "\n"
+     "ename": "InvalidParameterError",
+     "evalue": "The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[31], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mlist\u001b[39m(models))):\n\u001b[0;32m     10\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(models\u001b[38;5;241m.\u001b[39mvalues())[i]\n\u001b[1;32m---> 11\u001b[0m     \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Train model\u001b[39;00m\n\u001b[0;32m     13\u001b[0m     \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[0;32m     14\u001b[0m     y_train_pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_train)\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\OneDrive\\Desktop\\ml project\\env\\lib\\site-packages\\sklearn\\base.py:1145\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1140\u001b[0m partial_fit_and_fitted \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1141\u001b[0m     fit_method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpartial_fit\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m _is_fitted(estimator)\n\u001b[0;32m   1142\u001b[0m )\n\u001b[0;32m   1144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m global_skip_validation \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m partial_fit_and_fitted:\n\u001b[1;32m-> 1145\u001b[0m     \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1147\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1148\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1150\u001b[0m     )\n\u001b[0;32m   1151\u001b[0m ):\n\u001b[0;32m   1152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\OneDrive\\Desktop\\ml project\\env\\lib\\site-packages\\sklearn\\base.py:638\u001b[0m, in \u001b[0;36mBaseEstimator._validate_params\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    630\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    631\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate types and values of constructor parameters\u001b[39;00m\n\u001b[0;32m    632\u001b[0m \n\u001b[0;32m    633\u001b[0m \u001b[38;5;124;03m    The expected type and values must be defined in the `_parameter_constraints`\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    636\u001b[0m \u001b[38;5;124;03m    accepted constraints.\u001b[39;00m\n\u001b[0;32m    637\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 638\u001b[0m     \u001b[43mvalidate_parameter_constraints\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    639\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parameter_constraints\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    640\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    641\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcaller_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    642\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\OneDrive\\Desktop\\ml project\\env\\lib\\site-packages\\sklearn\\utils\\_param_validation.py:96\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[1;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     91\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     92\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     93\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     94\u001b[0m     )\n\u001b[1;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     98\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     99\u001b[0m )\n",
+      "\u001b[1;31mInvalidParameterError\u001b[0m: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead."
      ]
     }
    ],
@@ -1628,7 +1587,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.20"
   }
  },
  "nbformat": 4,
diff --git a/13-Gradient Boosting/Projects/Classification/GradientBoost Classification Implementation.ipynb b/13-Gradient Boosting/Projects/Classification/GradientBoost Classification Implementation.ipynb
index 1ca713b6..5427c8f8 100644
--- a/13-Gradient Boosting/Projects/Classification/GradientBoost Classification Implementation.ipynb	
+++ b/13-Gradient Boosting/Projects/Classification/GradientBoost Classification Implementation.ipynb	
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -240,7 +240,7 @@
        "4   Executive        18468.0  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -293,7 +293,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -304,19 +304,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "Gender\n",
        "Male       2916\n",
        "Female     1817\n",
        "Fe Male     155\n",
-       "Name: Gender, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -328,20 +329,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "MaritalStatus\n",
        "Married      2340\n",
        "Divorced      950\n",
        "Single        916\n",
        "Unmarried     682\n",
-       "Name: MaritalStatus, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -352,18 +354,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "TypeofContact\n",
        "Self Enquiry       3444\n",
        "Company Invited    1419\n",
-       "Name: TypeofContact, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -374,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,18 +387,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "Gender\n",
        "Male      2916\n",
        "Female    1972\n",
-       "Name: Gender, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -407,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -610,7 +614,7 @@
        "4   Executive        18468.0  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -621,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -649,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -789,7 +793,7 @@
        "max        22.000000                  3.000000   98678.000000  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -816,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -847,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -876,7 +880,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -888,7 +892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -906,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1103,7 +1107,7 @@
        "4   Executive        18468.0  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1114,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1125,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1144,7 +1148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1163,7 +1167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1182,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1201,7 +1205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1385,7 +1389,7 @@
        "4                       5       1   Executive        18468.0            2.0  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1403,7 +1407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1414,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1592,7 +1596,7 @@
        "4        18468.0            2.0  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1603,18 +1607,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "ProdTaken\n",
        "0    3968\n",
        "1     920\n",
-       "Name: ProdTaken, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1625,7 +1630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1803,7 +1808,7 @@
        "4        18468.0            2.0  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1814,7 +1819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1823,7 +1828,7 @@
        "((3910, 17), (978, 17))"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1836,7 +1841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1876,7 +1881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1900,11 +1905,439 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;OneHotEncoder&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n",
+       "                                 Index([&#x27;TypeofContact&#x27;, &#x27;Occupation&#x27;, &#x27;Gender&#x27;, &#x27;ProductPitched&#x27;,\n",
+       "       &#x27;MaritalStatus&#x27;, &#x27;Designation&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;StandardScaler&#x27;, StandardScaler(),\n",
+       "                                 Index([&#x27;Age&#x27;, &#x27;CityTier&#x27;, &#x27;DurationOfPitch&#x27;, &#x27;NumberOfFollowups&#x27;,\n",
+       "       &#x27;PreferredPropertyStar&#x27;, &#x27;NumberOfTrips&#x27;, &#x27;Passport&#x27;,\n",
+       "       &#x27;PitchSatisfactionScore&#x27;, &#x27;OwnCar&#x27;, &#x27;MonthlyIncome&#x27;, &#x27;TotalVisiting&#x27;],\n",
+       "      dtype=&#x27;object&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;OneHotEncoder&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n",
+       "                                 Index([&#x27;TypeofContact&#x27;, &#x27;Occupation&#x27;, &#x27;Gender&#x27;, &#x27;ProductPitched&#x27;,\n",
+       "       &#x27;MaritalStatus&#x27;, &#x27;Designation&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;StandardScaler&#x27;, StandardScaler(),\n",
+       "                                 Index([&#x27;Age&#x27;, &#x27;CityTier&#x27;, &#x27;DurationOfPitch&#x27;, &#x27;NumberOfFollowups&#x27;,\n",
+       "       &#x27;PreferredPropertyStar&#x27;, &#x27;NumberOfTrips&#x27;, &#x27;Passport&#x27;,\n",
+       "       &#x27;PitchSatisfactionScore&#x27;, &#x27;OwnCar&#x27;, &#x27;MonthlyIncome&#x27;, &#x27;TotalVisiting&#x27;],\n",
+       "      dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">OneHotEncoder</label><div class=\"sk-toggleable__content \"><pre>Index([&#x27;TypeofContact&#x27;, &#x27;Occupation&#x27;, &#x27;Gender&#x27;, &#x27;ProductPitched&#x27;,\n",
+       "       &#x27;MaritalStatus&#x27;, &#x27;Designation&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">StandardScaler</label><div class=\"sk-toggleable__content \"><pre>Index([&#x27;Age&#x27;, &#x27;CityTier&#x27;, &#x27;DurationOfPitch&#x27;, &#x27;NumberOfFollowups&#x27;,\n",
+       "       &#x27;PreferredPropertyStar&#x27;, &#x27;NumberOfTrips&#x27;, &#x27;Passport&#x27;,\n",
+       "       &#x27;PitchSatisfactionScore&#x27;, &#x27;OwnCar&#x27;, &#x27;MonthlyIncome&#x27;, &#x27;TotalVisiting&#x27;],\n",
+       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
       "text/plain": [
        "ColumnTransformer(transformers=[('OneHotEncoder', OneHotEncoder(drop='first'),\n",
        "                                 Index(['TypeofContact', 'Occupation', 'Gender', 'ProductPitched',\n",
@@ -1917,7 +2350,7 @@
        "      dtype='object'))])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1928,7 +2361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1938,7 +2371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -2298,7 +2731,7 @@
        "[3910 rows x 26 columns]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2309,7 +2742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2319,7 +2752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -2340,7 +2773,7 @@
        "        -0.44611323,  2.06138184]])"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2359,7 +2792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -2719,7 +3152,7 @@
        "[3910 rows x 26 columns]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2730,7 +3163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -2750,7 +3183,7 @@
        "Name: ProdTaken, Length: 3910, dtype: int64"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2761,7 +3194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2776,7 +3209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -2785,18 +3218,18 @@
      "text": [
       "Logisitic Regression\n",
       "Model performance for Training set\n",
-      "- Accuracy: 0.8458\n",
-      "- F1 score: 0.8200\n",
-      "- Precision: 0.6994\n",
+      "- Accuracy: 0.8460\n",
+      "- F1 score: 0.8202\n",
+      "- Precision: 0.7016\n",
       "- Recall: 0.3032\n",
-      "- Roc Auc Score: 0.6366\n",
+      "- Roc Auc Score: 0.6368\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Accuracy: 0.8354\n",
-      "- F1 score: 0.8078\n",
-      "- Precision: 0.6829\n",
+      "- Accuracy: 0.8364\n",
+      "- F1 score: 0.8087\n",
+      "- Precision: 0.6914\n",
       "- Recall: 0.2932\n",
-      "- Roc Auc Score: 0.6301\n",
+      "- Roc Auc Score: 0.6307\n",
       "===================================\n",
       "\n",
       "\n",
@@ -2809,11 +3242,11 @@
       "- Roc Auc Score: 1.0000\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Accuracy: 0.9162\n",
-      "- F1 score: 0.9151\n",
-      "- Precision: 0.8045\n",
-      "- Recall: 0.7539\n",
-      "- Roc Auc Score: 0.8547\n",
+      "- Accuracy: 0.9264\n",
+      "- F1 score: 0.9256\n",
+      "- Precision: 0.8287\n",
+      "- Recall: 0.7853\n",
+      "- Roc Auc Score: 0.8730\n",
       "===================================\n",
       "\n",
       "\n",
@@ -2826,11 +3259,11 @@
       "- Roc Auc Score: 1.0000\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Accuracy: 0.9264\n",
-      "- F1 score: 0.9204\n",
-      "- Precision: 0.9685\n",
-      "- Recall: 0.6440\n",
-      "- Roc Auc Score: 0.8194\n",
+      "- Accuracy: 0.9274\n",
+      "- F1 score: 0.9219\n",
+      "- Precision: 0.9615\n",
+      "- Recall: 0.6545\n",
+      "- Roc Auc Score: 0.8240\n",
       "===================================\n",
       "\n",
       "\n",
@@ -2931,7 +3364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2950,7 +3383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -2962,7 +3395,7 @@
        " 'n_estimators': [100, 200, 500, 1000]}"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2973,7 +3406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -2986,7 +3419,7 @@
        " 'max_depth': [5, 8, 15, None, 10]}"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2997,7 +3430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3011,7 +3444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -3032,7 +3465,7 @@
        "   'max_depth': [5, 8, 15, None, 10]})]"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3043,50 +3476,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  98 tasks      | elapsed:    9.5s\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:   20.4s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done 134 tasks      | elapsed:    9.2s\n",
-      "[Parallel(n_jobs=-1)]: Done 237 out of 300 | elapsed:   18.3s remaining:    4.8s\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:   42.9s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
+      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
       "---------------- Best Params for RF -------------------\n",
-      "{'n_estimators': 200, 'min_samples_split': 2, 'max_features': 8, 'max_depth': 15}\n",
+      "{'n_estimators': 200, 'min_samples_split': 2, 'max_features': 8, 'max_depth': None}\n",
       "---------------- Best Params for GradientBoost -------------------\n",
-      "{'n_estimators': 500, 'min_samples_split': 20, 'max_depth': 15, 'loss': 'exponential', 'criterion': 'mse'}\n"
+      "{'n_estimators': 500, 'min_samples_split': 2, 'max_depth': 10, 'loss': 'exponential', 'criterion': 'squared_error'}\n"
      ]
     }
    ],
@@ -3111,7 +3513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -3127,32 +3529,29 @@
       "- Roc Auc Score: 1.0000\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Accuracy: 0.9366\n",
-      "- F1 score: 0.9324\n",
-      "- Precision: 0.9708\n",
-      "- Recall: 0.6963\n",
-      "- Roc Auc Score: 0.8456\n",
-      "===================================\n",
-      "\n",
-      "\n",
-      "GradientBoostclassifier\n",
-      "Model performance for Training set\n",
-      "- Accuracy: 1.0000\n",
-      "- F1 score: 1.0000\n",
-      "- Precision: 1.0000\n",
-      "- Recall: 1.0000\n",
-      "- Roc Auc Score: 1.0000\n",
-      "----------------------------------\n",
-      "Model performance for Test set\n",
-      "- Accuracy: 0.9581\n",
-      "- F1 score: 0.9566\n",
-      "- Precision: 0.9688\n",
-      "- Recall: 0.8115\n",
-      "- Roc Auc Score: 0.9026\n",
+      "- Accuracy: 0.9315\n",
+      "- F1 score: 0.9267\n",
+      "- Precision: 0.9627\n",
+      "- Recall: 0.6754\n",
+      "- Roc Auc Score: 0.8345\n",
       "===================================\n",
       "\n",
       "\n"
      ]
+    },
+    {
+     "ename": "InvalidParameterError",
+     "evalue": "The 'criterion' parameter of GradientBoostingClassifier must be a str among {'friedman_mse', 'squared_error'}. Got 'mse' instead.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[52], line 13\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mlist\u001b[39m(models))):\n\u001b[0;32m     12\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(models\u001b[38;5;241m.\u001b[39mvalues())[i]\n\u001b[1;32m---> 13\u001b[0m     model\u001b[38;5;241m.\u001b[39mfit(X_train, y_train) \u001b[38;5;66;03m# Train model\u001b[39;00m\n\u001b[0;32m     15\u001b[0m     \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[0;32m     16\u001b[0m     y_train_pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_train)\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:1466\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1461\u001b[0m partial_fit_and_fitted \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1462\u001b[0m     fit_method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpartial_fit\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m _is_fitted(estimator)\n\u001b[0;32m   1463\u001b[0m )\n\u001b[0;32m   1465\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m global_skip_validation \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m partial_fit_and_fitted:\n\u001b[1;32m-> 1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1471\u001b[0m     )\n\u001b[0;32m   1472\u001b[0m ):\n\u001b[0;32m   1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:666\u001b[0m, in \u001b[0;36mBaseEstimator._validate_params\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    659\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate types and values of constructor parameters\u001b[39;00m\n\u001b[0;32m    660\u001b[0m \n\u001b[0;32m    661\u001b[0m \u001b[38;5;124;03m    The expected type and values must be defined in the `_parameter_constraints`\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    664\u001b[0m \u001b[38;5;124;03m    accepted constraints.\u001b[39;00m\n\u001b[0;32m    665\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 666\u001b[0m     validate_parameter_constraints(\n\u001b[0;32m    667\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parameter_constraints,\n\u001b[0;32m    668\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_params(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m    669\u001b[0m         caller_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m,\n\u001b[0;32m    670\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:95\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[1;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     90\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     91\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     92\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     93\u001b[0m     )\n\u001b[1;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     98\u001b[0m )\n",
+      "\u001b[1;31mInvalidParameterError\u001b[0m: The 'criterion' parameter of GradientBoostingClassifier must be a str among {'friedman_mse', 'squared_error'}. Got 'mse' instead."
+     ]
     }
    ],
    "source": [
@@ -3218,12 +3617,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3282,7 +3681,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -3296,7 +3695,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/13-Gradient Boosting/Projects/Regression/Gradientboost Regression Implementation.ipynb b/13-Gradient Boosting/Projects/Regression/Gradientboost Regression Implementation.ipynb
index d5906eec..9dc308de 100644
--- a/13-Gradient Boosting/Projects/Regression/Gradientboost Regression Implementation.ipynb	
+++ b/13-Gradient Boosting/Projects/Regression/Gradientboost Regression Implementation.ipynb	
@@ -652,18 +652,19 @@
     {
      "data": {
       "text/plain": [
+       "model\n",
        "i20            906\n",
        "Swift Dzire    890\n",
        "Swift          781\n",
        "Alto           778\n",
        "City           757\n",
        "              ... \n",
-       "Ghost            1\n",
+       "Ghibli           1\n",
+       "Altroz           1\n",
        "GTC4Lusso        1\n",
-       "Gurkha           1\n",
        "Aura             1\n",
-       "Ghibli           1\n",
-       "Name: model, Length: 120, dtype: int64"
+       "Gurkha           1\n",
+       "Name: count, Length: 120, dtype: int64"
       ]
      },
      "execution_count": 12,
@@ -1296,14 +1297,14 @@
       "\n",
       "K-Neighbors Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 325886.8736\n",
-      "- Mean Absolute Error: 91467.6671\n",
+      "- Root Mean Squared Error: 325873.0088\n",
+      "- Mean Absolute Error: 91425.4705\n",
       "- R2 Score: 0.8691\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 253118.4156\n",
-      "- Mean Absolute Error: 112704.3545\n",
-      "- R2 Score: 0.9149\n",
+      "- Root Mean Squared Error: 253024.3951\n",
+      "- Mean Absolute Error: 112526.3461\n",
+      "- R2 Score: 0.9150\n",
       "===================================\n",
       "\n",
       "\n",
@@ -1314,35 +1315,35 @@
       "- R2 Score: 0.9995\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 305299.7207\n",
-      "- Mean Absolute Error: 125466.7991\n",
-      "- R2 Score: 0.8762\n",
+      "- Root Mean Squared Error: 305590.0520\n",
+      "- Mean Absolute Error: 124898.2512\n",
+      "- R2 Score: 0.8759\n",
       "===================================\n",
       "\n",
       "\n",
       "Random Forest Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 155076.8819\n",
-      "- Mean Absolute Error: 40176.2373\n",
-      "- R2 Score: 0.9703\n",
+      "- Root Mean Squared Error: 126269.9485\n",
+      "- Mean Absolute Error: 39841.5849\n",
+      "- R2 Score: 0.9803\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 229169.8191\n",
-      "- Mean Absolute Error: 102220.2001\n",
-      "- R2 Score: 0.9302\n",
+      "- Root Mean Squared Error: 228022.4703\n",
+      "- Mean Absolute Error: 101693.9021\n",
+      "- R2 Score: 0.9309\n",
       "===================================\n",
       "\n",
       "\n",
       "Adaboost Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 418464.4830\n",
-      "- Mean Absolute Error: 306779.6276\n",
-      "- R2 Score: 0.7841\n",
+      "- Root Mean Squared Error: 448206.7247\n",
+      "- Mean Absolute Error: 344322.2238\n",
+      "- R2 Score: 0.7523\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 454609.4495\n",
-      "- Mean Absolute Error: 327688.7964\n",
-      "- R2 Score: 0.7255\n",
+      "- Root Mean Squared Error: 477538.1159\n",
+      "- Mean Absolute Error: 359776.8696\n",
+      "- R2 Score: 0.6971\n",
       "===================================\n",
       "\n",
       "\n",
@@ -1353,9 +1354,9 @@
       "- R2 Score: 0.9482\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 256880.4088\n",
-      "- Mean Absolute Error: 126637.5099\n",
-      "- R2 Score: 0.9123\n",
+      "- Root Mean Squared Error: 254160.0289\n",
+      "- Mean Absolute Error: 125953.4555\n",
+      "- R2 Score: 0.9142\n",
       "===================================\n",
       "\n",
       "\n"
@@ -1452,43 +1453,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  98 tasks      | elapsed:   20.2s\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:   54.0s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done 144 tasks      | elapsed:    0.9s\n",
-      "[Parallel(n_jobs=-1)]: Done 237 out of 300 | elapsed:   16.6s remaining:    4.3s\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  9.9min finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
+      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
       "---------------- Best Params for RF -------------------\n",
-      "{'n_estimators': 100, 'min_samples_split': 2, 'max_features': 8, 'max_depth': 15}\n",
+      "{'n_estimators': 100, 'min_samples_split': 2, 'max_features': 5, 'max_depth': 15}\n",
       "---------------- Best Params for GradientBoost -------------------\n",
-      "{'n_estimators': 200, 'min_samples_split': 8, 'max_depth': 10, 'loss': 'huber', 'criterion': 'mse'}\n"
+      "{'n_estimators': 200, 'min_samples_split': 2, 'max_depth': 15, 'loss': 'huber', 'criterion': 'squared_error'}\n"
      ]
     }
    ],
@@ -1514,7 +1484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1523,27 +1493,27 @@
      "text": [
       "Random Forest Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 133489.8497\n",
-      "- Mean Absolute Error: 39724.3165\n",
-      "- R2 Score: 0.9780\n",
+      "- Root Mean Squared Error: 128227.7974\n",
+      "- Mean Absolute Error: 56311.0901\n",
+      "- R2 Score: 0.9797\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 222711.7974\n",
-      "- Mean Absolute Error: 101412.0000\n",
-      "- R2 Score: 0.9341\n",
+      "- Root Mean Squared Error: 205038.0659\n",
+      "- Mean Absolute Error: 97150.2658\n",
+      "- R2 Score: 0.9442\n",
       "===================================\n",
       "\n",
       "\n",
       "GradientBoost Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 68673.6455\n",
-      "- Mean Absolute Error: 37702.1429\n",
-      "- R2 Score: 0.9942\n",
+      "- Root Mean Squared Error: 25277.8555\n",
+      "- Mean Absolute Error: 5915.7977\n",
+      "- R2 Score: 0.9992\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 227779.4363\n",
-      "- Mean Absolute Error: 97038.0649\n",
-      "- R2 Score: 0.9311\n",
+      "- Root Mean Squared Error: 255151.1310\n",
+      "- Mean Absolute Error: 108318.0849\n",
+      "- R2 Score: 0.9135\n",
       "===================================\n",
       "\n",
       "\n"
@@ -1553,10 +1523,10 @@
    "source": [
     "## Retraining the models with best parameters\n",
     "models = {\n",
-    "    \"Random Forest Regressor\": RandomForestRegressor(n_estimators=100, min_samples_split=2, max_features='auto', max_depth=None, \n",
+    "    \"Random Forest Regressor\": RandomForestRegressor(n_estimators=100, min_samples_split=2, max_features=5, max_depth=15, \n",
     "                                                     n_jobs=-1),\n",
     "     \"GradientBoost Regressor\":GradientBoostingRegressor(n_estimators= 200,\n",
-    "                                                         min_samples_split=8, max_depth=10, loss= 'huber', criterion='mse')\n",
+    "                                                         min_samples_split=2, max_depth=15, loss= 'huber', criterion='squared_error')\n",
     "    \n",
     "}\n",
     "for i in range(len(list(models))):\n",
@@ -1599,7 +1569,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -1613,7 +1583,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/16-PCA/PCA Project/Principal Component Analysis (PCA) Implementation.ipynb b/16-PCA/PCA Project/Principal Component Analysis (PCA) Implementation.ipynb
index 4f7db2de..2308d059 100644
--- a/16-PCA/PCA Project/Principal Component Analysis (PCA) Implementation.ipynb	
+++ b/16-PCA/PCA Project/Principal Component Analysis (PCA) Implementation.ipynb	
@@ -9,7 +9,7 @@
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
+    "import pandas as pd  \n",
     "%matplotlib inline"
    ]
   },
@@ -40,7 +40,7 @@
     {
      "data": {
       "text/plain": [
-       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
+       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
       ]
      },
      "execution_count": 4,
@@ -68,77 +68,77 @@
       "\n",
       "**Data Set Characteristics:**\n",
       "\n",
-      "    :Number of Instances: 569\n",
+      ":Number of Instances: 569\n",
       "\n",
-      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
+      ":Number of Attributes: 30 numeric, predictive attributes and the class\n",
       "\n",
-      "    :Attribute Information:\n",
-      "        - radius (mean of distances from center to points on the perimeter)\n",
-      "        - texture (standard deviation of gray-scale values)\n",
-      "        - perimeter\n",
-      "        - area\n",
-      "        - smoothness (local variation in radius lengths)\n",
-      "        - compactness (perimeter^2 / area - 1.0)\n",
-      "        - concavity (severity of concave portions of the contour)\n",
-      "        - concave points (number of concave portions of the contour)\n",
-      "        - symmetry\n",
-      "        - fractal dimension (\"coastline approximation\" - 1)\n",
+      ":Attribute Information:\n",
+      "    - radius (mean of distances from center to points on the perimeter)\n",
+      "    - texture (standard deviation of gray-scale values)\n",
+      "    - perimeter\n",
+      "    - area\n",
+      "    - smoothness (local variation in radius lengths)\n",
+      "    - compactness (perimeter^2 / area - 1.0)\n",
+      "    - concavity (severity of concave portions of the contour)\n",
+      "    - concave points (number of concave portions of the contour)\n",
+      "    - symmetry\n",
+      "    - fractal dimension (\"coastline approximation\" - 1)\n",
       "\n",
-      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
-      "        worst/largest values) of these features were computed for each image,\n",
-      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
-      "        10 is Radius SE, field 20 is Worst Radius.\n",
+      "    The mean, standard error, and \"worst\" or largest (mean of the three\n",
+      "    worst/largest values) of these features were computed for each image,\n",
+      "    resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
+      "    10 is Radius SE, field 20 is Worst Radius.\n",
       "\n",
-      "        - class:\n",
-      "                - WDBC-Malignant\n",
-      "                - WDBC-Benign\n",
+      "    - class:\n",
+      "            - WDBC-Malignant\n",
+      "            - WDBC-Benign\n",
       "\n",
-      "    :Summary Statistics:\n",
+      ":Summary Statistics:\n",
       "\n",
-      "    ===================================== ====== ======\n",
-      "                                           Min    Max\n",
-      "    ===================================== ====== ======\n",
-      "    radius (mean):                        6.981  28.11\n",
-      "    texture (mean):                       9.71   39.28\n",
-      "    perimeter (mean):                     43.79  188.5\n",
-      "    area (mean):                          143.5  2501.0\n",
-      "    smoothness (mean):                    0.053  0.163\n",
-      "    compactness (mean):                   0.019  0.345\n",
-      "    concavity (mean):                     0.0    0.427\n",
-      "    concave points (mean):                0.0    0.201\n",
-      "    symmetry (mean):                      0.106  0.304\n",
-      "    fractal dimension (mean):             0.05   0.097\n",
-      "    radius (standard error):              0.112  2.873\n",
-      "    texture (standard error):             0.36   4.885\n",
-      "    perimeter (standard error):           0.757  21.98\n",
-      "    area (standard error):                6.802  542.2\n",
-      "    smoothness (standard error):          0.002  0.031\n",
-      "    compactness (standard error):         0.002  0.135\n",
-      "    concavity (standard error):           0.0    0.396\n",
-      "    concave points (standard error):      0.0    0.053\n",
-      "    symmetry (standard error):            0.008  0.079\n",
-      "    fractal dimension (standard error):   0.001  0.03\n",
-      "    radius (worst):                       7.93   36.04\n",
-      "    texture (worst):                      12.02  49.54\n",
-      "    perimeter (worst):                    50.41  251.2\n",
-      "    area (worst):                         185.2  4254.0\n",
-      "    smoothness (worst):                   0.071  0.223\n",
-      "    compactness (worst):                  0.027  1.058\n",
-      "    concavity (worst):                    0.0    1.252\n",
-      "    concave points (worst):               0.0    0.291\n",
-      "    symmetry (worst):                     0.156  0.664\n",
-      "    fractal dimension (worst):            0.055  0.208\n",
-      "    ===================================== ====== ======\n",
+      "===================================== ====== ======\n",
+      "                                        Min    Max\n",
+      "===================================== ====== ======\n",
+      "radius (mean):                        6.981  28.11\n",
+      "texture (mean):                       9.71   39.28\n",
+      "perimeter (mean):                     43.79  188.5\n",
+      "area (mean):                          143.5  2501.0\n",
+      "smoothness (mean):                    0.053  0.163\n",
+      "compactness (mean):                   0.019  0.345\n",
+      "concavity (mean):                     0.0    0.427\n",
+      "concave points (mean):                0.0    0.201\n",
+      "symmetry (mean):                      0.106  0.304\n",
+      "fractal dimension (mean):             0.05   0.097\n",
+      "radius (standard error):              0.112  2.873\n",
+      "texture (standard error):             0.36   4.885\n",
+      "perimeter (standard error):           0.757  21.98\n",
+      "area (standard error):                6.802  542.2\n",
+      "smoothness (standard error):          0.002  0.031\n",
+      "compactness (standard error):         0.002  0.135\n",
+      "concavity (standard error):           0.0    0.396\n",
+      "concave points (standard error):      0.0    0.053\n",
+      "symmetry (standard error):            0.008  0.079\n",
+      "fractal dimension (standard error):   0.001  0.03\n",
+      "radius (worst):                       7.93   36.04\n",
+      "texture (worst):                      12.02  49.54\n",
+      "perimeter (worst):                    50.41  251.2\n",
+      "area (worst):                         185.2  4254.0\n",
+      "smoothness (worst):                   0.071  0.223\n",
+      "compactness (worst):                  0.027  1.058\n",
+      "concavity (worst):                    0.0    1.252\n",
+      "concave points (worst):               0.0    0.291\n",
+      "symmetry (worst):                     0.156  0.664\n",
+      "fractal dimension (worst):            0.055  0.208\n",
+      "===================================== ====== ======\n",
       "\n",
-      "    :Missing Attribute Values: None\n",
+      ":Missing Attribute Values: None\n",
       "\n",
-      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
+      ":Class Distribution: 212 - Malignant, 357 - Benign\n",
       "\n",
-      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
+      ":Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
       "\n",
-      "    :Donor: Nick Street\n",
+      ":Donor: Nick Street\n",
       "\n",
-      "    :Date: November, 1995\n",
+      ":Date: November, 1995\n",
       "\n",
       "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
       "https://goo.gl/U2Uwz2\n",
@@ -167,18 +167,19 @@
       "ftp ftp.cs.wisc.edu\n",
       "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
       "\n",
-      ".. topic:: References\n",
+      ".. dropdown:: References\n",
       "\n",
-      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
-      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
-      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
-      "     San Jose, CA, 1993.\n",
-      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
-      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
-      "     July-August 1995.\n",
-      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
-      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
-      "     163-171.\n"
+      "  - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\n",
+      "    for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\n",
+      "    Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
+      "    San Jose, CA, 1993.\n",
+      "  - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\n",
+      "    prognosis via linear programming. Operations Research, 43(4), pages 570-577,\n",
+      "    July-August 1995.\n",
+      "  - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
+      "    to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\n",
+      "    163-171.\n",
+      "\n"
      ]
     }
    ],
@@ -414,6 +415,59 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 569 entries, 0 to 568\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   mean radius              569 non-null    float64\n",
+      " 1   mean texture             569 non-null    float64\n",
+      " 2   mean perimeter           569 non-null    float64\n",
+      " 3   mean area                569 non-null    float64\n",
+      " 4   mean smoothness          569 non-null    float64\n",
+      " 5   mean compactness         569 non-null    float64\n",
+      " 6   mean concavity           569 non-null    float64\n",
+      " 7   mean concave points      569 non-null    float64\n",
+      " 8   mean symmetry            569 non-null    float64\n",
+      " 9   mean fractal dimension   569 non-null    float64\n",
+      " 10  radius error             569 non-null    float64\n",
+      " 11  texture error            569 non-null    float64\n",
+      " 12  perimeter error          569 non-null    float64\n",
+      " 13  area error               569 non-null    float64\n",
+      " 14  smoothness error         569 non-null    float64\n",
+      " 15  compactness error        569 non-null    float64\n",
+      " 16  concavity error          569 non-null    float64\n",
+      " 17  concave points error     569 non-null    float64\n",
+      " 18  symmetry error           569 non-null    float64\n",
+      " 19  fractal dimension error  569 non-null    float64\n",
+      " 20  worst radius             569 non-null    float64\n",
+      " 21  worst texture            569 non-null    float64\n",
+      " 22  worst perimeter          569 non-null    float64\n",
+      " 23  worst area               569 non-null    float64\n",
+      " 24  worst smoothness         569 non-null    float64\n",
+      " 25  worst compactness        569 non-null    float64\n",
+      " 26  worst concavity          569 non-null    float64\n",
+      " 27  worst concave points     569 non-null    float64\n",
+      " 28  worst symmetry           569 non-null    float64\n",
+      " 29  worst fractal dimension  569 non-null    float64\n",
+      "dtypes: float64(30)\n",
+      "memory usage: 133.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "## Standardization\n",
@@ -423,16 +477,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "StandardScaler()"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -443,7 +904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -473,7 +934,7 @@
        "        -0.04813821, -0.75120669]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -484,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -494,7 +955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,7 +964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -527,7 +988,7 @@
        "       [-5.4752433 , -0.67063679]])"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -538,7 +999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -547,7 +1008,7 @@
        "array([13.30499079,  5.7013746 ])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -558,37 +1019,119 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "Text(0, 0.5, 'Second Principal Component')"
+       "<Figure size 800x600 with 1 Axes>"
       ]
      },
-     "execution_count": 33,
      "metadata": {},
-     "output_type": "execute_result"
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,6))\n",
+    "plt.scatter(data_pca[:,0],data_pca[:,1],c=cancer_dataset['target'],cmap='plasma')\n",
+    "plt.xlabel('First principal component')\n",
+    "plt.ylabel('Second Principal Component')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
+       "<Figure size 800x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import libraries\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "# Load sample dataset\n",
+    "iris = load_iris()\n",
+    "X = iris.data  # Features\n",
+    "y = iris.target  # Labels\n",
+    "\n",
+    "# Apply PCA\n",
+    "pca = PCA(n_components=2)  # Reduce to 2 components for visualization\n",
+    "X_pca = pca.fit_transform(X)\n",
+    "\n",
+    "# Plot PCA result\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "scatter = plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap='viridis', s=50)\n",
+    "plt.xlabel('Principal Component 1')\n",
+    "plt.ylabel('Principal Component 2')\n",
+    "plt.title('PCA Result')\n",
+    "plt.legend(*scatter.legend_elements(), title=\"Classes\")\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.figure(figsize=(8,6))\n",
-    "plt.scatter(data_pca[:,0],data_pca[:,1],c=cancer_dataset['target'],cmap='plasma')\n",
-    "plt.xlabel('First principal component')\n",
-    "plt.ylabel('Second Principal Component')"
+    "# Import libraries\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "# Load dataset\n",
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "\n",
+    "# Apply LDA\n",
+    "lda = LDA(n_components=2)  # Up to (number of classes - 1) components, so here 2\n",
+    "X_lda = lda.fit_transform(X, y)\n",
+    "\n",
+    "# Plot LDA result\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "scatter = plt.scatter(X_lda[:, 0], X_lda[:, 1], c=y, cmap='rainbow', s=50)\n",
+    "plt.xlabel('Linear Discriminant 1')\n",
+    "plt.ylabel('Linear Discriminant 2')\n",
+    "plt.title('LDA Result')\n",
+    "plt.legend(*scatter.legend_elements(), title=\"Classes\")\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -601,7 +1144,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -615,7 +1158,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/17-K Means Clutering Unsupervised ML/K Means Clustering Algorithms implementation.ipynb b/17-K Means Clutering Unsupervised ML/K Means Clustering Algorithms implementation.ipynb
index 228f4bbc..3823ffc5 100644
--- a/17-K Means Clutering Unsupervised ML/K Means Clustering Algorithms implementation.ipynb	
+++ b/17-K Means Clutering Unsupervised ML/K Means Clustering Algorithms implementation.ipynb	
@@ -30,13 +30,13 @@
     {
      "data": {
       "text/plain": [
-       "array([[-1.90199764, -3.52956243],\n",
-       "       [-1.98393837, -3.57768991],\n",
-       "       [-9.70183114, -6.02341839],\n",
+       "array([[ 5.61671493,  0.5762826 ],\n",
+       "       [ 6.82669147,  0.54344322],\n",
+       "       [ 5.60701102,  1.02899068],\n",
        "       ...,\n",
-       "       [-9.65961562, -6.01237169],\n",
-       "       [-8.61886216, -6.1579854 ],\n",
-       "       [-2.74729721, -5.85448802]])"
+       "       [ 7.80715916,  0.12705569],\n",
+       "       [ 4.4567158 , -0.980362  ],\n",
+       "       [ 2.92023151, -3.55346599]])"
       ]
      },
      "execution_count": 3,
@@ -56,52 +56,52 @@
     {
      "data": {
       "text/plain": [
-       "array([0, 0, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 0, 1, 0, 1, 0, 2, 1,\n",
-       "       2, 2, 2, 2, 1, 1, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 2, 0,\n",
-       "       1, 2, 0, 2, 2, 2, 0, 0, 1, 0, 2, 0, 1, 0, 1, 2, 0, 0, 1, 2, 0, 2,\n",
-       "       1, 2, 0, 0, 0, 2, 2, 1, 0, 1, 2, 1, 0, 2, 0, 0, 1, 1, 2, 0, 2, 0,\n",
-       "       2, 2, 0, 2, 0, 1, 0, 2, 2, 0, 1, 1, 0, 2, 0, 2, 1, 1, 2, 0, 2, 2,\n",
-       "       0, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 1, 0, 1, 1, 2, 0, 2, 2, 1, 0, 2,\n",
-       "       2, 0, 0, 2, 2, 0, 1, 1, 0, 0, 0, 2, 2, 1, 1, 0, 0, 1, 2, 1, 2, 0,\n",
-       "       1, 1, 1, 1, 0, 0, 2, 0, 1, 0, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 0, 2,\n",
-       "       0, 0, 2, 1, 2, 0, 1, 0, 0, 0, 0, 0, 2, 0, 1, 0, 2, 2, 1, 1, 0, 0,\n",
-       "       0, 1, 0, 2, 1, 2, 2, 2, 1, 0, 2, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
-       "       0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0,\n",
-       "       1, 1, 0, 2, 2, 0, 2, 2, 2, 1, 0, 0, 2, 2, 2, 1, 0, 2, 0, 1, 1, 2,\n",
-       "       0, 2, 0, 1, 0, 1, 0, 1, 1, 0, 1, 2, 1, 0, 0, 2, 1, 1, 2, 1, 1, 2,\n",
-       "       1, 0, 2, 1, 1, 0, 0, 2, 2, 1, 1, 0, 1, 2, 1, 0, 1, 1, 1, 1, 1, 1,\n",
-       "       0, 1, 1, 1, 0, 2, 2, 1, 2, 1, 0, 1, 2, 1, 0, 1, 2, 2, 1, 1, 0, 2,\n",
-       "       2, 2, 1, 2, 2, 0, 2, 1, 0, 0, 1, 0, 0, 1, 1, 0, 2, 1, 0, 1, 2, 1,\n",
-       "       0, 1, 0, 1, 0, 1, 0, 1, 2, 2, 2, 2, 1, 1, 0, 2, 1, 1, 0, 1, 1, 2,\n",
-       "       2, 1, 2, 2, 1, 0, 0, 2, 0, 2, 0, 0, 2, 0, 1, 1, 0, 1, 1, 0, 2, 1,\n",
-       "       2, 1, 0, 1, 0, 2, 2, 0, 2, 2, 0, 1, 0, 2, 2, 0, 1, 1, 2, 0, 1, 2,\n",
-       "       1, 1, 0, 0, 1, 2, 1, 2, 1, 2, 0, 0, 2, 1, 1, 2, 0, 2, 0, 2, 2, 0,\n",
-       "       0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2,\n",
-       "       2, 1, 0, 1, 2, 1, 2, 1, 1, 0, 0, 1, 0, 0, 1, 2, 1, 0, 0, 1, 0, 0,\n",
-       "       0, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 2, 0, 0, 2, 0, 1, 2, 0, 0, 2, 1,\n",
-       "       1, 2, 2, 0, 0, 2, 2, 1, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, 1,\n",
-       "       0, 0, 0, 2, 0, 2, 0, 1, 2, 2, 1, 0, 2, 0, 2, 1, 2, 2, 2, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 0, 2, 0, 1, 1, 1, 2, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 2,\n",
-       "       1, 2, 2, 0, 1, 2, 1, 0, 2, 1, 2, 2, 1, 1, 1, 0, 2, 1, 1, 0, 2, 1,\n",
-       "       0, 2, 0, 1, 2, 2, 1, 1, 2, 1, 0, 0, 2, 2, 2, 1, 1, 2, 0, 0, 0, 2,\n",
-       "       0, 1, 0, 2, 0, 0, 0, 2, 1, 0, 2, 2, 1, 2, 1, 2, 1, 1, 2, 0, 0, 1,\n",
-       "       1, 1, 2, 1, 1, 0, 1, 0, 0, 0, 0, 2, 1, 0, 0, 0, 1, 2, 1, 2, 2, 0,\n",
-       "       1, 2, 2, 0, 2, 1, 2, 2, 2, 0, 1, 2, 1, 2, 2, 0, 0, 1, 0, 2, 1, 2,\n",
-       "       1, 1, 0, 0, 0, 1, 1, 2, 0, 0, 2, 1, 2, 2, 2, 2, 0, 2, 1, 1, 0, 1,\n",
-       "       1, 0, 0, 0, 2, 1, 0, 0, 0, 2, 0, 1, 1, 0, 0, 1, 2, 2, 1, 2, 0, 0,\n",
-       "       1, 1, 0, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 0, 2, 1, 1, 0, 0, 0, 1, 0,\n",
-       "       1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0, 1, 0, 0, 2, 1,\n",
-       "       1, 2, 1, 1, 0, 2, 1, 0, 2, 1, 2, 0, 0, 0, 0, 2, 0, 2, 1, 1, 2, 1,\n",
-       "       0, 1, 2, 2, 0, 1, 2, 0, 0, 2, 1, 2, 1, 2, 2, 0, 1, 2, 2, 0, 0, 1,\n",
-       "       2, 1, 2, 0, 2, 2, 2, 2, 0, 0, 0, 1, 0, 2, 1, 1, 2, 2, 2, 0, 2, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 1, 1, 1, 2, 0, 1, 0, 1, 0, 0, 2, 1,\n",
-       "       1, 1, 0, 2, 2, 1, 0, 0, 0, 2, 2, 2, 2, 1, 0, 2, 2, 1, 1, 0, 2, 1,\n",
-       "       0, 2, 1, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1,\n",
-       "       1, 0, 0, 1, 0, 0, 2, 0, 2, 0, 2, 1, 2, 2, 1, 0, 0, 0, 1, 2, 0, 2,\n",
-       "       0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 1, 1, 2, 2, 0,\n",
-       "       1, 1, 0, 2, 1, 0, 2, 0, 2, 0, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 0, 1,\n",
-       "       2, 2, 2, 2, 0, 2, 2, 2, 1, 1, 0, 2, 2, 2, 1, 2, 2, 1, 2, 2, 0, 1,\n",
-       "       2, 1, 1, 2, 2, 0, 2, 2, 2, 0])"
+       "array([1, 1, 1, 1, 0, 2, 1, 2, 0, 0, 1, 0, 2, 2, 1, 2, 0, 0, 2, 2, 1, 2,\n",
+       "       1, 2, 1, 0, 0, 1, 0, 2, 1, 2, 1, 2, 2, 1, 0, 0, 0, 1, 1, 1, 2, 0,\n",
+       "       1, 2, 2, 1, 2, 2, 2, 0, 0, 1, 1, 0, 2, 0, 1, 1, 0, 0, 1, 2, 0, 1,\n",
+       "       0, 0, 0, 1, 0, 0, 2, 0, 1, 1, 2, 0, 2, 1, 2, 0, 1, 2, 2, 1, 0, 2,\n",
+       "       1, 1, 1, 0, 0, 0, 0, 2, 1, 2, 1, 2, 0, 1, 0, 2, 0, 2, 1, 0, 2, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 2, 0, 1, 0, 0, 1, 2, 1, 2, 1, 0, 0, 0, 1, 0, 2,\n",
+       "       0, 0, 2, 1, 2, 1, 2, 1, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 0, 1, 0,\n",
+       "       2, 0, 0, 0, 2, 1, 2, 2, 2, 2, 1, 1, 1, 0, 1, 2, 0, 0, 1, 2, 2, 2,\n",
+       "       2, 2, 2, 0, 2, 1, 1, 0, 1, 2, 0, 1, 1, 2, 0, 1, 1, 1, 0, 0, 2, 2,\n",
+       "       0, 2, 1, 1, 1, 0, 0, 1, 0, 0, 1, 2, 1, 2, 0, 1, 1, 2, 2, 1, 2, 1,\n",
+       "       0, 2, 1, 0, 0, 2, 2, 1, 2, 1, 1, 0, 0, 1, 1, 1, 2, 1, 2, 0, 0, 1,\n",
+       "       0, 0, 2, 0, 2, 0, 1, 0, 2, 1, 1, 0, 1, 2, 2, 2, 1, 2, 1, 1, 2, 0,\n",
+       "       1, 0, 2, 2, 0, 1, 2, 1, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 1, 1, 1, 2,\n",
+       "       1, 1, 0, 2, 1, 2, 0, 0, 2, 0, 1, 1, 1, 2, 2, 0, 2, 0, 1, 0, 0, 1,\n",
+       "       0, 0, 0, 2, 2, 0, 1, 0, 0, 0, 1, 2, 1, 2, 0, 2, 2, 2, 0, 1, 2, 0,\n",
+       "       2, 0, 0, 0, 2, 0, 1, 2, 0, 0, 0, 2, 1, 1, 2, 1, 0, 0, 2, 1, 1, 2,\n",
+       "       2, 2, 0, 0, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 2, 2, 1, 0, 2, 1, 1, 1,\n",
+       "       1, 2, 2, 0, 1, 2, 0, 1, 2, 0, 0, 2, 2, 0, 1, 1, 1, 0, 1, 2, 0, 0,\n",
+       "       2, 2, 0, 1, 1, 1, 1, 2, 2, 2, 0, 1, 1, 0, 2, 2, 0, 2, 1, 0, 0, 1,\n",
+       "       2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 1, 2, 1, 2, 1, 1, 0, 0, 2, 1,\n",
+       "       1, 0, 0, 0, 0, 1, 2, 0, 0, 1, 1, 2, 1, 0, 2, 1, 1, 2, 2, 0, 2, 2,\n",
+       "       2, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 2, 2, 0, 1, 2, 1, 2, 0, 0, 1,\n",
+       "       0, 2, 1, 2, 2, 1, 2, 2, 0, 2, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 1, 1,\n",
+       "       1, 1, 2, 0, 2, 1, 2, 2, 0, 0, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       0, 2, 0, 1, 2, 0, 0, 2, 1, 2, 2, 0, 2, 2, 0, 1, 0, 2, 1, 0, 1, 1,\n",
+       "       2, 1, 0, 1, 0, 1, 2, 2, 0, 2, 0, 0, 1, 2, 1, 1, 2, 0, 1, 0, 0, 2,\n",
+       "       0, 1, 1, 0, 2, 1, 2, 0, 2, 0, 0, 1, 1, 2, 0, 0, 0, 0, 2, 2, 0, 1,\n",
+       "       0, 2, 2, 1, 0, 1, 0, 0, 1, 1, 1, 2, 1, 0, 0, 1, 0, 0, 2, 1, 1, 2,\n",
+       "       2, 1, 0, 2, 1, 2, 1, 0, 0, 1, 2, 0, 1, 1, 1, 2, 2, 0, 0, 0, 1, 0,\n",
+       "       0, 1, 0, 1, 2, 2, 0, 0, 0, 2, 2, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
+       "       1, 2, 2, 1, 1, 1, 1, 0, 2, 0, 2, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0,\n",
+       "       0, 2, 0, 2, 0, 2, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 2, 1, 2, 1, 2, 0,\n",
+       "       2, 0, 2, 1, 1, 0, 0, 0, 0, 0, 1, 0, 2, 1, 2, 1, 2, 2, 0, 2, 2, 0,\n",
+       "       2, 1, 0, 1, 1, 2, 0, 0, 2, 0, 1, 2, 2, 1, 0, 0, 1, 0, 2, 2, 0, 1,\n",
+       "       1, 0, 0, 0, 1, 0, 2, 2, 2, 2, 1, 0, 1, 2, 1, 2, 1, 1, 1, 1, 0, 0,\n",
+       "       2, 1, 2, 1, 0, 0, 1, 2, 1, 1, 0, 0, 2, 2, 0, 0, 2, 2, 1, 0, 0, 1,\n",
+       "       2, 1, 2, 2, 0, 2, 2, 2, 2, 1, 1, 2, 2, 0, 1, 2, 1, 0, 1, 0, 2, 1,\n",
+       "       2, 0, 0, 1, 2, 0, 2, 2, 1, 2, 1, 2, 1, 2, 0, 0, 1, 2, 2, 2, 0, 1,\n",
+       "       1, 1, 1, 0, 1, 0, 2, 2, 1, 2, 2, 0, 2, 2, 0, 1, 1, 2, 1, 2, 2, 0,\n",
+       "       1, 0, 2, 2, 0, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 2, 0, 2, 1, 1, 2, 2,\n",
+       "       0, 1, 1, 2, 0, 0, 2, 2, 1, 2, 2, 1, 2, 2, 0, 1, 1, 1, 1, 2, 2, 0,\n",
+       "       2, 2, 0, 0, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 0, 2, 1, 1, 1, 0, 1,\n",
+       "       0, 2, 1, 0, 1, 1, 0, 1, 2, 0, 2, 1, 2, 1, 2, 1, 1, 0, 2, 2, 0, 2,\n",
+       "       1, 1, 0, 1, 1, 2, 0, 1, 2, 0, 1, 1, 0, 2, 1, 1, 2, 0, 2, 2, 1, 1,\n",
+       "       1, 2, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 2, 2, 0, 0, 0, 1, 2, 1, 2, 2,\n",
+       "       2, 1, 1, 1, 1, 0, 0, 1, 0, 2])"
       ]
      },
      "execution_count": 4,
@@ -115,39 +115,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x22737c46d60>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.scatter(X[:,0],X[:,1],c=y)"
+    "plt.scatter(X[:,0],X[:,1],c=y)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,9 +185,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "## Elbow method To select K Value\n",
     "wcss=[]\n",
@@ -210,25 +226,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[1340.0000000000023,\n",
-       " 571.2724981447043,\n",
-       " 129.06920301404648,\n",
-       " 112.8929467989705,\n",
-       " 98.43762944408469,\n",
-       " 84.2587068347742,\n",
-       " 75.1016410696618,\n",
-       " 66.33725974517868,\n",
-       " 58.10165910172263,\n",
-       " 52.30910767336157]"
+       "[1340.0000000000005,\n",
+       " 419.722174664577,\n",
+       " 278.707707235791,\n",
+       " 238.42390623091075,\n",
+       " 197.42408797091883,\n",
+       " 177.24260080572822,\n",
+       " 146.71804503810756,\n",
+       " 128.8103903541581,\n",
+       " 111.65868393377545,\n",
+       " 105.05695194652985]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -239,19 +255,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -266,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -275,46 +289,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "array([0, 0, 0, 2, 1, 2, 1, 0, 1, 1, 2, 2, 2, 2, 2, 1, 0, 1, 1, 1, 1, 0,\n",
-       "       2, 1, 1, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 1, 0, 1, 1, 0,\n",
-       "       2, 1, 0, 0, 2, 0, 1, 0, 1, 0, 1, 2, 0, 2, 2, 1, 1, 2, 2, 2, 2, 0,\n",
-       "       2, 0, 2, 2, 2, 0, 1, 2, 1, 2, 0, 1, 1, 2, 1, 0, 1, 1, 1, 1, 0, 2,\n",
-       "       1, 1, 0, 0, 1, 0, 2, 2, 0, 1, 0, 0, 2, 2, 1, 0, 1, 2, 0, 0, 2, 2,\n",
-       "       2, 0, 2, 1, 2, 2, 0, 1, 0, 2, 0, 2, 1, 1, 2, 2, 1, 1, 0, 0, 1, 0,\n",
-       "       2, 0, 1, 0, 1, 2, 0, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0, 2, 1, 0, 2,\n",
-       "       2, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 0, 1, 0, 1, 0, 0,\n",
-       "       1, 2, 2, 0, 0, 0, 1, 1, 2, 0, 2, 0, 2, 2, 1, 2, 2, 0, 0, 0, 1, 2,\n",
-       "       0, 0, 0, 1, 1, 0, 2, 1, 1, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 1, 1,\n",
-       "       2, 0, 0, 1, 0, 2, 0, 1, 1, 0, 0, 1, 2, 2, 2, 0, 1, 0, 1, 1, 0, 0,\n",
-       "       1, 1, 0, 1, 1, 0, 0, 1, 2, 1, 2, 2, 0, 0, 2, 1, 1, 2, 2, 0, 2, 2,\n",
-       "       0, 0, 1, 2, 0, 1, 1, 2, 1, 2, 2, 2, 0, 1, 1, 2, 2, 1, 0, 1, 2, 1,\n",
-       "       2, 1, 2, 2, 1, 0, 0, 0, 0, 2, 2, 2, 0, 1, 2, 1, 0, 1, 1, 2, 0, 2,\n",
-       "       2, 1, 0, 2, 1, 2, 2, 0, 0, 1, 0, 1, 0, 1, 1, 1, 2, 2, 1, 2, 0, 2,\n",
-       "       2, 2, 2, 2, 0, 2, 0, 2, 1, 0, 0, 0, 2, 0, 1, 1, 0, 1, 1, 1, 2, 2,\n",
-       "       1, 0, 1, 1, 0, 1, 1, 0, 0, 2, 2, 2, 2, 0, 0, 1, 2, 2, 0, 2, 2, 0,\n",
-       "       0, 0, 1, 0, 2, 1, 1, 1, 1, 0, 2, 1, 0, 0, 2, 2, 2, 1, 0, 2, 2, 1,\n",
-       "       1, 1, 0, 2, 2, 1, 1, 0, 1, 1, 0, 0, 0, 2, 1, 0, 2, 0, 1, 0, 2, 0,\n",
-       "       2, 1, 0, 2, 1, 2, 1, 1, 0, 2, 0, 1, 2, 1, 0, 2, 2, 2, 2, 2, 1, 1,\n",
-       "       2, 1, 0, 1, 2, 2, 0, 1, 1, 1, 2, 0, 2, 2, 1, 2, 1, 2, 1, 0, 0, 0,\n",
-       "       2, 0, 0, 2, 1, 2, 1, 0, 1, 0, 2, 2, 1, 0, 1, 1, 1, 1, 2, 0, 0, 2,\n",
-       "       0, 0, 2, 0, 2, 1, 1, 0, 2, 1, 2, 0, 0, 0, 0, 2, 0, 1, 0, 1, 0, 2,\n",
-       "       0, 1, 1, 2, 2, 1, 0, 0, 0, 0, 2, 1, 1, 2, 1, 0, 1, 1, 1, 0, 1, 1,\n",
-       "       0, 0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 1, 1, 2, 0, 1, 1, 2, 1, 1, 2, 0,\n",
-       "       0, 1, 1, 1, 2, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 2,\n",
-       "       0, 2, 1, 2, 2, 2, 1, 0, 2, 2, 0, 1, 0, 1, 1, 0, 1, 1, 2, 1, 2, 2,\n",
-       "       2, 0, 1, 2, 1, 0, 0, 1, 0, 1, 2, 0, 0, 1, 1, 0, 1, 0, 1, 2, 1, 2,\n",
-       "       2, 2, 0, 0, 0, 1, 1, 1, 1, 2, 1, 2, 1, 0, 1, 2, 2, 1, 1, 0, 2, 1,\n",
-       "       2, 0, 1, 1, 1, 2, 0, 1, 0, 0, 1, 2, 1, 1, 0, 0, 0, 1, 1, 2, 0, 2,\n",
-       "       0, 1, 2, 0, 2, 2, 1, 1, 2, 1])"
+       "array([2, 0, 2, 1, 1, 2, 0, 2, 0, 0, 1, 0, 1, 1, 2, 0, 2, 0, 1, 1, 1, 2,\n",
+       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 2, 1, 2, 1, 1, 0, 2, 2, 0,\n",
+       "       1, 0, 0, 2, 1, 1, 0, 0, 0, 1, 2, 1, 2, 2, 2, 2, 0, 1, 2, 0, 1, 0,\n",
+       "       0, 2, 0, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 0, 1, 2, 1, 1, 2, 2, 0,\n",
+       "       1, 2, 0, 2, 1, 1, 2, 0, 0, 2, 2, 0, 2, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 0, 2, 2, 0, 1, 2, 0, 0, 0, 2, 1, 2, 2, 2, 0, 2, 1, 0,\n",
+       "       2, 1, 1, 2, 1, 1, 1, 0, 2, 0, 2, 1, 1, 2, 1, 2, 0, 1, 2, 2, 0, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 0, 2, 2, 0, 2, 2, 0, 1, 2, 0, 2, 1, 1, 1,\n",
+       "       2, 2, 1, 2, 0, 0, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 0, 2, 0, 1, 0, 2,\n",
+       "       1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 0, 1, 2, 0, 2, 2, 2, 0, 0, 0, 2,\n",
+       "       0, 2, 1, 2, 1, 1, 2, 2, 0, 1, 2, 0, 2, 1, 2, 2, 0, 1, 0, 1, 2, 2,\n",
+       "       0, 0, 2, 2, 0, 2, 1, 1, 1, 0, 2, 2, 1, 1, 0, 1, 0, 0, 2, 0, 2, 0,\n",
+       "       1, 0, 1, 1, 2, 2, 2, 0, 2, 0, 1, 0, 2, 2, 0, 1, 0, 2, 2, 2, 0, 0,\n",
+       "       2, 2, 2, 0, 0, 2, 1, 2, 2, 1, 2, 1, 1, 0, 2, 2, 1, 0, 1, 0, 2, 0,\n",
+       "       2, 2, 0, 1, 2, 0, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 0, 1, 2, 1, 0, 0,\n",
+       "       1, 1, 1, 0, 1, 0, 0, 0, 2, 0, 2, 2, 2, 2, 1, 1, 2, 0, 0, 2, 1, 0,\n",
+       "       1, 2, 0, 2, 1, 1, 2, 2, 2, 0, 2, 2, 1, 0, 0, 1, 1, 0, 1, 2, 0, 2,\n",
+       "       0, 0, 0, 0, 0, 1, 1, 1, 0, 2, 2, 2, 1, 0, 2, 0, 2, 0, 1, 0, 2, 1,\n",
+       "       0, 2, 1, 0, 0, 2, 2, 1, 1, 1, 0, 1, 2, 1, 2, 0, 2, 1, 1, 1, 0, 1,\n",
+       "       1, 1, 2, 2, 1, 2, 1, 2, 0, 2, 2, 0, 2, 2, 2, 0, 2, 2, 1, 0, 0, 1,\n",
+       "       2, 1, 2, 0, 0, 1, 2, 0, 1, 0, 2, 2, 1, 0, 2, 2, 1, 0, 2, 2, 1, 1,\n",
+       "       1, 1, 0, 2, 0, 2, 2, 0, 0, 1, 1, 2, 1, 2, 0, 1, 1, 1, 0, 2, 1, 2,\n",
+       "       2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 0, 1, 1, 2, 1, 0, 0, 1, 0, 1, 2, 2,\n",
+       "       0, 1, 0, 0, 1, 2, 0, 1, 0, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1,\n",
+       "       1, 0, 1, 2, 0, 2, 0, 2, 1, 0, 1, 1, 0, 2, 2, 2, 1, 0, 2, 0, 1, 2,\n",
+       "       1, 2, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 2, 1, 1, 0, 0, 1, 2, 2, 2,\n",
+       "       1, 1, 0, 1, 1, 1, 0, 2, 0, 1, 0, 1, 1, 1, 1, 2, 2, 2, 1, 0, 2, 0,\n",
+       "       0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 0, 2, 0, 2, 2, 1, 1, 1, 2, 2, 2, 0,\n",
+       "       1, 0, 2, 0, 0, 1, 2, 2, 0, 1, 0, 2, 0, 1, 0, 2, 0, 1, 1, 0, 0, 2,\n",
+       "       0, 0, 2, 1, 0, 2, 0, 0, 1, 2, 2, 0, 2, 1, 0, 0, 1, 2, 0, 0, 1, 2,\n",
+       "       1, 1, 1, 2, 2, 1, 2, 0, 1, 2])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,30 +356,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2, 2, 2, 0, 1, 1, 2, 0, 0, 2, 1, 2, 1, 2, 1, 0, 0, 1, 2, 0, 0, 1,\n",
-       "       0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 0, 2, 1, 2, 1, 2,\n",
-       "       2, 1, 1, 0, 1, 1, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0, 2, 2, 0, 1, 2,\n",
-       "       2, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 2, 0, 0, 2, 0, 2, 2, 1, 0, 2, 1,\n",
-       "       2, 0, 1, 0, 1, 0, 2, 1, 1, 2, 2, 1, 0, 0, 0, 2, 1, 0, 1, 2, 2, 1,\n",
-       "       1, 1, 0, 2, 2, 0, 1, 1, 1, 2, 2, 2, 2, 1, 0, 0, 2, 1, 2, 1, 2, 2,\n",
-       "       2, 1, 1, 1, 1, 1, 2, 2, 0, 1, 2, 0, 2, 2, 2, 1, 1, 0, 1, 1, 0, 1,\n",
-       "       0, 2, 0, 2, 1, 1, 2, 1, 2, 0, 1, 2, 0, 2, 2, 0, 2, 1, 1, 0, 2, 2,\n",
-       "       2, 2, 0, 0, 0, 0, 2, 2, 1, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n",
-       "       1, 1, 2, 0, 2, 0, 1, 1, 2, 1, 2, 1, 2, 1, 0, 2, 2, 2, 2, 2, 1, 2,\n",
-       "       0, 0, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 0, 1, 1, 1, 1, 0, 2, 2, 0, 2,\n",
-       "       2, 1, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 0, 1, 1,\n",
-       "       0, 2, 2, 1, 2, 2, 0, 1, 2, 2, 1, 2, 1, 0, 1, 1, 1, 0, 2, 0, 1, 1,\n",
-       "       0, 0, 2, 0, 0, 1, 2, 1, 2, 2, 1, 1, 2, 0, 1, 1, 1, 0, 2, 2, 2, 0,\n",
-       "       2, 0, 1, 1, 0, 1, 1, 0, 2, 1, 1, 1, 2, 1, 0, 0, 1, 0, 1, 1, 1, 1])"
+       "array([0, 0, 2, 1, 0, 2, 2, 0, 2, 0, 0, 0, 1, 2, 0, 0, 2, 2, 0, 0, 1, 0,\n",
+       "       1, 2, 1, 1, 1, 0, 0, 1, 0, 1, 0, 2, 0, 2, 0, 1, 2, 1, 0, 1, 0, 2,\n",
+       "       0, 2, 1, 2, 1, 2, 2, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 2, 2, 1, 2,\n",
+       "       0, 2, 0, 1, 2, 0, 0, 2, 2, 0, 1, 1, 0, 1, 2, 2, 0, 2, 1, 1, 2, 1,\n",
+       "       2, 1, 0, 0, 1, 0, 2, 2, 1, 1, 0, 1, 0, 2, 2, 0, 2, 2, 2, 0, 1, 2,\n",
+       "       2, 2, 1, 2, 2, 1, 1, 2, 0, 0, 0, 2, 1, 1, 1, 0, 1, 1, 2, 0, 1, 0,\n",
+       "       2, 2, 0, 2, 2, 0, 2, 2, 1, 2, 0, 0, 0, 1, 1, 2, 1, 0, 0, 0, 1, 0,\n",
+       "       2, 0, 2, 2, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2, 1, 2, 0, 1, 2, 0, 1, 1,\n",
+       "       1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 2, 2, 0, 2, 2, 0, 2, 0, 1, 1, 2, 1,\n",
+       "       1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 2, 1, 1, 1, 1,\n",
+       "       2, 0, 2, 0, 1, 1, 2, 1, 2, 1, 0, 1, 2, 2, 0, 0, 2, 2, 1, 2, 1, 2,\n",
+       "       2, 0, 2, 0, 1, 2, 0, 1, 1, 1, 2, 2, 2, 0, 1, 0, 2, 0, 0, 1, 0, 0,\n",
+       "       1, 2, 0, 2, 0, 1, 0, 1, 0, 0, 1, 2, 0, 1, 2, 0, 2, 2, 2, 1, 0, 1,\n",
+       "       2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 1, 2, 0, 1, 1, 0, 0,\n",
+       "       0, 2, 2, 2, 2, 1, 2, 2, 0, 0, 2, 2, 1, 2, 0, 1, 0, 1, 1, 0, 2, 2])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -368,39 +390,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x2273f345c40>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 23,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.scatter(X_test[:,0],X_test[:,1],c=y_pred)"
+    "plt.scatter(X_test[:,0],X_test[:,1],c=y_pred)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,16 +422,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: kneed in c:\\users\\win10\\anaconda3\\lib\\site-packages (0.8.1)\n",
-      "Requirement already satisfied: numpy>=1.14.2 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from kneed) (1.19.2)\n",
-      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from kneed) (1.5.2)\n"
+      "Requirement already satisfied: kneed in c:\\users\\mudas\\anaconda3\\lib\\site-packages (0.8.5)\n",
+      "Requirement already satisfied: numpy>=1.14.2 in c:\\users\\mudas\\anaconda3\\lib\\site-packages (from kneed) (1.26.4)\n",
+      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\mudas\\anaconda3\\lib\\site-packages (from kneed) (1.13.1)\n"
      ]
     }
    ],
@@ -431,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -458,7 +469,7 @@
        "3"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -479,13 +490,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "silhouette_coefficients=[]\n",
     "for k in range(2,11):\n",
-    "    kmeans=KMeans(n_clusters=k,init=\"k-means++\")\n",
+    "    kmeans=KMeans(n_clusters=k,init=\"k-means++\",)\n",
     "    kmeans.fit(X_train_scaled)\n",
     "    score=silhouette_score(X_train_scaled,kmeans.labels_)\n",
     "    silhouette_coefficients.append(score)"
@@ -493,24 +529,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.5791208264052049,\n",
-       " 0.7345149600137074,\n",
-       " 0.590061277437689,\n",
-       " 0.46306511562346064,\n",
-       " 0.3194384017662996,\n",
-       " 0.3260433893207664,\n",
-       " 0.3295975251169475,\n",
-       " 0.33870715403767093,\n",
-       " 0.33711491411125977]"
+       "[0.5844606489844084,\n",
+       " 0.44087033083332816,\n",
+       " 0.39092402192946496,\n",
+       " 0.3346532490001125,\n",
+       " 0.3307317574483552,\n",
+       " 0.334785074594518,\n",
+       " 0.33373056699260695,\n",
+       " 0.3476448433526777,\n",
+       " 0.35022943895812547]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,19 +557,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,7 +590,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -570,7 +604,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/18-Hierarichal Clustering/Hierarichal Clustering Implementation.ipynb b/18-Hierarichal Clustering/Hierarichal Clustering Implementation.ipynb
index 3ddd0716..1afe0463 100644
--- a/18-Hierarichal Clustering/Hierarichal Clustering Implementation.ipynb	
+++ b/18-Hierarichal Clustering/Hierarichal Clustering Implementation.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -172,7 +172,7 @@
        "[150 rows x 4 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -511,7 +511,7 @@
        "         7.90670654e-01]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -522,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -532,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -550,61 +550,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1b2ae6d5850>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 18,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.scatter(pca_scaled[:,0],pca_scaled[:,1],c=iris.target)"
+    "plt.scatter(pca_scaled[:,0],pca_scaled[:,1],c=iris.target)\n",
+    "plt.show();"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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       "text/plain": [
-       "Text(0, 0.5, 'Eucledian Distance')"
+       "<Figure size 2000x700 with 1 Axes>"
       ]
      },
-     "execution_count": 20,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
@@ -620,34 +597,442 @@
     "sc.dendrogram(sc.linkage(pca_scaled,method='ward'))\n",
     "plt.title('Dendogram')\n",
     "plt.xlabel('Sample Index')\n",
-    "plt.ylabel('Eucledian Distance')"
+    "plt.ylabel('Eucledian Distance')\n",
+    "plt.show();"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AgglomerativeClustering()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AgglomerativeClustering<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.AgglomerativeClustering.html\">?<span>Documentation for AgglomerativeClustering</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AgglomerativeClustering()</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "AgglomerativeClustering()"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from sklearn.cluster import AgglomerativeClustering\n",
-    "cluster=AgglomerativeClustering(n_clusters=2,affinity='euclidean',linkage='ward')\n",
+    "cluster=AgglomerativeClustering(n_clusters=2,linkage='ward')\n",
     "cluster.fit(pca_scaled)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -662,7 +1047,7 @@
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -673,39 +1058,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1b2af624a30>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 24,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.scatter(pca_scaled[:,0],pca_scaled[:,1],c=cluster.labels_)"
+    "plt.scatter(pca_scaled[:,0],pca_scaled[:,1],c=cluster.labels_)\n",
+    "plt.show();"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -715,7 +1089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -723,7 +1097,7 @@
     "\n",
     "# Notice you start at 2 clusters for silhouette coefficient\n",
     "for k in range(2, 11):\n",
-    "    agglo = AgglomerativeClustering(n_clusters=k,affinity='euclidean',linkage='ward')\n",
+    "    agglo = AgglomerativeClustering(n_clusters=k,linkage='ward')\n",
     "    agglo.fit(X_scaled)\n",
     "    score = silhouette_score(X_scaled, agglo.labels_)\n",
     "    silhouette_coefficients.append(score)"
@@ -731,19 +1105,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -787,7 +1159,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -801,7 +1173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/19-DBSCAN Clustering/DBSCAN Implementation.ipynb b/19-DBSCAN Clustering/DBSCAN Implementation.ipynb
index e24e38d2..64dfbde6 100644
--- a/19-DBSCAN Clustering/DBSCAN Implementation.ipynb	
+++ b/19-DBSCAN Clustering/DBSCAN Implementation.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,265 +23,265 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[-1.04155200e+00,  2.02937451e-01],\n",
-       "       [ 1.49039958e-01, -4.36691520e-02],\n",
-       "       [-9.48063597e-01,  3.80958522e-02],\n",
-       "       [ 1.11838024e-01,  1.08896949e+00],\n",
-       "       [ 5.81612525e-01, -2.83722184e-01],\n",
-       "       [ 1.05372013e+00, -5.59900004e-01],\n",
-       "       [ 1.89202171e-01, -7.03597031e-02],\n",
-       "       [ 1.54191019e+00, -3.59492608e-01],\n",
-       "       [ 8.33103985e-01, -4.57329478e-01],\n",
-       "       [ 1.28911430e+00, -3.04276010e-01],\n",
-       "       [ 6.96491444e-01,  6.88976643e-01],\n",
-       "       [-1.48474899e-01,  1.02535661e+00],\n",
-       "       [-7.86389564e-01,  6.34293605e-01],\n",
-       "       [ 5.98650996e-01, -4.52425308e-01],\n",
-       "       [ 1.79499086e+00,  6.04055027e-03],\n",
-       "       [-9.58546955e-01,  4.22612779e-01],\n",
-       "       [-9.68257288e-01,  3.51469656e-01],\n",
-       "       [ 8.44609790e-02,  1.18788202e-01],\n",
-       "       [ 6.79607749e-01, -4.30773986e-01],\n",
-       "       [ 1.95655827e+00,  1.69335269e-01],\n",
-       "       [ 8.38735325e-01, -5.28938139e-01],\n",
-       "       [-2.83832664e-02,  2.87732703e-01],\n",
-       "       [ 3.62814860e-01, -2.39887729e-01],\n",
-       "       [ 7.80966015e-01, -4.75112479e-01],\n",
-       "       [ 2.20991221e-01, -1.52704615e-01],\n",
-       "       [-7.43285660e-01,  6.92238648e-01],\n",
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+       "       [ 1.45006686, -0.31858949],\n",
+       "       [-0.8217458 ,  0.50185395],\n",
+       "       [-0.60862515,  0.82925197],\n",
+       "       [ 0.14036971,  0.98896613],\n",
+       "       [-0.7320256 ,  0.70825659],\n",
+       "       [ 0.10161245,  0.95127205],\n",
+       "       [-0.96936736,  0.03638311],\n",
+       "       [-0.94830988,  0.46798328],\n",
+       "       [ 1.09684292, -0.00326875],\n",
+       "       [ 0.37976996,  0.82587573],\n",
+       "       [ 0.94319799,  0.29193365],\n",
+       "       [-1.10194867,  0.18804544],\n",
+       "       [ 0.73717473,  0.66297009],\n",
+       "       [ 0.17895663,  0.06038958],\n",
+       "       [ 1.02187877,  0.0658728 ],\n",
+       "       [ 0.43949244,  0.90965283],\n",
+       "       [ 1.59598381, -0.32379657],\n",
+       "       [ 0.94984984,  0.03684798],\n",
+       "       [ 0.28699395,  0.99854077],\n",
+       "       [ 1.05938204, -0.48081154],\n",
+       "       [ 0.99766735,  0.23974295],\n",
+       "       [ 0.04375739,  0.38661925],\n",
+       "       [ 1.51942733, -0.27351272],\n",
+       "       [ 0.54342878,  0.80034923],\n",
+       "       [ 0.25782574, -0.15003618],\n",
+       "       [ 0.95558466, -0.49172668],\n",
+       "       [ 0.32761065, -0.29042719],\n",
+       "       [-0.5321903 ,  0.89002667],\n",
+       "       [-0.82685866,  0.54537262],\n",
+       "       [-0.79903646,  0.55895229]])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -292,39 +292,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x18eb63ae430>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.scatter(X[:,0],X[:,1])"
+    "plt.scatter(X[:,0],X[:,1])\n",
+    "plt.show();"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,265 +333,265 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
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+       "       [-5.43551532e-01, -2.22687832e-01],\n",
+       "       [-1.69566118e+00, -1.20957852e-01],\n",
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+       "       [ 2.34864899e-01,  1.01376882e+00],\n",
+       "       [ 2.59381787e-01,  7.81986669e-01],\n",
+       "       [-5.01262354e-01,  2.64213375e-03],\n",
+       "       [ 4.57587777e-01, -4.21098294e-02],\n",
+       "       [ 5.59520955e-01,  1.06435019e-01],\n",
+       "       [-7.60267959e-01,  1.50382049e+00],\n",
+       "       [ 2.67620632e-01,  8.33659354e-01],\n",
+       "       [ 5.61341642e-01, -1.37169132e-02],\n",
+       "       [ 1.57623931e-01,  1.16079553e+00],\n",
+       "       [ 6.15705948e-01, -1.60593709e+00],\n",
+       "       [ 1.57462361e+00, -5.10214104e-01],\n",
+       "       [-1.83825645e-01, -9.54789857e-01],\n",
+       "       [ 1.50509841e+00, -7.08539990e-01],\n",
+       "       [-1.06513526e+00,  1.26785782e+00],\n",
+       "       [-4.54702202e-01, -6.66073951e-01],\n",
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+       "       [-8.49847267e-01,  1.69721735e+00],\n",
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+       "       [ 1.27627885e+00, -1.14154794e+00],\n",
+       "       [-6.10420967e-01,  1.41610388e+00],\n",
+       "       [-4.48082014e-01,  1.66105489e+00],\n",
+       "       [-9.12829605e-01,  1.39802670e+00],\n",
+       "       [ 1.50994466e-02,  1.12965796e+00],\n",
+       "       [-9.82027488e-01,  1.48186568e+00],\n",
+       "       [-1.70986293e+00,  2.63473876e-01],\n",
+       "       [-8.27191806e-01,  1.36890031e+00],\n",
+       "       [-5.33803587e-01,  6.49594795e-02],\n",
+       "       [ 7.45743925e-01, -1.63779110e+00],\n",
+       "       [ 2.58507824e-01,  1.12878563e+00],\n",
+       "       [-1.63360081e+00, -2.39005723e-01],\n",
+       "       [ 1.35590430e+00, -1.03642858e+00],\n",
+       "       [ 1.64464198e+00,  5.07301233e-01],\n",
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+       "       [ 1.35924483e+00, -1.00991267e+00],\n",
+       "       [-1.34610416e+00,  1.11247494e+00],\n",
+       "       [ 1.57552446e+00, -4.68458643e-01],\n",
+       "       [-1.10868445e+00,  1.24073855e+00],\n",
+       "       [-4.01258370e-01, -3.20832608e-01],\n",
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+       "       [-1.62243001e+00,  3.91952378e-01],\n",
+       "       [ 1.63193026e+00, -6.88404007e-01],\n",
+       "       [-1.92762296e-01, -1.00694485e+00],\n",
+       "       [ 9.19255348e-01, -1.41786135e+00],\n",
+       "       [ 5.47189003e-01, -1.56091146e+00],\n",
+       "       [ 1.09029294e+00, -1.14822238e+00],\n",
+       "       [-1.51365548e+00,  5.12143778e-01],\n",
+       "       [-1.26937689e+00,  1.17471302e+00],\n",
+       "       [-4.10880054e-01,  1.49793334e+00],\n",
+       "       [-1.41081831e+00,  9.29849546e-01],\n",
+       "       [-4.55303573e-01,  1.42165023e+00],\n",
+       "       [-1.68285910e+00, -4.29849297e-01],\n",
+       "       [-1.65872305e+00,  4.43598263e-01],\n",
+       "       [ 6.85428171e-01, -5.10094454e-01],\n",
+       "       [-1.36479834e-01,  1.16788038e+00],\n",
+       "       [ 5.09320567e-01,  8.73191628e-02],\n",
+       "       [-1.83482361e+00, -1.22923813e-01],\n",
+       "       [ 2.73177002e-01,  8.38201324e-01],\n",
+       "       [-3.66651784e-01, -3.81266385e-01],\n",
+       "       [ 5.99504371e-01, -3.70169762e-01],\n",
+       "       [-6.80260142e-02,  1.33742364e+00],\n",
+       "       [ 1.25754274e+00, -1.15876017e+00],\n",
+       "       [ 5.16944908e-01, -4.28908522e-01],\n",
+       "       [-2.42819566e-01,  1.51730994e+00],\n",
+       "       [ 6.42490560e-01, -1.47651803e+00],\n",
+       "       [ 5.71753267e-01, -1.83013725e-02],\n",
+       "       [-5.21616965e-01,  2.78938416e-01],\n",
+       "       [ 1.16979382e+00, -1.05699863e+00],\n",
+       "       [ 5.11056740e-02,  1.11622132e+00],\n",
+       "       [-2.76252130e-01, -8.07113910e-01],\n",
+       "       [ 5.23518151e-01, -1.49860747e+00],\n",
+       "       [-1.96264756e-01, -1.09122915e+00],\n",
+       "       [-1.18176739e+00,  1.29770536e+00],\n",
+       "       [-1.51951583e+00,  6.00214352e-01],\n",
+       "       [-1.48762607e+00,  6.27696108e-01]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -613,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -622,16 +611,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DBSCAN(eps=0.3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DBSCAN<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.DBSCAN.html\">?<span>Documentation for DBSCAN</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DBSCAN(eps=0.3)</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "DBSCAN(eps=0.3)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -642,27 +1038,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
-       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
-       "       0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
-       "       1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1,\n",
-       "       0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,\n",
-       "       1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,\n",
-       "       0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
-       "       0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
-       "       0, 1, 0, 1, 0, 1, 1, 0], dtype=int64)"
+       "array([0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,\n",
+       "       1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0,\n",
+       "       0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
+       "       1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
+       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
+       "       1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n",
+       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
+       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
+       "       0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
+       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1,\n",
+       "       1, 0, 1, 1, 1, 0, 0, 0], dtype=int64)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -673,66 +1069,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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+8jk+d8pXK0twWJDHNo5UJ0f3RvLy9aN5Y+B7LP5mOSn/SXa2LN/h07iY2s1ytoq3UoqrB3bMVs/PiYOnmPj0VF6/+V2czqz/3Ac82SftAllc0/B+LafDyaCqQ+lpH8hTnV/mz1lrMuz/ftwvHNyWeT2v88nQpGemcvroGR9ejRCiNMjX5CYuLo5NmzaxadMmwDXVe9OmTRw6dAhw9agMGTIkvf2DDz7Ivn37ePbZZ9m5cyf/+9//mDlzJk899VR6m2HDhjF58mSmTp3Kjh07eOihh4iPj+fuu+/Oz5ciCkiDK+r69MXbqI33KdW+PN4xTIOYM3E+xeaJ3c9On6HXup1mff5Lf828jfw1dwN//vgXY4Z8zN2NnuDonuPpzbytOXVeWAXfB/v+V7u+rdDuurKyol1JxKqf17FwyrIsm9RrUZsXpw/DZre5khl1IakJCg306TKWpbEszfbVu3ntxnHp9Xgsy2LuxIVYlod1u5Tity+zjk0IUfrka3Kzfv16WrZsScuWLQFXYtKyZUtGjhwJwPHjx9MTHYDatWszb948Fi1aRPPmzXn33Xf57LPP6NGjR3qbgQMHMm7cOEaOHEmLFi3YtGkTCxYsyDTIWBRPFaqXo0P/1hhuHnGYNoOWXZtSvUFV7+eqUS7LnoSLOR1OKtWqkJNQM7n95Rup16JWpp4Kw1Tpj8XO98pYaf89fewsz3V/g9QUV8Xj+q3q+HStBlf41u5iB7cf5tfPl/DP8h206XW5+0TMDWUoZo//1e3+Kwe05btDE7nnzcF07N+Gzje14+nPHuLh9+/K1nXO985MHzObv3/fQmJcEtGnvSeqR/49lq3rCCFKrgKrc1OUSJ2bou3cyWiGdX6Zo3siMzyiMQxFhRrl+eDPNyhfLfNaS1l5se/brP9ts9uZPIFlAph5/DMCgnK5ZlSaxPgkfvpgHr/8bwFnj0dhmAZh5csQdSrG4+Oml6Y/lV65eESvN9m4ZAuWI3PMhqlo2bUZoxe85FM86xb8zYyxP7N1xY4MFYlREF4hjKiT0Zg2E621Tyua2+wmvyZP9+naAEu+XcGYIR+7eoqy+Ulj2gza9b2CF6c/Sd/g2z3GZ9pM+gztxmPj73PbRghR/Pn6/V2kxtwIARBRMYzxa0dz36jbqFavMgHB/lSuXZE7Xx/E/zaM8TmxARg6dgj+QX6ZeynSOlce+fCeDImN0+FM70XJicDgAG578UamH/mUX2K/Zk7s114TG8M0WPPrxvTfn/7sIULCgrNsazk1rXu28CmWz57/hhd6v83mpdsyJjYA2vXYrkbDqvQZ2o1rBnekRqOqXnu67AF2n64NrsHR4+79n2tcTA5uoZwOi51r92D3s9Ph+is89jQ5HU463+x+3J0QonSR5EYUScGhQdwyvB9Tdn/MnNhv+HrvBG59YUC2C+9dcml1Plr1Ni2uuSzD9ur1qzDy+6fpcZdreYK1v/7NM11epZf/IHoH3MrQ5k+z4Mulnsd5eKCUchUaVFnXvclA6wwzxBJik4iLinfbfNIzX7F/6yG3+wH+mruBGe/87LGN5bQ4vOsYl3VsxPNfPc7g5wd4TEJMm8GVA9p5POd5ruUYJvo8hsgdv7RkavALAzAMleXgZMM0aNr5Upp1bpyrawkhSg5JbkSJV+uyGoxZOJJvD/yPcUtfZdKmcXyx40OuvNH1Rf39u3N4sc/bbFmxI70GzIFth3n33v/x7r2f+FTnxR0/fzvVG1b1uNCmBhpcfmEMzS8TFnhsbxiKn8cv8HjdHz+Y69OYGsNQLEpbq+qqW9pTqVaFLKd0n1/h+6ZhfbN+DVpzNvIcp4+ewbIsZo79hZWzcregrWEadOjXGoAGrery+i/Pp89Is9nN9Dhbdm3K67Ofk2J+Qoh0RapCsRD5qWLNClSsmXHw8MHth/l0+FcAGcZ0nO9tWTh1GW37XE7nmzI+8khOTCbmTBwh4UEEhnieDTTg8d589OhnWe9UYLPb6H7X1emb/l6yJfNjpIs4HRYbF//j8Zo7/trt0xgay9KcjYwCwC/Aj7GLX+H5nm9ybE+kaywOrrE4/kF+vDzzaWo3vSTD8VprfvtyKTPG/syRXa4BvWWrRBB10vfaQVlVdVZKYdpN+j3SM31b6x4tmHH0U1b8uIb9Ww7iH+hP++uvoF7L2j5fSwhROkhyI0q1uZMWYdiMLAfvgqv3YPb4X9OTm+P7T/D1a9+zdNqfOFKdGKZBpwFtGPLKLRkWsrxY76Hd2LjkH/78aW2GL3LTZqA1PP/1Y4SVz9uB7b4uKWGYRobZYlXqVOKL7R+wZt5G1s7fSGqqg4ZX1KPr7VdmWogT4NPhX/PDe3My9DSdPe5b9WhlKOq1rM2xvZHERyegLhrw4x/ox2uzn6VKnYyzIP0C/Oh625XAlT5dQwhROklyI0q17at3u01swNWbs/fvAwAc3nWUJzq8RHxsQvoxltPiz5/Wsmbe37y77DUaXlE30zlM0+SlGcNYOGUZsz6ez4Gth7H72ejQvzU3Dbs+0zEtuzbl6J7jbntvTJvB5d2aeXxdV/Rowaqf13rsAToff697u/7n/CYd+rVOfyTkzva/dvPDe3MAvC7pkBVtaYa+cwf1W9Vh8dfL2bR0K1prmnRsRI+7r6FMREj2TyqEEEhyI0qx6NMxXgfmwoUZQh8+NJn4mIRMj3ssp0Vqcirv3Pkxn219P8uxH6Zp0uvervS6tytaa4/jQ65/pCdzJi50u9+yNP0e7el2P8BNw/qy4ifPlZeVoWjVvTlterf02M6d+Z8uSl8EMyce/vBuWlzTBIB+j/TM8AgqO5ITk4mLSiAkPAj/wLyZ0i+EKN5kQLEotX76YB4OL+tYGabBlQPacuTf42xets3tOBbLaXFox1F2/LXb63W9DXyt2agaz019FMM0MhQzNG0GylA88/nD1G7iefmFxu0b8tSkB1FuZhjZ/W3c8FhvXpv1LKZpeo05K/u2HMpRYqOUokmnRtzwWG+f2h/aeZQPH/qUGyvcTd/g23i07fMs+uoPDu86ypg7P6Z/+J0MqjaU/uF3MubOjzNUfBZClE7ScyNKrfmfLfY6E0prTf/He3N451Gfznlw+xEat2+Y69i63HoldVvU4pf//caGxf+A1lzetRnXP9KTWpf5tqp97/u60uyqxsyduJDtq3eD0lxyaQ1a92xBq+7NsxxDkx3BoUEolb1HUspQ2OwmD753l0/tNy7Zwkt938ZyWumJ1O4N+3jnrvFpY5Y0ltMVgCPVydJpf7L6l/W8v+INrwmgEKLkkuRGlEqWZRF1MsZru4at63HJpdV9HiQbGBKQ29DSXdK4Rq4r7lavX4UH370zjyLKqPPN7dm0bKvb/cpQ2P3sGRYHvaRxdZ6a9ECWY5P+KzE+idduHIsj1ZlhNtX5/59Vr5HTYZEYl8S7937C+DWjsvNyhBAliCQ3otSJi4pn/uTFGIZKX+MpK4ZpUKep6+7/so6NKFM2hNiz7hfZtAfYuaJHi7wOt8jqdvuVTBv1E2eOncv0uM4wDQKC/fnf+jEc33eS+Kh4KtepRINWdXyuR7N02koSYhKzHZfltNi1bg/7/jlInWaXeD9ACFHiyJgbUegO7TzKBw9M4oayd9E7cDAPtHyG+ZMX43Q48/xaJw+d4oEWz/DZiG89Jjbg+pK8dshVgKsY320v3ui+sYIbn+hDSHjWyyaURIEhgYz7/VWq1nVN1zbtJja7a/xOWPkyjFn4MtXqVeGK7s256pYONPRxxffzdq39F9OWs/FA4HpEKIQonaTnRhSqv3/fwot9R2E5nOmPGfZvOcT7D0xi5c/reG3WcGz2vPtn+sYt73Hm2FmvSyIoQ9Gubysu69gofduAJ/sQcyaWaaNmpQ/U1ZaryF3fB7pz15uD8izO4qJq3cp8vv0D1i3YxN+L/8HptLisQ0M63tAGu5/v61BlJTeJDUBAsMycEqK0klXBZVXwQpOUkMygakNJiEnIclCqUop7R93GwGf7uT2H1pq4qHgsp0VouTIeewZ2b9jLI62f9xqXaTPpcc81PPLB3fgF+GXaf/LwaRZ/vZzTR84QUSmcLrd1olq9Kl7PK7Jn1c/reOWGd3J0bEBIADOPT3at7yWEKDF8/f6WnhtRaOZMXEh8dILb/VprZn88n5ufuS5TxV2tNfM+XcTUV2aml/o3TINW1zbj2amPEl4hLNP5tq3clWWp//969adnaNf3Crf7K9Yoz60vDPB4DpF7bftcTtW6lYg8cMqnpSQudsvT10tiI0QpJmNuRKGZOdbzqtUAp4+eJfpU5llNE574gg8fmpxhDSPLabFuwSbuqPMIJw+fznSMr+M9yvxn5fG4qHj+3biPI7uP5WoRTZE9ps1k1IKXKFc1AiC9Xs/5BTMbd2gAypXUmnbTtVCoghse781tL3sYHyWEKPGk50YUin837iPqhG+LK5ppg1QdqQ7+3biffZsPeFwVOyk+mZHXj2Hi32MzbG9+zWVee22CygRSt0UtAM5GnuOz579NX0cKoHrDqgx55RauGdTRp9hF7lStW5kvdnzIshmr+PPHv0iMT6JOs0vo+8C1XNK4Bsf2RrL46+WcPX6OslUi6HZHZ6rWrVzYYQshCpkkN6JQbF62zacCcDUaVqVMRAjfvzuHGe/MzrIXJyt7Nx/g+L4TGRZerN2kJi2uacI/y7dn+ZhDGYrrH+5BQJA/505G83j7Fzl19EyGtaeO7j7G27d+QNTJaG543LcKuyJ3AoL86Xn3NfS8+5pM+6rWrcyQV28phKiEEEWZPJYShcaXBzzXDrmKSU9P5dPhX/mc2Jy3e8O+TNte+O4JqjdwDf49/5jDMF1vg7Z9LmfIa64vym/f+IFTR85kWlTzfDI26ZmpnDvpW8+TyF8pyaksnb6S8Y99zoQnvmD1nPU4nXlfRkAIUXxIz40oFJe2b+A1uzHtJq26t+CR1s/l6Bp2P9c/7+jTMSz4Yimb/9gGWnP1wI4EhwWx6ud1RJ2MpkrdSvS+rxtt+1yOYRikJKey4MulHgexWpZm8dfLufnp63IUW0mxf8tB5n+2hKP/HickIpirB3akbZ/Lc7xeVXbtXPsvL18/hqiT0emPL2d//CtV6lTirXkjqNGwWoHEIYQoWiS5EYXi1KHMA37/q0aDqiyd9meOVp42TIOmnS9l45ItvNJvDMlJKenjbdYv3Izd386rPz5D656ZV8SOOR1DckKy1/Mf3xuZrZhKEq01nz33DTPH/ZL+92OYBkunraT+5XUY/dtLhJYr4/1EuXDy8GmevfZ1khNcyzs4Uy/01pw4eIpnurzGFzs+yPUaWkKI4kceS4lCsXL2WrxNXjq27wQnDp5MXxgxO7rd3pnEuCRevn50hsQGXGsTpSal8soN73B834lMxwaFBoG3iVVaE1yKqhH/19xJi5g57hfgwhpP53u69m4+wBu3vJfvMfwyYQHJCSlZ9rBZTouzkedY9NUf+R6HEKLokeRGFIrEuCSvg4kdyamUKVsGw/S9ZD9Ak06NeGLiUOZOXIgjxZHlDCmtNU6HxZxPfsu0L6hMIG16tUwfi5MVp8Pi6oEdshVXSWFZFtNHz3K/32mxaelW9mzan69xLJu5ymv9m+Xfr87XGIQQRZMkNyLfHNsbyZSXpzP2nglMfvZr9m85mL6v1mU1PCYPADZ/GxEVQz0+klKGIjgsiHJVy9KkYyPe/vUF3vvjdfz87ayes97zuBmnxapf1me5746RN6OUSh90/N9rdhrQhrrNa3mMv6Q6svs4J708VjRMg7Xz/87XOJLiPT86RENCjPsikb5wOpxsWbGDVT+vy/DvVwhRtMmYG5HnLh6PYZhG+uOnmeN+odvtnbnjlZvRGq933alJDr596yciKocTfTI600KX51ee/mTDOxmmfJ/nSPE+YyY1OTXL7Y3a1OeNOc8z5o6PiD4di2k30U4LS2uuHtiBpz97yOu5SypHisNrG6WUT+201vw1dwOzPprPrrV7sPmZtO3TigFP9qFei9oej611WQ22rNjh9t+RaTOok8MENOZMLD9+MI+5kxYSczo2fXu9lrV54pP7adSmfo7OK4QoGJLciDx15N/jTB89i9++XApkTmAWf7Ocxd8sxzANDNPwmOCcrwZ8LjKK+lfU5d8Ne1G4vjgtS1O1biVenPZUlokNwGUdGnB8X6Tbnh/TZnBZh4Zur9+6RwumHZnE6l/Wc3DbEQJCAujYv3WpLxJXtV5lAoL9PfacOB1OGlxR1+N5tNZ8MmwKsz6cn+Hfwu/frWDJtyt44bsnuerm9m6Pv+6hHmxets1DDBZ9H7jWy6vJ6NyJKCY98xVLp/2Z5arx+zYfYNjVr/DBijdo0Mrz6xNCFB5JbkSeOLj9MO8/MIltK3f51D47awUpBYmxiXyz73+s/fVvUpNSqduiFs2uauxxSYXrHu7Jb1OWud3vdFj0e7SXx2vb/ex0vqk93ORzuCVeQJA/ve/rxuzxv2b592iYBmWrRNC6VwuP5/lz1lpmfTgfyPjvwemwQMHo2z/kso4NKV+1bJbHX3ljW64e2IFlM1dlKCtwvjjkjU/1pXF798nrf8WcieXxDi9y8tDpLBMbcJUAINXJ5Ge/YeySV3w+txCiYMmYG5FrR/49zuPtX/Q5sckureHIrmOEVQjluge7M+DJPjS/+jKva0U1vKIu94+5HbiwHhFcKNp31xuDPPbcCPfuemMg9VrWzjQmybQZ+Af68eqPz3itdTPrw3nux12lPbb8dfISt8cbhsHz3zzOQ+/eRcVLKqRvr9agCg++eyd3vzXY9xcETB89y5XYeEm8zw+YPnnoVMaQtcaysleyQAiRP5QuhSsB+rpkuvDNm4Pe44+Z+T8rZV7id/j527N93MYlW/jx/TlsXroNrTXNOjdmwFN9ad2jRd4HWYokJSQz55OFzJ24kMgDJwkMCaDrbVdy41N9fXp01ztwMKnJnsfltO7Zgrfnv+j1XJZl8feSLfz4/lzWL9yMtjR2fzsd+rfmpmF9qXVZTQKC/N0e73Q6ubH8PR5Xqf+vD1e9ReN2Dfj79y3MHPsLGxf/g7Ys6raozYAn+tDtjs4+L9YqhPCNr9/f8lhK+MzpdLJ52XZOHzmDX4Ady7JIiEnkj3yebmuYrrExOUlsAC7v2pTLuzbN46hEQJA/Nz99XY6rNBuGl45j5VoZ3Bdblu/gpetGYzmc6VP/U5NT+WPGKv6YsQqA8tXK0u/RXvR7pAeBIYEZjk+MTcpWYgMQUSmMX/73Gx8/+lmGMUN7Nx/gnbvGs/mPbTz92UOS4AhRCCS5ET5ZOXst4x/7nNNHzxb4tS2nxS3D+xX4dUX+at2zhWsdKA9T/Vt1b+71PE6Hk1G3f4gz1YmnjujTR8/y+Yhv+emDubz3x+tUb1A1fV9AsD+m3cxQ5dgdZSgata2P02Ex/rHPgYxjhs4nV799uZTWPVt6HBQthMgfMuZGeLV6znpevXEsp4/lX2JjmAY2Pxso0u90jbRxMkPHDqFd31b5dm1ROG4cdh1ON+NbDFMREh7MtXd09nqedQs2cebYOY+JzcXOnYzm+Z5v4ki98EjMZrfR+cZ26f/m3FJgGIr7R9/OvEmLsqyDdOE1GMz+eL5PMQkh8pYkN8IjrTUTh01xrUaQy9FZLbo0wbSbmQaRGqZBYJkAPlr9Fk9NfIDWvVrQ7KrG9H+kF59te7/UL05ZUjXp2IinJj2IMlSGfxNKKYJCgxi94CWCw7wvcXFw+xGPSUYmGk4cOMXqORsybB78wgBsNtPjuSrVrMBb816g6ZWXsnvDXq9FIvf8nb9VmoUQWZPHUsKjXev2cGxv5vWXsqvXvV0YNvkhdq3bw5SRM1i/cBNo1+yaK29qx12vD6JavSrUb1mH3vd3y33goljofV9Xml/dmHmTFrFjzb/Y/W207d2K7nddTZmIEJ/OERDsn+USG54YpsGG3zZx5YC26dtqN6nJmEUjeWvwB5w+cgbDNNCWRmtNozb1uOOVW7iiR/P0sUJ+AX7p087dsfnJR6wQhUHeecKjs5FRuT5H4/YNeOj9uwBo2Loeo359kejTMcSciaVs5XCf7s5FyVWtXhWGjh2S4+PbX38F4x//PNs9i06Ha3xNzNlYThw4RXBYEJd1aMg3+yewYeE/7N9yCP9AP9pd14rKtSpmOr5d31auJN0N02bQsX+b7AUlhMgTktwIj8pXy7qAmjs2P5OwCmEkxydRtX4Vrn+oB9cM7pRpplNY+VDCyss0fJF7FWuUp8utnfj92z99PsayLKrVr8Jbt77Pih/+Sh/UXLtpTe56fRAd+rWmTa+WHs/R7Y7OfP3698Sejcv8eEq5/ufGJ/tk89UIIfKC1LmROjceaa2597KnOLL7mNeuf8M0uOHx3jz47p0FFJ0QLinJqTzS+nkObD3kta1SEBAcgH+QP7FnYzPM1lJKobXm6c8fpufd13g91/4tB3mu+xucOxGNYRiuQc0K7H42Xpz2FB36tc7V6xJCZOTr97ckN5LceLVh0WZe6P12+viDrJg2g7AKYXyyYQxlK0cUcIRCuBLx8Y99zpyJC90m4spQ2PxsNO3UiM3Ltrmdhu4f6MeM45MJDg3yet3kxGSWzVjF+t824XQ4adSmPj3uvkZ6JoXIB5LceCDJTfatX7iZCY9/zpHdx7Pcf0X35jw56QEqXVQGX4jCEHsujpWz13Fw+2G2rtjBiYOniIuKJzAkgKtu7kDPe7vwRIcXcXiqaaPgqYkPyOB2IYoYqVAs8tQV3ZvzxY4P2bl2D6ePnCEoNBBHqhNHioPaTWuW+pWyRdFRJiLE4yOlgzuOeE5sAJvN5NjeSI9tju8/wV9zNpCcmELtpjW5okdzr+tpCSEKRoEkNxMmTGDs2LFERkbSvHlzPv74Y9q0yXoWwdVXX80ff/yRaXvv3r2ZN28eAHfddRdTp07NsL9Hjx4sWLAg74MX6ZRSXNq2PrStX9ihCJFjIeHeZ+dZlnbbLikhmffu/4Sl01eiUKBcVYnLVyvLi9OfoknHRnkdshAim/K9iN+MGTMYNmwYr7zyChs3bqR58+b06NGDkydPZtn+p59+4vjx4+k/W7duxTRNbr755gztevbsmaHdtGnT8vulCCFKgHJVIrisY0MMD8X6LMviqls6ZNqutebNQe+zbMYq0K7fz4/vOX30LM9c8yr7fRjULITIX/me3Lz33nvcf//93H333TRu3JiJEycSFBTEF198kWX7smXLUrly5fSfRYsWERQUlCm58ff3z9AuIkIGsRYlTqeTdb9tYu6kRSz/YTWJ8UmFHZIQ6e58baCrLE4W+Y0yFN3vvJoqdSpl2rdz7R7WzN3gdsCy0+HkrcEf5GmsQojsy9fHUikpKWzYsIERI0akbzMMg27durF6tW8rSX/++ecMGjSI4OCMXcTLli2jYsWKRERE0KVLF958803KlSuX5TmSk5NJTk5O/z0mJiYHr0b4as28Dbz/wCTOHDuXvi0wJIA7XrmFm4b1lVWSRaFr2aUpL898mnfv+x/xUQmYdtNVq0ZDz7uv4bEJ92V53NJpf7oSIg/TMA5uO8ypI6epUL18/gQvhPAqX5Ob06dP43Q6qVQp4x1QpUqV2Llzp9fj165dy9atW/n8888zbO/ZsycDBgygdu3a7N27lxdeeIFevXqxevXqLAf0jRo1itdeey13L0b4ZOPif3i535hMNekT45L4dPhXWE6Lgc/KCt+i8F05oC1te7fkz1lrObr7OEGhgVx5Y1sq1nQ/4+/0sbM+VUJe8s0KBj1/Qx5GK4TIjiI9W+rzzz+nadOmmQYfDxo0KP3/N23alGbNmlG3bl2WLVtG165dM51nxIgRDBs2LP33mJgYatSokX+Bl2KfPvs14H69na9enUHfB6/1qX6IEPnNL8CPLoM7+dw+rFwZn9rFxyTkNCQhRB7I1zE35cuXxzRNTpzIuPDiiRMnqFzZ89Th+Ph4pk+fzr333uv1OnXq1KF8+fLs2bMny/3+/v6EhoZm+BF579DOo+zddMBjJeOUpFRWzlpbgFEJkXd63Zf55ikrFWvIIykhClO+Jjd+fn60atWKJUuWpG+zLIslS5bQvn17j8d+//33JCcnc/vtt3u9zpEjRzhz5gxVqlTJdcziAqfDSWJcotuqxP8VdTLaaxvDNHxqJ0RR1KBVXao38Pw5Y9pNrhqYeaaVEKLg5PtsqWHDhjF58mSmTp3Kjh07eOihh4iPj+fuu+8GYMiQIRkGHJ/3+eef079//0yDhOPi4hg+fDh//fUXBw4cYMmSJfTr14969erRo0eP/H45pcKu9Xt57aZx9A68letDhzCw2lC+eeMHrzOeKtTIekD3xSynRQW5qxXF2MgfnsHm5/6J/j1vDia0rG+Pr4QQ+SPfx9wMHDiQU6dOMXLkSCIjI2nRogULFixIH2R86NAhDCNjjrVr1y7+/PNPFi5cmOl8pmnyzz//MHXqVKKioqhatSrdu3fnjTfewN/fP79fTom3Zv5GXun/DqDTVzo+FxnF16/NZNXP63h32asEhgRmeWyV2pVo0qkR21fvzrxKcpqg0EA69Lsiv8IXIt/VblKTj/96mw8f/JSday88Cg+vGMaQV2/huge7F2J0QgiQtaVk/M1FkhKSGVRtKAkxWT+KMkyDm4Zdx/1j3D8q3LVuD09dNRJnigMri7E3T3/2ED3v6ZKncQtRWA5sO8yxPZEEhwVxWceG2OxFeo6GEMWer9/f+f5YShQff8xcRXx0gtsxNpbTYt6ni4iNimfPpv3s33oIpyPjGj0NW9fjvWWvUbdl7QzbK9YszwvfPSmJjShRal1Wgw79WtP86ssksRGiCJF3o0i3b/NBbHbT46KC8dEJDK4+lOSEFAAiKodz89PXc+NTfdIfLzZqU5//rRvDgW2HOXHgJGXKlaFRm3qZHj8KIYQQ+UGSG5HOL9DPbX2ai51PbMA1HufT4V9x5N9jPPnJ0AzVh2tdVoNal0k9ISGEEAVLbqVFujrNamZ6zOSr+Z8uZsdfu/M4IiGEECL7JLkRJCcm8/ot7/L2rR/m+BymzWD+Z0u8NxRCCCHymTyWKmV2rdvD3EmL2PP3fgLLBND5xvZsWrqV1b+sc3uMMpTHqsMATofFkd3H8zpcIYQQItskuSkltNZ8+dI0po2ahWkzcDosULBl+Q6vx5arEoHD4STqhPvKwoahCC0bkpchC1GsrF+4iW/f+JHDu4/hH+hH55vbc/3DPahSu5L3g4UQeUoeS5USS6evZNqoWYCrlwXwaXVjgNNHz3LlgLYYpvt/LpaluSYbCxAKUVI4Uh082eklRvR8i60rdxJ9KoaTh07zw7tzuOfSJ1m34O/CDlGIUkeSm1Ji5juzUYby3tCNq27pQHBoYJYJjmkzqNWkBp0GtMniSCFKthG93mLbql1Z7nOkOHjlhrGcOxFVsEEJUcpJclMKxEfHs3fzQa/jZtyxB9hp0KoO7y57jcq1KwKuhOZ8otO4fUPeWfwKdj97nsUsRHGwZ9N+Nv2+1WOb1JRUfv389wKKSAgBMuamVHC6WefJF4bN4NrbOxMYEkjtppfw5c4P2fT7Vnb89S+mzaBV9+bUv7xOHkYrRPGx5JsV3htp2LxsK7e+MCD/AxJCAJLclAplIkKoWrcSx/eddLu0AoBSKsN+w2ZQoVo57npj0IVthsHl3Zpxebdm+RqzEMVB1Cn3g+wvlj7OTQhRIOSxVCmglGLAk33dJjbKUASWCaBx+wbp2/wC7PS6pysfrxlFRKXwAopUiOKlfLWyPrVLik/yeGMhhMhb0nNTSlz3UHe2r97F79/9iWEaWGmPqgzTwC/AztvzX6RJx0acOxlNQkwC5aqWJSDIv5CjFqJoS4pP9qndrnV7mT95MX2GXuuxnWVZpCSl4h/ol2EpEyFE9ihdCm8nfF0yvaSxLIuVs9by8/8WsP+fQ/gH+XH1LR24/pGeVK5VsbDDE6JYiTxwkjvqPuJbSQUF1etX4YsdH2aZtBz59zjTR8/i9+/+JDU5lTIRwfQZei03D7+e0LJl8j54IYopX7+/JbkpRcmNECLvTH1lBt+9/VN6L6gvZkZ+RkTFsAzbdq3fy/Aur5KSlJJhbI5hGlS6pAIfrnor0zFClFa+fn/LmBshhMiB4/tO5PoclmXx9uD3SU5MyTTo2HJanDh0ik+empLr6whR2khyI4QQORASHuzzuBilFFXrVSa8QsY7zU1Lt3Fs7wm3vT+Ww2L596uJPh2T63iFKE0kuRFCiBy4ZnAnnA6nT2211tw07LpMydC+zQcwDM8fw06Hk8M7j+Y4TiFKI0luhBAiBxq3b0Cr7s196r3pdV9X+j6QeaaUX4AfWnsfs2MP8MtRjEKUVpLciBzZv/UQsz6az08fzGPX+r2FHY4QBU4pxb1v3+q1fs3Nz1zPU5MeyDIJatO7pdfJVhGVw6nXolbOAxWiFJI6NyJbzp2M5u3BH7Bp6db0hTi1pWlwRV1enjlMppSLUmXx18sxbAaWmwrEhqHYsny7296dyrUq0mVwJ5bNWOV23M3g52/AtJl5FrMQpYEkN4VMa83aX//ml09+48CWQwSGBHD1wI70eeDaQpv+6XQ62fT7Vk4dPkNYhVBadW+On7+dlKQUhnd5lcO7jrliv2ghzj2b9vNU55FM2jRW6nKIUmPPpv1uExsAy9Ls33qIyAMnWbdgE44UB/Va1qZJp0bpCc9Tnz5I3Ll41v76N6bNRFsWGArLYXHL8H70f6xXQb0cIUoMqXNTiHVuLMvi3Xs/YeHUZRmrBhuK4PBgxv3+KnWaXVKgMa36eR0fPTKZM8fOpW8rExHMfaNvx7SbjLvnf26PVYbinrduZdBz/QsgUiEK34t932bdr5s8Ppqy2U0caQOPlVJoS1Pz0mq8NP0pajd1vb+11mxfvZsl364g9mwslS6pQM97ulC9QdUCeR1CFBdSxM+DopLc/DxhAeMf+zzLfYZpULZKBF/vHY/NXjAdbGvmb+Tl60aj0VlWXa3esCpH/z2eocfmv2o0qsYX2z/IvyCFKELmT17M+w9M8txIken9ZJgGQWUC+Wj1Wxzfd5LEuCRqXlqN2k1qZnkKR6qD37/7k7mTFnFsbyRlIkLodkdnrnuwO6HlpKdUlB6S3HhQFJIbrTV31n+U4/tPeizfPvL7p7nyxnYFEs89lz7B0X8jPS6w6SmxAQirEMoPJ7JO2IQoaRLjk7in0ROcjYzKVqVicL2fTJuBI+XCdPJL29bnqckPZkhyUpJSeLHPqPRxbuffg8pQRFQK570/XqNavSp584KEKOKkQnERdzYyiuP7PCc2pt3kn+XbCySePX/v58ju4x6717Wl0wcRZ0UZisq1ZUCxKD0CgwMYu+QVKlQvB4BhKldPjQ+0pTMkNuBaiuHJTi9x5N/j6dumjpzB5j+2pR9z8fFRJ6N57cZxsuK4EP8hyU0h8XXB37xeGfj00TN89epMRvR6k5euG8Xsj38lPjqecyeifTreU8+NtjR9vax6LERJU71BVabs/ogR3z5BWPnc9QRbToukhGS+fm0mAEkJycyZtNDt+85yWuzfcohtK3fm6rpClDSS3BSSiErhVK1X2WPy4kx10vzqy/Lsmn/MXMXtdR7h2zd/ZP1vm1kzfyP/e/JLbq/zCHHn4nw6x6Vt62cZs2EaNOnUiK63X5ln8QpRXNjsNpypTtdNQi47USyHxR8zV5MYn8ShHUdIjE3y2N4wDbau3JW7iwpRwkhyU0iUUtw07Dq33cmGaVCxZnna9W2VJ9fb8/d+3r7tQ5ypTiwrbWyAdo21SYhJ5ONHP6dm4+oeHzuFlS/DmMUvM3jEDQSFBqZv9w/04/qHezBqwUvY/ex5Eq8Qxc28Txd5fP9kh9PhJPZMrNelGc4z8ui6QpQUUuemEPV94Fr2bNrP/E8XZygEpgxFmYhg3pr3Qq6Ld21csoUf35/D+gWbsDx0bcdHJ9Dl1k4c3X0Mi6wfPz38wd0EBgdy95uDufXFAezddADL0tRpdglBZQIzn1iIUuT4/hNeB9z7yrQZlCkbQkTlcMqUDSH2rPueVctp0aJLkzy5rhAlhfTcFCKlFE9+MpQxC1+mw/WtqVavMvUur829b9/GFzs+pNZlNXJ1/u/H/cJz177O+oWb3SY252mtObzrGKN/e5majapl2FexZnlenjmMLrdeeOTkH+hP4/YNadKxkSQ2QuCaKejLYGLT5vlj1zANOt3YjsCQQOx+dgY80cftGD3TZnBZh4Y0aFU3BxELUXJJz00hU0pxebdmXN6tWZ6dU2vNV6/O5Js3fgDwWEH1YqnJqbS4pgmTt7zHvxv3pVcobty+gc/d40KUVt2HXM2nz37tcebSLc9cz83Dr2f6qFn8+MG8TPsN08A/0I8hr9ySvm3wiBs4uP0wy2asSu/hVUqhtaZK3cq8/P3T+fJ6hCjOJLkpYbTWfPTIZOZOXJSt4wzToHG7BoAr4WrQqq7cDRYibcVB4mx00s9gRYFZCxU0EPy7oJQkmkVRr3u7MHv8r5w6cibTDYVhGlSrX4Uhr92Cf6A/Q8cNITg8mJljfyYpPjm9Xe2mNXnmi4cz9J6eOHiK8tXLUb1BFaJOxWCzmVStV4Ve93bhmsEd8Q/0L7DXKERxIUX8CrFCcX5YM28DL103OtvHGYbiy10fUbVu5XyISmSHdh5Hn70NnEfPbwFMwAn+3VDhH6KUDNwuik4ePs1bg95n++rd6bMKtda06NKEF759gohK4RnaJ8Yl8veSrekViutfXifD/qXTVzJmyEdozYXlWUwD02by6k/DadOrZYG8LiGKCqlQ7EFJTm5e6P0WGxb943O1VNNm4HRaDPv0QXrd2zWfoxPeaK3RZ24Cx3bAmUULBcEPY5R5oqBDE9mw5+/9bP1zJyhocU2THI2f27/1EA+2HJ7le1kpsPnZmLLrIyrWrJAXIQtRLPj6/S2PpUqYPZsO+J7Y2E06XN+aG5/qy2UdGuZzZMInqf+AY4uHBhoSvkaHPIRSfgUWlsieei1rU69l7VydY/ZH890OJNYanA6LORMXce/bt+bqOkKURJLclDD+gb594dn9bXy+7QOq1KmUzxGJbElZi2sSo4cEVceA41+w512BR1H0rPttE04PkwEsp8X63zZJciNEFmRkYgnT+cZ2GKbnv1bTZvLa7OcksSmSfH1KXOqeJpc6vvTAZnexTiFKC0luSpjrH+mJ3d/usWJp7aY18nzNKpFH/K7AY68NgAoBW/0CCUcUnqadL/VYE8ewGTS7qnEBRiSKK601OmUdVsyrWFFPY8V+iE6fsFAyFUhyM2HCBGrVqkVAQABt27Zl7dq1bttOmTIFpVSGn4CAgAxttNaMHDmSKlWqEBgYSLdu3fj333/z+2UUC5UuqcCoX18kMNR9Yb19mw8xotebLP5meQFGJnxibwm2xrhmR2VFQdDtKCXTf0u6Gx7r7fmxlMPi5OHTrJy9Fqcjq8HnQrjKSuhzd7pmYCbMgKR5EP8J+lQXdNykwg4v3+R7cjNjxgyGDRvGK6+8wsaNG2nevDk9evTg5MmTbo8JDQ3l+PHj6T8HDx7MsP+dd97ho48+YuLEiaxZs4bg4GB69OhBUpLnBeZKi6ZXXsq0w5No2bVplhVTLcsCDe8PnUhcVHzBByjcUkqhwseDURHXX975v8C0t6rflaiQRwspOlGQGrdvyAPjhgBuqhor+GvuBl4dMJahzZ/m1JEzBRyhKA509DNpY/nANQPTSvvR6Lh30YmzCy22/JTvyc17773H/fffz913303jxo2ZOHEiQUFBfPHFF26PUUpRuXLl9J9KlS6MDdFa88EHH/DSSy/Rr18/mjVrxldffcWxY8eYPXt2fr+cYsO0mexc+6/HoRmpyQ6WfLui4IISPlG26qjyc1BlngNbIzAqg701KuwDVMQkmSVVitw07DreX/EGnW5oS3B4UMad+kL18aP/HmdEr7cuLIorBKAdeyD5d9w/6lbouAkeq2oXV/ma3KSkpLBhwwa6det24YKGQbdu3Vi9erXb4+Li4rjkkkuoUaMG/fr1Y9u2ben79u/fT2RkZIZzhoWF0bZtW7fnTE5OJiYmJsNPSRd1MprEWM89WabN4PDOkv3ctbhSRigq+B6M8j9jVFyOUe5rVGBvlMrdQqqi+GnSsREvzRhG/ZZ13E4WcDosDm47zLoFmwo2OFG0JS3B89e8BudB108Jk6/JzenTp3E6nRl6XgAqVapEZGRklsc0bNiQL774gp9//plvvvkGy7Lo0KEDR44cAUg/LjvnHDVqFGFhYek/NWrkbkHK4iAg2PuYDK01gbLopRBFXnJiMpuWbvU4O8q0mfw1Z30BRiWKOq2T8OlrXpe8IR1FbrZU+/btGTJkCC1atOCqq67ip59+okKFCkyalPOBTyNGjCA6Ojr95/Dhw3kYcf5KjE9i68qdbP1zBwmxiT4fF1q2DM2uauxxWrjTYdH5pnZ5EaYQIh85Uhw+tNKkJKfmeyyi+FD2RoC3fzv+YFYviHAKVL4W8StfvjymaXLixIkM20+cOEHlyr6tYWS322nZsiV79uwBSD/uxIkTVKlSJcM5W7RokeU5/P398fcvXrNLUpJTmfLSNOZMXJi+sJ5/kD+97+vKvaNu9WmxvNtfvonnrn3DNSb1P49UDdOg1bXNMq1lIwqeTtmMTvgGUjeB8gP/rqigwSizitdjRekQFBpE+erlOO1h0LDl1NRtXqvgghJFn38XMMqBdY6sx90oUDb0mZvQAV1QgbeibCUj0cnXnhs/Pz9atWrFkiVL0rdZlsWSJUto3769T+dwOp1s2bIlPZGpXbs2lStXznDOmJgY1qxZ4/M5izqn08mrN7zDD+/PzbBicHJCMrPH/8qLfUbhSPV+J9eyS1Ne+O4JVyKkwGY302ddtOrenJdmDMu31yB8o+M+QZ+9GZLmup57O/6F+E/Rp3qgk9cUdngiH2mt2bR0KxOfnsrHj37G/M+WkBif9eMBpRT9H+2Fcle/Srmqjl875Kp8jFgUN0rZUWEf4OrHyGq8ngYdD859EP8l+nQvdPLK9L2W4zBW1LNYp7pjnboWK+oZdOo/BRR97uT7wpkzZszgzjvvZNKkSbRp04YPPviAmTNnsnPnTipVqsSQIUOoVq0ao0aNAuD111+nXbt21KtXj6ioKMaOHcvs2bPZsGEDjRu7ClaNGTOG0aNHM3XqVGrXrs3LL7/MP//8w/bt2zPVxMlKUV84c8VPa3j9pnEe2zz/9eN0ve1Kn86XEJvI79/9yaEdRwgMCeDKG9vlet0bkXs6eTn63H1u9hqgAlAVlqGM8IIMSxSAs5HneKnvKP7duB/TZoICp8NJYEggL01/KsvVvlNTUnmxzyg2/b4VjU7vjTVtBlrDi9OepPNNJeMGT+QtnboTHf8pJC3A82MqBQRAhSUQNwESv826WcBNqLA3UargR7YUmYUzBw4cyKlTpxg5ciSRkZG0aNGCBQsWpA8IPnToEIZx4Q/o3Llz3H///URGRhIREUGrVq1YtWpVemID8OyzzxIfH8/QoUOJioqiU6dOLFiwwKfEpjiYP3kxhmm4HTxoGIp5kxf5nNwElQmk7wPX5mWIIg/o+C9w3U1lVYDNAp0IiT9B8D0FHJnIT06Hk+d7vMmhHUfSfz8vKS6Jkf3GMH7NqEw3IHY/O2/NG8Gc/y1k9vj5HN93EsM0aHfdFQx8tj+XtpWq1SJryt4IFf4eWr+DPnsPpK4l68dUGkiC6OGQsjKL/WmSfgBbDQh5KJ8izr1877kpiop6z81dDR/n6L/HPbYpVzWCbw984rrrE8WSFXkZ4GkAqAL/azAiJhZUSKIArPp5Ha/c8I7b/abNoPPN7Xnh2yc9nic1JRXTZma4ORTCGyuyCZDipVUWAzUzNQlDVVxZ4HW3fP3+lndFERReIdTr2k9njp2jX/idfPzoZ5w+draAIhN5y9t9hYa0ew+tNVr7MmNGFHV/zlqD4WHNKKfDYsUPf3ktrGb3s0tiI/KJD30eOhpSt3lvV0jknVEEXTvkKtczdS+SE5KZO2kRD7d6lsgD7pezEEWUX2vcryEFrlHgNbHOPYE+0QR9ojHWqW7o+Clo7e3OSxRVSfFJaC+reTtSnbLit8gffm3w/LmTHUW39IAkN0VQ19s7U6NBVY8rAp9nOS2iT8fy4UOTCyAykZdU8N1kPd4GXN3Cdkj4BpIXkv4h4jyMjh2FPnefJDjFVM1G1VGeelwUVK5dUR45i3yhgu/B8+eOr2VTTLAV3XFektwUQQFB/oxb+ipNOjUCwMsTKiynxfqFm6T3pphR/lejQp5I++3iLzITsOP6oLHI+EGkXT8payDe/fpsoujqdV9Xj4+clFL0e6Rnpu2J8UmcOHiK+JiE/AxPlHDKvxMq5HwZkIs/dwzAD8IngtnA+4kCrkcZEfkQYd6Q5KaIKls5gnG/v8anm8dx1S0dvR+g4eD2I/kfmMhTKuQRVNlpENDLVSXUrO2aHRX8EK5Bf+6+BDU6/nO0lkcXxU2lSyrw4Lt3Aq6ZjxczDEWTjo24/uEe6duO7z/BmDs/ZkDZu7i99sMMKHsXr904lv1bSt56QKJgqJAHUWW/h4C+YNYEsx4E34+q8BtGQEdU+Dt47MExa6JCXyiweHMi36eCi9yp3fQS2vVtxbIZHqblpfEPlNWiiyPl1wrl1yrDNiv6JVx3VR4GEetodOIsVNCN+RqfyHsDnuhD5VoV+e7tn9i1zlV9PaxCKP0e6cnAZ/vhF+B6Lx/ZfYzH279IQmwCzrQVwC1Ls+qX9axdsImxS16hcbvMd9mWZbHjr3+JORNL5doVqd2kZsG9OFEsKL/mKL/mWe+zN4bys9AxYyFl6UV7AiFoMCrkUZQRUjCB5pBMBS+CU8H/K/p0DIOqDcWR6u45KYSEBzPj+GT8/O0FGJnIL1bM65AwHe/rwoSjKq1EKfl7L65izsSSkpxKRMWwTONshnd9jX+Wb89ycLFhKqrUqcyXOz/MMLvy92l/8tnz33Dq8IWlGupfXofHJtwntXBEtmkrzjUzSoWjjODCDkemgpckYeVD6ftgd4/Twwc+118SmxJE+XfBe2IDEAXJf+RzNCI/hZYrQ/mqZTMlNsf2RnpcCdxyao7+e5ytf+5M37bgy6WMuu3DDIkNwN5N+3n66pHpvURC+EoZISizWpFIbLJDkpti4oFxQ+hyayfAVeTLSPsBuPGpvgx8tl9hhifyml8HMGr40NAAp4y1KokO7zrmU7vzlY6TE5P55Kkvs2xjWRqnw2Li01PzLD4hijJJbooJm93G818/zqRN4xjwRB869m/DZR0a0bRzY47+e5yfJywgPjq+sMMUeUQpA0Jf86GlBbL2VIkUGOLbcjLn2636eT0JMYlu21lOi61/7uT4vhN5Ep8QRZkkN8VMnWaX0KhtfVb/vI5tf+5gy/LtrJm3gQlPfMHtdR6RbucSRPl3BOMSL638wL9rgcQjCk58TMKFRTU9sPvbaJ22yObpI2cwTO8f6aeOnPHaRojiTmZLFTN7Nx/grcEfYFlW+izh80PCE2ISea7HG3y1ZzyhZcsUXpAiTyilIPRZdNQj7tuEPIwyyqCdR9EJMyB1C+CHCrgGAq4rds/JBRzfd4JhV7/CmaNnvdTDgRse70OZCNeslfCKYT5VNQ6vGJZnsYrSQ+sUsM6CCinyM6VAem6KjMT4JPZvPcTx/Sc8fqDN+nCeq6hfFk0sp0VCdCILpyzLtzhFwVIB16LC3gV1Plk9fyfvhwp5HIIfQif8gD7VFeI/da3km7IMHTMSfbobOnVXYYUuckBrzcvXj+Zc5Dm3nwPKUKCg74PdueftwenbO/Rv7bEchDIU9VrWpmajanketyi5tHUWK+YN9MnW6FOd0SdbYZ29D52yqbBD80h6bgpZzNlYprw0nd+mLCUlyVVi/5LG1bn95Zu4emBHjvx7nNizcVSsWZ5yVSJYPWdDer2LrGitWTN/IzcNu66gXoLIZyrwOgjoDsm/g/MYqHAI6IYywtAp69ExL5Ix2037/1YU+tzdUGEJSgUWQuQiu/5essVrMc7AkADGrx1FjQYZk5Tg0CCGvHoLk5/7JtMx52da3v/OHXkXrCjxtPMM+uzN4DzOhUrpGlJWos+uhIhPUP5XF2KE7klyU4jiouJ5suNLHN0TmaE7+dCOo7w1+AMmPj2VM8fOpW9v2+dyUpK8ryeUmlR0FzMTOaOUv6uK8X/o+M9xdcBmVQPJCdZpSJwHQTfld4giD2xauhXTZuJ0uK9plRCTiGlmPRbn5meux7SZTH11BomxSenby1Ury5MTh3J516Z5HrMouXTsuP8kNuc5AYWOGg4VV6JU0SsgK8lNIZo+elamxAZI746+OLEBWDNvI8pQKEOhray7rA3T4NIsKpaKkkdrDcnLcb8IHoCBTl6OkuSmWNAa15JiXtvpTL9vX72b/VsOEVq+DJP/eZfdG/YTeyaWSrUq0KJLE7cJkRBZ0VYcJP2C+88X7Srul7QQAvsWZGg+keSmkDidTuZ9utinAYAXc5fUpO/Xmj4PXJub0ESx4imxAdfCm7J6eHHRpFMjpo+e5bFNRKUwKteqmP77nr/3M+r2Dzm042j6NtNm0vfBa3lg3BDsflLcU2SmtetzwW2vi/MI4O0pgA3t2OtLPl7gZEBxIYmPSiAuKnd1aS6e9mnaDFDw5CdDqV6/Sm7DE8WAUgpsjfH8NjZQ9mYFFZLIpSt6NKdKnUpup3Qrpbjh8T7pU8SP7D7GsKtGcmT38QztnA4nv0z4jXfv/STfYxbFi076FevMzegTTdAnmmCd7o9O/CXzAHYV5MPZLJRP7QqeJDeFJCDYP9OKwNkx6IUBXNahITY/G/6BfrTt04r3/3id3vd3y8MoRVGngu/E1TuT5V7AgMCbCzAikRumafL6z89RJiLYNSsqzflkp0P/1twy/Pr07d++9SMpSSlZ9gBrrVny7QpZPVyks2LfQ0c9kVYyIo1jJzr6GXTs2xkTHLOGa7Vwj/0yFgQUzScF8liqkPgF+NH++tasnrM+24+mABq2qsO9bw723lAUS9pxCJJ+Q+t4lK0WBPTIesZTwPWQvBqSfsJ1r3L+35IJaFTYWJRZocDiFrlX67IaTN76PvM/XcyS71aQEJNAjUbV6PtAd668sS2G4Up0UlNSWTZ9pcfZk6bNYPE3K7h/jLdikKKks5I3QPzE879dvMf1n4Sp4H81+HcE0nqGyzyOjnrczRkN8O/h+nwqgmRV8EJcFXz3hr080eFFnE7L61ia/+rQrzU2PxsNr6hLj7uvIax80V/dXHindQo6+iVImo0rWTEAh6twVtgoVECPLI7RkDQPnfAVpG4HZQf/a1DBd6PsMjumpIo5E8uNFe7x2Ma0GXS9vTPDv3BfCFKUfFpr9KkeYB3w0MoE/6sxIjI+ytTx36Bj38Y1vs910wRO8O+GCn+3wMtM+Pr9LclNISY3AOt+28So2z4k9mwcpt1EW9qnnhzDNNK7EG12GyO+fYIrB7TN73BFPrOinoakuWSu0ujqGlYRU1D+7Qs8LlH0OB1O+oXfSXJCsts2hmkweMQN3PX6oAKMTBQ1OmkJOuoh7w2NKhgV/8h8vHUWEme7epSNEFRAb5S9cRbtYiBhBjrxR1c1Y7MaKmggBN7gKmeRByS58aAoJTcAKcmprJq9lv1bDuEf5M+BbYdZOu1Pn49XSqFMxYQ1o6nXsnY+Riryk3bsQ5/u6aGFAfaWGOWmFVhMomj7+NHPmPvpIix3j6YUTN39MVXrVi7YwESRYp29E1JWe29o1sao8FuOrqGdx9FnBoN1nAs3Z2nl9G1NUWWn5smyDb5+f8uA4iLAz9/O1QM7cvebg7n1hQE8N/VRBjzZJ30QofIy8FhrjQJ+eH9OAUQr8k3SAjy/JS1I3YB2niqoiEQRN/iFAYSVD3XNlszCLc/0k8RGZBxA7EkWj719paOGgXWCLKulO7ahY0fn+Nw5IclNEWTaTB567y6mH5nE4/+7n3veupWGret6HLTudFismr2u4IIUeU5bsfjyltSxo9EJM9FWQv4HJYq08lXL8tGqt2jVvXmGz4cyZUMYOnYI942+rfCCE0WIL7WODFRQziap6NSdkLoB93W3LEichbaicnT+nJDZUkVYRKVwrnuwOwCbl23NcrHMi6Umy7ILxZmyXYL2WpQPSJqPTpoDsaMg7D3XCuCi1KpcqyJvzX2BEwdPcWjHEfyD/Lm0XX0p3icu8O8KSbPwWPQz8DaUmcMaaal/+9IIUndAAY0ZlJ6bYqJeyzpuC3uB69FVnea1Ci4gkfcC+gK+DLpL+4DSCeioh7ESF6BTd6dXHBWlU6VLKtC6Z0uadW4siY3IwFUPC7Lu/ldAKKqMuynfPl3Bx3YFl3JIclNM9L6/a9rCM1nTlqb/Y5kXVhRFm3aeQMdPxYr90LVGS5kRaXt8eWumTcmMfhx9pi/6ZAes2PclySnFtNasnL2W4V1fo3/EndxU8R7eHzqRA9sOF3ZoohApe0NU+Ie4Htac/2xJS0hUGKrcFJQRlvML+LXzIYhAsDfJ+TWySWZLFYHZUr6aO2kRHz70KYZppE8XV0qhteaawZ14/uvH0gt8iaJNa6drgF3C17iSFBNXPZtACBgIqWvBsT0HZzbArx0qYjJKyd17aaK15qNHJjN34qIMi+sqQ2EYild+HE77664o5ChFYdLO05D4PTplEygbyr8jBFyfJ7OYrHNDIXkFWT/6UhB0D0boc7m+jkwF96C4JjcAf/++hZljf2Hjos1YlqZWkxrc8Hgfet5zTZaJzbkTUSz4Yin/btyLzc9Guz6t6HRjO/z85YuvMFkxoyHhC7f7VfjHYG+Cjn4ZUnwvC5B+fNhoVOCA3IQoipnfv1vBqNs/crtfGYpv9k2gYk2pWC3ynrai0GfvBMcOLlRLN3EV/LsGFf6x+0U6s0GSGw8KO7nRWvPP8u3sWL0bwzS4/Npm1GuRvfo0luWqanx+Ab2sLJ2+knfu/Bin0wLt+nCznBYVapTjnUUjqd6gam5fisgB7TyDPtUJ94P7FJi1Iex9iHoArMhsXsEAe1NXN3TyctApYL8U7Fe4SqqLEiM1JZVVs9dxaOdRfv18CacOn/HYvlr9Kny580P5d1CCaSsedBwYEXmSTGTr2jrFtWxM4iywToNZHRV4M/hfhVJ581RBkhsPCjO5ObzrKK/dOI6D24+4BghrV6LS9MpLeXnmMCIqhefJdXas+ZcnOr6Y5bIOhmlQtkoEU3d/hF9Awf7jF6ATpqNjXsHr9DcCgBTcL4zpiR1wXHxVMOugwt9H2S/NwflEUbNm/kbeuXM8MWdiMW0mTocPM+2AsUteocU1BTf2QRQMnfoPOm4CJC/DdTcbCIE3oYIfRpnlCju8PCNF/IqgcyejearzSI7sPgaA5bSwLNcX1/bVuxje9TVS8mg69w/v/uJ21XHLaXH6yBn++N6HipUi71kx+PbWy2liA5CKK3k6/wM4D6LP3o52yODS4m7bql280n8MsWfjAHxObFCw+Ovl+RiZKAw6eSX6zCBXT+3597tOhITv0GduKpWFPyW5KUBz/vcbsWfjslzF1+mwOLj9CMvzKOFYPWe9x9WClaH4a+6GPLmWyCbbJXisN5Eup4mNO07X9PH4z/P4vKKgffXqDLSGbHe8a4g6FZ0/QYlCoXUqOvppXJ8p//1ccYIViY59pxAiK1yS3BSgRV//4XFRTMNQLPluRa6vo7XGker5y1NbmtQkKfpXKPyvARWO+9oQ+fm2dELSrOx/KYoiI/ZcHBsXb/Fpgd3/Mm2GDCguaZJ/dy1S6fYxtxOS5qGt0pXUSnJTgGLPxXncb1mamFMxub6OUoraTWt6XJPKMA1ZZLOQKOWHCnsLV3Lz37egCQT5eqacBaATyTgeRxQn8dE5X3bD6bDoeY9UtC5RHHvwvtiAA5yl63G0JDcFqGrdyh4TDtNmUK1BDstf/8cNj/XOcjDxxXrd1zVPriWyTwVci4r4AuxNL9pqgH9XVPlZYL8Cz2/PtNV2c8IoKzVwiinLslg+c3XO8loFPe+5hgat6uZ5XKIQqUB8eoStAvM9lKJEkpsC1PeB7h4TDqfDovd93fLkWtfeeRWdb3at4XHxtE/DZoCCpz59kArVS84I+uJI+XfAKPc9qsJSVLnZqIqrMCLGo2yXoMo8g+vt6eZbzH4FOVsazoDAQTkPWhQarTXv3DmeySO+8ZrXKqUy3EgFhwUxZOQtPDnpgXyOUhQ4/254/gehwKwFZp1cXUbrFHTK3+jkNWin55IDRYEsnFmAut3RmcVf/8G2lTux/pvkKOh665U0v/qyPLmWaZq88N0TtOrWjFkfz+fA1sMYpkGbni25+Znrada5cZ5cR+SeMquBWS3jNr/LIeIzdPQIsI5ftCcAFfIAWoVD6vpsXsl01Z0Ivju3IYtC8NfcDSz51suYvLQOvcfG30vHG9qw759D2P1tNGpTD/9AX9YtE8WNstVEB/SFpHlk3YOjUSGP5bi2kdYWJHyBjvsUdFTaVhMd0AtV5sUiO81c6twUcJ2b5MRkvnxxGvMmLyYpPhmAsPJlGPBkXwY+1w/TzFyU73yhrt0b9mH3s9Gmd0subdcgW/9YnU4nhmFI8a5iRmsLUla7nperEPC/GmWEoK2z6JOd8H3sjAEBvYv0h5Hw7MU+b7N+4WaPA4kDywTw9GcPc9XNBbPysigatE5CRz0DyQtxjdtTuBIdhSrzbK5uaKzoNyDx6yz2mGBWQ5X7MXfrUmVTkSriN2HCBMaOHUtkZCTNmzfn448/pk2bNlm2nTx5Ml999RVbt24FoFWrVrz99tsZ2t91111MnTo1w3E9evRgwYIFPsVT2BWKARLjkzi04yimaXDJZdXdruK79c8dvHbTu0SdjMZmN9Fa43RYNG7fgFdnPUtExYL7RyWKFiv2XYif5ENLBbYGqLLT8mQNGVE4bqv1ECcPnfbYpmq9SkzdPR5wLb3yz/IdWE6LRm3qUaVOpYIIUxQinboDnTQPrFiUrQYE9EeZ5XNxvt3oM309tDAg+CGMMk/k+BrZ5ev3d74/lpoxYwbDhg1j4sSJtG3blg8++IAePXqwa9cuKlasmKn9smXLGDx4MB06dCAgIIAxY8bQvXt3tm3bRrVqF7rue/bsyZdffpn+u79/8epyDQwOoOEVngf2Hd51lOd7vJle2O/i6d271u1hRI83mLBujMclGETJpUKeQmNC/GRcRfvc0eD4Fx33ISr0xYIKT+SxoDLeB4QGhwWTGJ/E+Mc+Z/HXf2A5L9y7hoQHc+frt3Ddgz3kM6OEUvZL3VYgd/UCr0KnbHAtheDXzuuSLDrxB9LXh8qSBYnToQCTG1/le89N27Ztad26NePHu+4mLMuiRo0aPPbYYzz//PNej3c6nURERDB+/HiGDBkCuHpuoqKimD17do5iKgo9N754775P+G3K0szjcy7y6k/D6dg/614wUTpoKxod+zEkfuW5oQpEVfwLVcpmTZQU3775I1NfneF2UoIyFPe+fSvrFmzinz+2u61l1OKaJrw1/wVZPLcU0Y496HMPg/MArj4Ny/WjwsC/EyqgO/h3yzSL0jr3KCQvwtsIdlVpW4HNwCwSyy+kpKSwYcMGunW7MAPIMAy6devG6tW+VeJNSEggNTWVsmXLZti+bNkyKlasSMOGDXnooYc4c8b96O3k5GRiYmIy/BR1W1fu5Ncvf/eY2BimwfIfZAmF0k4ZYWCUwWtHrE4Ex6ECiUnkrTPHz7nWQ/W3Z3mnbZgGYeVDKVe1LJuXbfNYpHHT0q081nYEo+/4iO/H/UL06aL/eShyTjvPoM/eflGdGwfpA491tKvAX9QT6FPXoh37Mh5sROA1TVCBFMW5Sfma3Jw+fRqn00mlShmf9VaqVInISN9WOn7uueeoWrVqhgSpZ8+efPXVVyxZsoQxY8bwxx9/0KtXL5zOrLvORo0aRVhYWPpPjRo1cv6iCsD+LQd57trXvU73tJxWrgp6iZLDtfqvD52wUt+mWLEsi0+Hf8WtNR9k6sgZOFOd6YmLMhWm3fV4qWLN8oxb+iorfvzLYy2t8/b9c5Dfp/3J5Oe/YVD1B/hj5qp8fR2iECVOBysKr0u+WCfQZ4e4VhVPowKv83KcCYE3FMmJKkUv3brI6NGjmT59OsuWLSMgICB9+6BBF+p0NG3alGbNmlG3bl2WLVtG166ZC9ONGDGCYcOGpf8eExNTpBOcb9/60aeF8AybQfUGVQsgIlHk+V8Fce97aWRHqwo5rWssCsGXL03n+/fmXFj71LrwuaCdmlY9mnPdg91p3asFpmly5thZr8U7049Pa+dIdfDWrR9QoWZ5GrdrkOevQRQunfgLvq1T5wTrJCTNgaC071h7a/DrBCmrsjiH6XrUHXRv3gacR/K156Z8+fKYpsmJEycybD9x4gSVK1f2eOy4ceMYPXo0CxcupFmzZh7b1qlTh/Lly7Nnz54s9/v7+xMaGprhp6hKSUphxY9rPC56eZ7lsOh9f94U/RPFm7I3Br+2Xlo5IGFigcQjci/mTCw/XJTYZOXwzqO07XN5egmJ8tVyMM1fu9a1+37cLzmMVBRpOjuPHRU6adGF35RChY+HgD64ppdftGSMeQmq7HeuWVlFUL4mN35+frRq1YolS5akb7MsiyVLltC+vfs6DO+88w5vvPEGCxYs4IorrvB6nSNHjnDmzBmqVMmbpQsKU2Jcks8L4t0x8mZqNqrmvaEoHfx7e2mgIWEaWqcUSDgid1bPWY8jxXMdo+P7TrB384H033vcnbN1o5wOi9Vz1suCqiWRWRvfv+p12tpzFygjCCP8XVcl9dDXUGVeQJX9FlX+V5S9UZ6Hm1fyffmFYcOGMXnyZKZOncqOHTt46KGHiI+P5+67XUWFhgwZwogRI9LbjxkzhpdffpkvvviCWrVqERkZSWRkJHFxrkUn4+LiGD58OH/99RcHDhxgyZIl9OvXj3r16tGjR4/8fjn5LiQ8GL8A7+MiOt3QliGv3lIAEYliw3kI17RND3QcOH0b7yYKV9y5eAwfxs/ER10Yd9emd0sCywR4aO2eM9WZo5XGRdGmgm7Ft8dSACa4mUquzKqooEGo4CEov9ZFcpzNxfI9uRk4cCDjxo1j5MiRtGjRgk2bNrFgwYL0QcaHDh3i+PEL5eU/+eQTUlJSuOmmm6hSpUr6z7hx4wDXsgL//PMP119/PQ0aNODee++lVatWrFixotjVuvkvp9PJ5y9OIyXJU80SV1fhQx/cVTBBieJD+eVtO1GoqtWv4nG25HlV6lyoF2aaJt2HXJ39iymo3rCq1L8piQJ6gt/V+LbaqhNVQtaek+UXisj4mz++X80nw6Zw5uhZr23vfG0gt798UwFEJYoTnbIZffZmDy0U2Oqjys0p8nddApwOJ4NrPEDUyZgsHxcZpkGLay5jzMKRGbYf3nWUey97yueBxeC6YXrko3vo90jPXMctih6tUyB+EjruKyA6ixYGYEHwYxhlHivg6LKnSNS5KU0cqQ7WzNvA/MmLWT1nPakpnntfLvb7tD95c+B7PiU2dj8bNw+/PjehipLK3ixttXB3d98aFfywJDbFhGkzefqzh1CGyvR4yjANAkMCePjDezIdV6NhNZ78ZKhrMWjbhY/483/vGf7+FSgFrXu1pO8D1+bPCxGFTik/1+KZlVZCuTkQ/KhrpfD/iv8Y6+yd6JS1BR5jXpOemzzoufn9uxX878kviT4dm76tTNkQho4dQk8vA/xSU1IZVP0BYi461ptvD35CxRo5Xy9ElFzaOoc+ez84/sGV5LgWzwONKjMcFXxf4QYosm3zH9v48qVpbFu5C3BVIm5/3RXcN/o2ajR0P6Fg++pd/Pj+3PTFNpt0bETzqy9j8x/b2bBwE1pDlbqVuOGx3lz3UHds9iJdGUTkMcsZA2cHgnMfGafkmYBGhb+PCuhVSNG5V6QWzixq8jK5WTZjJW8N/sDt/uFfPkL3O692u/+vuRt4+frR2brmrLNTCAkPztYxovRwrSGzEp30K+h4MGujgm5GmTKzrjg7efg0sWfjKFc1gvAKuVsw15HqwJHqxD/QT3rySikrZhQkfEXWRfoUEICquLLILbZbZBbOLMmcTieThme1FPwFk5/7mi63dnJ7V3R8/4kst2fl/DN2SWyEJ0oZ4H8lyv/Kwg5F5KGKNcrnWY+tzW6TnppSTOsUSJyJ++rDGkiCpLkXCvoVMzLmJhe2rdzF6SPu17QCiDoZw9+/b3W//0RWg7vcu/1lTwNGhRBCCC+sU65eXY9MtCPrwrjFgaTuuXDOx8TEUwLjaxE+vwA7L3z3JE2vzLoGgRCi5LMsi8M7j5KcmEK1epUJDpNeXJEDKtCHRtrHdkWTJDe5UKGGb6XOPbWr06KWT+cY9/urXCrrvghRai34cinfvvkDkftPAmD3t9H1ts7cN/o2wsoXjZIWonhQRlm0vSWkbsZ9gT8nKqD4FsaVx1K5cGnb+lStV9ntgDylXIlNs6sauz1H7SY1ubRtfffnMBT1W9WRxEaIUuzbt37k3Xv/l57YAKQmO1g4dRlPdHyJ2HNxhRidKI5UyKO4X7jMAL+OKHuTggwpT0lykwtKKR4bf5+rVsR/6lC4khXFox/fi2F4/mN++vOHCAoLxDD/W8tCERgSwPAvHs7r0IUQxcSJg6eYOnJGlvssp8XxfSeY+c7PBRyVKO6U/5WosFGAH67ZUTbSa2T5tUWFf1R4weUBSW5y6YruzRn164vUaFg1w/Zq9Svzxi/P0eH61l7PcUnjGvxv3Ri63tYZm931j8u0m1wzuBMT1o2hdtNL8iV2UbJpnYpO3YlO3Y7WSYUdjsih375cmunm6WKW02Lep4uwLFkXSmSPChyAqvgnqswLEHgzBN2FKvs9KmIKyihT2OHlitS5yaPlF7TW7Pl7P6ePnqVslQgatKrj9lFTSnIqfy/ZQuzZOCrXqsBlHRult01OTCb2XDxlIoLxDyzea2WJ/Kets5C8CnQK2Buh7I3R2gnxn6Ljp4A+52qoQiBoMCrkcZSSf1fFyeghH7F02kqvi1rOjppKcGhQAUUlijutEyHpV3TqTlD+KP8uYG9R5OseSZ2bAqaUov7ldah/eR2P7eZMXMiXL35H7LkL0/Cq1q3Ek5MeoGWXpvgH+ktSI7zSOgUd83ZarQrHhe1GebASgIT/HBAH8Z+jU7dBxGSU8r7yvCgayoSHeP3CMW0G/oGyIKrImtYpcH5at60OpKxFRz0FOhZXGqDR8ZPA3goiJqCMsoUZbp6Q5KYA/fThPD55akqm7cf3n2REzzd5Z/ErNOuc9eDjmLOx/PblMtb/9jdOh0Xj9g3o+8C1VKxZIZ+jFkWN1hodNQySF5FpQKB12sORFqSsgqQ5EDggP0MUeejqQR2ZPf5Xt/sNm0Hnm9tLUT6RidYO14KZ8VNAp5UkUYGgk7jw2XHh5ojUTa7lW8p97yoGWowV7+iLkYTYRL54cVqW+7SlsSzNp26qHW9fvYs76jzC5Oe+ZuPiLWxeto0Z7/zMkHqP8vu0P/MzbFEUpW6G5IW4n+ngiYFOmJ7XEYl81Lh9A1p1b45hZv64NgyFaTMZ/PwNhRCZKMpcN0FPo+M+upDYAOhE3H92OMGxBVJWFkSI+UqSmwKycvZakhOS3e7XlmbXuj0c+fd4hu3Rp2MY0estkuKS0NaFf5CW08LpsBh9x0f8u3FfvsUtih6dNBv3K397Y4HjUB5GI/KbUopXfniaDv1ckxPOJzQA4ZXCGb3gJZl0IDLQVjw6aQ4k/0r2b4JMdNKC/AirQEk/ZgE5FxmFYRpeBwWei4yiev0q6b8v+GIpif9JbC5mGIqfPpzHc1Mfy9N4RRHmPIP7wls+MKTgW3ETGBLIKz88w5Hdx1g9ZwMpiSnUblqTtn0uT090hNCpW9FxEyD5d3LWs4vrOK9LMxR9ktwUkHJVy3pNbFztIjL8vm7B324TGwCnw2LNvI25jk8UI2YlXJ2u7ha988RABfbP23hEganeoCo3P13Ve0NR6ujk1ehz9+JKanI5Cdr0PDGmOJDHUgWkQ//WBAS7nwVlGIpL2zegat3KGbY7Hd6/wHxJmkTJoQIHkLPExgSjbLFd5VcIkTWtHejop3H16ObksyEjFVT8F2iW5KaABAYHMPSdO7LcpwyFYRo8OG5Ipn2N2zXIciDheYZpcGnb+nkWpyj6lL2xq+BWdpm1UGW/LRHTPIUQF0leljZTMjc3uq7vGVXmeZRZxUvbok+SmwJ03UM9ePqzhwivGJZhe81G1Xhn8Ss0bt8w0zF9HrgWPNRZtJwW/R/vneexiqJNhb4OwY+A8lS0Le3t7XcNKuJrVPn5KFvtAolPCFGAHHvI2SSDi2oj2Rqhwj9GBd+VR0EVLqlQnEcVirPDkergn+U7iD0TS+XaFWlwRV2PRbp+m7KUd+/9BMNUOB2uzPz84OSBz/bjvtG3F1TooojROhFSNoBORqtASF4MycsBC+xtUMG3e138TltnIXEu2jrh6tUJ6IMyK3s8RghRdOj4r9Cxb5GjsTZl3kQFXIMyi0fNNF+/vyW5KYTkxpOkhGR+/3YFy2asJPZcPJdcVp2+Q6/FtNv46cO5rPt1E06nReN29RnwRB/a9mlV2CGLYkprDfGfoePex/Wc3iS9Wzv4HlTIM8W+kJcQpYF2HkWf6kL2kxsD7C0xymVdg60okuUXiqETB0/xTJdXidx/EqUUWmv2bznIkm9W0PfB7rzw7ZNFft0PUYwkTkfHjb1ow0WVSuM/c1UyDZESA0IUdcqshg7oB0m/kL1xNxakbkDr5BK35pwkN0WE1pqR/cZw6vDp9N+B9MdQcycupNZlNej3SM9Ci1GUHFqnomM/8twmbjIE3Y0yQgooKlGQEuOTWDptJX/MXEV8dAK1LqtO76HX0rhdg8IOTeSACnsDrZPTCveZgCLDDYsn2ulqXoJIclNE/PPHdvb9c9Bjm5njfuG6h7pjGPKoQPhOOw6BdQ7MSuljaXTKBtBnvByZ5Bq/EygD1ouLXev28NOH81g7/28sp0XjDg254fHetOnVMkO7yAMneabLq5w4cCq9l3jP3/v4bcoybniiNw+9d5f0EhczSvmjIj5Epz6MTpoLVjQ4T0GKp4J+yjWL0ih5q8lLclNEbFz8D6bN9FjX5uTBU5w4cIoqdSoVYGSiuNLJa9Bx41xrUZ3f5tcBVeY5SPzRx5PE5VN0Iq8tnLqMcff8D8M00j9HNi7+h/W/bWLwiBu4ZXg/lny7gkM7jrBsxipio1x/t//tJZ714XwuubQ6fYZeWzgvROSKsjdE2V0zb7UVhz7VycN6UrrEzI76L0luigjLafnULeiUgn3CBzp5Ofrc0Mw7Utagz9xChimgns7j2A2OIyhb9bwNUOSp4/tO8O69/0NrneEG6XyBz2mjZvH9uF9wOiyUoTwW/lRKMXPsz/S+v5v03hRzygiB8PHocw+QscCf4fo9oC8EDiy8APORJDdFROMODXGOme2xTVj5MlSuVTym64nCo7UTHf0iWZdhd+L6kHO/iGsGCV+jE75G+10FZk3XoyyjPCqwP8p+WZ7GLXJu7sSFoBSeZss4Ul1fbNrpeUaN1ppje09w6sgZKtYon5dhikKg/DtB+Z/R8V9B0m9AMtgaoILucJV9KKEzIiW5KSLa9G5JxZrlOX30bJZ3VUop+j3aC5td/sqEFymrwTrhoUF2poumtU1Zlva7ASh0wlR0QB9U2DsoZc9RmCLvbFu1K8+XYfFl6RdRPChbPVTY6xD2emGHUmDkmzIPWZbFpt+3smbeRlJTHNS/vDZXD+pIYHCA12NN0+T1n5/jmWteJSE2Mf2DyjAUlqVp3bMFg0fckN8vQZQEzsP5ePKLvkCT5qNVWVTYy/l4PeEL0563K4NHVAqTXhtRrElyk0dOHz3Di31HsW/zQUybCQqcqU4mPj2Vl2YMo3WPFl7PUbd5LSZvfY85//uNJd+tICEmkRoNq9L3we50GdzJdV4hvFEFVZhSu2rllHkUZUR4by7yTeueLdmyYgfayn1NVmUo+j/WWz5vShitEyFxDjrxF9DnwKyNChoIfp1K5NgqSW7ygCPVwXPd3+Dov8eBjN25iXFJjOw3hglrR1On2SVezxVRKYxGbeuTEJPoqkTcvgGdb2onHzTCd/5XAQFAkvs2qjL4t4Ok2bhqYuT0EUQqJK+EwL45PF7k1sEdRzh56BTKULlKbs73ErfpfTm3DL8+DyMUhU07I9Fnh4DzAK6ZKxoc+9DJCyGgD4SNQ6mS9R0jyU0eWPXzOg7tOJrlPm1ptGXx/bu/8NxUz9Vej/x7nBd7v8WxvSfSu5nnfPIbnzw1hdd/fo7LOmReWFOI/1JGCIQ8jI57z32b0Gcg4DoIvA6dMB1S94B1kBytKqxTch6syDGtNV++NI1po2Zh2gwsR+a/O8M00JYFynPiExQWyCWX1uD6h3twzaCOcjNVwuioxy96XH3+30HaDU3SfLA1gJCHCiO0fFMyh0kXsD9nrcEw3f9ROh0WK374y+M5EmITGd7lVSIPnnIdk+rEmTa7Ie5cHM/3eIPIAyfzLGZRwgU/gAp5HLDjulNLu49RgajQ11GB16OUQvlfiRExAaPibxA0hBytLGyXpLswzJ+8mGmjZgEXatRcLCQ8mFuG92PSpnepeWn1TJ9RhmmglGLEN4/z87mv+GjVW3S7vbMkNiWMTt0CqZtw3zur0fFfokvYTYokN3kgKT7Z60yFlKQUPK1RuuSb5Zw+djbLuy/L0qQkpfLz+AW5jlWUDkopVMijqIorXclMyGOosDGoCqtQQYOyPibkQTAr43uCY4LtMpkSXggsy+K7tMTGnbioeLoM7kjtpjX58M83uOmpvgSFXqhE2/TKSxm98GW63HplfocrClPyary+p3UUOPYWRDQFRh5L5YFLLq3Omnkb3SY4Simq1q/scdDW8h/+wlOVCstpsWzGSh4YNyT3AYtSQxnhEORbkS5llIWyM9GxY1xd1enr0hhkrpljggpBhY/NdB6R/w7vOsbJtF5edwzTYM28jdRuegnBYcHc/84d3PXmIKJOxhAQ7E+ZCFkzrLjRzmPohG8gcS7oeLDVQQXdira3QSX9hE7ZDMqG8usEgf3T1oXz9VFz7gejFyWS3OSB3vd3Y/o7sz226f9IL4/7E+MS8dCxA0BSgo+F14TIIWVWQIWPQ1svguMgqAC0CoeELyDxe9cHKgEQOAAVcj/KrFbYIZdKjhTvCyIqpUhNztjO7menQvVy+RWWyEc6datrULBOJP0RU+oWdPRzgEKjcCUyCp28FOI+grJfgF8rvE4YUCFgq5Ov8Rc0eSyVB6rUqcTQd1w9KoaRsXdGGYrmV19Gnwe6eTxH7aaXYNrc/3UYpsEljWvkPlghfKCMCJRfC5S9EYatMkboC6iKG1EVN6EqbcIIe1USm0JUtV5l/AM9L6HhdDipd3ntAopI5CetU9HnHgSdQMZE5XyvjP7P/9egY9Bn70abDcBWH/ePpgwIGoxS3uuxFSeS3OSRm5++jld+fIb6reqmb4uoHM6drw3krfkvYPfzXMW174PdsxwUeJ7ltOj3SM88i1eI7FJKoYygEluuvTgJDA6g5z1d3E5kMAyD8tXL0aZ3yyz3i2Im+XewTpK92YwW6BhU0s+o8AlgRJDxKz/t//u1Q4U8kXexFhEF8ik1YcIEatWqRUBAAG3btmXt2rUe23///fc0atSIgIAAmjZtyvz58zPs11ozcuRIqlSpQmBgIN26dePff//Nz5fgk043tGX8mlH8ePoLph/9lGmHJ3Lbizfi5++9PH3DK+oy6HlXBWL1394fpeh8c3uuuqV9vsQthCh+7n5rMLWb1Mj0eWHaDPwC7bw8cximmbOZT3s27eerV2cy+dmvWfT1HyQnyiPxwqRTNpHTUSQ6eSnKVgtVfq4riTFrg1EO7C1cy6dETEYp3xbSLU6U9jSFJw/MmDGDIUOGMHHiRNq2bcsHH3zA999/z65du6hYsWKm9qtWraJz586MGjWKvn378t133zFmzBg2btxIkyZNABgzZgyjRo1i6tSp1K5dm5dffpktW7awfft2AgK8d63FxMQQFhZGdHQ0oaEFVc3VO601S75dwYx3ZnNgq6smQcVLKnDjE33o91jPHH9QCSFKpsS4RGZ/vIA5E3/j1OEzBAT70/XWK7npmeupXr9Kts8XHx3Pm4PeZ/1vmzFsBoZSOFKdBIcHMeKbJ2jb+/J8eBXCGyt2LMR/yYVB/tlgb41R7ts8j6mw+Pr9ne/JTdu2bWndujXjx48HXFMYa9SowWOPPcbzzz+fqf3AgQOJj49n7ty56dvatWtHixYtmDhxIlprqlatytNPP80zzzwDQHR0NJUqVWLKlCkMGpT1NNeLFdXk5jytNbFn43A6nIRVCMUw5DGAEMIzy7Jy9VmhtWZ4t9fYsnxHppmfSikM0+CDP9+gUZv6uQ1VZJNOXoU+d1fODg66DyP02TyNpzD5+v2dr9+aKSkpbNiwgW7dLgymNQyDbt26sXr16iyPWb16dYb2AD169Ehvv3//fiIjIzO0CQsLo23btm7PWdwopQgtV4aISuGS2IgiRaduw4p6HuvkVVgnr8aKegGduqOwwxKQ68+KbSt3snnptixLWmit0VqnFw0UBcyvPRiVc3ZsKa1Dla/fnKdPn8bpdFKpUqUM2ytVqkRkZGSWx0RGRnpsf/6/2TlncnIyMTExGX6EENmjE6ajzwyApJ/BOg7WMUiahT7TH53wU2GHJ3Lpj+9Xe6xObDktVs9ZT0pSyapkWyykrAQr6+83zwxIXZ/n4RQHpaJbYNSoUYSFhaX/1KghU6qFyA6duh0d8wquaaYXT0V1Ahod8wLasadwghN5IjE2CW+F3LSlpd5WIdBxH5Gzr2sFVmxeh1Ms5GtyU758eUzT5MSJExm2nzhxgsqVs+5iq1y5ssf25/+bnXOOGDGC6Ojo9J/Dhw9n2S6/Hdh2mPmTF/Pr50s4tjcnWbgQhUPHf4PnjwuFTig5gxZLo+oNqmB5WVW8TEQwwWFBHtuIvKWdp9LWhsrBorZolK101jrK1+TGz8+PVq1asWTJkvRtlmWxZMkS2rfPelpz+/btM7QHWLRoUXr72rVrU7ly5QxtYmJiWLNmjdtz+vv7ExoamuGnIJ0+dpZnurzC/U2H8f4Dk3jv/oncWf8xXr5+NDFnS2dWLYqZ1L/wXOXUCcmeF4cVRVv3u67OVIT0YoZp0PfB7jJrs6DphNwdH3hj3sRRzOT7Y6lhw4YxefJkpk6dyo4dO3jooYeIj4/n7rvvBmDIkCGMGDEivf0TTzzBggULePfdd9m5cyevvvoq69ev59FHHwVcg22ffPJJ3nzzTX755Re2bNnCkCFDqFq1Kv3798/vl5Nt8dHxDOs8ki1/7sy0b+2vf/Nst9dJSU4thMiEyA73X3rZayOKqrKVI3jwvbuAzLW2DNOgRqNqDHy2XyFEVsqZFYHsVg92/f2pMsNRZg4HIhdz+b621MCBAzl16hQjR44kMjKSFi1asGDBgvQBwYcOHcowyr9Dhw589913vPTSS7zwwgvUr1+f2bNnp9e4AXj22WeJj49n6NChREVF0alTJxYsWOBTjZuC9uvnvxO5/2SWK4JbTou9mw6w/PvVdLu9cyFEJ4SP/K6ExBm4770xwb9TQUYk8kH/R3tRrmpZvn3je/ZuPghAQLA/Pe/pwp2vDSQ4LLiQIyx9lApEBw7w/v7DD0hM+7UeKuRhVGCfggmyCMr3OjdFUUHWubm/2TAObDvsdpyeYSiaX9OEdxaNzNc4hMgNnfov+sx1ZP3cXwEGqvyvKFutgg1M5JvTR8+QlJBChepl8Q/0L+xwSjVtnUWfuRmcx8iY4BiARoW9CwFdwXkUCACzGkqVzJ7UIlHnRkDUyRiPExAsS3PuRFSBxSNETih7fVTYWFwfGRePuTABAxX+viQ2JUz5auWoXr+KJDZFgDLKosp9D0EDyfCIyt4SFfEFKrAvSgWibPVQtuolNrHJjnx/LFXaVaxZnujTMWg3sxAM06ByrczLUAhR1KjA68DeBJ0wDVJWAQr8OqKCbkXZahZ2eEKUaMooiwp9FV3meXCeBCMEZZQt7LCKLElu8lmf+7vx/gOT3O63nBa97utagBEJkXPKVhsV+kJhhyFEqaVUAMjNhFfyWCqfdbujMw1b18MwM/9RK0NxRY8WtO0ji9EJIYQQeUWSm3zmF+DHO4tH0uOuq7HZzYu22+n/aC9emzVc6kYIIYQQeUhmSxVgQb+YM7H8u3EfyjBo2LouwaFS6VMUDJ26HR3/VdpYGQ1+7VDBd6DszQo7NCGE8Jmv39+S3BRwtWIhCppO+BEd8wKujtrz00hNwEKFjkQF3eZqp52Q/Ac6xVVpWPm1Av+uKCVD84QQRYOv39/yqSVECaYde9ISm6wWvAQd8zrYm4MKRJ+7H5xHOP+xoBOmgFEJIj5F2S/1fi2tIeUvdOL34DgIRgQq8HoI6IlSfnn90oQQPtDOM5D4I9qxC1QAyr8b+HdGqZI9HEKSGyFKMNdilhf32PyXgY77wrV2lHUubZvjwm7rNPrsECj/K8os7/462omOfhaS5uDqFXK6zp2yHOI/hbJfybRVIfKB1qmQNMdVosFxEIww101F4K2Qshwd/SIXim8q182HrQFEfI4yKxVm6PlKHkvJYylRglmn+4DjX8+NVCjoWNxXmzRQIY+iQh51ewodNwEd95Gbc5hgb41R7isfoxZCnKe1htT16KQFoONRZi0IHIAyK2I59sPZe8A6+p+jDFAhoGPcnNUEW11UuV9QqnjNK5LHUkIIfFrMUifisYw2FjpxNhjlQMeDWSetWzvt8ZVOQcdP8XAOJ6T+hU7djbI3yFb0QpRm2opBn3sYUtdyvjK4RkPcB+iAvmk9pVm97ywPiQ2AExy7IWUF+F+VD5EXPkluhCjJ/DqBYy8eF9xTAaC9rEzvPISOeZX0R1xGeQh7B+XfCRy7QEd7CcSAlJUgyY0QPtNRT0DqhrTf/vMeTvoll2e3oZMWo0poclO8+qOEENmigm7F1XvjrgdHg60ZGdeLcueiQcnWGfS5oeiUTaCzWkwzK+4SLCHEf+nU7a4bgnx732jQyfl07sInyY0QJZiy1USFf4grecliwcuwcaiQoWT/A1QDlmucja0eGRbzy5IF9pbZvIYQpZdOWoRvNx05ZaHsDfPx/IVLkhshSjgVcC2q/EIIvgdsjcF2KQTdgSr/GyqwL/i1g8BB51tn48xW2p1lKgTdjPuPE9M1O8Muy4wI4TOdRPbej9llg8AB+Xj+wiVjboQoBZStOqrMcCgzPPM+pSD0NbBfio7/HJyHsnFmDVYMKuRpdOpWSP0b1wfy+UGOhqveTfjHrusIIXyi7A3RF5dlyNFJgkEnkHHQsQloVNgYlBGRu/MXYdJzI4RAKYUKGowqvwhCR2XjSDsY5VBGEKrs16jQ1109Q6oMmNVRIY+gys1B2WrnW+xClEgBPV3vo5z23hjVodwvrpsao0raRgV+V6LKfuPqtS3BpOdGCJFOqbQiXxhcKPzljgkB16GM4LRj/SBoECpokJfjhBDeKBUA4e+6poJnqjBuuBIfHU2W79XAgajQF13nsN0HQfcCSYANpewF9AoKlyQ3Qoh0WqdA6kYfWiowwlFlnsj3mIQoibTzODp+KiTOdtWkMauiggZD4GCU4VpUWflfDeWmo+MmQvLvgAUqHIIGQ9B9qNSVrnOkbgFlB/9rUMF3o+xNM1zL9Ug4sIBfYeGSCsVSoViIdFonoU/4sFK4URlV9jvAiU6YDo7tQAAqoEtab46seC+EOzp1N/rsra6imBl6ZBTYGqLKfosyymQ8Rqe4Cm6qMsWuqnBe8vX7u/T+CQkhMlEqAMx6eHvOr0IehOTf0ae7Q8IUSFkNKcvQMS+jT3dHO/YWSLxCFDdaa3TUY1kkNgAaHP+iY9/JdJxSfigjrFQnNtkhf0pCiAxU8F24X0pBgQpCq/Lo2DfJOBYg7RjrDPrsPa47TSFERqnrwLkf97WlnJA4C23FFmRUJY4kN0KIjAJvgoAb0n65+CPCBOyo8AmQ+A3uC4w5wToOSQvzNUwhiqXUf/D+1ZsCjj0FEU2JJcmNECIDpQxU2GhU+Edgv8I1K8Mo75oJVX4O+LWBlDV4rmpsopNXFFTIQhQjdjwvVJumlMxqyi8yW0oIkYlSCgJ6ogJ6Ztrnetzk7cNZQ24LkAlRjGmdCklz0QkzwHnYVcwy8Aa0vTVe3z9GWbCV3KURCoIkN0KIbFHKD23WA+de3H9I60zTUYUoLbRORJ+9H1LXkl6HxjrlGihsVHEteZKyFre1pMz6yNdz7shjKSFEtqngIXgcdIwfBN7gZr8QJZuOfQ9S16f9dnECo8E6AVZcWvVhN1LXQMI3F47SGp28Cuvck1in+2GdvQud8CNaJ+VL/CWBpIZCiOwLvMU17iZpHhkrpJqAQoV/iDLCCi8+IQqJtuIhYQbuK3w7wbHV+3niJ0LQra7/H/0cJP2C6/3lBBQ6ZRXEfwplv0KZlfIo+pJDem6EENnmGnT8LipsLNibAP6uO9GAfqhyP7mK+QlRGjl24lrqwBMf1ouyTrnOFT85LbGBTGUXnIfQUY/nLM4STnpuhBA5opQBgf1Qgf0KOxQhihBfFrrUae08DyzWVjzEf+mhhRNS/0anbpExbv8hPTdCCCFEXrFfCirYh4beZhza0vKfc17aGZC8yrfYShFJboQQQog8olQgBN2G+x4cE2ytwayO+69gEwL6oFSIL1fE/fie0kuSGyGEECIPqZDHwf+atN/OV/JOS3bMS1ARH6LCPwDlT+ZK3waY1VGhI8BWx4deICfYL8+r0EsMSW6EEG5pKwadsh6dsknWihLCR0r5ocL/hwr/BPw7g1kX7FegQt9ClZ+FMsuj7M0g+LEsjrbA3hpUqGsh26Bb8djDY9ZzVQ0XGciAYiFEJtqKdRUcS5wFpCU1KgyC74LgB1HK3bpSHs6ZuhMdPxVS/gDtAL/LUUF3oPw75mnsQhQFShkQ0BUV0BVIq+yd/CckzkWbldFWMsSNJcuxN0k/opU/KuwVVMjj6NQtkPIXGcsuGGCEoyLGuyqKiwyU1tqHRS5KlpiYGMLCwoiOjiY0NLSwwxGiSNE6EX1mMDh2keX6UQH9UGHvZOsDVSfOQ0c/jatr/vw502p2BD+CUeaJ3AcuRBGlE2agY8eBjr5o6/maNe4YqArLUGZl11IOiXPQidPAcRCMMFRgfwgchDLL5W/wRYyv39/ScyOEyChhJjh24HY2R9LPEDQQ/K7w6XTaeQwdPZzMgx7TPtjjJ6D9Lkf5X5nTiIUosnTCDHTMy1ns8ZTYAGhIWgjBQ1DKDkEDUEED8iPEEknG3AghMtAJ07y0MNEJP2Q8Rmu0zvrDWnus1pp2vvip2YpRiOJA62R07NgcHm2Cjs3TeEoT6bkRQmTkPIbnGhxOcB4CQDv2oeM+g6S5QBLaqIQKuhWChqCMtFkeKevxnNw4IXWDz+HplM3oxO/BeRBUOCqwL/h3cd3dClGUJK8AHZPDgx1g1szTcEoTSW6EEBkZYWB5Kh9vgFHWNYPq7J24Bhyn9dpYJ9BxH0LSfCj7HcooAz4NPvbeiay1hY55HRK/48J4BQOd/BvYLoOyX6CMCB+uJUTB0M4TuTjaRPtf61O9Y5FZvj2WOnv2LLfddhuhoaGEh4dz7733EhcX57H9Y489RsOGDQkMDKRmzZo8/vjjREdHZ2inlMr0M3369Px6GUKUPoE34PmjwYKAPuiox4BkMo8dsMCxBx33nutXs5aXC5rg58N4m4QpaYkNF10zrUfIsR19ui9W1DPo+K/RlvvPGiEKgk78BeLezcUZnKjUTXkVTqmTb8nNbbfdxrZt21i0aBFz585l+fLlDB061G37Y8eOcezYMcaNG8fWrVuZMmUKCxYs4N57783U9ssvv+T48ePpP/3798+vlyFEqaOC7gAjnMzFxXBtszUFZQPrBB5XPk74FutkL0j80csVnajguz220NqBjv/MUwvXQoNJc9Gxb6JPdUIn/+nlukLkD504Fx39DOjcJNkmOnFWnsVU2uTLVPAdO3bQuHFj1q1bxxVXuGZULFiwgN69e3PkyBGqVq3q03m+//57br/9duLj47HZXE/QlFLMmjUrVwmNTAUXwjPt2O/qmXHsxnUPpF0//le7VgJP+Bod9z/AkfuL+V2NUfZTz/Gk7kCfyc4CnQqwo8r/jLLVzVV4QmSH1g70qc5gnc79yfw6YJSdkvvzlCC+fn/nS8/N6tWrCQ8PT09sALp164ZhGKxZs8bn85wP/nxic94jjzxC+fLladOmDV988QXe8rPk5GRiYmIy/Agh3FO22qhyc1Blp6PKPI8q8xKq/G8YEZ+ijDDAjzxbz8Z52JdG2TypBpzo+K9zEJAQuZCy1sfExtvXrwlm5byIqFTKlwHFkZGRVKxYMeOFbDbKli1LZGSkT+c4ffo0b7zxRqZHWa+//jpdunQhKCiIhQsX8vDDDxMXF8fjjz/u9lyjRo3itddey/4LEaIUU0qB3+Wun//yvwrixuXRlZK9NzFrAwGAp4HO/+WEpAUQ9mrOwhIlntYaUtehE+e5CuyZNVCBN6Fsl2Ru69iPTvwJnCfALI8KuB5lb5S5XdpMQq/8u0HyYjw92lWBUtcmp7KV3Dz//POMGTPGY5sdO3bkKiBwdTv16dOHxo0b8+qrr2bY9/LLF4ohtWzZkvj4eMaOHesxuRkxYgTDhg3LcP4aNWrkOk4hSitlb4j2uxJSVpH9XpWLmWBr5v16RjA66GZI+Jbs9RjJelgia9qKQ0c9AimrcY0v04BCx09CBz+CCnkcpZRrll7sKEiYysXj0HT8Z+iA/qiwtzKWIUjd6VsAgTdC6ta0sWv/fQ8p8O/qWmNK5Ei2kpunn36au+66y2ObOnXqULlyZU6ePJlhu8Ph4OzZs1Su7LmbLTY2lp49e1KmTBlmzZqF3e65dkXbtm154403SE5Oxt/fP8s2/v7+bvcJIXJGhb+PPnc/pP6Na4xLTobvOVHBt/l2vZBh6JTN4NiStsXb9QywZb6zFgJARz8PKeeHSfwnuYif4HokFDQQ4ielJTZZtEv6GW2Eo0JfuLDNsdeHqxso/6ug3HR09IuQsuKifX4QOBAV+pysGZUL2UpuKlSoQIUKFby2a9++PVFRUWzYsIFWrVoB8Pvvv2NZFm3btnV7XExMDD169MDf359ffvmFgIAAr9fatGkTERERkrwIUcCUEQplp0HKKnTcREhdm42j0xYADH4YzDpoxyEwK7pWQXZ7vWAo9w0kzEAnfJc2VsfTgGYLFXR7NmISpYV27IfkhZ7bxH2CDrgO4id7agUJ36JDHkYZ4WnbUn2IwM+1sKZZGVX2c9e/f8c2wA5+rdPGtYncyJcxN5deeik9e/bk/vvvZ+LEiaSmpvLoo48yaNCg9JlSR48epWvXrnz11Ve0adOGmJgYunfvTkJCAt98802Ggb8VKlTANE3mzJnDiRMnaNeuHQEBASxatIi3336bZ555Jj9ehhDCC6UM8O8Efm3Qp3qAFYlvj6kCIKAbpKxGx/8vbVsgOmiA63GAm2J8SgVA8J2o4DuBtEcDse+QcbXktF6kgP4Q0DM3L0+UVMlLyfhvJgvWMUia48N07lTXat+BfV2/2ltA6mbcvw+MTOPYlK0m2KQacV7KtwrF3377LY8++ihdu3bFMAxuvPFGPvroo/T9qamp7Nq1i4SEBAA2btyYPpOqXr16Gc61f/9+atWqhd1uZ8KECTz11FNoralXrx7vvfce999/f369DCGED5Tyg7JfuioWW8e58MVx/r//fWyVCEm/pG2/aFvCdFd9mnIzfao2rILvc/X8xH8GqetdG826ruQn8GZX8iXEf+lk8KX2rxWfjfO5qKDB6IQvPZ0UFTTEt/OKHMuXOjdFndS5EeL/7d15mFTVmfjx77m1dvVKdwMNkSCgwURRVAIDzigJKCpxxBgXJIoGRY0aiYaIWSRo1Lj8RqNxRn8x0WjcE5WJOxGXqIgGYVBERgiKW0Okl+q9q+q+88etru6ia7mNXb0U7+d5+rHr1rnnnrpdVr2c5T25IdIKrU8hrStBWp08OfYOejYJ2AMFJ2A8o5D4nAjjn+IEK57KDNduB+yMQ1tKAUjrC0jduVlK+aH8YaiZk7U+U/EnjK9zYryzE/gVOMF9Rw9OPNAPzccU/0Tn0+wmt9/fGtxocKNUTkj0A+Tzo75ADV17eyzAhxlyGyZw+BdvnMp7Et0Krc8g0ojx7A3BYxObuYrEkH9+I0Pg7YHgCVhl12DXnBmfeJxqmMkD3n0xFcu7BSvS/nek6ffOkBUx8E3EFJ4BgaM0sPkCNLjJQIMbpXJP2l5Bar/XizV2ZB1+GuPVVA4qNZE2pP4nznwZPDjvmyiYAkzJLzEFxznl2tfGN36NkBy4WOAZjal4CGOVIdGPkJqTwa7rXo4ADPk9VuDQPnltqp8zFCulFL2+4iOedbhFN8pV6Un9T6H1yfijGIkVddKC1P8IaXsZAOM/GFP5KAT/HYinHDFDoPBcTMUjidVPxjsKU/EYhE4FCuL1Gpwenxao/S523SVI9OO+eHnKJe250Z4bpXJCxEY+nwmxXv7Q94zFGvpM79ap8oKroVDvgViVf0o+T2ychI+BjENGduMfoPGajrO6POMBU+LMvdFexZzSnhulVL8yxsIU5SJNwxfbrFMkgrQ+ix1ehh3+hbODs2gm47zQ+ixZV0FF1yOx5G2AjLEwJpg2sBG7FrtmATReTWIT2SQxkDDSkDmDv+o7OVsKrpRSpuBYkGak4WqQJpw5EDbgBc+XIPYBSfMiEs+n61D2gG9Smueyk8j7SO3Z8eXqzsefcD80DIUhd2B8B+x23ar/iTTgJlO2RLdiXG5KKdKO1JwF0WzbKsSg7a+IXYOxyl3VrXJHgxulVE6Z0HegYDa0roDYJ2BVQPAoZ7JmZBPS+gxIE8Y7DvGMhtpMOUDcb9ewK7HrkNrTwa6PH+nSA2TvdCaXVj6N8QxLeb4aBIzLeV6R9RCY6q5s6wqIvuuyATZEPwK/Bjf9TYMbpVTOGVMABf/e/bhvPMY3vvMxIMU/jmcd9tC5OsX53RRfhvFN2L1GtPwJ7FpS/8veBmlCmh/AFF+8e/Wr/udzs2rJJCXdy0ZalpM1m3FXVrHrulXu6JwbpdSAYgrPxgy5BwKHgyl0fgJHYIbcgylcsNv1SsvTZB6ysKH1qd2uX/U/4xtD9szD4mx34JbU4C6wMeDZFzxj3NetckZ7bpRSA44J/Asm8C+JxyJRaHseu+4SsBvBOxYTOhnjHeu+UnGRSl+ad6O1aqAw1hAkcDS0PUvagMQUQnCW+0o9oyCygex7pgmm+GJN0DdAaHCjlBrQJPZPpPYsZyuHjqGq9r8hzb+HokUQOgMQMEWZv1i8+0HsQ9J/SXnAOz7Nc2qwMMWLkfbXQcJ0T7onUHwZND+EHf0ArEJM8OiMQ52m4CQkkTcnHS+m5CpM8Itk5Fa9SfPcaJ4bpQYsEUFqTnL3L2fP3pjCs6DglJQbZkrbamdCcQam7A5M8Bs9ah+xj4AIeEY5G4iqfifRj5GGG5J7cLz7O3NyWh6gc2WekxgS/+GYspsxVlH3ukSQuh9CW6phTQPWSKh4BCvDvmeq92ieG6XU4BdZ46xsyTokAMQ+RMJLkfrLSflvNv9kKEgV3MR7e4LfhsB0V80SEaT5YeTzmfGfY5AdU7EbbkCkxVUdeyKxa5HoVsRuyN01Iu8gTf/lpBnwHgChM6HiMUzRD6DlHpztFgQnwIm/r9pfQeovSVmfMQZTdiMUfh9M1+DHDwUnYyr/ooHNAKTDUkqpAUvaXiJ51VTG0s5/Wh+D4EwIHpn8dPR/Ifp+99OsSkzRBVBwquv5EtJ4IzT9lqTJq9IATb9D2tdA+R8wJuCqrj2BRN5GGn4N7X/D+Tt5kODRmKJFGO/o3rtO42+QxltIes9E34aWxxGrgvSrnmxoexGJvIfx7dftWWO8mOKLkaJzIbIRiIJ3P4yujBqwtOdGKTVwSYTsq1925UGa79ulmveczQ8jb3Yvbv8TrCEph7JSN+m9eGAD3YcpbIisheaHetjm/CXtbyI7T4X2V+i8XzFnx+6d30aiW3rnOq3PxQObeP2dzzjzb2JbyLzqyUDbiozXMCbo7Enl/7oGNgOcBjdKqQHLyRjc0+0WYhBJziYrDdeAtJOuB0jql7regkFaHsHpGchQpvl+V3XlK4lVYzf8GvvzU5zsvkToHljEnOzV4WW9c82mO0n/leZmKbcgkQ+7H41td3qeYp98keapPqbDUkqpgSt4FISHgNTjOokagAkmfpXYJ9D+eubyUgdtL7hbIhz9B5mHyQRi29y0Mi9J60qk7iKce5TtbxaD9teR6Lae5Z7Z9ZrSDpF1u31+Z3M6/24Sec9JJtn+Kh09TuI7GFP8I4z/61/8WiqntOdGKTVgGePHDLkV8JGtt6STBwqO7Xzo6l/clvvdy60Ssn50mkJ3deUZiW5D6i7E6W3rQTAa695j0jM9uFbGapz3ikTeRXaeAu2rSBp6jPwPUnMG0vZq71xP5YwGN0qpAc34J2MqH4OCb4MJ4czBsUg/F8eLCXXZf8qUubiKDWaIu/YEjyXzl6kHCo5zVVe+ceY6pdo1OwuTZgm2uBuSNCYI3q/Q8/lZu3K+EiX8C6CN7j10NmAj9T9BpJcCKpUTGtwopQY8490Hq/RqrOHrMMPfw1T8JcMmiW3Q9rfOh959wTOOzF98fgjOSDoi0oa0r0Pa1yB2Y+cTgW/Gk/2l6kmywPgxoTNdva680/YS7la2dWENA9+BiYcS/Qg7/Atkx8HI9q9h75iK3fBrJLHhaWom9D3SB1UWTu9fpq88D/gPcyY4R9aRPoAVZ1f5bEOdql9pcKOUGlSMMWACztLrNCS8FIm8kyhvin+UudLChRjLCZZEos6X6Y6pSM3JSM1cJ4dN+ErEbsYYH2bIXV2+kD0kpi9aFc4eWF9g/sjg1sPABjBFF2GMEyhKZCOyc46z2qxjKwx7JzT9F7LzJMSuSV9RwQlQcEr8QdfA0wP4oORXZO7xszGFp0PU5XypPXhe1WCgwY1SatDJvhrJQpruTTwywRmY0v8HpiOjaceXnw8KL8QUXeTUK4LUXQJN/wnSpbeGNmi+H6k9C5F2jKcSU/4gpvwRKDwfChdgyn6DGfoSxn9Qb73Mwcc3iexzozqGFb2YosWYkBOQOPd+UTyoSTEcFPsICV+dvlZjMCVXQunN4JsIlDnZg0NnYCqfxAodhym7Kd6+XYMf42yf4NsfrHQ9grteULPbD2S6WkopNfi0ryJzL0EM2l9LOmIKvoX4DoTme8D+3EnCFjoVY5V1qfdVaHsmTZ3xHDYtj0HoFKcHyX/Qnh3M7MIUfhdp/XPmQsHjML4DoeBbGKu883jk7xDbmuHEGLQ+hdg/TT4vTmLbkYaboPUvOEvPAc8+mMA3Ez1pJjgLKp925ga1vQLY4J+CCZ2G8cX3FfMdBNZwsLdnehEQOCLz61T9SoMbpVSe6hx+ELsRCV8BrU/ROZfiKaT9TSj9FcYz1CnX/DCZMyIbpPnBRG+DSmZ8+0Pxz5CGX5J8H53fTfHPMIVnpD458q6LK8QgutnZSqMLiVUjO7/jDGF1/dtF3kJq50PZrZh4xmrjHY0p+Un612A8UPxDpH5J+jJF52OsPXNF3GChw1JKqcHHP42swx9WRWLFjdSevUtgE9f+mjOnpmOvo4y7hoOTw0aTuWViCs/AlD8AgaOclWqmDAJHYsofSB/YABL7zOUVum9OKg03dA9sAOfvLUj9EkTaXNYPpuDbmJKlQEe+JC9OsOyFwguh8DzXdan+oT03SqlBx4TmIs1/yFwougGa7wXPcIi8laZQzNnVu+VhKFwAVjnp9x+K6zqMpVIy/kMx/kN7dpKr3DFl4Ns/6YjY9dD6NOmDUnEmn7c+16Ml+iY0D4LHQ+uzzuooqwKCs1IOiamBR3tulFKDjvF+GUqvz1pOGm9Bmh8h80edxMuAKZhD5hw2FqbghB60VLkh0S0Q25S9YPBwjPElH4t9QvYtOrxINNN8ntSMVYQJnYgpuhATmquBzSCiwY1SalAyloukexKG6AdkzWBrf+78N3hMPBlcqiEvD1iVEDq1Zw1V2cV2uChkwLt/isPdEwB2Z2MsN+VUvtDgRik1OEnYXTmrmMzzcwx4qpzfjB9Tfg/4/6XzuY6PSe9+mPL73QVVeyCJfY403or9z1lO4r2d30VankTERe4bq9LNFRITv5N4RrnITizOHCC1x9A5N0qpwckzyl254DHQuCFjEVNwcufvVjmm/C4k8n7nknPfIeA70Fn+rbqRyPtIzXeTNzi1a5H6N6D1CSi7pftwUtfzPWPBGgX2R+kvYkJOduhdDxsDRYuQuu+nOdGC4ByMdy/3L0gNetpzo5QanLz7x//Fnu5jzALP3hD6Xnx1VapyHvDuAwUnAk52YhEnhb/x7eus/Ck8C+M/aEAGNiIRZ0fsnF+nDbv5T9h1l2DXLkaaH0TspvhzNlJ3XrwnrevwX/z3tpXQdGf6utv/B3YemTmwAUzRpRgrlPq54ExMyTVAgMSqpo6/d/A4TOmVbl6myiNGOv5P3oOEw2FKS0upr6+npESzTCo1WEn7WqTmdLrvQm0BFqb8box/MiKtSPhX0PIIiQRvWBCYBSU/xbQ+4SR2i30E+JxVMYVnY3xf6+NX5I60voA03QmRN50D3q9gQmdBwbd7PQizmx+G8DI671uHIkz5HSDNSO05mSuxKjBDX07qvRE7jLStgvofxetOMy/KlGKKL8GE5mZtq9gN0PoEEt2GsUogeDTGOybreWrwcPv9rcGNBjdKDWoSeRsJXw+R1Z0HfZMwxT/G+Ccml7XroH0tzlDTBLDKkJrvOdlxnRLx/8ZT8g+5HRM4POevoSek6XdIw3UkL1k3gEDwO5jSq3stwLGbH4TwFRlKBJ3d2lseJtuKJVP5HMa7NxLbgTTc6AxXZV3lBJQ/gqVZoFWc2+9vnXOjlBrUjG8CpuJeJwlcbAd4hmI8I1OXtcog+I3EY2m8FSJr6L6bdAwwSN0ipGQpRN8H/JjgdGfrgB4QuwZpuscJAOydYMohdCImdCbG42YibZe6Iu/HAxtI7umIt7/1TxA8AoKzelRv6nbXxntsMmmD6Lt0v3+pWM6k450nx7c2cLPJpgdanwENblQPaXCjlMoLxjMCPCO6HRe7GaTWGd7oshxYJIo03Uf6ZeLibJ5Zvxjno1KQpt8gvkmYIb9xlfPE2RbgVLCrO68jO6Hpd0jLY1D+UI8mukrLA2TeHsJCmv7o7KGUsV07wK4Hz3Bn+CaVlsczXCdRE0Q/yl7OqgLPl5DwVT0IbDouUeu+rFJxGtwopfKSRLc5PTOtT+IMf1hIYCam6CJnk0R7J0iNy9q6DJ9E1jpDWRV/dvYhytSG+p/Fv8x3DaBiYNcg9ZdhKu5z/6IiG8gcGNjxnpQ07Wl/E2m4uXOuDhbiOwS8Y8Eqxfgng//fMMZCov/rslG2M7E7uiVt20zh94AotDyapf3dWozxfMl9aRGnJy7yHhg/BA7HxJf5qz2LBjdKqbwj0S3IzlNAmuj8MrWh7Xmk7WUovxfiO0X3XMwJINpeThri6t6Gj6D9b6QfsolB5E0kuhnj3afzPLsBIuudB779k3ctN0ES82vSMd33XoL4JOS683c5ajvzjSJ/BzxI02/BMxqG/H931wLwfhVTepUzsdvu2B9KSPQwBb8DoTPiz7Vmrqt7qyHoLiO0RDYidZdAbEuXdltI8HhM6TKMCWapQeUTXQqulMo7Ur90l8CmQwxoR+ovczZ09E5g9z4GPUjrU5mLuJ2LEnFy8Ii0YYevQnZMQ2rPcn52HIZd/1PEbgTABGZkqcyA7xB2XSciEkHCl8fbk24YLn6vYh87OWt801y13xTOx3hHYSqfcDab9B0C3n2dpHml/wmhUyC6GSH1Mu6MCr/vathOotuQmnkQ+6DjSPy/NrQuR+ou7nZPVH7T4EYplVck+gFE3iD98IcNsX9A5C1M0Xlk3ZohpZgzHyej1D0o3fkQiSG150HzfUDX3asj0PJnZOeJ2M3LEe9XwRpC+ozLAm0rkM+PRNpe6jzc9iLYNbib+BtztqOIbYtvd5Bh5ZVvOgSmAx37MJ2GVfEApvxeMAVQfzHUnITs/BbUfBs84zK0vQtrKKb4CkzRD1y0F6fHSVpI/Te3oe0FiKxzVZfKDxrcKKXyS/QfLsttwQSPxBT/hM5tFgyuvnwBPFl6FPyTcJLKZSa+A6HteWh/ldSBlg2xrRBeDLXznB4nUxx/Lk3gEfsIqT0Xafub8zj6Aa5fl9MqaH0CM+S34M2Q68fe5gQOXc+0651J1K3LScqNE/s0PmRkp2m3Ad9kTMWfMENfwhR+19WSdhGBluVknsvjQVqXZ61L5Y+cBTc1NTXMmzePkpISysrKWLBgAY2Nmf+lM336dIwxST/nnXdeUplt27Yxe/ZsQqEQw4YNY/HixUSjLnIlKKX2DKbQZblQvPiZmMrnofA8CBwJbvPaWN1XZiVVbxVD4N+yN6PteRc7l3cR+wCkFQp/AKTb0kAAQcK/dL78rWJ63EMVfQ+kEVPxZyheRkfun+S2bEXqzkdanui8ctNv48kQdw02uvYadaxa68gkbJxMwuW/w/gOxJieTAdtJ/tcHgFbV13tSXI2oXjevHl89tlnrFixgkgkwllnncXChQu5//77M553zjnncOWVnamyQ6HOcdpYLMbs2bOpqqritdde47PPPuOMM87A5/NxzTXX5OqlKKUGE//BTu+G1GUqBIEjEo+Mdy9M8SIApO1VZJfeiFRcfQFL9oBCWh4GyTQXZlc2EHGGmsi09YI4PT7RdyAwA1hGz1YqxZCa+Zihz0Lb05117noNDBL+BQSPArzQ/FCW61hQOA/jHY9Et2CsQggcidntCd5+MKXxfa3SMWClzn2k8lNOem42btzIM888w5133smUKVP413/9V2699VYefPBBPv3004znhkIhqqqqEj9dMxA+99xzvPvuu/zxj39k4sSJHHPMMVx11VXcdttttLfnfn8VpdTAZ4wfU3RB5kKFZzk9K6mkSQC4W+Xs7dnLxLaDp5KefRzHILreZdEdzm7aoXlk3jk7BfszZ1uK9tdJH7CIs69U2/MgzVmCjI42fYopmI1V/ANM4YIvENjEN84MnUzmYbcYJnTibl9DDT45CW5WrVpFWVkZkyZNShybOXMmlmWxevXqDGfCfffdR2VlJQcccACXX345zc3NSfVOmDCB4cOHJ47NmjWLcDjMhg3pd/1ta2sjHA4n/Sil8ljoDCi8gI49ppxO6viwSsF3MUWL0p5qvGPAN5GMH4+mzN3wlWc4Wee6WJWYgjns3sRmFzxDATDFS6BgLk6A0/GTjQWtK91cBGKfxJePu+jRskpd1OmeKVwAnirS3uvQ/KTl9ir/5WRYqrq6mmHDhiVfyOulvLyc6urqtOeddtppjB49mpEjR7J+/Xouu+wyNm3axKOPPpqot2tgAyQeZ6r32muvZdmybGnElVL5whiDKb4YCZ0KLcsRuxpjVTrzOryjsp9f8jNk52l035DTxJ//BSZNPpmkegpOQNoyBQcGEzoJgrOh6W6IbsLdiqaO07MPx0jzE1A8CmMNwZT+AilaCK1PI7Gd0Py7LNezXbYnBqYMY7xI8GhofZr0PT0xTPBbLup0z1jlUP4QEv4ltD1H4m9myjBFCyG0oFevpwa+HgU3S5Ys4brrrstYZuPGjbvdmIULFyZ+nzBhAiNGjGDGjBls2bKFcePG7Xa9l19+OZdccknicTgcZtSo7B9wSqnBzXiGQ9HCng7GOPtHVdzvfFl2XULs2RtTfBkm+E13FQVmgG8SRN6ie8+Mx1lxVXAKxgSQslvh8yNdttDjZBIuOMbJ2ZOWQMu9SPvzUPEIxip39t0qXIAB7PbXILqR9AGMB3yHOgFUdHOGcn4IOm03hechrc+ROqeOBf7D4j1jvct4hmGG3OJsLRHdDCYAvgmuglCVf3oU3Fx66aWceeaZGcuMHTuWqqoqduzYkXQ8Go1SU1NDVZX7VNhTpkwBYPPmzYwbN46qqireeOONpDLbtztj2pnqDQQCBALZl2QqpVQH4zsQU/EwEt3qLGO2hjjZeHuw47YxXhjyWyS8DFr/m6Qve/9hmNJrE/tdGanvQZ+NB1P8Q4zvq4CFNPzK2U4ipRjEPkXCN2DKrk1uX+j0eHK/dAQTOgUChyK156YtZYrOxcSHmozvK1B+N1K3COwdOENF8UAncCSm9Lpe27U8ZVs8w8AzLHtBldd6FNwMHTqUoUOHZi03depU6urqWLNmDYceeigAK1euxLbtRMDixrp16wAYMWJEot6rr76aHTt2JIa9VqxYQUlJCV/7WoZcDEoptZuMdwx4x+z++VYhpux6JPYjaH8DsME3McUk2gL3lRZdEg9swBQcj+CF+h9mOCEGrf+N2D9JnkhdMAfaXoK2Z+MHOsIrZ+sEU7LUyRDs3QvKbo5nfq7DmY9kA35M0blQeGHya/ZPgqEvOltURDc5c3EC38R4R7t/jUp9AUZylJP6mGOOYfv27dx+++2JpeCTJk1KLAX/5JNPmDFjBvfccw+TJ09my5Yt3H///Rx77LFUVFSwfv16fvjDH7LXXnvx0ktOps1YLMbEiRMZOXIk119/PdXV1Zx++umcffbZPVoKHg6HKS0tpb6+Pmk1llJK9RcRG/nnDLA/yVLSAwUnYJV2fuZJ461I43+RtMFnCqbicfCMgta/OD1SphACMzHRt5Gmu52l4xjwT8MUno0JHLZLG9udpH2xj8EMgeDM9LuKK5UDbr+/c5bn5r777uPCCy9kxowZWJbFiSeeyC233JJ4PhKJsGnTpsRqKL/fz1//+lduvvlmmpqaGDVqFCeeeCI/+9nPEud4PB6eeOIJzj//fKZOnUphYSHz589PyoujlFKDkTEWUnQehH+epaQ4S66TTi7AzWoraVsFjafg5MeJDxc1/ScSmI6peBSMs6osXQ4fY/wQnJX9xSjVz3LWczOQac+NUmqgsrdPcvLGpGVhii5KyuUj0a3I55mCDgPWsAx5dywIHIE15I7dabJSfcbt97fuLaWUUjkk0Y+ww9dg7/g37O2TsHfOQ1qfRtJkL3Y288wy4bYgOSGd8Y6BwNGk/0iXeO9OuuedzSUl8l7m6xIfPmtbjbQ8irS+4AxVKTXA5GxYSiml9nTS/iZSczbOMFA870tkDVL3JgS/BaU3YswuAUfodGh9HiJrSR5qcibxmpIrMJ7uq0NN2XVIXTu0raQzmV28Y77oImj8dZbWepDWZzG+/dK/nraXkfBSJ2Ff4sKlUHwpJnRqlvqV6jsa3CilVA6ItCC13wfaSA5S4r+3Pgm+Q6Dwu0nnGROA8rug6XdI8x87l3j7DnaWXAemp7yeMQWYIbcjkbeRlqdAwhjPl6HgBJBWJGtwY0Ca0r+etteQ2oV0y3Uj9Uj4CpAoZpfXolR/0eBGKaVyoeWprPssSfPdEJrXLe+LMUEousDZqdyuBRNIvxfWLoxvAsY3Ifk60oaz1Lwlw5kxjHds6naKIA3X0rHbeMoyjTdCwbcxVijl80r1JZ1zo5RSOSCRtWT+96NAbFvGAMgYD8ZT6TqwSV9PAEInkn6fKwMEnaGyVGJbsm8NIc3xITGl+p8GN0oplRNZNszscbkvxhRd5OS46XY952vAlF6dyJbcTeyfLq5ggf35F2miUr1GgxullMoBE5hG5qR6lrOdwxfslXHdHmsIpuIhCJ0WXzkV5zsEM+QuTEGGzSxdbWdgx3dBV6r/6ZwbpZTKhcA3wfoS2NWk3iHbxhSe06dNMtYQTMnPkeIfO70xVsjZUTvbed5xiPcAiL5L2mSBpggC3+jdBiu1m7TnRimlcsAYH6b8TrDKcea0dEwajg8LFV6Qubckp20LYLx7uQpsEueU/ATnKyP114YpvtyZCK3UAKA9N0oplSPGOw4qn4XW5UjrM2A3g++rmNBcjG///m5ejxj/JCj/g7N5Zmxz5xPWMEzxYkzB8f3XOKV2odsv6PYLSinlmohA9B2IfQrWEPAdijF9MylaqX7fOFMppVT+McaAb4Lzo9QApXNulFJKKZVXNLhRSimlVF7R4EYppZRSeUWDG6WUUkrlFQ1ulFJKKZVXNLhRSimlVF7R4EYppZRSeUWDG6WUUkrlFQ1ulFJKKZVX9sgMxR07ToTD4X5uiVJKKaXc6vjezrZz1B4Z3DQ0NAAwatSofm6JUkoppXqqoaGB0tLStM/vkRtn2rbNp59+SnFxsbNPygAQDocZNWoUH330kW7m2YXel9T0vqSm9yU1vS+p6X1JbSDfFxGhoaGBkSNHYlnpZ9bskT03lmWx11579XczUiopKRlwb6aBQO9LanpfUtP7kprel9T0vqQ2UO9Lph6bDjqhWCmllFJ5RYMbpZRSSuUVDW4GiEAgwNKlSwkEAv3dlAFF70tqel9S0/uSmt6X1PS+pJYP92WPnFCslFJKqfylPTdKKaWUyisa3CillFIqr2hwo5RSSqm8osGNUkoppfKKBjf95Oqrr2batGmEQiHKyspcnSMiXHHFFYwYMYKCggJmzpzJ+++/n9uG9rGamhrmzZtHSUkJZWVlLFiwgMbGxoznTJ8+HWNM0s95553XRy3Ondtuu429996bYDDIlClTeOONNzKWf+SRR9hvv/0IBoNMmDCBp556qo9a2rd6cl/uvvvubu+NYDDYh63NvZdffpnjjjuOkSNHYozh8ccfz3rOiy++yCGHHEIgEGCfffbh7rvvznk7+1pP78uLL77Y7b1ijKG6urpvGtxHrr32Wr7+9a9TXFzMsGHDmDNnDps2bcp63mD7fNHgpp+0t7dz0kkncf7557s+5/rrr+eWW27h9ttvZ/Xq1RQWFjJr1ixaW1tz2NK+NW/ePDZs2MCKFSt44oknePnll1m4cGHW88455xw+++yzxM/111/fB63NnYceeohLLrmEpUuX8tZbb3HQQQcxa9YsduzYkbL8a6+9xty5c1mwYAFr165lzpw5zJkzh3feeaePW55bPb0v4GRZ7fre+PDDD/uwxbnX1NTEQQcdxG233eaq/NatW5k9ezbf+MY3WLduHYsWLeLss8/m2WefzXFL+1ZP70uHTZs2Jb1fhg0blqMW9o+XXnqJCy64gNdff50VK1YQiUQ46qijaGpqSnvOoPx8EdWv7rrrLiktLc1azrZtqaqqkhtuuCFxrK6uTgKBgDzwwAM5bGHfeffddwWQN998M3Hs6aefFmOMfPLJJ2nPO+KII+Tiiy/ugxb2ncmTJ8sFF1yQeByLxWTkyJFy7bXXpix/8skny+zZs5OOTZkyRc4999yctrOv9fS+uP3/K18A8thjj2Us8+Mf/1j233//pGOnnHKKzJo1K4ct619u7ssLL7wggNTW1vZJmwaKHTt2CCAvvfRS2jKD8fNFe24Gia1bt1JdXc3MmTMTx0pLS5kyZQqrVq3qx5b1nlWrVlFWVsakSZMSx2bOnIllWaxevTrjuffddx+VlZUccMABXH755TQ3N+e6uTnT3t7OmjVrkv7WlmUxc+bMtH/rVatWJZUHmDVrVt68N2D37gtAY2Mjo0ePZtSoURx//PFs2LChL5o7YO0J75UvYuLEiYwYMYIjjzySV199tb+bk3P19fUAlJeXpy0zGN8ze+TGmYNRx7jv8OHDk44PHz48b8aEq6uru3UBe71eysvLM77G0047jdGjRzNy5EjWr1/PZZddxqZNm3j00Udz3eSc+Pzzz4nFYin/1u+9917Kc6qrq/P6vQG7d1/Gjx/P73//ew488EDq6+u58cYbmTZtGhs2bBiwm+fmWrr3SjgcpqWlhYKCgn5qWf8aMWIEt99+O5MmTaKtrY0777yT6dOns3r1ag455JD+bl5O2LbNokWLOOywwzjggAPSlhuMny8a3PSiJUuWcN1112Uss3HjRvbbb78+atHA4Pa+7K6uc3ImTJjAiBEjmDFjBlu2bGHcuHG7Xa8a/KZOncrUqVMTj6dNm8ZXv/pV7rjjDq666qp+bJkaaMaPH8/48eMTj6dNm8aWLVu46aabuPfee/uxZblzwQUX8M477/DKK6/0d1N6nQY3vejSSy/lzDPPzFhm7Nixu1V3VVUVANu3b2fEiBGJ49u3b2fixIm7VWdfcXtfqqqquk0MjUaj1NTUJF6/G1OmTAFg8+bNgzK4qaysxOPxsH379qTj27dvT3sfqqqqelR+MNqd+7Irn8/HwQcfzObNm3PRxEEh3XulpKRkj+21SWfy5Ml5+cUPcOGFFyYWbWTrxRyMny8656YXDR06lP322y/jj9/v3626x4wZQ1VVFc8//3ziWDgcZvXq1Un/Mh2I3N6XqVOnUldXx5o1axLnrly5Etu2EwGLG+vWrQNICgIHE7/fz6GHHpr0t7Ztm+effz7t33rq1KlJ5QFWrFgx4N8bPbE792VXsViMt99+e9C+N3rDnvBe6S3r1q3Lu/eKiHDhhRfy2GOPsXLlSsaMGZP1nEH5nunvGc17qg8//FDWrl0ry5Ytk6KiIlm7dq2sXbtWGhoaEmXGjx8vjz76aOLxr371KykrK5Ply5fL+vXr5fjjj5cxY8ZIS0tLf7yEnDj66KPl4IMPltWrV8srr7wi++67r8ydOzfx/Mcffyzjx4+X1atXi4jI5s2b5corr5S///3vsnXrVlm+fLmMHTtWDj/88P56Cb3iwQcflEAgIHfffbe8++67snDhQikrK5Pq6moRETn99NNlyZIlifKvvvqqeL1eufHGG2Xjxo2ydOlS8fl88vbbb/fXS8iJnt6XZcuWybPPPitbtmyRNWvWyKmnnirBYFA2bNjQXy+h1zU0NCQ+PwD5j//4D1m7dq18+OGHIiKyZMkSOf300xPl//GPf0goFJLFixfLxo0b5bbbbhOPxyPPPPNMf72EnOjpfbnpppvk8ccfl/fff1/efvttufjii8WyLPnrX//aXy8hJ84//3wpLS2VF198UT777LPET3Nzc6JMPny+aHDTT+bPny9At58XXnghUQaQu+66K/HYtm35+c9/LsOHD5dAICAzZsyQTZs29X3jc2jnzp0yd+5cKSoqkpKSEjnrrLOSAr6tW7cm3adt27bJ4YcfLuXl5RIIBGSfffaRxYsXS319fT+9gt5z6623ype//GXx+/0yefJkef311xPPHXHEETJ//vyk8g8//LB85StfEb/fL/vvv788+eSTfdzivtGT+7Jo0aJE2eHDh8uxxx4rb731Vj+0Onc6ljDv+tNxH+bPny9HHHFEt3MmTpwofr9fxo4dm/Q5ky96el+uu+46GTdunASDQSkvL5fp06fLypUr+6fxOZTqnuz6XZMPny9GRKTPuomUUkoppXJM59wopZRSKq9ocKOUUkqpvKLBjVJKKaXyigY3SimllMorGtwopZRSKq9ocKOUUkqpvKLBjVJKKaXyigY3SimllMorGtwopZRSKq9ocKOUUkqpvKLBjVJKKaXyigY3SimllMor/we4dZmPQJ4VmAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x18eb5ecc1f0>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 15,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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g8qfOY/60BUn7tdmpJTe9cTnOCk649zE9yVuxocLNPcaaf9dx/ymPl1NGXfnPGluJ7Vg0xu+TdD6lpu7RjqABEwlHuOmw+xg+9Dm+fOU73h/+KRfveQPTxs3A5XaiVPKQyNNvOTHhhp6AwKLZS8ptuuPd6zjh8qPxZXkRQ9jjwN15avIDtTZ3Pfq+D8rdPAFCJSE+HvFV0hKSUz/9lamfTCdUErKmcAJhQiVhHjr9qXKOrSp03qcTw8ffzacb3uL1ec/gy/LhdNs/8TucBidcOZAjzzmETze+xaPf3c3wb+/iy8AYjjrnYNsRGsDs7/5g6qe/lv7fvHU20bD9tbXesSXFm0uY8c1vzP95QZWE+DSamqIXi2sZ0zSZNWEui2b/S7tdWtPvxP1we6omaTD+zR/4Z/aS0tj/WDRGLBrjwSFP8fGGN/H4PZRsDiT0MxwGx156FABv3fcB4YD9zdGMmfiyfOW2uT0uLn1iKJc+MXR7LrParF+eb7s9FolSUhggOzcrYd+E0T8mXSP586e/2PfIHtW2x+F02D6tg5XXsO8RewFW4tq+h289T4v2uUnDc82YyafPfc1BJ+0PQG7bHHod1YNZE+YQCW11CB6/h47dOzCk3SU4XU6UaZKZk8Ej39xFx247VPuaNJptoUcEtUhJYYAr+9zGg6c9yah73uepYS9x7i5Xsmbpuir1nzhmsm0CWDQc5d2HPuacu09NeOoXQ7jh1cvIyPZzyvXH0ff4PrbSzCJCyw4t2HmPHRP21SW79Ohou92f5bNdZK1t+hzT07bimtvn5vKnL0jab6+Du1Vqb97K8g7v9jHX0ueYfXB5nPgyvWQ083PydYP46rWJhANhSjaXECgKkrcin1sHPKhHBppaRTuCWmT0fR/w3/wVBIqCpVr+G9du5LHzR26z76a8zSyctTjp/h8/msapNxzPZU8OJbddDgjs0KUdD467jaPPPwywnm7vev96Xv3jKQ4c3AeHy4Evy4svy0urHVvw0Be3JX36rSsuHn52Qn0Cj9/DRcPPThqPP+C8Q23rHwDs2b9bjezxZ/m4+4Pr8fjceDM8uL0u3F4Xp910PN0P2C1pP8MweGLSfbZSHABrl65nwcyt76c/y8f9n9zCe8tfZuSvw/lo7WtsWFWQMHpTCko2l9iW2dRoUoUOH61FTmt7MRvXJUazOFwOPsl/E1+mz6aXxWPnj2TimJ+SqoPmtmvOBytfTdgeCUdY+18eOa2zyWhW/gk1f3UB86ctILtlFnv179ZgEp/m/7yA129/l8Vzl9J6x5YMvW9I6TSKHUopHr/geSb/3y9Ew5H4iEe47+ObS5VQa8rmDYVM+3QGwZIQ+w3cp1x5zsqYOGYyw899LmG7COw3aF8e+vz2pH3vOv4Rpn85O2G7P9vHraOvpt8JqU3m06QfWmKiPqjEyW7L/075ZHpSJ4BAr6MSb3jjXviG1+94F9NUxCIxDjm9H9e/NAx3vEpYi3Y59D+l+tm6tUX3vl158of7q9xeRLhl1FWcePVAZn07N17/oG+VE7mqQnZuFsdcePh29+vapzMevydhAVwpWDL3v0r7HnTS/sz5YV7C+kc0HGXPg3bfbls0mqrSMB4JmyiHnn5gwvy8GMLufTrjz0o+GgCShoYCZDbP4PwHTi+3beqnv/LKLe9QsjlAsChIJBRh8kc/M+LyxFFDU2G3Xrty5u0nccIVR6fUCdSEFh1yk3r5jt0rX/A9/Oz+tO3UOmG6bp8j97JdNNdoUoV2BLXI+Q+eTofObfFlWhmy3kwv2S2yuHmUbWmGchwypK/tIm+7Xdrw2p9PJ2SfvvvwxwlPoeFAmEkfTKWkMDGySFM7+DK8HH/F0Xj85dcwPH435947pNK+LrfTdhT4+/fz+O37P1Jqp0ZTFj01VItkNMvg5d+fYPqXs1n027+07dSa/Qbtgy/Ti2mazBw/h58/n0lmTgZHn3coO+zWvrTvJY+dy7xpC1i/PJ9QIIzH7yYrJ5OnJj9Ai3Y5CefKX7XB1gbDMNicX7jNEYgmdVzy6DlkNPMz9qkvKNpYzE7dOnDFMxdUutgMsPTPZaxblkfFdbtQSYixT3/BPofvVZtma9IYvVhcRyxfsJLHL3iBhfHIkYzmfoLFIcKBMOIQnE4HN7x2OUeefXBpn1gsxsxvfuffP5bRoUs7+p7QO6lO/gNDnmTKx9MTYtkzczL4v7Wv6wpc9YRpmlVelJ87eT53nzDcNjfE43fzcd6bpes9Gk110FpD9UjxpmKuPfAu/p6+sDQpbHNeYak6qIopIqEoj503ko3rNpb2czgc7H9sL8647ST6n3JApcVSzn/gDLwZHowylbk8fg/DHj9XO4F6ZHsis7r02iWpRlQsEmPimJ9SZZZGUw7tCOqAiWOmEAlGthkppEzFk5e8VK1z7LR7B57/dTgHn9aXVju2YI9+Xbn3/25k4IVHbLuzpl5YMvc/Pnryc75543uKNhbjy/AyYKi99HY0EuPncY1rFKxpPOg1gjpg5T+rEhQok/HbxD+rfZ4du3bgTl0hq8GjlOKpS15i0vtTiEVNnC4Hz1/7Bg99fjt7HrQ737w5KUGLyDCE5m2a1ZPFmqZOShyBiBwDjAAcwGtKqeEV9t8MnF3mnN2AVkqpDSKyFCgEYkDUbv6qsbNb7874Mr3brA8AJM8d0DQZfh43s1z96C03/duOeQgEe7E9EVYsWMV370zm0NP7VTpNqNFsLzWeGhIRB/A8MBDoDpwpIt3LtlFKPa6U6qmU6gncDvyolCob5nJYfH+TcwIA/U89gGatshOkiysihtDrqOoLpmkaB+NHTbIVzYuGo0RDUSg7hRhf8jFjJn/89BcjLn+FW496kFi08noTGs32kIo1gv2ARUqpJUqpMPA+MLiS9mcC76XgvI0Gt8fFyOmPcNR5hyZ1BiJCVm4mV4xILmymaRqsW55X9cYV1pWCxSEWzlrClE9+tW+v0VSDVDiCDsDyMv+viG9LQET8wDHA2DKbFfCtiMwSkWHJTiIiw0RkpojMXL9+fQrMrluatczmkNP62kbwOJwGZ9x2IqMWPEu7Tm3qwTpNXVK4oahG/YPFwXL1DTSampKKiUY7LYRk8THHA1MrTAsdqJRaJSKtgQki8rdSanLCAZV6BXgFrDyCmhpdlyyYuZg37nyX37//03YNwO11M/S+IXreN02IbE/xHCHh22Q4DLJyM1Nqkya9ScWIYAVQVtR+B2BVkrZnUGFaSCm1Kv57HfAJ1lRTk2HBzMXceOi9zJ4wN+lCcCwaq/FToqbx0POwPREj8fnJcBil8tpOtxO3z22bEe5yOxl0sQ4L1qSOVDyCzgC6iEgnYCXWzf6sio1EpBlwCHBOmW0ZgKGUKoz/PQB4IAU2NQiUUjx58QsJGkAVcXlcZLfQomLpwnn3n870L2cTLA4Si1oPBx6/h2tfvBiHw8EPH0zD4XJw/OUDyMrJ5I6B/yMUjCBi5RNcMeICdt175/q9CE2TosaOQCkVFZGrgPFY4aNvKKXmichl8f1bMqROAr5VShWX6d4G+CSutugE3lVKfVNTmxoKL90win/nLqu0jcfv4ey7TtHZv2lE+13b8tJvj/Puwx/z509/0Wbn1pxx24ns1msX7jpuOAtmLsbhMJjx9W/0OKQ7oxePZOHMJQSLg+zZvxsZ2f76vgRNE0NrDdUSa5au46Lu1xEOJp8PbrVDC86++xQGXXxkvVcK09QvkXCEmw6/n79+XlhOdM7tdXHClccw7LFzmfvjfP79YxntO7el14AeOBz64UGzfejCNLXI2v/W886DH/H79/PIbdec0289kaKCYgynA0h0BIbD4JTrj2XYY3VTIF7TsInFYtw64CHmT1uQsC8cjPD1axOZO3k+y/9aSTQSw+l2kNOmOc/89CA5bZrXvcGaJod2BDVk3bL1XLbvzZRsDmDGTNYsXccjZ4/gsDMOKicAV5bO+3TioofPtt2nST+mfzmbf2YvSbq/pDDAv3OXlUYbRUIRwoH1PH3pyzzw6a11ZaamCaNF52rIe498QqAwWC4iKFgcYtL7U2wLy7i9Lu5491q9JqApZea3cwhWIj9iOIyEkNNYNMavX/1mL0eh0Wwn2hHUkDk/zLNN9zccBte+cAmtdmyBL9OLP9uHP8vHbe9cS4fO7erBUk1DJad1tu1DA4A3w4MryT6lFKbZ+Nb4NA0PPTVUQ1rt1JLlCxLTJgKFQfJWbuDtJc+z+PelhANhduvTGbfHVQ9WahoyA847jA8e+4xouPx2t8/F6/Of4c273mPSe1PLPXCIIex50O64PS5isRiGYeiAA0210SOCGnLGrScm1KcF62ntzbve44kLX2C3Xruy50HdtBPQ2NKmYyvuev8GMpr58Wf78GV6adE+lxFT/kfrHVsy7LFzadE+B++W2tcZHrJyMjhq6KFcsPs1HOM6g5Nyz+ftBz4kFouxcf0m1i1PLHmp0SRDh4+mgG/e/J6RV79eKitcFrfPzQszH6Vjtx3qwTJNYyISjrBgxmJcHhdd9u1UrrpZKBBi8ke/sHD2Enbq2p4OXdpyz+DHCAW2fubcXhdZuZlszitEDKFF+1xuHX01e/TrWh+Xo2mAJAsf1Y4gRTxx8QuMf2NSwnaP38MVz1ygJQE0KeW2Yx5i1rdzttnOm+Hhzb9H0LJDizqwStPQ0TWLa4n/5i/np7G/4PG6cXkSl1wcDoPcts3r3jBNk2bZXyuq1C4WjfH169/XsjWaxo5eLK4mwZIQ9574KPOmLcDhdFhFRSoUHhcRPH4PvY/eu56s1DRVdunRkbwV+dusgx0JRVm1eA0LZi5m8e9LabdLa/Y+dI9y004ajXYE1eSVm9/mzyl/l5OQcLocGG4DZSpipknL9jkMH3+3lpfWpJyh9w3h90nztilo6PG7WThjMTcddi8oK9qoRYdcnvrhfp2VrClFPxZsB+uWreet+z7gyYte4JvXJyboCEUjMWubgBk12byhmBeufVOXFdSknN167crwb+6ka59dcboctGifwy57d8Tjd5e2cbmdOF1O1ixdR7A4RLAkRKAoyOrFa3n8whfq0XpNQ0MvFleRWRPmcN9JjxONxkqLjVcFj9/DVc9eyDEXHl6L1mk01nrApyO/5ouXJxAOhOl/6gFMHPMTG9duSmjrdDn4pOAtvDahz5qmi44aqgGxWIzT2w9j0/rN1eq/50G78/TkB1NslSYdmf7VbMY8NJZ1y/Lo3m83zrv/9EpDk09ueYFt0SOHy8HY9W9oSes0Q0cN1YClfy4nHEjMEQBKC3UmkwgAtK6QJiV8/fpEHhzyFH/9spD8VRuY8vF0rt7/dv6bvzxpn36D+9h+/nbZayftBDSlaEdQCbFYjGBJCLfXlVTTpUX7HPY/dl9Ov3UwzVtlJ+z3ZngYeJHOIdDUjFg0xis3v11ucViZimBxiFH3fJC030WPnE1uu+alJTDdPjcZzfzc9MaVtW6zpvGgw1lsiIQjvHbrO3z56kQioQhtd25FdotM8gKhcuF63gwPF/3vbI4aeggABxzbi1sHPIhpKqLhKA6nwQHH9eKwMw+spyvRNBXyV20gYrM2pZRizg/zuLrvHSz7awXtd23LBQ+dyX4D9wEgp3UzXp//DJPencJf0/9hx93bc/T5h9GsZeJDiyZ90WsENjx2/kgmf/RzQvq+x+8hFolhmiZmzOSwMw7khtcuL43J3rCmgGBJmHlT/mZT3mZ6HNKd3XrtWmt2atKHQFGAU1tfZF/xToAyX2OP382tb11N/1MOqDP7NI0DXaGsimzOL+THD6clfOEioQi9BuzNsZccyYY1G9nzoN3ZsWsHAJbOW87DZz3DioWWCmnH7jtyx7vXlu7XaGqKL9PHYWcdxKT3piauV1V4lguVhHnl5tHaEWiqjHYEFVi3LA+n25ngCJSCFQtWsf+xvcptLykMcP3Bd1O8sbh02mjx70u5/uB7GLP0BTw+HZ6nSQ3XPH8Jm9cX8vPn2x4Nr1m6nlg0pgMVNFVCLxZXoN2ubWzzBAyHwW59Oids//HDaUTD0XJrB0opwoEwUz/5tTZNTQuUCqFCU1ChqSiVJHIrTXB7XHTv1xXDse2vrS/TS8G6xPwBjcaOlDgCETlGRBaIyCIRuc1m/6EisklEfo//3FPVvnVNRrafwVcNTKgx4Pa6OeuOkxPar1uWR7A4Mc0/FAizbnl+rdnZ0FDKREX+REXmoZS57Q5VOWZwEmrdAaiN16A2Xo1a1xcVmpaSYzdWMrJ9tuKGFYmEIwzd9Sreuu/DOrBKUxuo8AzMgisw807FLBqJMmvPsdfYEYiIA3geGAh0B84Uke42TX9SSvWM/zywnX3rlEsePYeLHjmLVju2wOP30PPwPXn6pwfYaffEOf/d9+uML14wpCwen5uufdJjoViFZ6HWH4TacC5qwzmo9f1R4d9qdszYOtTGa0EVgyqK/xSiNl5eq1+Ihs7Bp/WtUrtoOEYkFOH/nhzHb9//UctWaVKNWfweasPFEPoOonOh6GXU+mMxw/NqpeBQKkYE+wGLlFJLlDV2fx8YXAd9U0o4FOGnsb/wybNf8fevizjxqoG8+99LfFH0Do9/dy+de3YiFouhlGLpvOV8/uJ4fhr7Cz0O7c4Ou7XD7d1afcztddFpr53oedie9XEpdYoyN6EKLgYzL37TLgZzPargQpRZWP0DB78gYRW0dN/46h+3kdOsZTa3j7kOt9eN2+vCm+nBl+nlgofPtH0gCRaH+PLV7+rBUk11USoARcOBQJmtIVDrYMPJqHX9UaFfUnrOVCwWdwDKpjauAPa3addXROYAq4CblFLztqMvIjIMGAaw0047pcDsMif9ZzXX97+bUCBsxf87DPY6uDsPfHYLTpeTOT/OY+TVr7P0z+U4HAaIlaLvcDpxuR089OUdTP98Jt+98xMiMOD8wxhy8wnpUUM2+BXYTQUp09rnP71ah1VmEWCzJqCioGrgYBo586Yt4LHznsPhNIhGYkRDUU6+7li69OyEGPaft5LNAdvtmgZK5G8g2SK/ArUOVXAptPoKcaQmMjEVjsDu01fxUW420FEpVSQig4BPgS5V7GttVOoV4BWw8giqba0ND572JJvWby4dckWAuZPn8elzX9Pz8D2589hHSjM6YzHrpheLmkCEAPDwmc8wetFIzn/wzFSa1TgwNwBBmx3h+D57VGwlquh1iPwGzs5IxsWIa2tJRfEciCp+nfJPRQAOcKdngl40EuXu4x9JuLGPe2E8ex3cnWgkMcjB4XJw2Onp+Xo1Wozm1gNPpURQJR8gWTek5pQpOMYKYMcy/++A9dRfilJqs1KqKP73V4BLRFpWpW9ts35FPisWrkqYdwuVhPnqtYm8+/DHyXWG4mxct4ml85LrvTRp3PuB+Ox2WPtsUNFFqLzjIfA+ROdB8HNU/hBU6OetjVz7gucwoIwejvjAdwLi2j2ll9BYmPvj/NIHkbIEi0N8/+4UOu2ZOFI2YyaGUwcHNibE2QmciRGK5YlCLHX3nFR8QmYAXUSkk4i4gTOAcWUbiEhbic+TiMh+8fPmV6VvbROLxpJO4cSiMZb+uXybizNiyHZJUzcpXL3B1Qco6wx84Olr3cxtUJsfs9YS2PKamUAAtbk0mAwRQZo/hTR/DDxHgmcA0uwZJDt9VVxts4rjFG8qZsnc/xK2K1Pxf0/W6VdKkwIk5yWQ1pW08CDuqgUOVIUaTw0ppaIichUwHmti6w2l1DwRuSy+/yXgVOByEYlijfXPUNbd1bZvTW3aHtp0bEVu+xxWL15bbrvL6+Lwsw5i1aI1rFy4KqnoHFihpbvs3bG2TW2QiAjkvAiBT1CB/wME8Z0KvhOTr5FEZmA7AxhbiTKLECMzfmwDvAMQ74Bas78x0eOQ7rZFjrwZHvYbuC9zfpjPVue6lQKbegSaho04WkPLT1F5R4IqSWxgtAbf8Sk7X0rGjEqpr5RSuymldlVK/S++7aW4E0ApNVIptYdSam+l1AFKqWmV9a1LRIQ7370Of5YPt8+K/HF5nLTbpQ1DbjqBM28/GbfXbdvX5bH0h+4Ycy0OR+LizoY1BWzOb/oLmyJOxH8aRosPMFq8j/hPRaSSZwxJJnjmAEnMxFbKtJLKSsagwjNrJXyuMeDP8nHdy5fi9rlLM4a9GR56HNKdgZccgdef+Dk1DGHvNIhea4qIoyWS+z4492LrcqoP/EORlh8jtlOy1TxXY/xS1Ybo3I8fTeORc54FFCIGInDGbScx9N4hLJixiBeue5OFMxeT0TyDPsf0xOFysnLhatavyCMrN5OTrh7EUUMPQUT4Z/YShp/7LKuXrEUp6NpnV+4Ycy2td2qVUpsbK2bxm1D4DOUXgj3gOwGjWflnARXLR204C8x1oGIgBjh3Q3JGIUZ66umvWLiK8W/9QFFBMX2P703vo/fGMAwmfTCVJy96gVCJtablcDrwZnp4ceZjtNulje2xgiUhJn/0M6sWr2HXvXem7wm9dY3tBohSIcBAxLXNtpWhK5RVQqA4yJC2FydkCHv8Hh6bcDfd+3Ytt72kMMCwHjeyYXVBqTSwx++m/8kHMOyJczm/yzWUFG69yRkOg5Ydchm9aKTWfiH+hL/5QQj8H4gbVBg8/a01ASkfC28WXAahyZSf8nCD/0yM7Dvr1O7GwJ9T/uL9Rz9lzb/r6HFId8649cSkDyCrl6zlmn53ECwJESwK4cv00qJDLs9O+x9ZOZl1bLmmLtDqo5Uwc/wcW/2WcCDMhNE/JjiC8aMmsXH9pnL68KGSMN+9M5mfPp6eMI9rxkwKC4qZ+e0c9h9kv4CaTogYSLN7UVlXQ3QJOHZAHG1RSqGiKwAFjh2ACIR+BCrOi4ch8BloR5DAngd146GDulWp7eMXPs+mvEJUfP0rUBRkzb/reOOOd7n2xWG1aaamgaHjyoBYJGo776yUIhJKjNSY/d3c0uF3RUIlIdsIIjMaY91/62tubBNCjFzE3dtyApEFqLyB8Z9jUXlHoyJ/kzS72GZRVFN1giUh5k9bUOoEthANR/nxw5+T9NI0VbQjAHoN2JtYxD4a4xCbZJy2O7fGsZ2x2WKIrXqpBpRZjNpwDsSWACEgCLGlUHABuHqQmHfoBI8u/1kT4sHc9vuSZChrmi7aEQBZOZlcNfIiKxrD5UDEcgIHnrgfvQfsndD+hCuOxunexqJNme+Sx+dmzwN3p2vv9BChqwrKLESFpqEif6OC40HZxMirGLgPjUcZbYmQ8IPREsm6pQ6tbXp4fFa0UcUpUZfHyeFnHVRPVmnqC71YXIYV/6zm+zE/UVIUoN8Jfdirf7eksfC/fv0bj50/kk3rNyfsc7oc7LL3zmxYXYDT7WTQxUdw6o3H49qW80gTzKLXoegZa6GYKIgfzAKsxLKyCJJ5NfjPQwU+g+gixLUX+AalNHQuXVm3bD3XHngXhRuLCAciOJ0O2u7Smud+eYSM7PSMyGrq6KihWsA0TZ6+9GUmvTultL6x4TDIbO7n1T+eIrdtTj1b2PBQoSmogispHzpqYK0FVPgsih9p/jzi0Vo5NeHPKX/x5avfUbI5wCFD+nHIaX1Lo9fGPvMFr936DqZSGCIYDoOLhp/NydccW89Wa2oD7QgqIRaN8dET4xj34ngCRUF6D9ibi4efQ5uO2477V0rx3duT+ejJcWzOL6LXgB6cd98QnTOQBHPDJRD+0WaPAC62Ko56wdUdyX3XyjDWVIv3hn/CmIfGloomItCsRTbDx99FRnM/F+9xfYJ0hdvn5tW5T9J+17b1YHHTRpklEP3Tmu50di0346DMzRD8AhVdibj3Bs/hlSdmVgPtCCrh4bNHMO2zX0sjgQxDyMzN5I35z9CsZbIsWE11MPNOgahdoZQM8A2GcFxn3XcyknEeYpNprKkaG9YUcM4uVxKx0ShyeZwcf/nRfP7ieCKh8hFYTreTCx48gyE310tpkCaLWfweFA4HcQAxMNojOa8izh1Qkb+sgAkVV+ERPzh2QnLfQ4yMlNmQzBGk/aPW6n/XMmXsL+XCQU1TESwK8vlL39ajZU0U7xGAzc1dTCT7VoxW31g/mcO0E6ghc36YnzS6LRKK8ssXs7B9DlTKfrum2qjwbCh8BAjEq+0FIPYvquAiK39m4w3xOhvxKVNVAtElqOJX6sS+tHcEr936TrnEsC2EgxHmT1tQ+n8sGuO/+cvJW5k+dYhrA/GfC45WwJYMYgF8kHWHXgBOMf4sb6XFkdYvz7cNmzacDg48yV5CXFM9VMlorNDospgQW4sKT0kiKR2G4lGo0ORa19dK68ziedMW8MsXs2z3GQ5hx93bAzDlk+k8PexlwqEIZjRGl167cM9HN+rF4GogRha0GIcqeQ9Ck8DRGvEPRdw64zrV7HtUj0olTSLhSML6vMvrYui9p7FDl3a1bF2aEcsnaXLk5vuxrcYHQABVcDX4jkOa1Z4mZ1qPCCaM/iFhfnQLZkwx5eNf+fatSQw/91k25xcSLAoSDkb4e/o/3H7M/9JWBbOmiJGJkXkJRot3MZo/o51ALeFyuxg+/u5y9bTLUeHjazgMjjirP6ffcmKt25Z2eI9g6yi4LCVVKDATgMAXqMiftWCYRVo7gmgkVunNfN2yPJ685KUEZxGLmqxavIbFc5bWsoVNG6XCqOiyeH1iTW3QtfeufJz/Jvsf1wun24k3w4Pb68LpSZwMMGMm/8xeUg9WNn3Edxo42lHeGbjjP1V5oAzFxRdrh7SeGjr09AP58cNpCaqjZTFjpu375HA6yF9VQOeenWrRwqaLWfQqFD3HlhdX+QYj2fcg4rZKWZaMBVWMeI8C90GVznVrKsfj8/DQuNsoKQywYc1GzFiMy/e1z8yuSsi0ZvsRIwNajEWVfAChb0GyrJ/QhCoewWlFEtUSae0Ieh3Vg4NP7cvk//s5uTNQVjhpxQpl4VCE3bRkRLUwN94FwQ/Lbwx8hsKJcnWDzf8DIkAMFRxnFatv/pzOJ6gh/iwf/ixrQb7bAbvx55S/yynlevxuTrsxdVWvNOURIxMyzkZF/4Dgd1gTMpXXQ9+KAd5BtWZbWn+zRISb3riC4ePvZtDFR9iG2rl9LvzZfpzurT7Tm+HhxKsGktO6WV2a2yRQ4d8h+JHNnpBVn2DzQ0CQUulpVQLhqRD6ofxxIvMxN1yKua4fZv5pqAr7NfaYpklhQRGHntEP0ywv6SEitNYjglpFbbwDghOwHEDQpoUbjPaADyTT+sEHzZ60ylfWEmk9IgDrw79Hv67s0a8rgaIg08bNKM0pcLocZLfI4unJDzLuhfFM+2wGWTkZnHzdcRx6er96trxxokreJvmcaAzbHANVggp+jXgPt/6NzEPln4X1RVJg5qEKrkVl34/hP7FW7G4KjHtxPKPufp9AUdBWKj1YHOKSHjfw9qLnyW6RVQ8WNm3M6BoIfZFkrwuMllZ0UMblgAHhnwEF7r4pTSqzQ2cWlyEWi/Hpc1/z+YvfEiwO0m9wH869d4h+8k8h5obzITwtyV6vlXWpiitsN8B3GkazB+PHuMAaJVREcpHW0/QUkg0T3v6REZe/ulVqohJOueE4LnvivDqwKr0wC66H0Jf2O402GK1/qnUbdGZxFTBjJp326sjxlw+gTcdWfPf2ZG44+B4mjqn9Nyht8ByJFSlhQ+aN2Gvku8F78tZ/k4XRqaK4iqmmIqPv+7BKTgBg6ie/1rI16YdSCkITkzdw9dpmfxWeiSp5HxX6JeWh6ymZGhKRY4ARgAN4TSk1vML+s4Fb4/8WAZcrpebE9y0FCrHmBaJ23qoumDt5Pvee9BiRcJRQmYXjFQtX8fSlL1NYUMiJV9XeYk26IP5TUIH3IPofWxfKBLynYmSeh3J3QxVcam1WW9YKQrD5NlSzhxB3H3C0gegmm6MbYOgpjS2YpomIICKs3Y7qeBnZOsM7lajYGlTBJdivCcTJvMa6ucdWgjgRx1bBP6tw03kQWwTKBDHA0QFyxyBG85TYWOMRgYg4gOeBgUB34EwR6V6h2b/AIUqpHsCDQEUBjcOUUj3rywksX7iKm4+4n6KC4nJOYAuhkhCj7v4goRaxZvsR8SG5H0HWzeDqDZ6jkJy3MJpbWZPi3g9pNRWMspmtKq7LcjEq+i+SeSUkyFF4wX86IklGG2nEnB/mcUmPGzjaeTon5pzHc1e9VuUnSI/fw+CrBtayhemFKhgG0UXJG7h6IRSh8o5C5Q1CrT8KM+8EVPRfq3/hExD92wqcIBjXIVqK2nxfymxMxYhgP2CRUmoJgIi8DwwG5m9poJQqOyn8C7BDCs6bEiLhCNceeKeVL1AJ0XCUjes306KdlpWoKWL4kYzzICPJPLS5BmIrSChar8Ko4lEYze5HxfKs4jYqAijwn4pk3WpzsPTin9lLuPO4h0sDHko2B/j69YkYDgMzmvgZF4dYdYvjfsKf7WPfI/eqS5ObNCq6KD76tXuINECaQdYdqA1Dy6+NRRegNpwFrX6E4GckhplGIPgtSpkpWRNLxRpBB6BsjvSK+LZkXAR8XeZ/BXwrIrNEZFiyTiIyTERmisjM9etTVwR+2mczCRZVMmTbagBZuZkpO2+6osxNmEUjMfNOxSy4HBWantgotgLEThYhBtHFABgZ5yKtf0FajUfa/IqRfU/KtdsbI2MeGks4UP6mEQlFbZ2A4TQwDKNcENem9Zu54ZB79eg3VZib4rLTdrQC34lQcL5NgISypkZDk+LS1LYHp2pZydsmFY7AbnXP1joROQzLEZR9dDtQKbUv1tTSlSJysF1fpdQrSqneSqnerVpVP9Z5c34hG9dvnV9e/vfKpHpDW/D4PQy+8mjcHl1qsiYocxMq7wQoehmicyE0EVVwCWbxmPINnbuBslvYdIN766KaiAtxtNOqpWVYOm+ZrYS0w2HgKqM5JGJlxzsq1Cw2YyaFBcXM+Ob3WrY0TXB1t2pvJ+AGw4SSd+Py0zaoCMTWgucQEm/VBrj3Q5I6me0jFY5gBbBjmf93AFZVbCQiPYDXgMFKqVItZ6XUqvjvdcAnWFNNKWfV4jVc3fcOTu8wjLN2vIxLe97E0nnL6dh9B3xZdmJQWGuYmV5Ouf5YLnz4rNowK61QxaPBzKe8HG8QCh+zKjfFEUdb8B1PeV0WA8SH+M+pI2sbJzt1s591jcVMTrx6IM1aZuH2uug1oCf9T94/oToZQCwSZc3SdbVtalog4oOs2wEfW5+ZPVZQg1lMojR1WRzg2hvJvhOMFvFjYP2WbCT7gZTZmYqx9Aygi4h0AlYCZwDl7poishPwMXCuUmphme0ZgKGUKoz/PQBI3dXFCYciXHfQ3Wxcv8maDwX+/eM/rj/4bkYtfJbmLbMJlYS3rhMINGuZzStzn6RZyywcjtR43bQn9AO2KfXisBbDyqiQSvZDKEdnKHnbCgt1H4hk3Yg4dOZrZezYtT0/22x3uBzse0QPhj16bum279/9iWnjEqdGDYeh5VNSiJFxBsrVGVX8FpjrwXOY9ZkufrmSXm5w9wZXD0tnq+W3qMDnEJ0Hzi6I70TESF31xBo7AqVUVESuAsZjhY++oZSaJyKXxfe/BNwDtABeiIuHbQkTbQN8Et/mBN5VSn1TU5sq8vO4mQRLgqVOwLLLWgCeMnY6I35+mBevf5Opn/yKUrDzHjvQvnNbJr37EwPOP4ysHL02kBKMFvbbVRQqhMGJOJDMi1C+Y60vjaNTyobBTRWlFL9+Ndt2n8NhsHrJ2nLbDjrlAEbf/xHr/ltfWpzJ7XPTtU9nuu3fpdbtTSfE3Rtxbw2KVIGPUfiBkuSdmj9fKrYoRgaScUat2ZeS1TWl1FfAVxW2vVTm74uBi236LQH2ToUNlbF26TrbIXCwOMSapevIad2MO8Zcx5ql67hq/9tZ9vcq/pn9L798Posx//uY5355mA6ddaGOmiIZ56PC0yktxweAAc5OiHOXcm1VbB1q49UQmR9fbHNDs4cR75F1aXKj4rfv/2TFP2ts94kh7Npz53Lb3B4Xz/3yMG8/8BE/fjANp8vJ0Rcexhm3nqjVXmsbzzEgj8ZDQu0IQ/hH8B5dJ+akRWZx1z6dcdks9Poyvey+39Ynn+evfZPC/MLSDMxQIEzRxmJGXP5qndnalBFPP8i6EWvuf4umkILoEszND6Li0RFKKVTBBRCZC4SsL4vaiNp4AyqyMMnRNRPfmWyrIQTQZufWtk/5WTmZXPH0BXyw6lXG/PciQ+8dgturczFqHXGCu2/lbSLz6sYW0sQR9DikO7vu3bFcpSaXx0nbTq054LitUSizvv09QW5amYo5P8zT1chShJExFJo9zNbAMgWEoOQjVOGj1qboPIiuJDH2OhKv/aqxI9ln1Ol2cs7dp+in/AaE2nQnhL6vvJH7gLoxhjRxBCLCo9/ezZBbBtN6p5a03CGXk645lmemPFSupqvTZT9TZidPrakBxa+RuGgchJIPUCoEsfVWGn0CMYglBKRp4hxxdn+8GYnqrQ6nwQHHVq5lo6k7lLkRgt9QqeSE0QrDU3cKx2mTgeN0Odmrf3c69+zEXv272crsHn52f74dNalcXoHL7eTQ0w/UT1OpJLY6yQ4F5kZw7QnKrmCH1ypSo7Fl3yN7cMQ5/fnu7clEQlGcLgeIcOvoa/Bl6lyLBkNsnTU1ZJsrAzj3gtxRdWpSWjiCxXOWctvRD1kZl2JFC1348Fmcct1x5dpd+vi5LJmzlH//WGZtEGHHru25csQF9WB1E8a1J4RtFF3FC0ZLRBwo/9lQ8j5bF5bdYOQi/tPr0tJGhYhw3YuXctywAUz/aja+TC+HDOmnZVEaGs4dLfG4BAQ8AzFynqlri5p+PYJYLMaZO1xKwdryapUev4fHvruH7gfsVm67Uoq/pv/Df/OWs+PuHdijX1c9GkgxKjIflX8m5aOHfJB9B0b8Rq+UguCXqJJRYBaC53DIuAjD0bI+TNZoUopZ9IKVYV8xgq7Z4xi+2isXmqweQZMfEfwx+S9CgcRphnAgzJcvT2DXvTsya8JcouEo+x7Zg8zmGXQ/YLcEB6FJHeLqDi3eQxU+BZE/wNEOybyqXGioiIDvOHB2RG26B0rehJK3MX2Dkaw7EaP2CnlrNLWNZFyOkmZQ+D9gy1S0gk13YqqS0geiuqLJO4KSzQHb7Uopli9cxWltL0ZEUApi0RjXvngJA4YeWrdGpiHi6o7kvlZpGxVdjtpwbplY67BV5D62Csl9s/aN1GhSgApOskq0mpvBOwDxn40YGShzA+XjdRSW5MojKN9gRJJI39QCTT4cZq+Du9nGVnv8HhbNXkKgMEjJ5gCBwgDhQJgRl7/KykXJFjM1dYkqGR0vTlOWMIRnoKJL6sUmjWZ7MAtHoDZdB+EpltBi0XOo/FNRKhAPH7ULijAg8ned2tnkHUFWTiaXPHoOHr8bMay5fm+Gh5YdcsuFjm4hFo0x8R1dmrJBEP4VS2q3IhFU5N+6tiYtmDZuBhftcR2DfGdxQbdr+eljG5lwTZVQsXwofhVU2VmJEMRWoUo+BSM3ScdEyZXapslPDQGcePUgdt+/C1++PIHCgiL6n9KXwo3FvHbL2wltzWiMkkL76SRNHZMwGijdAViSIUopMFeDeJFkXyxNlZj66a88cs6I0qI2Kxas4pGznyH65pUcdsZB9WxdIyTyG4jbJhQ6AKFJSMYFqPBMyi8YO8C5K+Lcue7sJE0cAcDu+3UpJyexeslaXr05MUvV4/fQ94R6qZipqYjRAmJ2T/5OIIaZfzZEZmxpjHLtizQfoRVKq8mrt71T6gS2EAlFefKiFzlkSD+riI2m6hi52JdmMcDRBvEchMq8xqq0J25rJODsiOS8ZNOndkkbR7CFGd/8xtsP/B9rl66j5Q4tWL8in2goglLWlNEBx/emx8EVSy5r6gPxnYiK/EGiZrsTNj0E5JfZZkJkplXku+WXOuS3EkzT5LORX/PRk59TuKGIPfp15dInhrJqkb1gXSgQ5ocPpnL4mf3r2NJGjqsnGDkQC1B+itMBsbWYhc8g/tPAP8TSFTJaIK76UX1t8nkEZfluzGSeufTl0qceEXB5XPQ6uif+LB+HnXEg+w3cp9xNZPpXs/lkxJdsyi/kwBP346SrB5LRLCNl16JJjlJh1IZzILIQS67XANzgHQzBsWwNuyuLB8l9G3H3rEtTGw2xaIybj7ifP6b8Ve5h1ZfpxeVxsjm/yLZf76N78sjXd9aRlU0HFV2GKrg0Lo1iYH2OXViLxC7AgeS8iHjqJmM+bfMItmCaJi/f+Fa5oa9SEA5GCJeEeOCTWxL6jPnfWN4f/gnBYuuJdNn8FXw76gde+u1x/Fk6Zb+2EXFD7hgIjkeFvgOxMotV4FPsnQBYMhWrgZ51Zmdj4r6TH+ePn/5K2B4sCdFul9ZJHYEeYFUPce4ELb+C2GJU0ah4IfotI9wIEEFtuhlaTUlJEfrqkjaOYHN+IcWb7LW/F8xcXPp3OBhm5rdzKFizkTEP/V853aFwMEL+6gK+fGUCp914Qq3brLHqEuM7DvGVkQOJdEHhYsuCcXkUOPeoK/MaFf/NX86sCXNs9ylTEYuaeDM8pQ8+W/BmeDj6gsPrwsQmiYiAszMqMhvb0pSqBGJLwNm5zm3bQtqs/mQ082M47C+3ZQcr2mT+zwsY0v4SHh36HC9c96ZtUftwIMz0L+2rQGnqCN9AELsyfQLuQ1CFj2Ku3QdzXT/MopEoZecw0o+FM5fYhkxvoWP3HXjo89vxZnjw+D04nI7S4In+p+xfh5Y2UZIliKkYW+tz1A9pMyJwuV0ce8mRfPHKd5b4XByn28GZt59EJBzhzuMeoXhjJaXjsLz7FsehqR9EfNByLKrgNoj+Et/qB/95EHgXVCFggiqGoldQkX+QnBH1aXKDoHXHlkkX0R1OgzNuO4ku++7Cu8teYvJHP1O4oYh9juxBV12/OCWI/2zU5gdIrNC3M+Lcsb7MAtLIEQCce+9pfP16+WIQSsEvX8wms3nm1uL1leD2uTjpmkG1ZaKmqpjFoFYBPmsCW7zW2oAKUj5CIwih71HRZdZ8bRqzV/9u5LbLYfWSteU+6yLCLW9dRZd9rXKhWTmZHDvsqPoys1Gjwr+jip60MoMdOyBZ1yKeQ62dvpMg8isEvoqXXxWQLKT58/VpMpBmjmDimJ8SKpDFIjGmfjKd7n13sw/5BQyHgTfDgzIVV428iK596m8uT1MmmkgVxDdgzbMGx2GbiSwuiP4Dae4IDMPgyR/uZ/g5I/hz6gJrdLtDLreNvprufbvWt3mNDqXCEJyAisxHnDujjPaw8XJKC85EN6EKrkE1+x+G73hEDKTZo6iMy6xkM6M1uPsikny6rq5IK0cw98f5pfWIy+JwOXB5nEQjiWsC3gwvFz18Jl336xIvd6nrudYXKrYGVTQCgt+Cso9uASHBo6soONLbCWyhRbscHp94H4UFRYSDEXLbNi+dLjJNk015hWQ08+O2qfGt2YoyC1D5p4GZB6oEhQ8rJLRiedUgFA5HeY8rfZ3F2Qmcnera5EpJyWKxiBwjIgtEZJGI3GazX0Tk2fj+uSKyb1X7ppL2ndvidNt4XwUdu+3ApU8MxeNzY2zRJMr0slf/3Tn+iqPptn8X7QTqEWVuQOUNhsCn8TUAu+GbSeJH2gWuveotUaehkpWTSYt2OaU3p29H/8CQtpdwdsfLObnF+bx04yhi0Yo3Nc0WVOETVm5AqTJugEQnEMcssNarGjA1HhGINa55HjgKWAHMEJFxSqn5ZZoNBLrEf/YHXgT2r2LflHHcpQP49LmviYa3vmEOp0GLDrnsceDu7HlQN7r368r4NyZRtKmY/icfwP7H7YvDUf9Dt3RHFb8T/zJVcnMSP2RcAcEvrKkgHOA9Bsm+r46sbJxM/2o2z17xarkcmy9enkAsanLliAvr0bIGTHA8yXNZKiAekIadd5SKqaH9gEVKqSUAIvI+MBgoezMfDIxWVhrzLyLSXETaATtXoW/KaNOxFQ9/dSePnT+SDasLME3FHv26cvuYa0ufjDr37ETnZxvWsE1DXInUTrJ3C15wdkYyLkQyh1kyvzitPARNpbx9/0cJGkOhkjBfvvIdFw8/G4+vfkMbGyZVnUzxgHM31KYbwX2QlRNTh3UGqkoqHEEHYHmZ/1dgPfVvq02HKvYFQESGAcMAdtqp+vO9e/XvxuhFI8lbuQGPz21bxF7TAHF2hMgsEkcEAo5dwHcaknE2ItZHWhr4E1hDYvWStbbbI6EI+asKaL9r2zq2qBHgPR4CH1D+4cQBRkdQeWUK00chMhciv6FCk6D4dWjxEWJk1oPRyUnFGoFdYHLFCdxkbarS19qo1CtKqd5Kqd6tWtVMXVJEaLVDC+0EGhGScQFQcY3GuTXywtXdZr+mKvizkzvNxXOW1p0hjQjJut7KBBY/4ALJAGkF7h5Ya1XC1sXj+MOLCkBshVVwqYGRCkewAiibDbEDsKqKbarSV6NBnJ2RnOfBaIeVhekATDDXQ2AMauNlqE230BhFFOubXfayH2E7Xc6kpV7THTEykRYfI82fR7JugOzHwNESgl/FI9qC2D/Thqw8ggZGKhzBDKCLiHQSETdwBjCuQptxwNB49NABwCal1Ooq9tVoABDPQUirHyD3Q0odASagrOiN0LcQnlavNjZGjhx6KG5f4mhKHELPw/asB4saByIGONoDLoj8DtHFVL6OFcfw17Jl20+N1wiUUlERuQoYj/XtfEMpNU9ELovvfwn4ChgELMLSYb2gsr41tUnTNLCyNF+E2H/g3hvJuAJxdoTY3yicJHzpVABV9AqYG8HTHzHs9Ig0Fek3uDe79+nMwlmLSwXnvBkejr/8aNp01EV+kmFufhxKRhPPaMReBDER8Z9dm2ZVi7SqR6BpPKjg96iN12GpNSrAAPEhuR9CdCFq811JYrNdW6s9NXsYw3d8ndrdWImEI0wcM4VJ7/2EN8PLscOOos8xPXWBnySo8AzUhosprxtUBYw2SKvJ9fa6JqtHoB2BpsGhlEKtPwTMihWzBNyHIs2fRK0/qEwyTzI8SKtvEUe72jJVk6aYm26HwMck1aVJQAA30uKDeGBD/ZDMEaSNDLWmEaE2gplvtwMiM62FuubPYQnOxaM27A9kLd5pNKlGxbB3Ag6s26oBzu7g2geMDuAZYC0u16MTqIy00hrSNBIkg6TPKEYLq4mnP7SeCqFJqOAECH1HYqZnNK5GqtGkGO+A+ENGxcVhJ7Saihj+0pyWxoAeEWgaIA5w7Gyz3QcZl5T+J0Ym4jseybrG6pOAgZIMVNFzqMAn8WxjjaZmqPAM2HQL5ZVuBXCC72zE8DYqJwDaEWgaIKroeStSqCLu/RDfqQmbxdkZPEfbHCkKhU9YjmDT/ah1h6GiS1NuryY9UJF/MIOTUBuGxXMFyo5AFeCA4IeodQeiInPrycrq0bjclqbJo5SCkjcp1XQvS/Sf5NEWyl4mYevQvQRUELXpFqTFhymwVJMuKHMDasMlcSFDsP1sAhAqlZZQGy6G1lMbjdaVHhFoGhhhKxXfDtsF5DiRqugUmhCZhzI3V8syTXqiNl4H0b+wHEBV15yicaHExoF2BJoGhjsuI2GDa/fk3RxVFUaLoAquQkWXb7upJu1RsTwIz6bKktPlOm8rvLnhoB2BpkEhIpB1J1BRqteLZCWvWySZV1Vd8z3yKyr/NJSZrMqZRhNHbYbqLPyqCLhthZQbJNoRaBochu9IJOdVcPUBoxW4+yMt3kHcvZL2Ee9A8A+r4hlMS44i8HlqDNY0XRwdsUQOt9kw/tsAvJB1Z6OSONGLxZoGiXj2RzxjtrPPAaiS16pYFjAA0b+rZ5wmbRBxoLIfgk03slXuJKGV9fRvtATJRvynIa5udWxpzdCOQNN0cHYpUxBkW/itzE+NZhsYvqNQzvdRxW9aZVATiiO5kczrEffe9WFeStBTQ5omgxjZ4DmsCi0NMPyI77hat0nTNBBXd4zmj0P2/TZ7TUvosBGjHYGmaZE9HPss4y0Y4DkcaTEWMTKqdEhlFqG0VIUGoORtm40R1Ob/1bkpqUQ7Ak2TwnBkge+85A08h2PkvFCqSKqiSzELrsZc2wdz/VGYxe+WVjlTkfmYeYNR6/qg1u6LWXAZytxQF5ehaYAoFYXoQvudkd/r1JZUo9cINE0O8Q1EBd4jUSveBc5dSv9TsdWo/FPii8smxDZB4aOo2H+QcQlqwzlxKYE4ocmo/HNRWbch5nJw7g6ufbRmfxNEqQgEx6PCP1k1BHyngmNHrLBmm4RHaVjF6LcX7Qg0TQ9XD3DuFC8dWCYRSFyI/8zSf1XRa/Es5rLiYQEoeRclXisWvBxRiC2CjVdasSMi4OwGuW8iVc1h0DR4lAqi8s+C6BKsgopOVPEopPkI8J8OJe9TPsPYCxmVjEIbAXpqSNPkEBEk9y1w98WqVeAGR0ck53XE0WFrw0iSjFFxQeQPrHDBiihKpQZUwJKsKHquFq5CUxuo6HLMTbdjrj8Sc8O5qNCUxDYl70F0S1VdsD4jQdSmmyHzWvAeDrhBsqzfvuOQjKrmsDRM9IhA0yQRIxfJfd3KHlZBMFokTuE4O8U1ZMzy21UYXHtDeBbbLkUYgpKPIeuWFFqvqQ1UdDkq/8S49EMMYstQ4Tmo7Dsx/KdvbRj4AntNoRgSW4w0fwYVWwux5eDohDha1M0F1CJ6RKBp0oiRCeZaVNEzmIXPoaJLtu7LuBioGPYnQBiKX8KKF68sAmkL1dCh0dQ5qmhkfD2obB5AEAofQ6kyBWaSTvOZbJE+EUcbxN27STgBqKEjEJFcEZkgIv/Ef+fYtNlRRCaJyF8iMk9Eri2z7z4RWSkiv8d/BtXEHo2mIubmR1D5Z0Lxy1D8AipvMGbxaMCKDZeckWC0Z2u5yy2ZozGsG7wHJNuSujBa25zBCd6jav06NDVDmUXxKnamzd4YxFbG222y3m8qBgCI9RlwdqllS+uHmo4IbgMmKqW6ABPj/1ckCtyolOoGHABcKSJlUzqfVkr1jP/oArOalKEif5RZ2DOxbu4h6wkwtgYA8RyMtJoEGdeRKHRnggjS7HGM1lOR3FHxm0S8nfjBaIVk3lQ3F6SpFkoF4tFhSUQGVRSMHCtfJP8kCE+mvJSEE4xcJOelJhshVtM1gsHAofG/3wJ+AG4t20AptRpYHf+7UET+AjoAVRGQ12iqjQp+g/2CrwGhH8B/BmAtLiu1Dtt5YRWx5oKJV0JrNREV+BSi/yKuHuAbhEhFB6JpSKiSTyG2BnudIDd4DkOM5pjFb0Esj8Q6xEDu/yHODonbmwg1dQRt4jd6lFKrRcRu7FyKiOwM7ANML7P5KhEZCszEGjkU1NAmjSaOk8QhPlbYZ4W5f3HthRK/jYa8QFxATKkQqBLEfybSyCUF0orwTyRd9Hd1Q5o9av0dmortw4B4keg8iDsCq/a1G5GqrB81DrY5NSQi34nInzY/g7fnRCKSCYwFrlNKbSkR9SKwK9ATa9TwZCX9h4nITBGZuX79+u05tSZNEe9xbJ37L4MywXtE+W3eY8BoQaLjCKNKxmFufhy1dj/U+qNR6/bHLHqlNAN5W6jgt5j5QzDXHY656e7SaSlNHeFoh/2ivx/JugUx/PF2HZK0M8Fogxmairm2L2rt3qi13THzhmCam2rP7jpEqvphtu0ssgA4ND4aaAf8oJTqatPOBXwBjFdKPZXkWDsDXyil9tzWeXv37q1mzpxZbbs16YNZ9DoUPYN1gxfAhGaPYNgIzpnRJZA3iMQFRSfWM1PZKQMfZN+J4R+yjfO/AkXPs/WJ1AmSibT8HHG0qdY1abYPFV2MyjuJ8k/7Yq33uHqCsyPiPxeIoPJOrtDOYdUkaPYkbDiFhM+GZCOtf240tYlFZJZSqnfF7TVdLB4HbEmpOw/4zObEArwO/FXRCcSdxxZOAv6soT0aTTmMzIuQVt9YT37ZtyOtfkhwAiq2EhWcCMEJJC4YgxXvUHHeOABFL5Q/jlKoyB+owKeoyFzMWBEUjaT8tEQUVDGq+PWaX1wjRqkYKjQVFfgCFVtdq+cS565I86dBmoNkYBWaMazqY+EfoWQ0Km8gKroOaf4MSI4VCIAHnHtYQQJFT2EbcaQ2owKf1Kr9dUFN1wiGAx+KyEXAMuA0ABFpD7ymlBoEHAicC/whIr/H+90RjxB6TER6Yq3iLAUuraE9Gk0C4ugAGeckbFcqhtp0GwS/sbKJVYhErflKMPO3HsssRhVcFE9QE1AKnDtiP9UQgfDP23sZTQYVXYzacJ4V068AIij/OUjWrbUWlSPeI8AzDaL/oDY/DpGKGcVR2Hg1tJmJtJ5myZMYmYijfdzmJGJzAKEfYRsjw4ZOjRyBUiofOMJm+ypgUPzvKdiu2IFS6tyanF+jqQmq+A0IjgdC21HQpgzO3bYeq/BRiPxJuZFD9F+SOhaj/fafrwmglEIVXArmespF8ZS8B+7e4D2y1s4t4gRXN1RkepIWhajYMgxnR3DtVn6XY1cw19p3c3ZOqZ31gc4s1qQvJe9gLyUA1hSRx/pxDyRxysiLZJdJmwl+RuL00RbRuorzxz4k8+LqWNz4iS5IdAIABFAl79baaZWKoAJfYBZcS6WZ4AlRY3Gy70rSwYH4z6qpefWO1hrSpC/JvvQ4IesGBAM8hyLOnVChn1FFz0JsKTi7IpnXIe6eZY5VUam0DK5+EPkZxBk/9t2Iu0/qrqMxoQIkle1IlvBV01OqMGrDUIj8zVYhOTsMpIxMebk9rs6YzV+AjTeydc0nA3JebBKL/toRaNIXT38IfkXCIqBjJ4yM88ttEk9fxNM3+bHcB0B4WoVjCbj7YuS+ahW0MQvAsVOjiTCpFVx7JNnhBW/NFWaUWYwKfGYpyzo7Ib4hVh5B5C8qFxAU8J2NiCdpC8N7JKrN7xBbZgkTOndFpGlMqmhHoElbJOsmVGhqfGQQwvo6uJBmD23/sbLvi8sYhLCmm7wgHiT7Xmu/kQtGbgqtb5yIuFHZD8OmW7CmzmKWyJujE1JWAbQaqFgeKv9kMDdh3fTdqOLX4gVltqEia3SELDuFnIr2Czg71sjOhoh2BJq0RRztodXX1tx0eDY4d0H8QxHnTrbtVfRfS6s+thyce0HGWRhGc+tYzp2g1QRUyUcQnQ/O7oj/VMRI0GFMe8TTH+U/HQKfAQq8R0PWXTWW6lBFT4OZx9Y1gLD15B79e9udzTVIeKKVWJiGaEegSWvEyEUyr9pmOxX6EVVwNdbIQUFoIhSPwMy4FMm83iqGYzRHMi+pdZsbM9Z8/RkQXUqpDlTwcyACW6QeqkvwO6ovCR5EBb5E0tQRNI0JLo2mFrHyDW7FmvIpG+2ioPg1VMk79WRZIyT4DUSXU04MUAUg8BUq+m+lXZVZiLn5Ycx1B2KuOwiz8Mm47k+cSub3q0QaiwdqR6DRbIvYv1aVM1uiVq0DTZVQ4WnYRu6Iw5qeS9ZPRVD5p0PJGCv81FwHxaNQG87bqvnkPx0r5Lc6uBH/adXs2/jRjkCj2RbiB1VJxrG5oe5sqSFKmZhFr2Cu64e5Zk/M/LNRkXl1Z4DRDlshQAQcLZP3C30P5iq25mYAhCC6EMJWgphkXAKevmz/jLcBGRcj7v22s1/TQTsCjWYbiKN9uSziBBpJ1SqllCWpUTQyvqgahsgM1IazypXwrE3EfyqJN2qxNIDcBybtpyJ/2Od9qDBELUcm4sbIeQWav4Cts5EMyLgKsu4D7+ng6gO+c6HldxhZ11XzipoGerFYo6kCkjMSlX+aNSVRDg+SdUe92LQ9qPAs1MYbwLQReFNhVPHLW3X5a8uGyEJrkd13AgS/xZL2MMGxI5LzvCUBkQRx7IjCR0IYqLhRsY0QXWQVDgIM76Go5s+hNt2MldehwGiB5Lxc2kZTnhrJUNcXWoZaUx8opVAlY6DkXWs6yLU7knkt4t6n+seMrbV0ikLfA07wnYhk3rBVIz8VdsfWoPKOjmf1JsHRBaPVlyk7Z0XMzU9AyWisqZ14ZnHGZYjveKQKcfnKLEKtP8xSDE2Qp8gEouDay7rZG5lWHxWByHxrEdnZtcmWmdwekslQ6xGBRlNFRATJOMdWybQ6KLMElX9qfJomvgZR8h4qPBNafJKyG5cKjK18jQMDXLU3vWXVjh7NVl2nuC3FL4P/zCodQ4xMaPEeauMtll4RMSyHoIC4NEVkDmrzXZaUNFgZ3O69U3YdTRm9RqDR1BMq8AWYhZRXKI1AdD4q7+jU6fTHVmBbh7cUN5JRewrwKvCV/fnFgNCkKh9HnJ0xWn5syURLKxJHBmEITrBKimq2C+0INJpaQpmFqPBMVHSZfYPoHyQVQYstReWfU+VymJUh7j7xQis2GDshOa8i8brMtUNlI5vtH/WI0YzkkhGqepLiaY6eGtJoUoBSYQh8gQp9F9cUMiDwSbzgTRTl2hP8Q62KWMoBjvYQW4UV3ZJEudRcjtp0EzR7okrTREpFAUdiW++xUPQyxFay9cncB57+GDkjq3/N5gZU8SgI/QBGSyTjQsRzUEI78R0XT7oLVjwAeA+r3sndB0JoPHaCgWJkV++YaYx2BBpNDVEqhMo/A2JL4guyQum0xZan08gs2DQb23KHlREcj3L3Q/ynJD9/ZC5q033xMEo3yneyVZYznikr4oEW/4cqfiWutuoB/5k10tFXZgEqb7ClqBp3Lio8C5V1PUbG+ajYeuu1cOyIuLqjMi6G4lexrj8+EZH9oCXGZ3f88O+o4FeAIL5jEVePcvsl62YrOU0F4ud3glRPMFCjo4Y0mhpjFr8LhY+yTYXL6uLsgtHyS1R0BcT+s+SPHW0BUNFlqPwTKsTYe8DT14qprwbKLLHCPM0CcPdFbBaSzcIR8Rt7xbl/D7i6WdE6GGA0Q5oNRzwHWhISoe8BF3iPTqrjb25+1IrMKpWhcEPGhQmx/iqWb0VxRWaBozOSMbRKEUjpTLKoIe0INJoaYuafC0nLH6YAoxW49oLQVKu4zZbiLkZ76yf6K4klMV1Iy6+TKqkmQ0XmojZcAJjxYjsG+AYh2Y+UTjlZAnzXAcU2R5D4T9mRjw9p+XmCLda9Jwh4SnX9VeRvVP4QEivHeZCWnyUtHKOpGskcgV4s1mhqitEsBQdxJlnQdYBkWk6AkFXwHROIgPkfRKdjXxc5giq4ABVbU2ULlIqhCi4DVRg/TxgIQuBrCH4NgFn8BqrgGuydAFhTYhWnv6IJZSjNwDeo9Yei1u6LWtcLs/BZlDLjUUR2aybmdkUYabYP7Qg0mhpizbX7KmtRlaNYJS3xsvVr6QbJii/yJouEqWTNIbYSVXBx1SOPIn8mSToLoAIfocxiKHyG5FNgBva3lIhV1SuOCk2xCtOYq4GY5XSKX0cVPgXiTnIMA3BX7To0202NHIGI5IrIBBH5J/7btgqHiCwVkT9E5HcRmbm9/TWahox4+kHmpVg37kxL08ZoCb7TrAI2ngHgvxRLGdOPvWOIWCUVnZ3Acxy49oaM86HFp1RfY9+0iuhEF1SptVIRUEnOpSLWcZLKQAg4d8c+/sQH7q1lPlXRsyRO/QSg5G2U5wiS3pa8R1dqv6b61DRq6DZgolJquIjcFv//1iRtD1NK5dWgv0bTYDEyr0D5z4DwDJDm4O6NSPki7SrzQghNRWFA6GcIjqX8TT4EsaVI9v2Iu2fpVtO52zaqbLksGQXb4u8OMPO3ab9SCopHYT/y8IHvOMu5qSShrq4DMFq8hbnpDgh8ydZRgwuMXMR38ta2yfIqUIh4Udn3web7LGlqBRCDZg8jjtbbvA5N9aipIxgMHBr/+y3gB7bvRl7T/hpNg0GM3EqfWsXIAd9x1lJq+Bdsn/SVsm76ZRyBZD+AKjgvHopacSrIA94jwNkVip4lYb1ARayF5m0RmWnlONgSgM33opx7WiOW6KIKtvuQrKvitj6EcvaEwGgwS8A7AMm8FDEyLGcTmQtGFsRspLvFCUYLDP8pKO9hVn4CAp5DdcnPWqamjqCNUmo1gFJqtYgkc9kK+FZEFPCyUuqV7eyv0TQtnLtgrQdUmCIRR7zYeplN7p7Q4jNU8ZuW9n5sNdaN2ADf8Uj2vZYGj92isff4KiVYqdCPJF+HAFDxTOgMcO5hOast00RZd1jZy4CIgWQMgYwh5XurKGrjVRCaZn8e8UHmVZY+EHGnWnYUoalVtukIROQ7oK3Nrju34zwHKqVWxW/0E0Tkb6XU5O3oj4gMA4YB7LTT9oXEaTQNDfGdhCoaGX/K37KY6wSjdbn59NL2zp2RZvcD8WkcVQCSgYgHM/wHhL62P5G5vIoWVfWZMAKeg5GcEVaegbOzlbC2DVTJh1sjnyoibSHrOsR3UhVtKHNcFUSVfGxFNRnNEP/ZiCfx9dNUzjbffaXUkcn2ichaEWkXf5pvB1QUa99yjFXx3+tE5BNgP2AyUKX+8b6vAK+AlUewLbs1moaMGM2gxfuoTXdA5A+sKZCDIeMaVNEIK/nKvR/iO7FUVrm0rwhImYzcTTclP1G0io7AMwiKX6hCwzBE/7GK9TjaV+3YAIGPSDricHfD8G//079SYSujO/ovW9YkVOgnVOYVGJm1J6LXFKlp+Og44Lz43+cBn1VsICIZIpK15W9gAPBnVftrNE0VcXbGaPEh0mY20uZ3JGMYFJwJxa9D6BsofByVNwgVS77Yq2Jr4uqiSXD1rJoxkRlULczVa0U0bS+qEvXT8K/bfzyAwDirnnS5cNYAFI1ENaLyoQ2BmjqC4cBRIvIPcFT8f0SkvYh8FW/TBpgiInOAX4EvlVLfVNZfo0knRHyAC7XpljLaOQABMPNQRc9V0jtGpbkENiJwtpSMIlHWuSIGiD9ebnI78Z6QfJ9kbP/xwBL4s8t7EBeEZ1XrmOlKjRaLlVL5wBE221cBg+J/LwFsHyGS9ddo0g4zD2yzgKMQmgDcZ9/PaI+VzGYfOiqV1iEog23oaUWckPN6XAZ6+5DM81Elr4PaWGGPG/xnbPfxgK0qrwmOUIHRvHrHTFN0ZrFG0xAQD0mfyCV51rKIQMbQJHvdVlhpVXAfxLZvBwqC46p2vAqIeKDleHB0xHr+dGOJ4x1c7aI4VkZ3xWxjsbKxXb2qdcx0RTsCjaYBIEY2uPuQOEj3gq9yuWjJvNSKNio3x+8GV1dw7Vu182fdAHHZ6uREILRdwX7lMBw5SMtvkRYfIM0fR1p+hpHzQmnI6PYirj0h+y7AG8/o9oPRHsl9s1TETlM19Kul0TQQpNkT1hOz+OPz5h4rmSrpE3+8n/iQFh+D93irnzSz6g3kjKpy3WNxtIPMm7EK5VRCkvoBVUVEENdeiHdgSpREDf8QpPXPSPPnkJy3kFbfI85da3zcdEMXptFoGgjiaAktv4LIbKt6mWuPKt8sxdEaaf5Ezc7vOwFV+BhJK6aJD8m4sEbnqA3EyADPgfVtRqNGjwg0mgaEiCDuXojv+DrX3hcjy0oUw4cljldGBRUPZFyJeHVsR1NEjwg0Gk0p4jkUWk+xtP9VGOXshGCCs1tCYpum6aAdgUajKYcYWeCz4v6rtsKgaezoqSGNRqNJc7Qj0Gg0mjRHOwKNRqNJc7Qj0Gg0mjRHOwKNRqNJc0SpxiftLyLrgf9SeMiWQMV6yo0NfQ31T2O3H/Q1NBRq6xo6KqVaVdzYKB1BqhGRmUqp3vVtR03Q11D/NHb7QV9DQ6Gur0FPDWk0Gk2aox2BRqPRpDnaEVi8Ut8GpAB9DfVPY7cf9DU0FOr0GvQagUaj0aQ5ekSg0Wg0aY52BBqNRpPmpKUjEJHTRGSeiJgikjRES0SOEZEFIrJIRG6rSxu3hYjkisgEEfkn/jsnSbulIvKHiPwuIjPr2k4beyp9TcXi2fj+uSJStVqLdUgVruFQEdkUf81/F5F76sPOZIjIGyKyTkT+TLK/MbwH27qGhv4e7Cgik0Tkr/i96FqbNnX3Piil0u4H6AZ0BX4Aeidp4wAWA7tgVeaYA3Svb9vL2PcYcFv879uAR5O0Wwq0rG97q/qaAoOAr7EUkA8Apte33dW4hkOBL+rb1kqu4WBgX+DPJPsb9HtQxWto6O9BO2Df+N9ZwML6/C6k5YhAKfWXUmrBNprtByxSSi1RSoWB94HBtW9dlRkMvBX/+y3gxPozpcpU5TUdDIxWFr8AzUWkXV0bWgkN/XOxTZRSk4ENlTRp6O9BVa6hQaOUWq2Umh3/uxD4C+hQoVmdvQ9p6QiqSAdgeZn/V5D4RtUnbZRSq8H6UAGtk7RTwLciMktEhtWZdfZU5TVt6K97Ve3rKyJzRORrEdmjbkxLGQ39PagqjeI9EJGdgX2A6RV21dn70GQrlInId0Bbm113KqU+q8ohbLbVaaxtZdewHYc5UCm1SkRaAxNE5O/401R9UJXXtN5f921QFftmY2m6FInIIOBToEttG5ZCGvp7UBUaxXsgIpnAWOA6pdTmirttutTK+9BkHYFS6sgaHmIFsGOZ/3cAVtXwmNtFZdcgImtFpJ1SanV8uLguyTFWxX+vE5FPsKY26ssRVOU1rffXfRts076yX2il1Fci8oKItFRKNRYhtIb+HmyTxvAeiIgLywmMUUp9bNOkzt4HPTWUnBlAFxHpJCJu4AxgXD3bVJZxwHnxv88DEkY5IpIhIllb/gYGALZRFnVEVV7TccDQeMTEAcCmLVNgDYRtXoOItBURif+9H9b3LL/OLa0+Df092CYN/T2I2/Y68JdS6qkkzerufajv1fP6+AFOwvK2IWAtMD6+vT3wVZl2g7BW8xdjTSnVu+1lbGsBTAT+if/OrXgNWJEtc+I/8xrCNdi9psBlwGXxvwV4Pr7/D5JEdTXwa7gq/nrPAX4B+tW3zRXsfw9YDUTi34OLGuF7sK1raOjvwUFY0zxzgd/jP4Pq633QEhMajUaT5uipIY1Go0lztCPQaDSaNEc7Ao1Go0lztCPQaDSaNEc7Ao1Go0lztCPQaDSaNEc7Ao1Go0lz/h/sY5v0Mmz8ggAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.scatter(X[:,0],X[:,1],c=dbcan.labels_)"
+    "plt.scatter(X[:,0],X[:,1],c=dbcan.labels_)\n",
+    "plt.show();"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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+8jk+d8pXK0twWJDHNo5UJ0f3RvLy9aN5Y+B7LP5mOSn/SXa2LN/h07iY2s1ytoq3UoqrB3bMVs/PiYOnmPj0VF6/+V2czqz/3Ac82SftAllc0/B+LafDyaCqQ+lpH8hTnV/mz1lrMuz/ftwvHNyWeT2v88nQpGemcvroGR9ejRCiNMjX5CYuLo5NmzaxadMmwDXVe9OmTRw6dAhw9agMGTIkvf2DDz7Ivn37ePbZZ9m5cyf/+9//mDlzJk899VR6m2HDhjF58mSmTp3Kjh07eOihh4iPj+fuu+/Oz5ciCkiDK+r69MXbqI33KdW+PN4xTIOYM3E+xeaJ3c9On6HXup1mff5Lf828jfw1dwN//vgXY4Z8zN2NnuDonuPpzbytOXVeWAXfB/v+V7u+rdDuurKyol1JxKqf17FwyrIsm9RrUZsXpw/DZre5khl1IakJCg306TKWpbEszfbVu3ntxnHp9Xgsy2LuxIVYlod1u5Tity+zjk0IUfrka3Kzfv16WrZsScuWLQFXYtKyZUtGjhwJwPHjx9MTHYDatWszb948Fi1aRPPmzXn33Xf57LPP6NGjR3qbgQMHMm7cOEaOHEmLFi3YtGkTCxYsyDTIWBRPFaqXo0P/1hhuHnGYNoOWXZtSvUFV7+eqUS7LnoSLOR1OKtWqkJNQM7n95Rup16JWpp4Kw1Tpj8XO98pYaf89fewsz3V/g9QUV8Xj+q3q+HStBlf41u5iB7cf5tfPl/DP8h206XW5+0TMDWUoZo//1e3+Kwe05btDE7nnzcF07N+Gzje14+nPHuLh9+/K1nXO985MHzObv3/fQmJcEtGnvSeqR/49lq3rCCFKrgKrc1OUSJ2bou3cyWiGdX6Zo3siMzyiMQxFhRrl+eDPNyhfLfNaS1l5se/brP9ts9uZPIFlAph5/DMCgnK5ZlSaxPgkfvpgHr/8bwFnj0dhmAZh5csQdSrG4+Oml6Y/lV65eESvN9m4ZAuWI3PMhqlo2bUZoxe85FM86xb8zYyxP7N1xY4MFYlREF4hjKiT0Zg2E621Tyua2+wmvyZP9+naAEu+XcGYIR+7eoqy+Ulj2gza9b2CF6c/Sd/g2z3GZ9pM+gztxmPj73PbRghR/Pn6/V2kxtwIARBRMYzxa0dz36jbqFavMgHB/lSuXZE7Xx/E/zaM8TmxARg6dgj+QX6ZeynSOlce+fCeDImN0+FM70XJicDgAG578UamH/mUX2K/Zk7s114TG8M0WPPrxvTfn/7sIULCgrNsazk1rXu28CmWz57/hhd6v83mpdsyJjYA2vXYrkbDqvQZ2o1rBnekRqOqXnu67AF2n64NrsHR4+79n2tcTA5uoZwOi51r92D3s9Ph+is89jQ5HU463+x+3J0QonSR5EYUScGhQdwyvB9Tdn/MnNhv+HrvBG59YUC2C+9dcml1Plr1Ni2uuSzD9ur1qzDy+6fpcZdreYK1v/7NM11epZf/IHoH3MrQ5k+z4Mulnsd5eKCUchUaVFnXvclA6wwzxBJik4iLinfbfNIzX7F/6yG3+wH+mruBGe/87LGN5bQ4vOsYl3VsxPNfPc7g5wd4TEJMm8GVA9p5POd5ruUYJvo8hsgdv7RkavALAzAMleXgZMM0aNr5Upp1bpyrawkhSg5JbkSJV+uyGoxZOJJvD/yPcUtfZdKmcXyx40OuvNH1Rf39u3N4sc/bbFmxI70GzIFth3n33v/x7r2f+FTnxR0/fzvVG1b1uNCmBhpcfmEMzS8TFnhsbxiKn8cv8HjdHz+Y69OYGsNQLEpbq+qqW9pTqVaFLKd0n1/h+6ZhfbN+DVpzNvIcp4+ewbIsZo79hZWzcregrWEadOjXGoAGrery+i/Pp89Is9nN9Dhbdm3K67Ofk2J+Qoh0RapCsRD5qWLNClSsmXHw8MHth/l0+FcAGcZ0nO9tWTh1GW37XE7nmzI+8khOTCbmTBwh4UEEhnieDTTg8d589OhnWe9UYLPb6H7X1emb/l6yJfNjpIs4HRYbF//j8Zo7/trt0xgay9KcjYwCwC/Aj7GLX+H5nm9ybE+kaywOrrE4/kF+vDzzaWo3vSTD8VprfvtyKTPG/syRXa4BvWWrRBB10vfaQVlVdVZKYdpN+j3SM31b6x4tmHH0U1b8uIb9Ww7iH+hP++uvoF7L2j5fSwhROkhyI0q1uZMWYdiMLAfvgqv3YPb4X9OTm+P7T/D1a9+zdNqfOFKdGKZBpwFtGPLKLRkWsrxY76Hd2LjkH/78aW2GL3LTZqA1PP/1Y4SVz9uB7b4uKWGYRobZYlXqVOKL7R+wZt5G1s7fSGqqg4ZX1KPr7VdmWogT4NPhX/PDe3My9DSdPe5b9WhlKOq1rM2xvZHERyegLhrw4x/ox2uzn6VKnYyzIP0C/Oh625XAlT5dQwhROklyI0q17at3u01swNWbs/fvAwAc3nWUJzq8RHxsQvoxltPiz5/Wsmbe37y77DUaXlE30zlM0+SlGcNYOGUZsz6ez4Gth7H72ejQvzU3Dbs+0zEtuzbl6J7jbntvTJvB5d2aeXxdV/Rowaqf13rsAToff697u/7n/CYd+rVOfyTkzva/dvPDe3MAvC7pkBVtaYa+cwf1W9Vh8dfL2bR0K1prmnRsRI+7r6FMREj2TyqEEEhyI0qx6NMxXgfmwoUZQh8+NJn4mIRMj3ssp0Vqcirv3Pkxn219P8uxH6Zp0uvervS6tytaa4/jQ65/pCdzJi50u9+yNP0e7el2P8BNw/qy4ifPlZeVoWjVvTlterf02M6d+Z8uSl8EMyce/vBuWlzTBIB+j/TM8AgqO5ITk4mLSiAkPAj/wLyZ0i+EKN5kQLEotX76YB4OL+tYGabBlQPacuTf42xets3tOBbLaXFox1F2/LXb63W9DXyt2agaz019FMM0MhQzNG0GylA88/nD1G7iefmFxu0b8tSkB1FuZhjZ/W3c8FhvXpv1LKZpeo05K/u2HMpRYqOUokmnRtzwWG+f2h/aeZQPH/qUGyvcTd/g23i07fMs+uoPDu86ypg7P6Z/+J0MqjaU/uF3MubOjzNUfBZClE7ScyNKrfmfLfY6E0prTf/He3N451Gfznlw+xEat2+Y69i63HoldVvU4pf//caGxf+A1lzetRnXP9KTWpf5tqp97/u60uyqxsyduJDtq3eD0lxyaQ1a92xBq+7NsxxDkx3BoUEolb1HUspQ2OwmD753l0/tNy7Zwkt938ZyWumJ1O4N+3jnrvFpY5Y0ltMVgCPVydJpf7L6l/W8v+INrwmgEKLkkuRGlEqWZRF1MsZru4at63HJpdV9HiQbGBKQ29DSXdK4Rq4r7lavX4UH370zjyLKqPPN7dm0bKvb/cpQ2P3sGRYHvaRxdZ6a9ECWY5P+KzE+idduHIsj1ZlhNtX5/59Vr5HTYZEYl8S7937C+DWjsvNyhBAliCQ3otSJi4pn/uTFGIZKX+MpK4ZpUKep6+7/so6NKFM2hNiz7hfZtAfYuaJHi7wOt8jqdvuVTBv1E2eOncv0uM4wDQKC/fnf+jEc33eS+Kh4KtepRINWdXyuR7N02koSYhKzHZfltNi1bg/7/jlInWaXeD9ACFHiyJgbUegO7TzKBw9M4oayd9E7cDAPtHyG+ZMX43Q48/xaJw+d4oEWz/DZiG89Jjbg+pK8dshVgKsY320v3ui+sYIbn+hDSHjWyyaURIEhgYz7/VWq1nVN1zbtJja7a/xOWPkyjFn4MtXqVeGK7s256pYONPRxxffzdq39F9OWs/FA4HpEKIQonaTnRhSqv3/fwot9R2E5nOmPGfZvOcT7D0xi5c/reG3WcGz2vPtn+sYt73Hm2FmvSyIoQ9Gubysu69gofduAJ/sQcyaWaaNmpQ/U1ZaryF3fB7pz15uD8izO4qJq3cp8vv0D1i3YxN+L/8HptLisQ0M63tAGu5/v61BlJTeJDUBAsMycEqK0klXBZVXwQpOUkMygakNJiEnIclCqUop7R93GwGf7uT2H1pq4qHgsp0VouTIeewZ2b9jLI62f9xqXaTPpcc81PPLB3fgF+GXaf/LwaRZ/vZzTR84QUSmcLrd1olq9Kl7PK7Jn1c/reOWGd3J0bEBIADOPT3at7yWEKDF8/f6WnhtRaOZMXEh8dILb/VprZn88n5ufuS5TxV2tNfM+XcTUV2aml/o3TINW1zbj2amPEl4hLNP5tq3clWWp//969adnaNf3Crf7K9Yoz60vDPB4DpF7bftcTtW6lYg8cMqnpSQudsvT10tiI0QpJmNuRKGZOdbzqtUAp4+eJfpU5llNE574gg8fmpxhDSPLabFuwSbuqPMIJw+fznSMr+M9yvxn5fG4qHj+3biPI7uP5WoRTZE9ps1k1IKXKFc1AiC9Xs/5BTMbd2gAypXUmnbTtVCoghse781tL3sYHyWEKPGk50YUin837iPqhG+LK5ppg1QdqQ7+3biffZsPeFwVOyk+mZHXj2Hi32MzbG9+zWVee22CygRSt0UtAM5GnuOz579NX0cKoHrDqgx55RauGdTRp9hF7lStW5kvdnzIshmr+PPHv0iMT6JOs0vo+8C1XNK4Bsf2RrL46+WcPX6OslUi6HZHZ6rWrVzYYQshCpkkN6JQbF62zacCcDUaVqVMRAjfvzuHGe/MzrIXJyt7Nx/g+L4TGRZerN2kJi2uacI/y7dn+ZhDGYrrH+5BQJA/505G83j7Fzl19EyGtaeO7j7G27d+QNTJaG543LcKuyJ3AoL86Xn3NfS8+5pM+6rWrcyQV28phKiEEEWZPJYShcaXBzzXDrmKSU9P5dPhX/mc2Jy3e8O+TNte+O4JqjdwDf49/5jDMF1vg7Z9LmfIa64vym/f+IFTR85kWlTzfDI26ZmpnDvpW8+TyF8pyaksnb6S8Y99zoQnvmD1nPU4nXlfRkAIUXxIz40oFJe2b+A1uzHtJq26t+CR1s/l6Bp2P9c/7+jTMSz4Yimb/9gGWnP1wI4EhwWx6ud1RJ2MpkrdSvS+rxtt+1yOYRikJKey4MulHgexWpZm8dfLufnp63IUW0mxf8tB5n+2hKP/HickIpirB3akbZ/Lc7xeVXbtXPsvL18/hqiT0emPL2d//CtV6lTirXkjqNGwWoHEIYQoWiS5EYXi1KHMA37/q0aDqiyd9meOVp42TIOmnS9l45ItvNJvDMlJKenjbdYv3Izd386rPz5D656ZV8SOOR1DckKy1/Mf3xuZrZhKEq01nz33DTPH/ZL+92OYBkunraT+5XUY/dtLhJYr4/1EuXDy8GmevfZ1khNcyzs4Uy/01pw4eIpnurzGFzs+yPUaWkKI4kceS4lCsXL2WrxNXjq27wQnDp5MXxgxO7rd3pnEuCRevn50hsQGXGsTpSal8soN73B834lMxwaFBoG3iVVaE1yKqhH/19xJi5g57hfgwhpP53u69m4+wBu3vJfvMfwyYQHJCSlZ9rBZTouzkedY9NUf+R6HEKLokeRGFIrEuCSvg4kdyamUKVsGw/S9ZD9Ak06NeGLiUOZOXIgjxZHlDCmtNU6HxZxPfsu0L6hMIG16tUwfi5MVp8Pi6oEdshVXSWFZFtNHz3K/32mxaelW9mzan69xLJu5ymv9m+Xfr87XGIQQRZMkNyLfHNsbyZSXpzP2nglMfvZr9m85mL6v1mU1PCYPADZ/GxEVQz0+klKGIjgsiHJVy9KkYyPe/vUF3vvjdfz87ayes97zuBmnxapf1me5746RN6OUSh90/N9rdhrQhrrNa3mMv6Q6svs4J708VjRMg7Xz/87XOJLiPT86RENCjPsikb5wOpxsWbGDVT+vy/DvVwhRtMmYG5HnLh6PYZhG+uOnmeN+odvtnbnjlZvRGq933alJDr596yciKocTfTI600KX51ee/mTDOxmmfJ/nSPE+YyY1OTXL7Y3a1OeNOc8z5o6PiD4di2k30U4LS2uuHtiBpz97yOu5SypHisNrG6WUT+201vw1dwOzPprPrrV7sPmZtO3TigFP9qFei9oej611WQ22rNjh9t+RaTOok8MENOZMLD9+MI+5kxYSczo2fXu9lrV54pP7adSmfo7OK4QoGJLciDx15N/jTB89i9++XApkTmAWf7Ocxd8sxzANDNPwmOCcrwZ8LjKK+lfU5d8Ne1G4vjgtS1O1biVenPZUlokNwGUdGnB8X6Tbnh/TZnBZh4Zur9+6RwumHZnE6l/Wc3DbEQJCAujYv3WpLxJXtV5lAoL9PfacOB1OGlxR1+N5tNZ8MmwKsz6cn+Hfwu/frWDJtyt44bsnuerm9m6Pv+6hHmxets1DDBZ9H7jWy6vJ6NyJKCY98xVLp/2Z5arx+zYfYNjVr/DBijdo0Mrz6xNCFB5JbkSeOLj9MO8/MIltK3f51D47awUpBYmxiXyz73+s/fVvUpNSqduiFs2uauxxSYXrHu7Jb1OWud3vdFj0e7SXx2vb/ex0vqk93ORzuCVeQJA/ve/rxuzxv2b592iYBmWrRNC6VwuP5/lz1lpmfTgfyPjvwemwQMHo2z/kso4NKV+1bJbHX3ljW64e2IFlM1dlKCtwvjjkjU/1pXF798nrf8WcieXxDi9y8tDpLBMbcJUAINXJ5Ge/YeySV3w+txCiYMmYG5FrR/49zuPtX/Q5sckureHIrmOEVQjluge7M+DJPjS/+jKva0U1vKIu94+5HbiwHhFcKNp31xuDPPbcCPfuemMg9VrWzjQmybQZ+Af68eqPz3itdTPrw3nux12lPbb8dfISt8cbhsHz3zzOQ+/eRcVLKqRvr9agCg++eyd3vzXY9xcETB89y5XYeEm8zw+YPnnoVMaQtcaysleyQAiRP5QuhSsB+rpkuvDNm4Pe44+Z+T8rZV7id/j527N93MYlW/jx/TlsXroNrTXNOjdmwFN9ad2jRd4HWYokJSQz55OFzJ24kMgDJwkMCaDrbVdy41N9fXp01ztwMKnJnsfltO7Zgrfnv+j1XJZl8feSLfz4/lzWL9yMtjR2fzsd+rfmpmF9qXVZTQKC/N0e73Q6ubH8PR5Xqf+vD1e9ReN2Dfj79y3MHPsLGxf/g7Ys6raozYAn+tDtjs4+L9YqhPCNr9/f8lhK+MzpdLJ52XZOHzmDX4Ady7JIiEnkj3yebmuYrrExOUlsAC7v2pTLuzbN46hEQJA/Nz99XY6rNBuGl45j5VoZ3Bdblu/gpetGYzmc6VP/U5NT+WPGKv6YsQqA8tXK0u/RXvR7pAeBIYEZjk+MTcpWYgMQUSmMX/73Gx8/+lmGMUN7Nx/gnbvGs/mPbTz92UOS4AhRCCS5ET5ZOXst4x/7nNNHzxb4tS2nxS3D+xX4dUX+at2zhWsdKA9T/Vt1b+71PE6Hk1G3f4gz1YmnjujTR8/y+Yhv+emDubz3x+tUb1A1fV9AsD+m3cxQ5dgdZSgata2P02Ex/rHPgYxjhs4nV799uZTWPVt6HBQthMgfMuZGeLV6znpevXEsp4/lX2JjmAY2Pxso0u90jbRxMkPHDqFd31b5dm1ROG4cdh1ON+NbDFMREh7MtXd09nqedQs2cebYOY+JzcXOnYzm+Z5v4ki98EjMZrfR+cZ26f/m3FJgGIr7R9/OvEmLsqyDdOE1GMz+eL5PMQkh8pYkN8IjrTUTh01xrUaQy9FZLbo0wbSbmQaRGqZBYJkAPlr9Fk9NfIDWvVrQ7KrG9H+kF59te7/UL05ZUjXp2IinJj2IMlSGfxNKKYJCgxi94CWCw7wvcXFw+xGPSUYmGk4cOMXqORsybB78wgBsNtPjuSrVrMBb816g6ZWXsnvDXq9FIvf8nb9VmoUQWZPHUsKjXev2cGxv5vWXsqvXvV0YNvkhdq3bw5SRM1i/cBNo1+yaK29qx12vD6JavSrUb1mH3vd3y33goljofV9Xml/dmHmTFrFjzb/Y/W207d2K7nddTZmIEJ/OERDsn+USG54YpsGG3zZx5YC26dtqN6nJmEUjeWvwB5w+cgbDNNCWRmtNozb1uOOVW7iiR/P0sUJ+AX7p087dsfnJR6wQhUHeecKjs5FRuT5H4/YNeOj9uwBo2Loeo359kejTMcSciaVs5XCf7s5FyVWtXhWGjh2S4+PbX38F4x//PNs9i06Ha3xNzNlYThw4RXBYEJd1aMg3+yewYeE/7N9yCP9AP9pd14rKtSpmOr5d31auJN0N02bQsX+b7AUlhMgTktwIj8pXy7qAmjs2P5OwCmEkxydRtX4Vrn+oB9cM7pRpplNY+VDCyss0fJF7FWuUp8utnfj92z99PsayLKrVr8Jbt77Pih/+Sh/UXLtpTe56fRAd+rWmTa+WHs/R7Y7OfP3698Sejcv8eEq5/ufGJ/tk89UIIfKC1LmROjceaa2597KnOLL7mNeuf8M0uOHx3jz47p0FFJ0QLinJqTzS+nkObD3kta1SEBAcgH+QP7FnYzPM1lJKobXm6c8fpufd13g91/4tB3mu+xucOxGNYRiuQc0K7H42Xpz2FB36tc7V6xJCZOTr97ckN5LceLVh0WZe6P12+viDrJg2g7AKYXyyYQxlK0cUcIRCuBLx8Y99zpyJC90m4spQ2PxsNO3UiM3Ltrmdhu4f6MeM45MJDg3yet3kxGSWzVjF+t824XQ4adSmPj3uvkZ6JoXIB5LceCDJTfatX7iZCY9/zpHdx7Pcf0X35jw56QEqXVQGX4jCEHsujpWz13Fw+2G2rtjBiYOniIuKJzAkgKtu7kDPe7vwRIcXcXiqaaPgqYkPyOB2IYoYqVAs8tQV3ZvzxY4P2bl2D6ePnCEoNBBHqhNHioPaTWuW+pWyRdFRJiLE4yOlgzuOeE5sAJvN5NjeSI9tju8/wV9zNpCcmELtpjW5okdzr+tpCSEKRoEkNxMmTGDs2LFERkbSvHlzPv74Y9q0yXoWwdVXX80ff/yRaXvv3r2ZN28eAHfddRdTp07NsL9Hjx4sWLAg74MX6ZRSXNq2PrStX9ihCJFjIeHeZ+dZlnbbLikhmffu/4Sl01eiUKBcVYnLVyvLi9OfoknHRnkdshAim/K9iN+MGTMYNmwYr7zyChs3bqR58+b06NGDkydPZtn+p59+4vjx4+k/W7duxTRNbr755gztevbsmaHdtGnT8vulCCFKgHJVIrisY0MMD8X6LMviqls6ZNqutebNQe+zbMYq0K7fz4/vOX30LM9c8yr7fRjULITIX/me3Lz33nvcf//93H333TRu3JiJEycSFBTEF198kWX7smXLUrly5fSfRYsWERQUlCm58ff3z9AuIkIGsRYlTqeTdb9tYu6kRSz/YTWJ8UmFHZIQ6e58baCrLE4W+Y0yFN3vvJoqdSpl2rdz7R7WzN3gdsCy0+HkrcEf5GmsQojsy9fHUikpKWzYsIERI0akbzMMg27durF6tW8rSX/++ecMGjSI4OCMXcTLli2jYsWKRERE0KVLF958803KlSuX5TmSk5NJTk5O/z0mJiYHr0b4as28Dbz/wCTOHDuXvi0wJIA7XrmFm4b1lVWSRaFr2aUpL898mnfv+x/xUQmYdtNVq0ZDz7uv4bEJ92V53NJpf7oSIg/TMA5uO8ypI6epUL18/gQvhPAqX5Ob06dP43Q6qVQp4x1QpUqV2Llzp9fj165dy9atW/n8888zbO/ZsycDBgygdu3a7N27lxdeeIFevXqxevXqLAf0jRo1itdeey13L0b4ZOPif3i535hMNekT45L4dPhXWE6Lgc/KCt+i8F05oC1te7fkz1lrObr7OEGhgVx5Y1sq1nQ/4+/0sbM+VUJe8s0KBj1/Qx5GK4TIjiI9W+rzzz+nadOmmQYfDxo0KP3/N23alGbNmlG3bl2WLVtG165dM51nxIgRDBs2LP33mJgYatSokX+Bl2KfPvs14H69na9enUHfB6/1qX6IEPnNL8CPLoM7+dw+rFwZn9rFxyTkNCQhRB7I1zE35cuXxzRNTpzIuPDiiRMnqFzZ89Th+Ph4pk+fzr333uv1OnXq1KF8+fLs2bMny/3+/v6EhoZm+BF579DOo+zddMBjJeOUpFRWzlpbgFEJkXd63Zf55ikrFWvIIykhClO+Jjd+fn60atWKJUuWpG+zLIslS5bQvn17j8d+//33JCcnc/vtt3u9zpEjRzhz5gxVqlTJdcziAqfDSWJcotuqxP8VdTLaaxvDNHxqJ0RR1KBVXao38Pw5Y9pNrhqYeaaVEKLg5PtsqWHDhjF58mSmTp3Kjh07eOihh4iPj+fuu+8GYMiQIRkGHJ/3+eef079//0yDhOPi4hg+fDh//fUXBw4cYMmSJfTr14969erRo0eP/H45pcKu9Xt57aZx9A68letDhzCw2lC+eeMHrzOeKtTIekD3xSynRQW5qxXF2MgfnsHm5/6J/j1vDia0rG+Pr4QQ+SPfx9wMHDiQU6dOMXLkSCIjI2nRogULFixIH2R86NAhDCNjjrVr1y7+/PNPFi5cmOl8pmnyzz//MHXqVKKioqhatSrdu3fnjTfewN/fP79fTom3Zv5GXun/DqDTVzo+FxnF16/NZNXP63h32asEhgRmeWyV2pVo0qkR21fvzrxKcpqg0EA69Lsiv8IXIt/VblKTj/96mw8f/JSday88Cg+vGMaQV2/huge7F2J0QgiQtaVk/M1FkhKSGVRtKAkxWT+KMkyDm4Zdx/1j3D8q3LVuD09dNRJnigMri7E3T3/2ED3v6ZKncQtRWA5sO8yxPZEEhwVxWceG2OxFeo6GEMWer9/f+f5YShQff8xcRXx0gtsxNpbTYt6ni4iNimfPpv3s33oIpyPjGj0NW9fjvWWvUbdl7QzbK9YszwvfPSmJjShRal1Wgw79WtP86ssksRGiCJF3o0i3b/NBbHbT46KC8dEJDK4+lOSEFAAiKodz89PXc+NTfdIfLzZqU5//rRvDgW2HOXHgJGXKlaFRm3qZHj8KIYQQ+UGSG5HOL9DPbX2ai51PbMA1HufT4V9x5N9jPPnJ0AzVh2tdVoNal0k9ISGEEAVLbqVFujrNamZ6zOSr+Z8uZsdfu/M4IiGEECL7JLkRJCcm8/ot7/L2rR/m+BymzWD+Z0u8NxRCCCHymTyWKmV2rdvD3EmL2PP3fgLLBND5xvZsWrqV1b+sc3uMMpTHqsMATofFkd3H8zpcIYQQItskuSkltNZ8+dI0po2ahWkzcDosULBl+Q6vx5arEoHD4STqhPvKwoahCC0bkpchC1GsrF+4iW/f+JHDu4/hH+hH55vbc/3DPahSu5L3g4UQeUoeS5USS6evZNqoWYCrlwXwaXVjgNNHz3LlgLYYpvt/LpaluSYbCxAKUVI4Uh082eklRvR8i60rdxJ9KoaTh07zw7tzuOfSJ1m34O/CDlGIUkeSm1Ji5juzUYby3tCNq27pQHBoYJYJjmkzqNWkBp0GtMniSCFKthG93mLbql1Z7nOkOHjlhrGcOxFVsEEJUcpJclMKxEfHs3fzQa/jZtyxB9hp0KoO7y57jcq1KwKuhOZ8otO4fUPeWfwKdj97nsUsRHGwZ9N+Nv2+1WOb1JRUfv389wKKSAgBMuamVHC6WefJF4bN4NrbOxMYEkjtppfw5c4P2fT7Vnb89S+mzaBV9+bUv7xOHkYrRPGx5JsV3htp2LxsK7e+MCD/AxJCAJLclAplIkKoWrcSx/eddLu0AoBSKsN+w2ZQoVo57npj0IVthsHl3Zpxebdm+RqzEMVB1Cn3g+wvlj7OTQhRIOSxVCmglGLAk33dJjbKUASWCaBx+wbp2/wC7PS6pysfrxlFRKXwAopUiOKlfLWyPrVLik/yeGMhhMhb0nNTSlz3UHe2r97F79/9iWEaWGmPqgzTwC/AztvzX6RJx0acOxlNQkwC5aqWJSDIv5CjFqJoS4pP9qndrnV7mT95MX2GXuuxnWVZpCSl4h/ol2EpEyFE9ihdCm8nfF0yvaSxLIuVs9by8/8WsP+fQ/gH+XH1LR24/pGeVK5VsbDDE6JYiTxwkjvqPuJbSQUF1etX4YsdH2aZtBz59zjTR8/i9+/+JDU5lTIRwfQZei03D7+e0LJl8j54IYopX7+/JbkpRcmNECLvTH1lBt+9/VN6L6gvZkZ+RkTFsAzbdq3fy/Aur5KSlJJhbI5hGlS6pAIfrnor0zFClFa+fn/LmBshhMiB4/tO5PoclmXx9uD3SU5MyTTo2HJanDh0ik+empLr6whR2khyI4QQORASHuzzuBilFFXrVSa8QsY7zU1Lt3Fs7wm3vT+Ww2L596uJPh2T63iFKE0kuRFCiBy4ZnAnnA6nT2211tw07LpMydC+zQcwDM8fw06Hk8M7j+Y4TiFKI0luhBAiBxq3b0Cr7s196r3pdV9X+j6QeaaUX4AfWnsfs2MP8MtRjEKUVpLciBzZv/UQsz6az08fzGPX+r2FHY4QBU4pxb1v3+q1fs3Nz1zPU5MeyDIJatO7pdfJVhGVw6nXolbOAxWiFJI6NyJbzp2M5u3BH7Bp6db0hTi1pWlwRV1enjlMppSLUmXx18sxbAaWmwrEhqHYsny7296dyrUq0mVwJ5bNWOV23M3g52/AtJl5FrMQpYEkN4VMa83aX//ml09+48CWQwSGBHD1wI70eeDaQpv+6XQ62fT7Vk4dPkNYhVBadW+On7+dlKQUhnd5lcO7jrliv2ghzj2b9vNU55FM2jRW6nKIUmPPpv1uExsAy9Ls33qIyAMnWbdgE44UB/Va1qZJp0bpCc9Tnz5I3Ll41v76N6bNRFsWGArLYXHL8H70f6xXQb0cIUoMqXNTiHVuLMvi3Xs/YeHUZRmrBhuK4PBgxv3+KnWaXVKgMa36eR0fPTKZM8fOpW8rExHMfaNvx7SbjLvnf26PVYbinrduZdBz/QsgUiEK34t932bdr5s8Ppqy2U0caQOPlVJoS1Pz0mq8NP0pajd1vb+11mxfvZsl364g9mwslS6pQM97ulC9QdUCeR1CFBdSxM+DopLc/DxhAeMf+zzLfYZpULZKBF/vHY/NXjAdbGvmb+Tl60aj0VlWXa3esCpH/z2eocfmv2o0qsYX2z/IvyCFKELmT17M+w9M8txIken9ZJgGQWUC+Wj1Wxzfd5LEuCRqXlqN2k1qZnkKR6qD37/7k7mTFnFsbyRlIkLodkdnrnuwO6HlpKdUlB6S3HhQFJIbrTV31n+U4/tPeizfPvL7p7nyxnYFEs89lz7B0X8jPS6w6SmxAQirEMoPJ7JO2IQoaRLjk7in0ROcjYzKVqVicL2fTJuBI+XCdPJL29bnqckPZkhyUpJSeLHPqPRxbuffg8pQRFQK570/XqNavSp584KEKOKkQnERdzYyiuP7PCc2pt3kn+XbCySePX/v58ju4x6717Wl0wcRZ0UZisq1ZUCxKD0CgwMYu+QVKlQvB4BhKldPjQ+0pTMkNuBaiuHJTi9x5N/j6dumjpzB5j+2pR9z8fFRJ6N57cZxsuK4EP8hyU0h8XXB37xeGfj00TN89epMRvR6k5euG8Xsj38lPjqecyeifTreU8+NtjR9vax6LERJU71BVabs/ogR3z5BWPnc9QRbToukhGS+fm0mAEkJycyZtNDt+85yWuzfcohtK3fm6rpClDSS3BSSiErhVK1X2WPy4kx10vzqy/Lsmn/MXMXtdR7h2zd/ZP1vm1kzfyP/e/JLbq/zCHHn4nw6x6Vt62cZs2EaNOnUiK63X5ln8QpRXNjsNpypTtdNQi47USyHxR8zV5MYn8ShHUdIjE3y2N4wDbau3JW7iwpRwkhyU0iUUtw07Dq33cmGaVCxZnna9W2VJ9fb8/d+3r7tQ5ypTiwrbWyAdo21SYhJ5ONHP6dm4+oeHzuFlS/DmMUvM3jEDQSFBqZv9w/04/qHezBqwUvY/ex5Eq8Qxc28Txd5fP9kh9PhJPZMrNelGc4z8ui6QpQUUuemEPV94Fr2bNrP/E8XZygEpgxFmYhg3pr3Qq6Ld21csoUf35/D+gWbsDx0bcdHJ9Dl1k4c3X0Mi6wfPz38wd0EBgdy95uDufXFAezddADL0tRpdglBZQIzn1iIUuT4/hNeB9z7yrQZlCkbQkTlcMqUDSH2rPueVctp0aJLkzy5rhAlhfTcFCKlFE9+MpQxC1+mw/WtqVavMvUur829b9/GFzs+pNZlNXJ1/u/H/cJz177O+oWb3SY252mtObzrGKN/e5majapl2FexZnlenjmMLrdeeOTkH+hP4/YNadKxkSQ2QuCaKejLYGLT5vlj1zANOt3YjsCQQOx+dgY80cftGD3TZnBZh4Y0aFU3BxELUXJJz00hU0pxebdmXN6tWZ6dU2vNV6/O5Js3fgDwWEH1YqnJqbS4pgmTt7zHvxv3pVcobty+gc/d40KUVt2HXM2nz37tcebSLc9cz83Dr2f6qFn8+MG8TPsN08A/0I8hr9ySvm3wiBs4uP0wy2asSu/hVUqhtaZK3cq8/P3T+fJ6hCjOJLkpYbTWfPTIZOZOXJSt4wzToHG7BoAr4WrQqq7cDRYibcVB4mx00s9gRYFZCxU0EPy7oJQkmkVRr3u7MHv8r5w6cibTDYVhGlSrX4Uhr92Cf6A/Q8cNITg8mJljfyYpPjm9Xe2mNXnmi4cz9J6eOHiK8tXLUb1BFaJOxWCzmVStV4Ve93bhmsEd8Q/0L7DXKERxIUX8CrFCcX5YM28DL103OtvHGYbiy10fUbVu5XyISmSHdh5Hn70NnEfPbwFMwAn+3VDhH6KUDNwuik4ePs1bg95n++rd6bMKtda06NKEF759gohK4RnaJ8Yl8veSrekViutfXifD/qXTVzJmyEdozYXlWUwD02by6k/DadOrZYG8LiGKCqlQ7EFJTm5e6P0WGxb943O1VNNm4HRaDPv0QXrd2zWfoxPeaK3RZ24Cx3bAmUULBcEPY5R5oqBDE9mw5+/9bP1zJyhocU2THI2f27/1EA+2HJ7le1kpsPnZmLLrIyrWrJAXIQtRLPj6/S2PpUqYPZsO+J7Y2E06XN+aG5/qy2UdGuZzZMInqf+AY4uHBhoSvkaHPIRSfgUWlsieei1rU69l7VydY/ZH890OJNYanA6LORMXce/bt+bqOkKURJLclDD+gb594dn9bXy+7QOq1KmUzxGJbElZi2sSo4cEVceA41+w512BR1H0rPttE04PkwEsp8X63zZJciNEFmRkYgnT+cZ2GKbnv1bTZvLa7OcksSmSfH1KXOqeJpc6vvTAZnexTiFKC0luSpjrH+mJ3d/usWJp7aY18nzNKpFH/K7AY68NgAoBW/0CCUcUnqadL/VYE8ewGTS7qnEBRiSKK601OmUdVsyrWFFPY8V+iE6fsFAyFUhyM2HCBGrVqkVAQABt27Zl7dq1bttOmTIFpVSGn4CAgAxttNaMHDmSKlWqEBgYSLdu3fj333/z+2UUC5UuqcCoX18kMNR9Yb19mw8xotebLP5meQFGJnxibwm2xrhmR2VFQdDtKCXTf0u6Gx7r7fmxlMPi5OHTrJy9Fqcjq8HnQrjKSuhzd7pmYCbMgKR5EP8J+lQXdNykwg4v3+R7cjNjxgyGDRvGK6+8wsaNG2nevDk9evTg5MmTbo8JDQ3l+PHj6T8HDx7MsP+dd97ho48+YuLEiaxZs4bg4GB69OhBUpLnBeZKi6ZXXsq0w5No2bVplhVTLcsCDe8PnUhcVHzBByjcUkqhwseDURHXX975v8C0t6rflaiQRwspOlGQGrdvyAPjhgBuqhor+GvuBl4dMJahzZ/m1JEzBRyhKA509DNpY/nANQPTSvvR6Lh30YmzCy22/JTvyc17773H/fffz913303jxo2ZOHEiQUFBfPHFF26PUUpRuXLl9J9KlS6MDdFa88EHH/DSSy/Rr18/mjVrxldffcWxY8eYPXt2fr+cYsO0mexc+6/HoRmpyQ6WfLui4IISPlG26qjyc1BlngNbIzAqg701KuwDVMQkmSVVitw07DreX/EGnW5oS3B4UMad+kL18aP/HmdEr7cuLIorBKAdeyD5d9w/6lbouAkeq2oXV/ma3KSkpLBhwwa6det24YKGQbdu3Vi9erXb4+Li4rjkkkuoUaMG/fr1Y9u2ben79u/fT2RkZIZzhoWF0bZtW7fnTE5OJiYmJsNPSRd1MprEWM89WabN4PDOkv3ctbhSRigq+B6M8j9jVFyOUe5rVGBvlMrdQqqi+GnSsREvzRhG/ZZ13E4WcDosDm47zLoFmwo2OFG0JS3B89e8BudB108Jk6/JzenTp3E6nRl6XgAqVapEZGRklsc0bNiQL774gp9//plvvvkGy7Lo0KEDR44cAUg/LjvnHDVqFGFhYek/NWrkbkHK4iAg2PuYDK01gbLopRBFXnJiMpuWbvU4O8q0mfw1Z30BRiWKOq2T8OlrXpe8IR1FbrZU+/btGTJkCC1atOCqq67ip59+okKFCkyalPOBTyNGjCA6Ojr95/Dhw3kYcf5KjE9i68qdbP1zBwmxiT4fF1q2DM2uauxxWrjTYdH5pnZ5EaYQIh85Uhw+tNKkJKfmeyyi+FD2RoC3fzv+YFYviHAKVL4W8StfvjymaXLixIkM20+cOEHlyr6tYWS322nZsiV79uwBSD/uxIkTVKlSJcM5W7RokeU5/P398fcvXrNLUpJTmfLSNOZMXJi+sJ5/kD+97+vKvaNu9WmxvNtfvonnrn3DNSb1P49UDdOg1bXNMq1lIwqeTtmMTvgGUjeB8gP/rqigwSizitdjRekQFBpE+erlOO1h0LDl1NRtXqvgghJFn38XMMqBdY6sx90oUDb0mZvQAV1QgbeibCUj0cnXnhs/Pz9atWrFkiVL0rdZlsWSJUto3769T+dwOp1s2bIlPZGpXbs2lStXznDOmJgY1qxZ4/M5izqn08mrN7zDD+/PzbBicHJCMrPH/8qLfUbhSPV+J9eyS1Ne+O4JVyKkwGY302ddtOrenJdmDMu31yB8o+M+QZ+9GZLmup57O/6F+E/Rp3qgk9cUdngiH2mt2bR0KxOfnsrHj37G/M+WkBif9eMBpRT9H+2Fcle/Srmqjl875Kp8jFgUN0rZUWEf4OrHyGq8ngYdD859EP8l+nQvdPLK9L2W4zBW1LNYp7pjnboWK+oZdOo/BRR97uT7wpkzZszgzjvvZNKkSbRp04YPPviAmTNnsnPnTipVqsSQIUOoVq0ao0aNAuD111+nXbt21KtXj6ioKMaOHcvs2bPZsGEDjRu7ClaNGTOG0aNHM3XqVGrXrs3LL7/MP//8w/bt2zPVxMlKUV84c8VPa3j9pnEe2zz/9eN0ve1Kn86XEJvI79/9yaEdRwgMCeDKG9vlet0bkXs6eTn63H1u9hqgAlAVlqGM8IIMSxSAs5HneKnvKP7duB/TZoICp8NJYEggL01/KsvVvlNTUnmxzyg2/b4VjU7vjTVtBlrDi9OepPNNJeMGT+QtnboTHf8pJC3A82MqBQRAhSUQNwESv826WcBNqLA3UargR7YUmYUzBw4cyKlTpxg5ciSRkZG0aNGCBQsWpA8IPnToEIZx4Q/o3Llz3H///URGRhIREUGrVq1YtWpVemID8OyzzxIfH8/QoUOJioqiU6dOLFiwwKfEpjiYP3kxhmm4HTxoGIp5kxf5nNwElQmk7wPX5mWIIg/o+C9w3U1lVYDNAp0IiT9B8D0FHJnIT06Hk+d7vMmhHUfSfz8vKS6Jkf3GMH7NqEw3IHY/O2/NG8Gc/y1k9vj5HN93EsM0aHfdFQx8tj+XtpWq1SJryt4IFf4eWr+DPnsPpK4l68dUGkiC6OGQsjKL/WmSfgBbDQh5KJ8izr1877kpiop6z81dDR/n6L/HPbYpVzWCbw984rrrE8WSFXkZ4GkAqAL/azAiJhZUSKIArPp5Ha/c8I7b/abNoPPN7Xnh2yc9nic1JRXTZma4ORTCGyuyCZDipVUWAzUzNQlDVVxZ4HW3fP3+lndFERReIdTr2k9njp2jX/idfPzoZ5w+draAIhN5y9t9hYa0ew+tNVr7MmNGFHV/zlqD4WHNKKfDYsUPf3ktrGb3s0tiI/KJD30eOhpSt3lvV0jknVEEXTvkKtczdS+SE5KZO2kRD7d6lsgD7pezEEWUX2vcryEFrlHgNbHOPYE+0QR9ojHWqW7o+Clo7e3OSxRVSfFJaC+reTtSnbLit8gffm3w/LmTHUW39IAkN0VQ19s7U6NBVY8rAp9nOS2iT8fy4UOTCyAykZdU8N1kPd4GXN3Cdkj4BpIXkv4h4jyMjh2FPnefJDjFVM1G1VGeelwUVK5dUR45i3yhgu/B8+eOr2VTTLAV3XFektwUQQFB/oxb+ipNOjUCwMsTKiynxfqFm6T3pphR/lejQp5I++3iLzITsOP6oLHI+EGkXT8payDe/fpsoujqdV9Xj4+clFL0e6Rnpu2J8UmcOHiK+JiE/AxPlHDKvxMq5HwZkIs/dwzAD8IngtnA+4kCrkcZEfkQYd6Q5KaIKls5gnG/v8anm8dx1S0dvR+g4eD2I/kfmMhTKuQRVNlpENDLVSXUrO2aHRX8EK5Bf+6+BDU6/nO0lkcXxU2lSyrw4Lt3Aq6ZjxczDEWTjo24/uEe6duO7z/BmDs/ZkDZu7i99sMMKHsXr904lv1bSt56QKJgqJAHUWW/h4C+YNYEsx4E34+q8BtGQEdU+Dt47MExa6JCXyiweHMi36eCi9yp3fQS2vVtxbIZHqblpfEPlNWiiyPl1wrl1yrDNiv6JVx3VR4GEetodOIsVNCN+RqfyHsDnuhD5VoV+e7tn9i1zlV9PaxCKP0e6cnAZ/vhF+B6Lx/ZfYzH279IQmwCzrQVwC1Ls+qX9axdsImxS16hcbvMd9mWZbHjr3+JORNL5doVqd2kZsG9OFEsKL/mKL/mWe+zN4bys9AxYyFl6UV7AiFoMCrkUZQRUjCB5pBMBS+CU8H/K/p0DIOqDcWR6u45KYSEBzPj+GT8/O0FGJnIL1bM65AwHe/rwoSjKq1EKfl7L65izsSSkpxKRMWwTONshnd9jX+Wb89ycLFhKqrUqcyXOz/MMLvy92l/8tnz33Dq8IWlGupfXofHJtwntXBEtmkrzjUzSoWjjODCDkemgpckYeVD6ftgd4/Twwc+118SmxJE+XfBe2IDEAXJf+RzNCI/hZYrQ/mqZTMlNsf2RnpcCdxyao7+e5ytf+5M37bgy6WMuu3DDIkNwN5N+3n66pHpvURC+EoZISizWpFIbLJDkpti4oFxQ+hyayfAVeTLSPsBuPGpvgx8tl9hhifyml8HMGr40NAAp4y1KokO7zrmU7vzlY6TE5P55Kkvs2xjWRqnw2Li01PzLD4hijJJbooJm93G818/zqRN4xjwRB869m/DZR0a0bRzY47+e5yfJywgPjq+sMMUeUQpA0Jf86GlBbL2VIkUGOLbcjLn2636eT0JMYlu21lOi61/7uT4vhN5Ep8QRZkkN8VMnWaX0KhtfVb/vI5tf+5gy/LtrJm3gQlPfMHtdR6RbucSRPl3BOMSL638wL9rgcQjCk58TMKFRTU9sPvbaJ22yObpI2cwTO8f6aeOnPHaRojiTmZLFTN7Nx/grcEfYFlW+izh80PCE2ISea7HG3y1ZzyhZcsUXpAiTyilIPRZdNQj7tuEPIwyyqCdR9EJMyB1C+CHCrgGAq4rds/JBRzfd4JhV7/CmaNnvdTDgRse70OZCNeslfCKYT5VNQ6vGJZnsYrSQ+sUsM6CCinyM6VAem6KjMT4JPZvPcTx/Sc8fqDN+nCeq6hfFk0sp0VCdCILpyzLtzhFwVIB16LC3gV1Plk9fyfvhwp5HIIfQif8gD7VFeI/da3km7IMHTMSfbobOnVXYYUuckBrzcvXj+Zc5Dm3nwPKUKCg74PdueftwenbO/Rv7bEchDIU9VrWpmajanketyi5tHUWK+YN9MnW6FOd0SdbYZ29D52yqbBD80h6bgpZzNlYprw0nd+mLCUlyVVi/5LG1bn95Zu4emBHjvx7nNizcVSsWZ5yVSJYPWdDer2LrGitWTN/IzcNu66gXoLIZyrwOgjoDsm/g/MYqHAI6IYywtAp69ExL5Ix2037/1YU+tzdUGEJSgUWQuQiu/5essVrMc7AkADGrx1FjQYZk5Tg0CCGvHoLk5/7JtMx52da3v/OHXkXrCjxtPMM+uzN4DzOhUrpGlJWos+uhIhPUP5XF2KE7klyU4jiouJ5suNLHN0TmaE7+dCOo7w1+AMmPj2VM8fOpW9v2+dyUpK8ryeUmlR0FzMTOaOUv6uK8X/o+M9xdcBmVQPJCdZpSJwHQTfld4giD2xauhXTZuJ0uK9plRCTiGlmPRbn5meux7SZTH11BomxSenby1Ury5MTh3J516Z5HrMouXTsuP8kNuc5AYWOGg4VV6JU0SsgK8lNIZo+elamxAZI746+OLEBWDNvI8pQKEOhray7rA3T4NIsKpaKkkdrDcnLcb8IHoCBTl6OkuSmWNAa15JiXtvpTL9vX72b/VsOEVq+DJP/eZfdG/YTeyaWSrUq0KJLE7cJkRBZ0VYcJP2C+88X7Srul7QQAvsWZGg+keSmkDidTuZ9utinAYAXc5fUpO/Xmj4PXJub0ESx4imxAdfCm7J6eHHRpFMjpo+e5bFNRKUwKteqmP77nr/3M+r2Dzm042j6NtNm0vfBa3lg3BDsflLcU2SmtetzwW2vi/MI4O0pgA3t2OtLPl7gZEBxIYmPSiAuKnd1aS6e9mnaDFDw5CdDqV6/Sm7DE8WAUgpsjfH8NjZQ9mYFFZLIpSt6NKdKnUpup3Qrpbjh8T7pU8SP7D7GsKtGcmT38QztnA4nv0z4jXfv/STfYxbFi076FevMzegTTdAnmmCd7o9O/CXzAHYV5MPZLJRP7QqeJDeFJCDYP9OKwNkx6IUBXNahITY/G/6BfrTt04r3/3id3vd3y8MoRVGngu/E1TuT5V7AgMCbCzAikRumafL6z89RJiLYNSsqzflkp0P/1twy/Pr07d++9SMpSSlZ9gBrrVny7QpZPVyks2LfQ0c9kVYyIo1jJzr6GXTs2xkTHLOGa7Vwj/0yFgQUzScF8liqkPgF+NH++tasnrM+24+mABq2qsO9bw723lAUS9pxCJJ+Q+t4lK0WBPTIesZTwPWQvBqSfsJ1r3L+35IJaFTYWJRZocDiFrlX67IaTN76PvM/XcyS71aQEJNAjUbV6PtAd668sS2G4Up0UlNSWTZ9pcfZk6bNYPE3K7h/jLdikKKks5I3QPzE879dvMf1n4Sp4H81+HcE0nqGyzyOjnrczRkN8O/h+nwqgmRV8EJcFXz3hr080eFFnE7L61ia/+rQrzU2PxsNr6hLj7uvIax80V/dXHindQo6+iVImo0rWTEAh6twVtgoVECPLI7RkDQPnfAVpG4HZQf/a1DBd6PsMjumpIo5E8uNFe7x2Ma0GXS9vTPDv3BfCFKUfFpr9KkeYB3w0MoE/6sxIjI+ytTx36Bj38Y1vs910wRO8O+GCn+3wMtM+Pr9LclNISY3AOt+28So2z4k9mwcpt1EW9qnnhzDNNK7EG12GyO+fYIrB7TN73BFPrOinoakuWSu0ujqGlYRU1D+7Qs8LlH0OB1O+oXfSXJCsts2hmkweMQN3PX6oAKMTBQ1OmkJOuoh7w2NKhgV/8h8vHUWEme7epSNEFRAb5S9cRbtYiBhBjrxR1c1Y7MaKmggBN7gKmeRByS58aAoJTcAKcmprJq9lv1bDuEf5M+BbYdZOu1Pn49XSqFMxYQ1o6nXsnY+Riryk3bsQ5/u6aGFAfaWGOWmFVhMomj7+NHPmPvpIix3j6YUTN39MVXrVi7YwESRYp29E1JWe29o1sao8FuOrqGdx9FnBoN1nAs3Z2nl9G1NUWWn5smyDb5+f8uA4iLAz9/O1QM7cvebg7n1hQE8N/VRBjzZJ30QofIy8FhrjQJ+eH9OAUQr8k3SAjy/JS1I3YB2niqoiEQRN/iFAYSVD3XNlszCLc/0k8RGZBxA7EkWj719paOGgXWCLKulO7ahY0fn+Nw5IclNEWTaTB567y6mH5nE4/+7n3veupWGret6HLTudFismr2u4IIUeU5bsfjyltSxo9EJM9FWQv4HJYq08lXL8tGqt2jVvXmGz4cyZUMYOnYI942+rfCCE0WIL7WODFRQziap6NSdkLoB93W3LEichbaicnT+nJDZUkVYRKVwrnuwOwCbl23NcrHMi6Umy7ILxZmyXYL2WpQPSJqPTpoDsaMg7D3XCuCi1KpcqyJvzX2BEwdPcWjHEfyD/Lm0XX0p3icu8O8KSbPwWPQz8DaUmcMaaal/+9IIUndAAY0ZlJ6bYqJeyzpuC3uB69FVnea1Ci4gkfcC+gK+DLpL+4DSCeioh7ESF6BTd6dXHBWlU6VLKtC6Z0uadW4siY3IwFUPC7Lu/ldAKKqMuynfPl3Bx3YFl3JIclNM9L6/a9rCM1nTlqb/Y5kXVhRFm3aeQMdPxYr90LVGS5kRaXt8eWumTcmMfhx9pi/6ZAes2PclySnFtNasnL2W4V1fo3/EndxU8R7eHzqRA9sOF3ZoohApe0NU+Ie4Htac/2xJS0hUGKrcFJQRlvML+LXzIYhAsDfJ+TWySWZLFYHZUr6aO2kRHz70KYZppE8XV0qhteaawZ14/uvH0gt8iaJNa6drgF3C17iSFBNXPZtACBgIqWvBsT0HZzbArx0qYjJKyd17aaK15qNHJjN34qIMi+sqQ2EYild+HE77664o5ChFYdLO05D4PTplEygbyr8jBFyfJ7OYrHNDIXkFWT/6UhB0D0boc7m+jkwF96C4JjcAf/++hZljf2Hjos1YlqZWkxrc8Hgfet5zTZaJzbkTUSz4Yin/btyLzc9Guz6t6HRjO/z85YuvMFkxoyHhC7f7VfjHYG+Cjn4ZUnwvC5B+fNhoVOCA3IQoipnfv1vBqNs/crtfGYpv9k2gYk2pWC3ynrai0GfvBMcOLlRLN3EV/LsGFf6x+0U6s0GSGw8KO7nRWvPP8u3sWL0bwzS4/Npm1GuRvfo0luWqanx+Ab2sLJ2+knfu/Bin0wLt+nCznBYVapTjnUUjqd6gam5fisgB7TyDPtUJ94P7FJi1Iex9iHoArMhsXsEAe1NXN3TyctApYL8U7Fe4SqqLEiM1JZVVs9dxaOdRfv18CacOn/HYvlr9Kny580P5d1CCaSsedBwYEXmSTGTr2jrFtWxM4iywToNZHRV4M/hfhVJ581RBkhsPCjO5ObzrKK/dOI6D24+4BghrV6LS9MpLeXnmMCIqhefJdXas+ZcnOr6Y5bIOhmlQtkoEU3d/hF9Awf7jF6ATpqNjXsHr9DcCgBTcL4zpiR1wXHxVMOugwt9H2S/NwflEUbNm/kbeuXM8MWdiMW0mTocPM+2AsUteocU1BTf2QRQMnfoPOm4CJC/DdTcbCIE3oYIfRpnlCju8PCNF/IqgcyejearzSI7sPgaA5bSwLNcX1/bVuxje9TVS8mg69w/v/uJ21XHLaXH6yBn++N6HipUi71kx+PbWy2liA5CKK3k6/wM4D6LP3o52yODS4m7bql280n8MsWfjAHxObFCw+Ovl+RiZKAw6eSX6zCBXT+3597tOhITv0GduKpWFPyW5KUBz/vcbsWfjslzF1+mwOLj9CMvzKOFYPWe9x9WClaH4a+6GPLmWyCbbJXisN5Eup4mNO07X9PH4z/P4vKKgffXqDLSGbHe8a4g6FZ0/QYlCoXUqOvppXJ8p//1ccYIViY59pxAiK1yS3BSgRV//4XFRTMNQLPluRa6vo7XGker5y1NbmtQkKfpXKPyvARWO+9oQ+fm2dELSrOx/KYoiI/ZcHBsXb/Fpgd3/Mm2GDCguaZJ/dy1S6fYxtxOS5qGt0pXUSnJTgGLPxXncb1mamFMxub6OUoraTWt6XJPKMA1ZZLOQKOWHCnsLV3Lz37egCQT5eqacBaATyTgeRxQn8dE5X3bD6bDoeY9UtC5RHHvwvtiAA5yl63G0JDcFqGrdyh4TDtNmUK1BDstf/8cNj/XOcjDxxXrd1zVPriWyTwVci4r4AuxNL9pqgH9XVPlZYL8Cz2/PtNV2c8IoKzVwiinLslg+c3XO8loFPe+5hgat6uZ5XKIQqUB8eoStAvM9lKJEkpsC1PeB7h4TDqfDovd93fLkWtfeeRWdb3at4XHxtE/DZoCCpz59kArVS84I+uJI+XfAKPc9qsJSVLnZqIqrMCLGo2yXoMo8g+vt6eZbzH4FOVsazoDAQTkPWhQarTXv3DmeySO+8ZrXKqUy3EgFhwUxZOQtPDnpgXyOUhQ4/254/gehwKwFZp1cXUbrFHTK3+jkNWin55IDRYEsnFmAut3RmcVf/8G2lTux/pvkKOh665U0v/qyPLmWaZq88N0TtOrWjFkfz+fA1sMYpkGbni25+Znrada5cZ5cR+SeMquBWS3jNr/LIeIzdPQIsI5ftCcAFfIAWoVD6vpsXsl01Z0Ivju3IYtC8NfcDSz51suYvLQOvcfG30vHG9qw759D2P1tNGpTD/9AX9YtE8WNstVEB/SFpHlk3YOjUSGP5bi2kdYWJHyBjvsUdFTaVhMd0AtV5sUiO81c6twUcJ2b5MRkvnxxGvMmLyYpPhmAsPJlGPBkXwY+1w/TzFyU73yhrt0b9mH3s9Gmd0subdcgW/9YnU4nhmFI8a5iRmsLUla7nperEPC/GmWEoK2z6JOd8H3sjAEBvYv0h5Hw7MU+b7N+4WaPA4kDywTw9GcPc9XNBbPysigatE5CRz0DyQtxjdtTuBIdhSrzbK5uaKzoNyDx6yz2mGBWQ5X7MXfrUmVTkSriN2HCBMaOHUtkZCTNmzfn448/pk2bNlm2nTx5Ml999RVbt24FoFWrVrz99tsZ2t91111MnTo1w3E9evRgwYIFPsVT2BWKARLjkzi04yimaXDJZdXdruK79c8dvHbTu0SdjMZmN9Fa43RYNG7fgFdnPUtExYL7RyWKFiv2XYif5ENLBbYGqLLT8mQNGVE4bqv1ECcPnfbYpmq9SkzdPR5wLb3yz/IdWE6LRm3qUaVOpYIIUxQinboDnTQPrFiUrQYE9EeZ5XNxvt3oM309tDAg+CGMMk/k+BrZ5ev3d74/lpoxYwbDhg1j4sSJtG3blg8++IAePXqwa9cuKlasmKn9smXLGDx4MB06dCAgIIAxY8bQvXt3tm3bRrVqF7rue/bsyZdffpn+u79/8epyDQwOoOEVngf2Hd51lOd7vJle2O/i6d271u1hRI83mLBujMclGETJpUKeQmNC/GRcRfvc0eD4Fx33ISr0xYIKT+SxoDLeB4QGhwWTGJ/E+Mc+Z/HXf2A5L9y7hoQHc+frt3Ddgz3kM6OEUvZL3VYgd/UCr0KnbHAtheDXzuuSLDrxB9LXh8qSBYnToQCTG1/le89N27Ztad26NePHu+4mLMuiRo0aPPbYYzz//PNej3c6nURERDB+/HiGDBkCuHpuoqKimD17do5iKgo9N754775P+G3K0szjcy7y6k/D6dg/614wUTpoKxod+zEkfuW5oQpEVfwLVcpmTZQU3775I1NfneF2UoIyFPe+fSvrFmzinz+2u61l1OKaJrw1/wVZPLcU0Y496HMPg/MArj4Ny/WjwsC/EyqgO/h3yzSL0jr3KCQvwtsIdlVpW4HNwCwSyy+kpKSwYcMGunW7MAPIMAy6devG6tW+VeJNSEggNTWVsmXLZti+bNkyKlasSMOGDXnooYc4c8b96O3k5GRiYmIy/BR1W1fu5Ncvf/eY2BimwfIfZAmF0k4ZYWCUwWtHrE4Ex6ECiUnkrTPHz7nWQ/W3Z3mnbZgGYeVDKVe1LJuXbfNYpHHT0q081nYEo+/4iO/H/UL06aL/eShyTjvPoM/eflGdGwfpA491tKvAX9QT6FPXoh37Mh5sROA1TVCBFMW5Sfma3Jw+fRqn00mlShmf9VaqVInISN9WOn7uueeoWrVqhgSpZ8+efPXVVyxZsoQxY8bwxx9/0KtXL5zOrLvORo0aRVhYWPpPjRo1cv6iCsD+LQd57trXvU73tJxWrgp6iZLDtfqvD52wUt+mWLEsi0+Hf8WtNR9k6sgZOFOd6YmLMhWm3fV4qWLN8oxb+iorfvzLYy2t8/b9c5Dfp/3J5Oe/YVD1B/hj5qp8fR2iECVOBysKr0u+WCfQZ4e4VhVPowKv83KcCYE3FMmJKkUv3brI6NGjmT59OsuWLSMgICB9+6BBF+p0NG3alGbNmlG3bl2WLVtG166ZC9ONGDGCYcOGpf8eExNTpBOcb9/60aeF8AybQfUGVQsgIlHk+V8Fce97aWRHqwo5rWssCsGXL03n+/fmXFj71LrwuaCdmlY9mnPdg91p3asFpmly5thZr8U7049Pa+dIdfDWrR9QoWZ5GrdrkOevQRQunfgLvq1T5wTrJCTNgaC071h7a/DrBCmrsjiH6XrUHXRv3gacR/K156Z8+fKYpsmJEycybD9x4gSVK1f2eOy4ceMYPXo0CxcupFmzZh7b1qlTh/Lly7Nnz54s9/v7+xMaGprhp6hKSUphxY9rPC56eZ7lsOh9f94U/RPFm7I3Br+2Xlo5IGFigcQjci/mTCw/XJTYZOXwzqO07XN5egmJ8tVyMM1fu9a1+37cLzmMVBRpOjuPHRU6adGF35RChY+HgD64ppdftGSMeQmq7HeuWVlFUL4mN35+frRq1YolS5akb7MsiyVLltC+vfs6DO+88w5vvPEGCxYs4IorrvB6nSNHjnDmzBmqVMmbpQsKU2Jcks8L4t0x8mZqNqrmvaEoHfx7e2mgIWEaWqcUSDgid1bPWY8jxXMdo+P7TrB384H033vcnbN1o5wOi9Vz1suCqiWRWRvfv+p12tpzFygjCCP8XVcl9dDXUGVeQJX9FlX+V5S9UZ6Hm1fyffmFYcOGMXnyZKZOncqOHTt46KGHiI+P5+67XUWFhgwZwogRI9LbjxkzhpdffpkvvviCWrVqERkZSWRkJHFxrkUn4+LiGD58OH/99RcHDhxgyZIl9OvXj3r16tGjR4/8fjn5LiQ8GL8A7+MiOt3QliGv3lIAEYliw3kI17RND3QcOH0b7yYKV9y5eAwfxs/ER10Yd9emd0sCywR4aO2eM9WZo5XGRdGmgm7Ft8dSACa4mUquzKqooEGo4CEov9ZFcpzNxfI9uRk4cCDjxo1j5MiRtGjRgk2bNrFgwYL0QcaHDh3i+PEL5eU/+eQTUlJSuOmmm6hSpUr6z7hx4wDXsgL//PMP119/PQ0aNODee++lVatWrFixotjVuvkvp9PJ5y9OIyXJU80SV1fhQx/cVTBBieJD+eVtO1GoqtWv4nG25HlV6lyoF2aaJt2HXJ39iymo3rCq1L8piQJ6gt/V+LbaqhNVQtaek+UXisj4mz++X80nw6Zw5uhZr23vfG0gt798UwFEJYoTnbIZffZmDy0U2Oqjys0p8nddApwOJ4NrPEDUyZgsHxcZpkGLay5jzMKRGbYf3nWUey97yueBxeC6YXrko3vo90jPXMctih6tUyB+EjruKyA6ixYGYEHwYxhlHivg6LKnSNS5KU0cqQ7WzNvA/MmLWT1nPakpnntfLvb7tD95c+B7PiU2dj8bNw+/PjehipLK3ixttXB3d98aFfywJDbFhGkzefqzh1CGyvR4yjANAkMCePjDezIdV6NhNZ78ZKhrMWjbhY/483/vGf7+FSgFrXu1pO8D1+bPCxGFTik/1+KZlVZCuTkQ/KhrpfD/iv8Y6+yd6JS1BR5jXpOemzzoufn9uxX878kviT4dm76tTNkQho4dQk8vA/xSU1IZVP0BYi461ptvD35CxRo5Xy9ElFzaOoc+ez84/sGV5LgWzwONKjMcFXxf4QYosm3zH9v48qVpbFu5C3BVIm5/3RXcN/o2ajR0P6Fg++pd/Pj+3PTFNpt0bETzqy9j8x/b2bBwE1pDlbqVuOGx3lz3UHds9iJdGUTkMcsZA2cHgnMfGafkmYBGhb+PCuhVSNG5V6QWzixq8jK5WTZjJW8N/sDt/uFfPkL3O692u/+vuRt4+frR2brmrLNTCAkPztYxovRwrSGzEp30K+h4MGujgm5GmTKzrjg7efg0sWfjKFc1gvAKuVsw15HqwJHqxD/QT3rySikrZhQkfEXWRfoUEICquLLILbZbZBbOLMmcTieThme1FPwFk5/7mi63dnJ7V3R8/4kst2fl/DN2SWyEJ0oZ4H8lyv/Kwg5F5KGKNcrnWY+tzW6TnppSTOsUSJyJ++rDGkiCpLkXCvoVMzLmJhe2rdzF6SPu17QCiDoZw9+/b3W//0RWg7vcu/1lTwNGhRBCCC+sU65eXY9MtCPrwrjFgaTuuXDOx8TEUwLjaxE+vwA7L3z3JE2vzLoGgRCi5LMsi8M7j5KcmEK1epUJDpNeXJEDKtCHRtrHdkWTJDe5UKGGb6XOPbWr06KWT+cY9/urXCrrvghRai34cinfvvkDkftPAmD3t9H1ts7cN/o2wsoXjZIWonhQRlm0vSWkbsZ9gT8nKqD4FsaVx1K5cGnb+lStV9ntgDylXIlNs6sauz1H7SY1ubRtfffnMBT1W9WRxEaIUuzbt37k3Xv/l57YAKQmO1g4dRlPdHyJ2HNxhRidKI5UyKO4X7jMAL+OKHuTggwpT0lykwtKKR4bf5+rVsR/6lC4khXFox/fi2F4/mN++vOHCAoLxDD/W8tCERgSwPAvHs7r0IUQxcSJg6eYOnJGlvssp8XxfSeY+c7PBRyVKO6U/5WosFGAH67ZUTbSa2T5tUWFf1R4weUBSW5y6YruzRn164vUaFg1w/Zq9Svzxi/P0eH61l7PcUnjGvxv3Ri63tYZm931j8u0m1wzuBMT1o2hdtNL8iV2UbJpnYpO3YlO3Y7WSYUdjsih375cmunm6WKW02Lep4uwLFkXSmSPChyAqvgnqswLEHgzBN2FKvs9KmIKyihT2OHlitS5yaPlF7TW7Pl7P6ePnqVslQgatKrj9lFTSnIqfy/ZQuzZOCrXqsBlHRult01OTCb2XDxlIoLxDyzea2WJ/Kets5C8CnQK2Buh7I3R2gnxn6Ljp4A+52qoQiBoMCrkcZSSf1fFyeghH7F02kqvi1rOjppKcGhQAUUlijutEyHpV3TqTlD+KP8uYG9R5OseSZ2bAqaUov7ldah/eR2P7eZMXMiXL35H7LkL0/Cq1q3Ek5MeoGWXpvgH+ktSI7zSOgUd83ZarQrHhe1GebASgIT/HBAH8Z+jU7dBxGSU8r7yvCgayoSHeP3CMW0G/oGyIKrImtYpcH5at60OpKxFRz0FOhZXGqDR8ZPA3goiJqCMsoUZbp6Q5KYA/fThPD55akqm7cf3n2REzzd5Z/ErNOuc9eDjmLOx/PblMtb/9jdOh0Xj9g3o+8C1VKxZIZ+jFkWN1hodNQySF5FpQKB12sORFqSsgqQ5EDggP0MUeejqQR2ZPf5Xt/sNm0Hnm9tLUT6RidYO14KZ8VNAp5UkUYGgk7jw2XHh5ojUTa7lW8p97yoGWowV7+iLkYTYRL54cVqW+7SlsSzNp26qHW9fvYs76jzC5Oe+ZuPiLWxeto0Z7/zMkHqP8vu0P/MzbFEUpW6G5IW4n+ngiYFOmJ7XEYl81Lh9A1p1b45hZv64NgyFaTMZ/PwNhRCZKMpcN0FPo+M+upDYAOhE3H92OMGxBVJWFkSI+UqSmwKycvZakhOS3e7XlmbXuj0c+fd4hu3Rp2MY0estkuKS0NaFf5CW08LpsBh9x0f8u3FfvsUtih6dNBv3K397Y4HjUB5GI/KbUopXfniaDv1ckxPOJzQA4ZXCGb3gJZl0IDLQVjw6aQ4k/0r2b4JMdNKC/AirQEk/ZgE5FxmFYRpeBwWei4yiev0q6b8v+GIpif9JbC5mGIqfPpzHc1Mfy9N4RRHmPIP7wls+MKTgW3ETGBLIKz88w5Hdx1g9ZwMpiSnUblqTtn0uT090hNCpW9FxEyD5d3LWs4vrOK9LMxR9ktwUkHJVy3pNbFztIjL8vm7B324TGwCnw2LNvI25jk8UI2YlXJ2u7ha988RABfbP23hEganeoCo3P13Ve0NR6ujk1ehz9+JKanI5Cdr0PDGmOJDHUgWkQ//WBAS7nwVlGIpL2zegat3KGbY7Hd6/wHxJmkTJoQIHkLPExgSjbLFd5VcIkTWtHejop3H16ObksyEjFVT8F2iW5KaABAYHMPSdO7LcpwyFYRo8OG5Ipn2N2zXIciDheYZpcGnb+nkWpyj6lL2xq+BWdpm1UGW/LRHTPIUQF0leljZTMjc3uq7vGVXmeZRZxUvbok+SmwJ03UM9ePqzhwivGJZhe81G1Xhn8Ss0bt8w0zF9HrgWPNRZtJwW/R/vneexiqJNhb4OwY+A8lS0Le3t7XcNKuJrVPn5KFvtAolPCFGAHHvI2SSDi2oj2Rqhwj9GBd+VR0EVLqlQnEcVirPDkergn+U7iD0TS+XaFWlwRV2PRbp+m7KUd+/9BMNUOB2uzPz84OSBz/bjvtG3F1TooojROhFSNoBORqtASF4MycsBC+xtUMG3e138TltnIXEu2jrh6tUJ6IMyK3s8RghRdOj4r9Cxb5GjsTZl3kQFXIMyi0fNNF+/vyW5KYTkxpOkhGR+/3YFy2asJPZcPJdcVp2+Q6/FtNv46cO5rPt1E06nReN29RnwRB/a9mlV2CGLYkprDfGfoePex/Wc3iS9Wzv4HlTIM8W+kJcQpYF2HkWf6kL2kxsD7C0xymVdg60okuUXiqETB0/xTJdXidx/EqUUWmv2bznIkm9W0PfB7rzw7ZNFft0PUYwkTkfHjb1ow0WVSuM/c1UyDZESA0IUdcqshg7oB0m/kL1xNxakbkDr5BK35pwkN0WE1pqR/cZw6vDp9N+B9MdQcycupNZlNej3SM9Ci1GUHFqnomM/8twmbjIE3Y0yQgooKlGQEuOTWDptJX/MXEV8dAK1LqtO76HX0rhdg8IOTeSACnsDrZPTCveZgCLDDYsn2ulqXoJIclNE/PPHdvb9c9Bjm5njfuG6h7pjGPKoQPhOOw6BdQ7MSuljaXTKBtBnvByZ5Bq/EygD1ouLXev28NOH81g7/28sp0XjDg254fHetOnVMkO7yAMneabLq5w4cCq9l3jP3/v4bcoybniiNw+9d5f0EhczSvmjIj5Epz6MTpoLVjQ4T0GKp4J+yjWL0ih5q8lLclNEbFz8D6bN9FjX5uTBU5w4cIoqdSoVYGSiuNLJa9Bx41xrUZ3f5tcBVeY5SPzRx5PE5VN0Iq8tnLqMcff8D8M00j9HNi7+h/W/bWLwiBu4ZXg/lny7gkM7jrBsxipio1x/t//tJZ714XwuubQ6fYZeWzgvROSKsjdE2V0zb7UVhz7VycN6UrrEzI76L0luigjLafnULeiUgn3CBzp5Ofrc0Mw7Utagz9xChimgns7j2A2OIyhb9bwNUOSp4/tO8O69/0NrneEG6XyBz2mjZvH9uF9wOiyUoTwW/lRKMXPsz/S+v5v03hRzygiB8PHocw+QscCf4fo9oC8EDiy8APORJDdFROMODXGOme2xTVj5MlSuVTym64nCo7UTHf0iWZdhd+L6kHO/iGsGCV+jE75G+10FZk3XoyyjPCqwP8p+WZ7GLXJu7sSFoBSeZss4Ul1fbNrpeUaN1ppje09w6sgZKtYon5dhikKg/DtB+Z/R8V9B0m9AMtgaoILucJV9KKEzIiW5KSLa9G5JxZrlOX30bJZ3VUop+j3aC5td/sqEFymrwTrhoUF2poumtU1Zlva7ASh0wlR0QB9U2DsoZc9RmCLvbFu1K8+XYfFl6RdRPChbPVTY6xD2emGHUmDkmzIPWZbFpt+3smbeRlJTHNS/vDZXD+pIYHCA12NN0+T1n5/jmWteJSE2Mf2DyjAUlqVp3bMFg0fckN8vQZQEzsP5ePKLvkCT5qNVWVTYy/l4PeEL0563K4NHVAqTXhtRrElyk0dOHz3Di31HsW/zQUybCQqcqU4mPj2Vl2YMo3WPFl7PUbd5LSZvfY85//uNJd+tICEmkRoNq9L3we50GdzJdV4hvFEFVZhSu2rllHkUZUR4by7yTeueLdmyYgfayn1NVmUo+j/WWz5vShitEyFxDjrxF9DnwKyNChoIfp1K5NgqSW7ygCPVwXPd3+Dov8eBjN25iXFJjOw3hglrR1On2SVezxVRKYxGbeuTEJPoqkTcvgGdb2onHzTCd/5XAQFAkvs2qjL4t4Ok2bhqYuT0EUQqJK+EwL45PF7k1sEdRzh56BTKULlKbs73ErfpfTm3DL8+DyMUhU07I9Fnh4DzAK6ZKxoc+9DJCyGgD4SNQ6mS9R0jyU0eWPXzOg7tOJrlPm1ptGXx/bu/8NxUz9Vej/x7nBd7v8WxvSfSu5nnfPIbnzw1hdd/fo7LOmReWFOI/1JGCIQ8jI57z32b0Gcg4DoIvA6dMB1S94B1kBytKqxTch6syDGtNV++NI1po2Zh2gwsR+a/O8M00JYFynPiExQWyCWX1uD6h3twzaCOcjNVwuioxy96XH3+30HaDU3SfLA1gJCHCiO0fFMyh0kXsD9nrcEw3f9ROh0WK374y+M5EmITGd7lVSIPnnIdk+rEmTa7Ie5cHM/3eIPIAyfzLGZRwgU/gAp5HLDjulNLu49RgajQ11GB16OUQvlfiRExAaPibxA0hBytLGyXpLswzJ+8mGmjZgEXatRcLCQ8mFuG92PSpnepeWn1TJ9RhmmglGLEN4/z87mv+GjVW3S7vbMkNiWMTt0CqZtw3zur0fFfokvYTYokN3kgKT7Z60yFlKQUPK1RuuSb5Zw+djbLuy/L0qQkpfLz+AW5jlWUDkopVMijqIorXclMyGOosDGoCqtQQYOyPibkQTAr43uCY4LtMpkSXggsy+K7tMTGnbioeLoM7kjtpjX58M83uOmpvgSFXqhE2/TKSxm98GW63HplfocrClPyary+p3UUOPYWRDQFRh5L5YFLLq3Omnkb3SY4Simq1q/scdDW8h/+wlOVCstpsWzGSh4YNyT3AYtSQxnhEORbkS5llIWyM9GxY1xd1enr0hhkrpljggpBhY/NdB6R/w7vOsbJtF5edwzTYM28jdRuegnBYcHc/84d3PXmIKJOxhAQ7E+ZCFkzrLjRzmPohG8gcS7oeLDVQQXdira3QSX9hE7ZDMqG8usEgf3T1oXz9VFz7gejFyWS3OSB3vd3Y/o7sz226f9IL4/7E+MS8dCxA0BSgo+F14TIIWVWQIWPQ1svguMgqAC0CoeELyDxe9cHKgEQOAAVcj/KrFbYIZdKjhTvCyIqpUhNztjO7menQvVy+RWWyEc6datrULBOJP0RU+oWdPRzgEKjcCUyCp28FOI+grJfgF8rvE4YUCFgq5Ov8Rc0eSyVB6rUqcTQd1w9KoaRsXdGGYrmV19Gnwe6eTxH7aaXYNrc/3UYpsEljWvkPlghfKCMCJRfC5S9EYatMkboC6iKG1EVN6EqbcIIe1USm0JUtV5l/AM9L6HhdDipd3ntAopI5CetU9HnHgSdQMZE5XyvjP7P/9egY9Bn70abDcBWH/ePpgwIGoxS3uuxFSeS3OSRm5++jld+fIb6reqmb4uoHM6drw3krfkvYPfzXMW174PdsxwUeJ7ltOj3SM88i1eI7FJKoYygEluuvTgJDA6g5z1d3E5kMAyD8tXL0aZ3yyz3i2Im+XewTpK92YwW6BhU0s+o8AlgRJDxKz/t//u1Q4U8kXexFhEF8ik1YcIEatWqRUBAAG3btmXt2rUe23///fc0atSIgIAAmjZtyvz58zPs11ozcuRIqlSpQmBgIN26dePff//Nz5fgk043tGX8mlH8ePoLph/9lGmHJ3Lbizfi5++9PH3DK+oy6HlXBWL1394fpeh8c3uuuqV9vsQthCh+7n5rMLWb1Mj0eWHaDPwC7bw8cximmbOZT3s27eerV2cy+dmvWfT1HyQnyiPxwqRTNpHTUSQ6eSnKVgtVfq4riTFrg1EO7C1cy6dETEYp3xbSLU6U9jSFJw/MmDGDIUOGMHHiRNq2bcsHH3zA999/z65du6hYsWKm9qtWraJz586MGjWKvn378t133zFmzBg2btxIkyZNABgzZgyjRo1i6tSp1K5dm5dffpktW7awfft2AgK8d63FxMQQFhZGdHQ0oaEFVc3VO601S75dwYx3ZnNgq6smQcVLKnDjE33o91jPHH9QCSFKpsS4RGZ/vIA5E3/j1OEzBAT70/XWK7npmeupXr9Kts8XHx3Pm4PeZ/1vmzFsBoZSOFKdBIcHMeKbJ2jb+/J8eBXCGyt2LMR/yYVB/tlgb41R7ts8j6mw+Pr9ne/JTdu2bWndujXjx48HXFMYa9SowWOPPcbzzz+fqf3AgQOJj49n7ty56dvatWtHixYtmDhxIlprqlatytNPP80zzzwDQHR0NJUqVWLKlCkMGpT1NNeLFdXk5jytNbFn43A6nIRVCMUw5DGAEMIzy7Jy9VmhtWZ4t9fYsnxHppmfSikM0+CDP9+gUZv6uQ1VZJNOXoU+d1fODg66DyP02TyNpzD5+v2dr9+aKSkpbNiwgW7dLgymNQyDbt26sXr16iyPWb16dYb2AD169Ehvv3//fiIjIzO0CQsLo23btm7PWdwopQgtV4aISuGS2IgiRaduw4p6HuvkVVgnr8aKegGduqOwwxKQ68+KbSt3snnptixLWmit0VqnFw0UBcyvPRiVc3ZsKa1Dla/fnKdPn8bpdFKpUqUM2ytVqkRkZGSWx0RGRnpsf/6/2TlncnIyMTExGX6EENmjE6ajzwyApJ/BOg7WMUiahT7TH53wU2GHJ3Lpj+9Xe6xObDktVs9ZT0pSyapkWyykrAQr6+83zwxIXZ/n4RQHpaJbYNSoUYSFhaX/1KghU6qFyA6duh0d8wquaaYXT0V1Ahod8wLasadwghN5IjE2CW+F3LSlpd5WIdBxH5Gzr2sFVmxeh1Ms5GtyU758eUzT5MSJExm2nzhxgsqVs+5iq1y5ssf25/+bnXOOGDGC6Ojo9J/Dhw9n2S6/Hdh2mPmTF/Pr50s4tjcnWbgQhUPHf4PnjwuFTig5gxZLo+oNqmB5WVW8TEQwwWFBHtuIvKWdp9LWhsrBorZolK101jrK1+TGz8+PVq1asWTJkvRtlmWxZMkS2rfPelpz+/btM7QHWLRoUXr72rVrU7ly5QxtYmJiWLNmjdtz+vv7ExoamuGnIJ0+dpZnurzC/U2H8f4Dk3jv/oncWf8xXr5+NDFnS2dWLYqZ1L/wXOXUCcmeF4cVRVv3u67OVIT0YoZp0PfB7jJrs6DphNwdH3hj3sRRzOT7Y6lhw4YxefJkpk6dyo4dO3jooYeIj4/n7rvvBmDIkCGMGDEivf0TTzzBggULePfdd9m5cyevvvoq69ev59FHHwVcg22ffPJJ3nzzTX755Re2bNnCkCFDqFq1Kv3798/vl5Nt8dHxDOs8ki1/7sy0b+2vf/Nst9dJSU4thMiEyA73X3rZayOKqrKVI3jwvbuAzLW2DNOgRqNqDHy2XyFEVsqZFYHsVg92/f2pMsNRZg4HIhdz+b621MCBAzl16hQjR44kMjKSFi1asGDBgvQBwYcOHcowyr9Dhw589913vPTSS7zwwgvUr1+f2bNnp9e4AXj22WeJj49n6NChREVF0alTJxYsWOBTjZuC9uvnvxO5/2SWK4JbTou9mw6w/PvVdLu9cyFEJ4SP/K6ExBm4770xwb9TQUYk8kH/R3tRrmpZvn3je/ZuPghAQLA/Pe/pwp2vDSQ4LLiQIyx9lApEBw7w/v7DD0hM+7UeKuRhVGCfggmyCMr3OjdFUUHWubm/2TAObDvsdpyeYSiaX9OEdxaNzNc4hMgNnfov+sx1ZP3cXwEGqvyvKFutgg1M5JvTR8+QlJBChepl8Q/0L+xwSjVtnUWfuRmcx8iY4BiARoW9CwFdwXkUCACzGkqVzJ7UIlHnRkDUyRiPExAsS3PuRFSBxSNETih7fVTYWFwfGRePuTABAxX+viQ2JUz5auWoXr+KJDZFgDLKosp9D0EDyfCIyt4SFfEFKrAvSgWibPVQtuolNrHJjnx/LFXaVaxZnujTMWg3sxAM06ByrczLUAhR1KjA68DeBJ0wDVJWAQr8OqKCbkXZahZ2eEKUaMooiwp9FV3meXCeBCMEZZQt7LCKLElu8lmf+7vx/gOT3O63nBa97utagBEJkXPKVhsV+kJhhyFEqaVUAMjNhFfyWCqfdbujMw1b18MwM/9RK0NxRY8WtO0ji9EJIYQQeUWSm3zmF+DHO4tH0uOuq7HZzYu22+n/aC9emzVc6kYIIYQQeUhmSxVgQb+YM7H8u3EfyjBo2LouwaFS6VMUDJ26HR3/VdpYGQ1+7VDBd6DszQo7NCGE8Jmv39+S3BRwtWIhCppO+BEd8wKujtrz00hNwEKFjkQF3eZqp52Q/Ac6xVVpWPm1Av+uKCVD84QQRYOv39/yqSVECaYde9ISm6wWvAQd8zrYm4MKRJ+7H5xHOP+xoBOmgFEJIj5F2S/1fi2tIeUvdOL34DgIRgQq8HoI6IlSfnn90oQQPtDOM5D4I9qxC1QAyr8b+HdGqZI9HEKSGyFKMNdilhf32PyXgY77wrV2lHUubZvjwm7rNPrsECj/K8os7/462omOfhaS5uDqFXK6zp2yHOI/hbJfybRVIfKB1qmQNMdVosFxEIww101F4K2Qshwd/SIXim8q182HrQFEfI4yKxVm6PlKHkvJYylRglmn+4DjX8+NVCjoWNxXmzRQIY+iQh51ewodNwEd95Gbc5hgb41R7isfoxZCnKe1htT16KQFoONRZi0IHIAyK2I59sPZe8A6+p+jDFAhoGPcnNUEW11UuV9QqnjNK5LHUkIIfFrMUifisYw2FjpxNhjlQMeDWSetWzvt8ZVOQcdP8XAOJ6T+hU7djbI3yFb0QpRm2opBn3sYUtdyvjK4RkPcB+iAvmk9pVm97ywPiQ2AExy7IWUF+F+VD5EXPkluhCjJ/DqBYy8eF9xTAaC9rEzvPISOeZX0R1xGeQh7B+XfCRy7QEd7CcSAlJUgyY0QPtNRT0DqhrTf/vMeTvoll2e3oZMWo0poclO8+qOEENmigm7F1XvjrgdHg60ZGdeLcueiQcnWGfS5oeiUTaCzWkwzK+4SLCHEf+nU7a4bgnx732jQyfl07sInyY0QJZiy1USFf4grecliwcuwcaiQoWT/A1QDlmucja0eGRbzy5IF9pbZvIYQpZdOWoRvNx05ZaHsDfPx/IVLkhshSjgVcC2q/EIIvgdsjcF2KQTdgSr/GyqwL/i1g8BB51tn48xW2p1lKgTdjPuPE9M1O8Muy4wI4TOdRPbej9llg8AB+Xj+wiVjboQoBZStOqrMcCgzPPM+pSD0NbBfio7/HJyHsnFmDVYMKuRpdOpWSP0b1wfy+UGOhqveTfjHrusIIXyi7A3RF5dlyNFJgkEnkHHQsQloVNgYlBGRu/MXYdJzI4RAKYUKGowqvwhCR2XjSDsY5VBGEKrs16jQ1109Q6oMmNVRIY+gys1B2WrnW+xClEgBPV3vo5z23hjVodwvrpsao0raRgV+V6LKfuPqtS3BpOdGCJFOqbQiXxhcKPzljgkB16GM4LRj/SBoECpokJfjhBDeKBUA4e+6poJnqjBuuBIfHU2W79XAgajQF13nsN0HQfcCSYANpewF9AoKlyQ3Qoh0WqdA6kYfWiowwlFlnsj3mIQoibTzODp+KiTOdtWkMauiggZD4GCU4VpUWflfDeWmo+MmQvLvgAUqHIIGQ9B9qNSVrnOkbgFlB/9rUMF3o+xNM1zL9Ug4sIBfYeGSCsVSoViIdFonoU/4sFK4URlV9jvAiU6YDo7tQAAqoEtab46seC+EOzp1N/rsra6imBl6ZBTYGqLKfosyymQ8Rqe4Cm6qMsWuqnBe8vX7u/T+CQkhMlEqAMx6eHvOr0IehOTf0ae7Q8IUSFkNKcvQMS+jT3dHO/YWSLxCFDdaa3TUY1kkNgAaHP+iY9/JdJxSfigjrFQnNtkhf0pCiAxU8F24X0pBgQpCq/Lo2DfJOBYg7RjrDPrsPa47TSFERqnrwLkf97WlnJA4C23FFmRUJY4kN0KIjAJvgoAb0n65+CPCBOyo8AmQ+A3uC4w5wToOSQvzNUwhiqXUf/D+1ZsCjj0FEU2JJcmNECIDpQxU2GhU+Edgv8I1K8Mo75oJVX4O+LWBlDV4rmpsopNXFFTIQhQjdjwvVJumlMxqyi8yW0oIkYlSCgJ6ogJ6Ztrnetzk7cNZQ24LkAlRjGmdCklz0QkzwHnYVcwy8Aa0vTVe3z9GWbCV3KURCoIkN0KIbFHKD23WA+de3H9I60zTUYUoLbRORJ+9H1LXkl6HxjrlGihsVHEteZKyFre1pMz6yNdz7shjKSFEtqngIXgcdIwfBN7gZr8QJZuOfQ9S16f9dnECo8E6AVZcWvVhN1LXQMI3F47SGp28Cuvck1in+2GdvQud8CNaJ+VL/CWBpIZCiOwLvMU17iZpHhkrpJqAQoV/iDLCCi8+IQqJtuIhYQbuK3w7wbHV+3niJ0LQra7/H/0cJP2C6/3lBBQ6ZRXEfwplv0KZlfIo+pJDem6EENnmGnT8LipsLNibAP6uO9GAfqhyP7mK+QlRGjl24lrqwBMf1ouyTrnOFT85LbGBTGUXnIfQUY/nLM4STnpuhBA5opQBgf1Qgf0KOxQhihBfFrrUae08DyzWVjzEf+mhhRNS/0anbpExbv8hPTdCCCFEXrFfCirYh4beZhza0vKfc17aGZC8yrfYShFJboQQQog8olQgBN2G+x4cE2ytwayO+69gEwL6oFSIL1fE/fie0kuSGyGEECIPqZDHwf+atN/OV/JOS3bMS1ARH6LCPwDlT+ZK3waY1VGhI8BWx4deICfYL8+r0EsMSW6EEG5pKwadsh6dsknWihLCR0r5ocL/hwr/BPw7g1kX7FegQt9ClZ+FMsuj7M0g+LEsjrbA3hpUqGsh26Bb8djDY9ZzVQ0XGciAYiFEJtqKdRUcS5wFpCU1KgyC74LgB1HK3bpSHs6ZuhMdPxVS/gDtAL/LUUF3oPw75mnsQhQFShkQ0BUV0BVIq+yd/CckzkWbldFWMsSNJcuxN0k/opU/KuwVVMjj6NQtkPIXGcsuGGCEoyLGuyqKiwyU1tqHRS5KlpiYGMLCwoiOjiY0NLSwwxGiSNE6EX1mMDh2keX6UQH9UGHvZOsDVSfOQ0c/jatr/vw502p2BD+CUeaJ3AcuRBGlE2agY8eBjr5o6/maNe4YqArLUGZl11IOiXPQidPAcRCMMFRgfwgchDLL5W/wRYyv39/ScyOEyChhJjh24HY2R9LPEDQQ/K7w6XTaeQwdPZzMgx7TPtjjJ6D9Lkf5X5nTiIUosnTCDHTMy1ns8ZTYAGhIWgjBQ1DKDkEDUEED8iPEEknG3AghMtAJ07y0MNEJP2Q8Rmu0zvrDWnus1pp2vvip2YpRiOJA62R07NgcHm2Cjs3TeEoT6bkRQmTkPIbnGhxOcB4CQDv2oeM+g6S5QBLaqIQKuhWChqCMtFkeKevxnNw4IXWDz+HplM3oxO/BeRBUOCqwL/h3cd3dClGUJK8AHZPDgx1g1szTcEoTSW6EEBkZYWB5Kh9vgFHWNYPq7J24Bhyn9dpYJ9BxH0LSfCj7HcooAz4NPvbeiay1hY55HRK/48J4BQOd/BvYLoOyX6CMCB+uJUTB0M4TuTjaRPtf61O9Y5FZvj2WOnv2LLfddhuhoaGEh4dz7733EhcX57H9Y489RsOGDQkMDKRmzZo8/vjjREdHZ2inlMr0M3369Px6GUKUPoE34PmjwYKAPuiox4BkMo8dsMCxBx33nutXs5aXC5rg58N4m4QpaYkNF10zrUfIsR19ui9W1DPo+K/RlvvPGiEKgk78BeLezcUZnKjUTXkVTqmTb8nNbbfdxrZt21i0aBFz585l+fLlDB061G37Y8eOcezYMcaNG8fWrVuZMmUKCxYs4N57783U9ssvv+T48ePpP/3798+vlyFEqaOC7gAjnMzFxXBtszUFZQPrBB5XPk74FutkL0j80csVnajguz220NqBjv/MUwvXQoNJc9Gxb6JPdUIn/+nlukLkD504Fx39DOjcJNkmOnFWnsVU2uTLVPAdO3bQuHFj1q1bxxVXuGZULFiwgN69e3PkyBGqVq3q03m+//57br/9duLj47HZXE/QlFLMmjUrVwmNTAUXwjPt2O/qmXHsxnUPpF0//le7VgJP+Bod9z/AkfuL+V2NUfZTz/Gk7kCfyc4CnQqwo8r/jLLVzVV4QmSH1g70qc5gnc79yfw6YJSdkvvzlCC+fn/nS8/N6tWrCQ8PT09sALp164ZhGKxZs8bn85wP/nxic94jjzxC+fLladOmDV988QXe8rPk5GRiYmIy/Agh3FO22qhyc1Blp6PKPI8q8xKq/G8YEZ+ijDDAjzxbz8Z52JdG2TypBpzo+K9zEJAQuZCy1sfExtvXrwlm5byIqFTKlwHFkZGRVKxYMeOFbDbKli1LZGSkT+c4ffo0b7zxRqZHWa+//jpdunQhKCiIhQsX8vDDDxMXF8fjjz/u9lyjRo3itddey/4LEaIUU0qB3+Wun//yvwrixuXRlZK9NzFrAwGAp4HO/+WEpAUQ9mrOwhIlntYaUtehE+e5CuyZNVCBN6Fsl2Ru69iPTvwJnCfALI8KuB5lb5S5XdpMQq/8u0HyYjw92lWBUtcmp7KV3Dz//POMGTPGY5sdO3bkKiBwdTv16dOHxo0b8+qrr2bY9/LLF4ohtWzZkvj4eMaOHesxuRkxYgTDhg3LcP4aNWrkOk4hSitlb4j2uxJSVpH9XpWLmWBr5v16RjA66GZI+Jbs9RjJelgia9qKQ0c9AimrcY0v04BCx09CBz+CCnkcpZRrll7sKEiYysXj0HT8Z+iA/qiwtzKWIUjd6VsAgTdC6ta0sWv/fQ8p8O/qWmNK5Ei2kpunn36au+66y2ObOnXqULlyZU6ePJlhu8Ph4OzZs1Su7LmbLTY2lp49e1KmTBlmzZqF3e65dkXbtm154403SE5Oxt/fP8s2/v7+bvcJIXJGhb+PPnc/pP6Na4xLTobvOVHBt/l2vZBh6JTN4NiStsXb9QywZb6zFgJARz8PKeeHSfwnuYif4HokFDQQ4ielJTZZtEv6GW2Eo0JfuLDNsdeHqxso/6ug3HR09IuQsuKifX4QOBAV+pysGZUL2UpuKlSoQIUKFby2a9++PVFRUWzYsIFWrVoB8Pvvv2NZFm3btnV7XExMDD169MDf359ffvmFgIAAr9fatGkTERERkrwIUcCUEQplp0HKKnTcREhdm42j0xYADH4YzDpoxyEwK7pWQXZ7vWAo9w0kzEAnfJc2VsfTgGYLFXR7NmISpYV27IfkhZ7bxH2CDrgO4id7agUJ36JDHkYZ4WnbUn2IwM+1sKZZGVX2c9e/f8c2wA5+rdPGtYncyJcxN5deeik9e/bk/vvvZ+LEiaSmpvLoo48yaNCg9JlSR48epWvXrnz11Ve0adOGmJgYunfvTkJCAt98802Ggb8VKlTANE3mzJnDiRMnaNeuHQEBASxatIi3336bZ555Jj9ehhDCC6UM8O8Efm3Qp3qAFYlvj6kCIKAbpKxGx/8vbVsgOmiA63GAm2J8SgVA8J2o4DuBtEcDse+QcbXktF6kgP4Q0DM3L0+UVMlLyfhvJgvWMUia48N07lTXat+BfV2/2ltA6mbcvw+MTOPYlK0m2KQacV7KtwrF3377LY8++ihdu3bFMAxuvPFGPvroo/T9qamp7Nq1i4SEBAA2btyYPpOqXr16Gc61f/9+atWqhd1uZ8KECTz11FNoralXrx7vvfce999/f369DCGED5Tyg7JfuioWW8e58MVx/r//fWyVCEm/pG2/aFvCdFd9mnIzfao2rILvc/X8xH8GqetdG826ruQn8GZX8iXEf+lk8KX2rxWfjfO5qKDB6IQvPZ0UFTTEt/OKHMuXOjdFndS5EeL/7d15mFTVmfjx77m1dvVKdwMNkSCgwURRVAIDzigJKCpxxBgXJIoGRY0aiYaIWSRo1Lj8RqNxRn8x0WjcE5WJOxGXqIgGYVBERgiKW0Okl+q9q+q+88etru6ia7mNXb0U7+d5+rHr1rnnnrpdVr2c5T25IdIKrU8hrStBWp08OfYOejYJ2AMFJ2A8o5D4nAjjn+IEK57KDNduB+yMQ1tKAUjrC0jduVlK+aH8YaiZk7U+U/EnjK9zYryzE/gVOMF9Rw9OPNAPzccU/0Tn0+wmt9/fGtxocKNUTkj0A+Tzo75ADV17eyzAhxlyGyZw+BdvnMp7Et0Krc8g0ojx7A3BYxObuYrEkH9+I0Pg7YHgCVhl12DXnBmfeJxqmMkD3n0xFcu7BSvS/nek6ffOkBUx8E3EFJ4BgaM0sPkCNLjJQIMbpXJP2l5Bar/XizV2ZB1+GuPVVA4qNZE2pP4nznwZPDjvmyiYAkzJLzEFxznl2tfGN36NkBy4WOAZjal4CGOVIdGPkJqTwa7rXo4ADPk9VuDQPnltqp8zFCulFL2+4iOedbhFN8pV6Un9T6H1yfijGIkVddKC1P8IaXsZAOM/GFP5KAT/HYinHDFDoPBcTMUjidVPxjsKU/EYhE4FCuL1Gpwenxao/S523SVI9OO+eHnKJe250Z4bpXJCxEY+nwmxXv7Q94zFGvpM79ap8oKroVDvgViVf0o+T2ychI+BjENGduMfoPGajrO6POMBU+LMvdFexZzSnhulVL8yxsIU5SJNwxfbrFMkgrQ+ix1ehh3+hbODs2gm47zQ+ixZV0FF1yOx5G2AjLEwJpg2sBG7FrtmATReTWIT2SQxkDDSkDmDv+o7OVsKrpRSpuBYkGak4WqQJpw5EDbgBc+XIPYBSfMiEs+n61D2gG9Smueyk8j7SO3Z8eXqzsefcD80DIUhd2B8B+x23ar/iTTgJlO2RLdiXG5KKdKO1JwF0WzbKsSg7a+IXYOxyl3VrXJHgxulVE6Z0HegYDa0roDYJ2BVQPAoZ7JmZBPS+gxIE8Y7DvGMhtpMOUDcb9ewK7HrkNrTwa6PH+nSA2TvdCaXVj6N8QxLeb4aBIzLeV6R9RCY6q5s6wqIvuuyATZEPwK/Bjf9TYMbpVTOGVMABf/e/bhvPMY3vvMxIMU/jmcd9tC5OsX53RRfhvFN2L1GtPwJ7FpS/8veBmlCmh/AFF+8e/Wr/udzs2rJJCXdy0ZalpM1m3FXVrHrulXu6JwbpdSAYgrPxgy5BwKHgyl0fgJHYIbcgylcsNv1SsvTZB6ysKH1qd2uX/U/4xtD9szD4mx34JbU4C6wMeDZFzxj3NetckZ7bpRSA44J/Asm8C+JxyJRaHseu+4SsBvBOxYTOhnjHeu+UnGRSl+ad6O1aqAw1hAkcDS0PUvagMQUQnCW+0o9oyCygex7pgmm+GJN0DdAaHCjlBrQJPZPpPYsZyuHjqGq9r8hzb+HokUQOgMQMEWZv1i8+0HsQ9J/SXnAOz7Nc2qwMMWLkfbXQcJ0T7onUHwZND+EHf0ArEJM8OiMQ52m4CQkkTcnHS+m5CpM8Itk5Fa9SfPcaJ4bpQYsEUFqTnL3L2fP3pjCs6DglJQbZkrbamdCcQam7A5M8Bs9ah+xj4AIeEY5G4iqfifRj5GGG5J7cLz7O3NyWh6gc2WekxgS/+GYspsxVlH3ukSQuh9CW6phTQPWSKh4BCvDvmeq92ieG6XU4BdZ46xsyTokAMQ+RMJLkfrLSflvNv9kKEgV3MR7e4LfhsB0V80SEaT5YeTzmfGfY5AdU7EbbkCkxVUdeyKxa5HoVsRuyN01Iu8gTf/lpBnwHgChM6HiMUzRD6DlHpztFgQnwIm/r9pfQeovSVmfMQZTdiMUfh9M1+DHDwUnYyr/ooHNAKTDUkqpAUvaXiJ51VTG0s5/Wh+D4EwIHpn8dPR/Ifp+99OsSkzRBVBwquv5EtJ4IzT9lqTJq9IATb9D2tdA+R8wJuCqrj2BRN5GGn4N7X/D+Tt5kODRmKJFGO/o3rtO42+QxltIes9E34aWxxGrgvSrnmxoexGJvIfx7dftWWO8mOKLkaJzIbIRiIJ3P4yujBqwtOdGKTVwSYTsq1925UGa79ulmveczQ8jb3Yvbv8TrCEph7JSN+m9eGAD3YcpbIisheaHetjm/CXtbyI7T4X2V+i8XzFnx+6d30aiW3rnOq3PxQObeP2dzzjzb2JbyLzqyUDbiozXMCbo7Enl/7oGNgOcBjdKqQHLyRjc0+0WYhBJziYrDdeAtJOuB0jql7regkFaHsHpGchQpvl+V3XlK4lVYzf8GvvzU5zsvkToHljEnOzV4WW9c82mO0n/leZmKbcgkQ+7H41td3qeYp98keapPqbDUkqpgSt4FISHgNTjOokagAkmfpXYJ9D+eubyUgdtL7hbIhz9B5mHyQRi29y0Mi9J60qk7iKce5TtbxaD9teR6Lae5Z7Z9ZrSDpF1u31+Z3M6/24Sec9JJtn+Kh09TuI7GFP8I4z/61/8WiqntOdGKTVgGePHDLkV8JGtt6STBwqO7Xzo6l/clvvdy60Ssn50mkJ3deUZiW5D6i7E6W3rQTAa695j0jM9uFbGapz3ikTeRXaeAu2rSBp6jPwPUnMG0vZq71xP5YwGN0qpAc34J2MqH4OCb4MJ4czBsUg/F8eLCXXZf8qUubiKDWaIu/YEjyXzl6kHCo5zVVe+ceY6pdo1OwuTZgm2uBuSNCYI3q/Q8/lZu3K+EiX8C6CN7j10NmAj9T9BpJcCKpUTGtwopQY8490Hq/RqrOHrMMPfw1T8JcMmiW3Q9rfOh959wTOOzF98fgjOSDoi0oa0r0Pa1yB2Y+cTgW/Gk/2l6kmywPgxoTNdva680/YS7la2dWENA9+BiYcS/Qg7/Atkx8HI9q9h75iK3fBrJLHhaWom9D3SB1UWTu9fpq88D/gPcyY4R9aRPoAVZ1f5bEOdql9pcKOUGlSMMWACztLrNCS8FIm8kyhvin+UudLChRjLCZZEos6X6Y6pSM3JSM1cJ4dN+ErEbsYYH2bIXV2+kD0kpi9aFc4eWF9g/sjg1sPABjBFF2GMEyhKZCOyc46z2qxjKwx7JzT9F7LzJMSuSV9RwQlQcEr8QdfA0wP4oORXZO7xszGFp0PU5XypPXhe1WCgwY1SatDJvhrJQpruTTwywRmY0v8HpiOjaceXnw8KL8QUXeTUK4LUXQJN/wnSpbeGNmi+H6k9C5F2jKcSU/4gpvwRKDwfChdgyn6DGfoSxn9Qb73Mwcc3iexzozqGFb2YosWYkBOQOPd+UTyoSTEcFPsICV+dvlZjMCVXQunN4JsIlDnZg0NnYCqfxAodhym7Kd6+XYMf42yf4NsfrHQ9grteULPbD2S6WkopNfi0ryJzL0EM2l9LOmIKvoX4DoTme8D+3EnCFjoVY5V1qfdVaHsmTZ3xHDYtj0HoFKcHyX/Qnh3M7MIUfhdp/XPmQsHjML4DoeBbGKu883jk7xDbmuHEGLQ+hdg/TT4vTmLbkYaboPUvOEvPAc8+mMA3Ez1pJjgLKp925ga1vQLY4J+CCZ2G8cX3FfMdBNZwsLdnehEQOCLz61T9SoMbpVSe6hx+ELsRCV8BrU/ROZfiKaT9TSj9FcYz1CnX/DCZMyIbpPnBRG+DSmZ8+0Pxz5CGX5J8H53fTfHPMIVnpD458q6LK8QgutnZSqMLiVUjO7/jDGF1/dtF3kJq50PZrZh4xmrjHY0p+Un612A8UPxDpH5J+jJF52OsPXNF3GChw1JKqcHHP42swx9WRWLFjdSevUtgE9f+mjOnpmOvo4y7hoOTw0aTuWViCs/AlD8AgaOclWqmDAJHYsofSB/YABL7zOUVum9OKg03dA9sAOfvLUj9EkTaXNYPpuDbmJKlQEe+JC9OsOyFwguh8DzXdan+oT03SqlBx4TmIs1/yFwougGa7wXPcIi8laZQzNnVu+VhKFwAVjnp9x+K6zqMpVIy/kMx/kN7dpKr3DFl4Ns/6YjY9dD6NOmDUnEmn7c+16Ml+iY0D4LHQ+uzzuooqwKCs1IOiamBR3tulFKDjvF+GUqvz1pOGm9Bmh8h80edxMuAKZhD5hw2FqbghB60VLkh0S0Q25S9YPBwjPElH4t9QvYtOrxINNN8ntSMVYQJnYgpuhATmquBzSCiwY1SalAyloukexKG6AdkzWBrf+78N3hMPBlcqiEvD1iVEDq1Zw1V2cV2uChkwLt/isPdEwB2Z2MsN+VUvtDgRik1OEnYXTmrmMzzcwx4qpzfjB9Tfg/4/6XzuY6PSe9+mPL73QVVeyCJfY403or9z1lO4r2d30VankTERe4bq9LNFRITv5N4RrnITizOHCC1x9A5N0qpwckzyl254DHQuCFjEVNwcufvVjmm/C4k8n7nknPfIeA70Fn+rbqRyPtIzXeTNzi1a5H6N6D1CSi7pftwUtfzPWPBGgX2R+kvYkJOduhdDxsDRYuQuu+nOdGC4ByMdy/3L0gNetpzo5QanLz7x//Fnu5jzALP3hD6Xnx1VapyHvDuAwUnAk52YhEnhb/x7eus/Ck8C+M/aEAGNiIRZ0fsnF+nDbv5T9h1l2DXLkaaH0TspvhzNlJ3XrwnrevwX/z3tpXQdGf6utv/B3YemTmwAUzRpRgrlPq54ExMyTVAgMSqpo6/d/A4TOmVbl6myiNGOv5P3oOEw2FKS0upr6+npESzTCo1WEn7WqTmdLrvQm0BFqb8box/MiKtSPhX0PIIiQRvWBCYBSU/xbQ+4SR2i30E+JxVMYVnY3xf6+NX5I60voA03QmRN50D3q9gQmdBwbd7PQizmx+G8DI671uHIkz5HSDNSO05mSuxKjBDX07qvRE7jLStgvofxetOMy/KlGKKL8GE5mZtq9gN0PoEEt2GsUogeDTGOybreWrwcPv9rcGNBjdKDWoSeRsJXw+R1Z0HfZMwxT/G+Ccml7XroH0tzlDTBLDKkJrvOdlxnRLx/8ZT8g+5HRM4POevoSek6XdIw3UkL1k3gEDwO5jSq3stwLGbH4TwFRlKBJ3d2lseJtuKJVP5HMa7NxLbgTTc6AxXZV3lBJQ/gqVZoFWc2+9vnXOjlBrUjG8CpuJeJwlcbAd4hmI8I1OXtcog+I3EY2m8FSJr6L6bdAwwSN0ipGQpRN8H/JjgdGfrgB4QuwZpuscJAOydYMohdCImdCbG42YibZe6Iu/HAxtI7umIt7/1TxA8AoKzelRv6nbXxntsMmmD6Lt0v3+pWM6k450nx7c2cLPJpgdanwENblQPaXCjlMoLxjMCPCO6HRe7GaTWGd7oshxYJIo03Uf6ZeLibJ5Zvxjno1KQpt8gvkmYIb9xlfPE2RbgVLCrO68jO6Hpd0jLY1D+UI8mukrLA2TeHsJCmv7o7KGUsV07wK4Hz3Bn+CaVlsczXCdRE0Q/yl7OqgLPl5DwVT0IbDouUeu+rFJxGtwopfKSRLc5PTOtT+IMf1hIYCam6CJnk0R7J0iNy9q6DJ9E1jpDWRV/dvYhytSG+p/Fv8x3DaBiYNcg9ZdhKu5z/6IiG8gcGNjxnpQ07Wl/E2m4uXOuDhbiOwS8Y8Eqxfgng//fMMZCov/rslG2M7E7uiVt20zh94AotDyapf3dWozxfMl9aRGnJy7yHhg/BA7HxJf5qz2LBjdKqbwj0S3IzlNAmuj8MrWh7Xmk7WUovxfiO0X3XMwJINpeThri6t6Gj6D9b6QfsolB5E0kuhnj3afzPLsBIuudB779k3ctN0ES82vSMd33XoL4JOS683c5ajvzjSJ/BzxI02/BMxqG/H931wLwfhVTepUzsdvu2B9KSPQwBb8DoTPiz7Vmrqt7qyHoLiO0RDYidZdAbEuXdltI8HhM6TKMCWapQeUTXQqulMo7Ur90l8CmQwxoR+ovczZ09E5g9z4GPUjrU5mLuJ2LEnFy8Ii0YYevQnZMQ2rPcn52HIZd/1PEbgTABGZkqcyA7xB2XSciEkHCl8fbk24YLn6vYh87OWt801y13xTOx3hHYSqfcDab9B0C3n2dpHml/wmhUyC6GSH1Mu6MCr/vathOotuQmnkQ+6DjSPy/NrQuR+ou7nZPVH7T4EYplVck+gFE3iD98IcNsX9A5C1M0Xlk3ZohpZgzHyej1D0o3fkQiSG150HzfUDX3asj0PJnZOeJ2M3LEe9XwRpC+ozLAm0rkM+PRNpe6jzc9iLYNbib+BtztqOIbYtvd5Bh5ZVvOgSmAx37MJ2GVfEApvxeMAVQfzHUnITs/BbUfBs84zK0vQtrKKb4CkzRD1y0F6fHSVpI/Te3oe0FiKxzVZfKDxrcKKXyS/QfLsttwQSPxBT/hM5tFgyuvnwBPFl6FPyTcJLKZSa+A6HteWh/ldSBlg2xrRBeDLXznB4nUxx/Lk3gEfsIqT0Xafub8zj6Aa5fl9MqaH0CM+S34M2Q68fe5gQOXc+0651J1K3LScqNE/s0PmRkp2m3Ad9kTMWfMENfwhR+19WSdhGBluVknsvjQVqXZ61L5Y+cBTc1NTXMmzePkpISysrKWLBgAY2Nmf+lM336dIwxST/nnXdeUplt27Yxe/ZsQqEQw4YNY/HixUSjLnIlKKX2DKbQZblQvPiZmMrnofA8CBwJbvPaWN1XZiVVbxVD4N+yN6PteRc7l3cR+wCkFQp/AKTb0kAAQcK/dL78rWJ63EMVfQ+kEVPxZyheRkfun+S2bEXqzkdanui8ctNv48kQdw02uvYadaxa68gkbJxMwuW/w/gOxJieTAdtJ/tcHgFbV13tSXI2oXjevHl89tlnrFixgkgkwllnncXChQu5//77M553zjnncOWVnamyQ6HOcdpYLMbs2bOpqqritdde47PPPuOMM87A5/NxzTXX5OqlKKUGE//BTu+G1GUqBIEjEo+Mdy9M8SIApO1VZJfeiFRcfQFL9oBCWh4GyTQXZlc2EHGGmsi09YI4PT7RdyAwA1hGz1YqxZCa+Zihz0Lb05117noNDBL+BQSPArzQ/FCW61hQOA/jHY9Et2CsQggcidntCd5+MKXxfa3SMWClzn2k8lNOem42btzIM888w5133smUKVP413/9V2699VYefPBBPv3004znhkIhqqqqEj9dMxA+99xzvPvuu/zxj39k4sSJHHPMMVx11VXcdttttLfnfn8VpdTAZ4wfU3RB5kKFZzk9K6mkSQC4W+Xs7dnLxLaDp5KefRzHILreZdEdzm7aoXlk3jk7BfszZ1uK9tdJH7CIs69U2/MgzVmCjI42fYopmI1V/ANM4YIvENjEN84MnUzmYbcYJnTibl9DDT45CW5WrVpFWVkZkyZNShybOXMmlmWxevXqDGfCfffdR2VlJQcccACXX345zc3NSfVOmDCB4cOHJ47NmjWLcDjMhg3pd/1ta2sjHA4n/Sil8ljoDCi8gI49ppxO6viwSsF3MUWL0p5qvGPAN5GMH4+mzN3wlWc4Wee6WJWYgjns3sRmFzxDATDFS6BgLk6A0/GTjQWtK91cBGKfxJePu+jRskpd1OmeKVwAnirS3uvQ/KTl9ir/5WRYqrq6mmHDhiVfyOulvLyc6urqtOeddtppjB49mpEjR7J+/Xouu+wyNm3axKOPPpqot2tgAyQeZ6r32muvZdmybGnElVL5whiDKb4YCZ0KLcsRuxpjVTrzOryjsp9f8jNk52l035DTxJ//BSZNPpmkegpOQNoyBQcGEzoJgrOh6W6IbsLdiqaO07MPx0jzE1A8CmMNwZT+AilaCK1PI7Gd0Py7LNezXbYnBqYMY7xI8GhofZr0PT0xTPBbLup0z1jlUP4QEv4ltD1H4m9myjBFCyG0oFevpwa+HgU3S5Ys4brrrstYZuPGjbvdmIULFyZ+nzBhAiNGjGDGjBls2bKFcePG7Xa9l19+OZdccknicTgcZtSo7B9wSqnBzXiGQ9HCng7GOPtHVdzvfFl2XULs2RtTfBkm+E13FQVmgG8SRN6ie8+Mx1lxVXAKxgSQslvh8yNdttDjZBIuOMbJ2ZOWQMu9SPvzUPEIxip39t0qXIAB7PbXILqR9AGMB3yHOgFUdHOGcn4IOm03hechrc+ROqeOBf7D4j1jvct4hmGG3OJsLRHdDCYAvgmuglCVf3oU3Fx66aWceeaZGcuMHTuWqqoqduzYkXQ8Go1SU1NDVZX7VNhTpkwBYPPmzYwbN46qqireeOONpDLbtztj2pnqDQQCBALZl2QqpVQH4zsQU/EwEt3qLGO2hjjZeHuw47YxXhjyWyS8DFr/m6Qve/9hmNJrE/tdGanvQZ+NB1P8Q4zvq4CFNPzK2U4ipRjEPkXCN2DKrk1uX+j0eHK/dAQTOgUChyK156YtZYrOxcSHmozvK1B+N1K3COwdOENF8UAncCSm9Lpe27U8ZVs8w8AzLHtBldd6FNwMHTqUoUOHZi03depU6urqWLNmDYceeigAK1euxLbtRMDixrp16wAYMWJEot6rr76aHTt2JIa9VqxYQUlJCV/7WoZcDEoptZuMdwx4x+z++VYhpux6JPYjaH8DsME3McUk2gL3lRZdEg9swBQcj+CF+h9mOCEGrf+N2D9JnkhdMAfaXoK2Z+MHOsIrZ+sEU7LUyRDs3QvKbo5nfq7DmY9kA35M0blQeGHya/ZPgqEvOltURDc5c3EC38R4R7t/jUp9AUZylJP6mGOOYfv27dx+++2JpeCTJk1KLAX/5JNPmDFjBvfccw+TJ09my5Yt3H///Rx77LFUVFSwfv16fvjDH7LXXnvx0ktOps1YLMbEiRMZOXIk119/PdXV1Zx++umcffbZPVoKHg6HKS0tpb6+Pmk1llJK9RcRG/nnDLA/yVLSAwUnYJV2fuZJ461I43+RtMFnCqbicfCMgta/OD1SphACMzHRt5Gmu52l4xjwT8MUno0JHLZLG9udpH2xj8EMgeDM9LuKK5UDbr+/c5bn5r777uPCCy9kxowZWJbFiSeeyC233JJ4PhKJsGnTpsRqKL/fz1//+lduvvlmmpqaGDVqFCeeeCI/+9nPEud4PB6eeOIJzj//fKZOnUphYSHz589PyoujlFKDkTEWUnQehH+epaQ4S66TTi7AzWoraVsFjafg5MeJDxc1/ScSmI6peBSMs6osXQ4fY/wQnJX9xSjVz3LWczOQac+NUmqgsrdPcvLGpGVhii5KyuUj0a3I55mCDgPWsAx5dywIHIE15I7dabJSfcbt97fuLaWUUjkk0Y+ww9dg7/g37O2TsHfOQ1qfRtJkL3Y288wy4bYgOSGd8Y6BwNGk/0iXeO9OuuedzSUl8l7m6xIfPmtbjbQ8irS+4AxVKTXA5GxYSiml9nTS/iZSczbOMFA870tkDVL3JgS/BaU3YswuAUfodGh9HiJrSR5qcibxmpIrMJ7uq0NN2XVIXTu0raQzmV28Y77oImj8dZbWepDWZzG+/dK/nraXkfBSJ2Ff4sKlUHwpJnRqlvqV6jsa3CilVA6ItCC13wfaSA5S4r+3Pgm+Q6Dwu0nnGROA8rug6XdI8x87l3j7DnaWXAemp7yeMQWYIbcjkbeRlqdAwhjPl6HgBJBWJGtwY0Ca0r+etteQ2oV0y3Uj9Uj4CpAoZpfXolR/0eBGKaVyoeWprPssSfPdEJrXLe+LMUEousDZqdyuBRNIvxfWLoxvAsY3Ifk60oaz1Lwlw5kxjHds6naKIA3X0rHbeMoyjTdCwbcxVijl80r1JZ1zo5RSOSCRtWT+96NAbFvGAMgYD8ZT6TqwSV9PAEInkn6fKwMEnaGyVGJbsm8NIc3xITGl+p8GN0oplRNZNszscbkvxhRd5OS46XY952vAlF6dyJbcTeyfLq5ggf35F2miUr1GgxullMoBE5hG5qR6lrOdwxfslXHdHmsIpuIhCJ0WXzkV5zsEM+QuTEGGzSxdbWdgx3dBV6r/6ZwbpZTKhcA3wfoS2NWk3iHbxhSe06dNMtYQTMnPkeIfO70xVsjZUTvbed5xiPcAiL5L2mSBpggC3+jdBiu1m7TnRimlcsAYH6b8TrDKcea0dEwajg8LFV6Qubckp20LYLx7uQpsEueU/ATnKyP114YpvtyZCK3UAKA9N0oplSPGOw4qn4XW5UjrM2A3g++rmNBcjG///m5ejxj/JCj/g7N5Zmxz5xPWMEzxYkzB8f3XOKV2odsv6PYLSinlmohA9B2IfQrWEPAdijF9MylaqX7fOFMppVT+McaAb4Lzo9QApXNulFJKKZVXNLhRSimlVF7R4EYppZRSeUWDG6WUUkrlFQ1ulFJKKZVXNLhRSimlVF7R4EYppZRSeUWDG6WUUkrlFQ1ulFJKKZVX9sgMxR07ToTD4X5uiVJKKaXc6vjezrZz1B4Z3DQ0NAAwatSofm6JUkoppXqqoaGB0tLStM/vkRtn2rbNp59+SnFxsbNPygAQDocZNWoUH330kW7m2YXel9T0vqSm9yU1vS+p6X1JbSDfFxGhoaGBkSNHYlnpZ9bskT03lmWx11579XczUiopKRlwb6aBQO9LanpfUtP7kprel9T0vqQ2UO9Lph6bDjqhWCmllFJ5RYMbpZRSSuUVDW4GiEAgwNKlSwkEAv3dlAFF70tqel9S0/uSmt6X1PS+pJYP92WPnFCslFJKqfylPTdKKaWUyisa3CillFIqr2hwo5RSSqm8osGNUkoppfKKBjf95Oqrr2batGmEQiHKyspcnSMiXHHFFYwYMYKCggJmzpzJ+++/n9uG9rGamhrmzZtHSUkJZWVlLFiwgMbGxoznTJ8+HWNM0s95553XRy3Ondtuu429996bYDDIlClTeOONNzKWf+SRR9hvv/0IBoNMmDCBp556qo9a2rd6cl/uvvvubu+NYDDYh63NvZdffpnjjjuOkSNHYozh8ccfz3rOiy++yCGHHEIgEGCfffbh7rvvznk7+1pP78uLL77Y7b1ijKG6urpvGtxHrr32Wr7+9a9TXFzMsGHDmDNnDps2bcp63mD7fNHgpp+0t7dz0kkncf7557s+5/rrr+eWW27h9ttvZ/Xq1RQWFjJr1ixaW1tz2NK+NW/ePDZs2MCKFSt44oknePnll1m4cGHW88455xw+++yzxM/111/fB63NnYceeohLLrmEpUuX8tZbb3HQQQcxa9YsduzYkbL8a6+9xty5c1mwYAFr165lzpw5zJkzh3feeaePW55bPb0v4GRZ7fre+PDDD/uwxbnX1NTEQQcdxG233eaq/NatW5k9ezbf+MY3WLduHYsWLeLss8/m2WefzXFL+1ZP70uHTZs2Jb1fhg0blqMW9o+XXnqJCy64gNdff50VK1YQiUQ46qijaGpqSnvOoPx8EdWv7rrrLiktLc1azrZtqaqqkhtuuCFxrK6uTgKBgDzwwAM5bGHfeffddwWQN998M3Hs6aefFmOMfPLJJ2nPO+KII+Tiiy/ugxb2ncmTJ8sFF1yQeByLxWTkyJFy7bXXpix/8skny+zZs5OOTZkyRc4999yctrOv9fS+uP3/K18A8thjj2Us8+Mf/1j233//pGOnnHKKzJo1K4ct619u7ssLL7wggNTW1vZJmwaKHTt2CCAvvfRS2jKD8fNFe24Gia1bt1JdXc3MmTMTx0pLS5kyZQqrVq3qx5b1nlWrVlFWVsakSZMSx2bOnIllWaxevTrjuffddx+VlZUccMABXH755TQ3N+e6uTnT3t7OmjVrkv7WlmUxc+bMtH/rVatWJZUHmDVrVt68N2D37gtAY2Mjo0ePZtSoURx//PFs2LChL5o7YO0J75UvYuLEiYwYMYIjjzySV199tb+bk3P19fUAlJeXpy0zGN8ze+TGmYNRx7jv8OHDk44PHz48b8aEq6uru3UBe71eysvLM77G0047jdGjRzNy5EjWr1/PZZddxqZNm3j00Udz3eSc+Pzzz4nFYin/1u+9917Kc6qrq/P6vQG7d1/Gjx/P73//ew488EDq6+u58cYbmTZtGhs2bBiwm+fmWrr3SjgcpqWlhYKCgn5qWf8aMWIEt99+O5MmTaKtrY0777yT6dOns3r1ag455JD+bl5O2LbNokWLOOywwzjggAPSlhuMny8a3PSiJUuWcN1112Uss3HjRvbbb78+atHA4Pa+7K6uc3ImTJjAiBEjmDFjBlu2bGHcuHG7Xa8a/KZOncrUqVMTj6dNm8ZXv/pV7rjjDq666qp+bJkaaMaPH8/48eMTj6dNm8aWLVu46aabuPfee/uxZblzwQUX8M477/DKK6/0d1N6nQY3vejSSy/lzDPPzFhm7Nixu1V3VVUVANu3b2fEiBGJ49u3b2fixIm7VWdfcXtfqqqquk0MjUaj1NTUJF6/G1OmTAFg8+bNgzK4qaysxOPxsH379qTj27dvT3sfqqqqelR+MNqd+7Irn8/HwQcfzObNm3PRxEEh3XulpKRkj+21SWfy5Ml5+cUPcOGFFyYWbWTrxRyMny8656YXDR06lP322y/jj9/v3626x4wZQ1VVFc8//3ziWDgcZvXq1Un/Mh2I3N6XqVOnUldXx5o1axLnrly5Etu2EwGLG+vWrQNICgIHE7/fz6GHHpr0t7Ztm+effz7t33rq1KlJ5QFWrFgx4N8bPbE792VXsViMt99+e9C+N3rDnvBe6S3r1q3Lu/eKiHDhhRfy2GOPsXLlSsaMGZP1nEH5nunvGc17qg8//FDWrl0ry5Ytk6KiIlm7dq2sXbtWGhoaEmXGjx8vjz76aOLxr371KykrK5Ply5fL+vXr5fjjj5cxY8ZIS0tLf7yEnDj66KPl4IMPltWrV8srr7wi++67r8ydOzfx/Mcffyzjx4+X1atXi4jI5s2b5corr5S///3vsnXrVlm+fLmMHTtWDj/88P56Cb3iwQcflEAgIHfffbe8++67snDhQikrK5Pq6moRETn99NNlyZIlifKvvvqqeL1eufHGG2Xjxo2ydOlS8fl88vbbb/fXS8iJnt6XZcuWybPPPitbtmyRNWvWyKmnnirBYFA2bNjQXy+h1zU0NCQ+PwD5j//4D1m7dq18+OGHIiKyZMkSOf300xPl//GPf0goFJLFixfLxo0b5bbbbhOPxyPPPPNMf72EnOjpfbnpppvk8ccfl/fff1/efvttufjii8WyLPnrX//aXy8hJ84//3wpLS2VF198UT777LPET3Nzc6JMPny+aHDTT+bPny9At58XXnghUQaQu+66K/HYtm35+c9/LsOHD5dAICAzZsyQTZs29X3jc2jnzp0yd+5cKSoqkpKSEjnrrLOSAr6tW7cm3adt27bJ4YcfLuXl5RIIBGSfffaRxYsXS319fT+9gt5z6623ype//GXx+/0yefJkef311xPPHXHEETJ//vyk8g8//LB85StfEb/fL/vvv788+eSTfdzivtGT+7Jo0aJE2eHDh8uxxx4rb731Vj+0Onc6ljDv+tNxH+bPny9HHHFEt3MmTpwofr9fxo4dm/Q5ky96el+uu+46GTdunASDQSkvL5fp06fLypUr+6fxOZTqnuz6XZMPny9GRKTPuomUUkoppXJM59wopZRSKq9ocKOUUkqpvKLBjVJKKaXyigY3SimllMorGtwopZRSKq9ocKOUUkqpvKLBjVJKKaXyigY3SimllMorGtwopZRSKq9ocKOUUkqpvKLBjVJKKaXyigY3SimllMor/we4dZmPQJ4VmAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x18eb67e16a0>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 16,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.scatter(X[:,0],X[:,1],c=y)"
+    "plt.scatter(X[:,0],X[:,1],c=y)\n",
+    "plt.show();"
    ]
   },
   {
@@ -745,7 +1119,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -759,7 +1133,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/21-Anomaly Detection ML/DBSCAN Implementation (1).ipynb b/21-Anomaly Detection ML/DBSCAN Implementation (1).ipynb
index efae8726..8407fb38 100644
--- a/21-Anomaly Detection ML/DBSCAN Implementation (1).ipynb	
+++ b/21-Anomaly Detection ML/DBSCAN Implementation (1).ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,22 +39,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fae2ccc1ff0>"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -64,12 +54,13 @@
     }
    ],
    "source": [
-    "plt.scatter(X[:,0],X[:,1])"
+    "plt.scatter(X[:,0],X[:,1])\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,60 +78,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0,  0,  0,  1,  1,  1,  0,  2,  3,  1,  4,  3,  4,  0,  0,  0,  0,\n",
-       "       -1,  0,  0, -1,  3,  0,  0,  2,  4,  5,  0,  0, -1,  2,  0,  4,  3,\n",
-       "        2,  2,  0,  5,  0,  0,  2,  2,  0,  0,  4,  0,  0,  0,  0,  6,  0,\n",
-       "        0,  1,  2,  0,  2,  0,  7,  1,  1,  0,  0,  2,  2,  0,  0,  0,  0,\n",
-       "        2,  0,  6,  0,  0,  3,  0,  0,  0,  6,  0,  0,  0,  2,  0, -1,  2,\n",
-       "        4,  0,  5,  2,  0,  2,  2,  0,  3,  0,  2,  2,  0,  0,  0,  0,  1,\n",
-       "        2,  2,  1,  0,  0,  0, -1,  0,  0,  1,  7, -1,  0,  6,  5,  0,  0,\n",
-       "        0,  0,  0,  8,  0,  2,  0,  0,  0, -1,  0,  2,  0,  0,  0,  0, -1,\n",
-       "        0,  6,  2,  2,  2,  0,  0,  1,  0,  4,  2,  0,  2,  7,  7,  0,  4,\n",
-       "        0,  5,  0, -1,  2,  0, -1,  6,  1,  8,  0, -1,  0, -1, -1,  2,  2,\n",
-       "        0,  5,  0,  0,  4,  2,  0,  0,  4, -1,  0,  1,  6,  0,  0,  4,  0,\n",
-       "        2,  0,  1,  4,  0,  0,  0,  0,  0,  0,  7,  6,  4,  9,  2,  2,  2,\n",
-       "        0,  3,  0,  0,  7,  6, -1,  0,  0,  0,  2,  0,  4,  2,  7,  9,  2,\n",
-       "        0,  0,  1,  0,  0,  0,  0,  2,  2,  6, -1,  9,  1,  7, -1,  0,  0,\n",
-       "        0,  4,  0,  0,  0,  3,  0,  0,  0,  2,  3,  2,  4,  0,  2, -1,  0,\n",
-       "        2,  0,  0,  0,  0, -1,  0,  0,  0,  7,  7,  0,  7, -1,  0,  3,  0,\n",
-       "        2,  0,  7,  0,  2, -1,  0,  0,  2,  0,  6,  3, -1,  2,  2,  0,  2,\n",
-       "        2,  2,  5, -1, -1,  2,  0,  0,  7,  6,  0,  2,  6,  8, -1,  2,  0,\n",
-       "        3,  0,  0,  4,  2,  7,  0,  2,  0,  0,  2,  0,  0,  0,  0,  0,  0,\n",
-       "        4,  0,  2,  5,  2,  0,  0,  9, 10,  0,  0,  0,  0,  0,  0,  0,  4,\n",
-       "        0,  7,  0,  2,  5,  0,  0,  0,  5,  0, -1,  0,  3,  0,  0,  5,  0,\n",
-       "        0,  0,  0, -1,  0,  2,  2,  8,  4,  9,  0,  4,  0,  0, -1,  0,  0,\n",
-       "        0,  0,  0,  2,  0,  6,  0,  0,  4,  0,  0,  2,  5,  0,  7,  0,  1,\n",
-       "        0,  0,  3,  0,  0,  0, -1,  0,  5,  0, -1,  4,  3,  0,  0,  6,  0,\n",
-       "        0,  5,  0,  8,  6,  0,  2,  0,  7,  3,  0,  2,  0,  2,  2,  0,  0,\n",
-       "        2,  0, 10,  0,  0,  6,  2,  2,  0,  0,  0,  3,  7,  2,  0,  0,  6,\n",
-       "        0,  0,  5,  2,  0,  2,  0,  0,  0, -1,  2,  4, -1,  4,  2,  0,  0,\n",
-       "        0,  2,  0,  0,  8,  4,  6,  0,  0,  0, -1,  5,  8,  0,  0,  0,  0,\n",
-       "        0,  4,  4,  7,  0,  4,  0,  0,  0,  0,  0, -1,  0,  2,  0,  5, -1,\n",
-       "        4,  0,  0,  0,  0,  3,  0,  7,  2, -1,  0,  6,  4,  0,  0,  3,  5,\n",
-       "        3,  0,  7,  9,  0,  0,  1,  7,  0,  0,  2,  2,  0,  2,  0,  0,  0,\n",
-       "        1,  2,  0,  2,  3,  0,  4,  0,  0,  0,  2,  2,  0,  0, -1,  5,  0,\n",
-       "        0,  2,  2,  2,  2,  0,  0,  0,  0,  0,  7,  2,  8,  0,  0,  6,  7,\n",
-       "        4,  0,  1,  0,  0,  0,  7,  0,  4,  1,  0,  0,  5,  5,  0,  0,  2,\n",
-       "        0,  2,  0,  0,  2,  0,  0,  1,  0,  8,  0,  0,  0,  0,  0,  9,  0,\n",
-       "        2,  3,  2,  0,  4,  0,  0,  0,  0,  0,  1,  7,  3,  2,  4,  2,  0,\n",
-       "        1,  0,  0,  0,  0,  0,  0,  2,  2,  8,  0,  7,  3,  6,  5,  6,  0,\n",
-       "        0,  0,  0,  2,  0,  5,  0,  0,  0, -1,  0,  0, -1,  2, -1,  2,  2,\n",
-       "        0,  2,  2,  0, 10,  2,  2,  0,  0,  0,  2,  2,  7,  3,  7,  2,  6,\n",
-       "        2,  3,  2,  0,  7,  0,  0,  1,  1,  0,  7,  0,  0,  0, -1,  7,  6,\n",
-       "        2, -1,  0,  3,  3,  0,  3,  3,  0,  0,  0,  2,  0,  0,  0,  6,  0,\n",
-       "        2,  0,  0, -1,  5,  0,  3,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,\n",
-       "        0, 10, -1,  1,  0,  2,  0,  8,  1,  0,  0,  0, -1,  1, 10, -1,  8,\n",
-       "        2,  2,  2,  0,  0, -1,  9,  4,  2,  0,  0,  0,  0,  4,  2,  2,  0,\n",
-       "        0,  2])"
+       "array([ 0, -1,  6,  1,  2,  2,  3,  2,  2,  2,  2,  2,  2,  2,  2, 11,  2,\n",
+       "        4,  2,  5,  6,  2,  1,  3,  7,  0, -1,  2,  2, -1,  0,  2,  2,  2,\n",
+       "        8,  4,  2,  6,  4,  7,  2,  2,  8,  2,  9,  2,  7,  2,  3,  2,  2,\n",
+       "        2,  0,  2,  2,  3,  3,  8,  2,  3,  2,  2,  2,  8,  2,  2,  3,  3,\n",
+       "        2, -1, 10,  2, 12, -1,  2,  8,  2,  3,  4, 11,  4,  2,  2,  2,  2,\n",
+       "        0, -1,  7,  7,  9,  2,  2,  0,  3, 11,  2,  9,  2,  2,  2,  8,  2,\n",
+       "       -1,  2,  2,  3, 11,  2,  2,  4, 12,  0, -1,  6,  2,  2,  2,  2,  3,\n",
+       "        2,  2,  2,  9, 12,  2,  2,  2,  3, -1,  2,  2,  7,  2,  0,  9,  7,\n",
+       "       -1,  2,  2,  8,  2, -1,  0,  2,  2, -1,  2,  2,  2,  2,  2,  2, -1,\n",
+       "        2,  2,  2,  2,  2,  6,  3,  1,  2,  2,  2,  2,  1,  2,  2,  3, 12,\n",
+       "       10,  8,  1,  2,  2,  2, 12,  9,  2,  2,  2, -1, 12,  8,  2,  2,  9,\n",
+       "        9,  2,  3, 13,  2,  5, 13,  2,  2,  2,  6,  9,  8,  2,  0,  8, 12,\n",
+       "        2,  2,  5,  0,  2,  2,  8,  6,  6,  3,  2,  9,  7,  7,  1,  8,  2,\n",
+       "        6,  0,  2,  2,  6,  3,  2,  4,  5,  2, 10,  2,  2,  2,  5,  9,  2,\n",
+       "       -1, 11,  2,  9,  2,  3,  7,  2,  1,  2,  2,  2,  0,  2,  0,  2,  2,\n",
+       "        2, -1, -1,  8,  2,  4,  2,  3, -1,  4,  8,  2, 13,  3,  6,  1,  6,\n",
+       "        1,  2,  4,  3,  2,  3,  1,  2, 11,  2,  2,  7,  2,  8,  2,  2,  1,\n",
+       "        2,  2,  1,  8,  2, -1, -1, 13, 10, 12,  2,  2,  2,  2, -1,  2,  9,\n",
+       "        9,  2,  2,  6,  8,  2,  0,  2,  9,  2, 11,  8,  2,  3,  6,  2,  2,\n",
+       "        7,  0,  7,  0,  2,  2,  3,  6,  2,  8,  3,  2,  7, -1,  2,  1,  9,\n",
+       "        2,  2,  2,  2, -1,  2,  2,  2,  1,  2,  2,  9,  6,  2,  1,  9, -1,\n",
+       "        0,  2,  2,  2,  2,  2,  9,  2,  2,  2,  2,  2,  2,  7,  2,  0,  2,\n",
+       "        2,  6,  2,  2,  2,  2,  2,  3, -1, -1,  3,  0, -1,  2,  5,  2, 10,\n",
+       "        2,  3,  2,  2,  5,  2,  3,  2,  2, 11,  2,  2,  4, -1,  7,  2, -1,\n",
+       "        3,  9,  2,  2,  2,  2,  0,  8,  2,  3,  9, -1,  3,  2,  3,  2,  2,\n",
+       "        5,  5,  6,  2,  6, -1,  8,  6,  3,  2,  2,  0,  9,  6, 11,  2,  4,\n",
+       "        2,  2,  6,  5,  2,  2,  0,  2,  9,  2,  2,  3,  2, 13,  2,  9,  8,\n",
+       "       -1,  2,  8,  2,  2,  2,  2, -1,  1,  7,  2,  2,  2,  2,  2,  8,  2,\n",
+       "        2, -1,  7,  2,  2,  1,  6,  1,  6,  6,  2, -1,  7,  2,  2,  3,  2,\n",
+       "       -1,  6,  6,  2,  2,  3,  2,  6,  2,  4,  6,  2,  3,  0,  2,  3, -1,\n",
+       "        0,  2,  2,  8,  0,  9,  2,  3,  2,  2, -1,  7, 12,  6,  0, 12,  0,\n",
+       "        3,  2,  2,  0, -1,  2,  0,  2,  2,  2,  3,  1,  2,  2, -1,  2,  2,\n",
+       "       -1,  5,  2, 13,  1,  2,  2,  8,  2,  2,  2,  2,  8,  2,  9,  2,  2,\n",
+       "        2,  2,  9,  4, -1,  6,  6,  2,  2,  9,  8,  2,  8,  3,  0,  1,  2,\n",
+       "        2,  2,  5,  2,  2,  0,  2,  2,  2,  6,  0,  2,  2,  3,  2,  4,  2,\n",
+       "        3, 12, 12,  2,  2,  4, 10,  2,  2,  2,  4,  2,  5,  2,  0,  2,  8,\n",
+       "        8,  2,  2,  7,  2,  2,  6,  2,  9,  2, 13,  7,  2,  4,  3,  2,  2,\n",
+       "        7,  2,  2,  2,  2,  2,  8,  2,  2,  3, 13,  0,  2,  2, -1,  8,  0,\n",
+       "        2,  2,  8,  3,  2,  2,  2, -1,  6,  2,  2,  2,  1,  2,  3, 13, -1,\n",
+       "        6,  1,  2,  2,  0,  2,  2,  2,  2,  2,  3,  6,  3,  6,  2,  1, -1,\n",
+       "       -1,  2,  5,  2,  2,  2,  2,  2,  2,  2,  2, 12,  3, -1, 10,  3,  2,\n",
+       "        6,  2,  2,  3,  2,  0,  9,  2,  0,  2,  7,  2, 12,  3,  2,  2,  2,\n",
+       "        2,  2,  2,  2, -1,  2, -1,  4,  9,  6,  2,  0,  3,  0,  2,  3,  2,\n",
+       "        2,  2,  2,  2,  6,  4,  9,  2,  2, -1,  2,  2,  5,  3,  4,  2,  3,\n",
+       "        2,  2], dtype=int64)"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -151,60 +142,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0,  0,  0,  1,  1,  1,  0,  2,  3,  1,  4,  3,  4,  0,  0,  0,  0,\n",
-       "       -1,  0,  0, -1,  3,  0,  0,  2,  4,  5,  0,  0, -1,  2,  0,  4,  3,\n",
-       "        2,  2,  0,  5,  0,  0,  2,  2,  0,  0,  4,  0,  0,  0,  0,  6,  0,\n",
-       "        0,  1,  2,  0,  2,  0,  7,  1,  1,  0,  0,  2,  2,  0,  0,  0,  0,\n",
-       "        2,  0,  6,  0,  0,  3,  0,  0,  0,  6,  0,  0,  0,  2,  0, -1,  2,\n",
-       "        4,  0,  5,  2,  0,  2,  2,  0,  3,  0,  2,  2,  0,  0,  0,  0,  1,\n",
-       "        2,  2,  1,  0,  0,  0, -1,  0,  0,  1,  7, -1,  0,  6,  5,  0,  0,\n",
-       "        0,  0,  0,  8,  0,  2,  0,  0,  0, -1,  0,  2,  0,  0,  0,  0, -1,\n",
-       "        0,  6,  2,  2,  2,  0,  0,  1,  0,  4,  2,  0,  2,  7,  7,  0,  4,\n",
-       "        0,  5,  0, -1,  2,  0, -1,  6,  1,  8,  0, -1,  0, -1, -1,  2,  2,\n",
-       "        0,  5,  0,  0,  4,  2,  0,  0,  4, -1,  0,  1,  6,  0,  0,  4,  0,\n",
-       "        2,  0,  1,  4,  0,  0,  0,  0,  0,  0,  7,  6,  4,  9,  2,  2,  2,\n",
-       "        0,  3,  0,  0,  7,  6, -1,  0,  0,  0,  2,  0,  4,  2,  7,  9,  2,\n",
-       "        0,  0,  1,  0,  0,  0,  0,  2,  2,  6, -1,  9,  1,  7, -1,  0,  0,\n",
-       "        0,  4,  0,  0,  0,  3,  0,  0,  0,  2,  3,  2,  4,  0,  2, -1,  0,\n",
-       "        2,  0,  0,  0,  0, -1,  0,  0,  0,  7,  7,  0,  7, -1,  0,  3,  0,\n",
-       "        2,  0,  7,  0,  2, -1,  0,  0,  2,  0,  6,  3, -1,  2,  2,  0,  2,\n",
-       "        2,  2,  5, -1, -1,  2,  0,  0,  7,  6,  0,  2,  6,  8, -1,  2,  0,\n",
-       "        3,  0,  0,  4,  2,  7,  0,  2,  0,  0,  2,  0,  0,  0,  0,  0,  0,\n",
-       "        4,  0,  2,  5,  2,  0,  0,  9, 10,  0,  0,  0,  0,  0,  0,  0,  4,\n",
-       "        0,  7,  0,  2,  5,  0,  0,  0,  5,  0, -1,  0,  3,  0,  0,  5,  0,\n",
-       "        0,  0,  0, -1,  0,  2,  2,  8,  4,  9,  0,  4,  0,  0, -1,  0,  0,\n",
-       "        0,  0,  0,  2,  0,  6,  0,  0,  4,  0,  0,  2,  5,  0,  7,  0,  1,\n",
-       "        0,  0,  3,  0,  0,  0, -1,  0,  5,  0, -1,  4,  3,  0,  0,  6,  0,\n",
-       "        0,  5,  0,  8,  6,  0,  2,  0,  7,  3,  0,  2,  0,  2,  2,  0,  0,\n",
-       "        2,  0, 10,  0,  0,  6,  2,  2,  0,  0,  0,  3,  7,  2,  0,  0,  6,\n",
-       "        0,  0,  5,  2,  0,  2,  0,  0,  0, -1,  2,  4, -1,  4,  2,  0,  0,\n",
-       "        0,  2,  0,  0,  8,  4,  6,  0,  0,  0, -1,  5,  8,  0,  0,  0,  0,\n",
-       "        0,  4,  4,  7,  0,  4,  0,  0,  0,  0,  0, -1,  0,  2,  0,  5, -1,\n",
-       "        4,  0,  0,  0,  0,  3,  0,  7,  2, -1,  0,  6,  4,  0,  0,  3,  5,\n",
-       "        3,  0,  7,  9,  0,  0,  1,  7,  0,  0,  2,  2,  0,  2,  0,  0,  0,\n",
-       "        1,  2,  0,  2,  3,  0,  4,  0,  0,  0,  2,  2,  0,  0, -1,  5,  0,\n",
-       "        0,  2,  2,  2,  2,  0,  0,  0,  0,  0,  7,  2,  8,  0,  0,  6,  7,\n",
-       "        4,  0,  1,  0,  0,  0,  7,  0,  4,  1,  0,  0,  5,  5,  0,  0,  2,\n",
-       "        0,  2,  0,  0,  2,  0,  0,  1,  0,  8,  0,  0,  0,  0,  0,  9,  0,\n",
-       "        2,  3,  2,  0,  4,  0,  0,  0,  0,  0,  1,  7,  3,  2,  4,  2,  0,\n",
-       "        1,  0,  0,  0,  0,  0,  0,  2,  2,  8,  0,  7,  3,  6,  5,  6,  0,\n",
-       "        0,  0,  0,  2,  0,  5,  0,  0,  0, -1,  0,  0, -1,  2, -1,  2,  2,\n",
-       "        0,  2,  2,  0, 10,  2,  2,  0,  0,  0,  2,  2,  7,  3,  7,  2,  6,\n",
-       "        2,  3,  2,  0,  7,  0,  0,  1,  1,  0,  7,  0,  0,  0, -1,  7,  6,\n",
-       "        2, -1,  0,  3,  3,  0,  3,  3,  0,  0,  0,  2,  0,  0,  0,  6,  0,\n",
-       "        2,  0,  0, -1,  5,  0,  3,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,\n",
-       "        0, 10, -1,  1,  0,  2,  0,  8,  1,  0,  0,  0, -1,  1, 10, -1,  8,\n",
-       "        2,  2,  2,  0,  0, -1,  9,  4,  2,  0,  0,  0,  0,  4,  2,  2,  0,\n",
-       "        0,  2])"
+       "array([ 0, -1,  6,  1,  2,  2,  3,  2,  2,  2,  2,  2,  2,  2,  2, 11,  2,\n",
+       "        4,  2,  5,  6,  2,  1,  3,  7,  0, -1,  2,  2, -1,  0,  2,  2,  2,\n",
+       "        8,  4,  2,  6,  4,  7,  2,  2,  8,  2,  9,  2,  7,  2,  3,  2,  2,\n",
+       "        2,  0,  2,  2,  3,  3,  8,  2,  3,  2,  2,  2,  8,  2,  2,  3,  3,\n",
+       "        2, -1, 10,  2, 12, -1,  2,  8,  2,  3,  4, 11,  4,  2,  2,  2,  2,\n",
+       "        0, -1,  7,  7,  9,  2,  2,  0,  3, 11,  2,  9,  2,  2,  2,  8,  2,\n",
+       "       -1,  2,  2,  3, 11,  2,  2,  4, 12,  0, -1,  6,  2,  2,  2,  2,  3,\n",
+       "        2,  2,  2,  9, 12,  2,  2,  2,  3, -1,  2,  2,  7,  2,  0,  9,  7,\n",
+       "       -1,  2,  2,  8,  2, -1,  0,  2,  2, -1,  2,  2,  2,  2,  2,  2, -1,\n",
+       "        2,  2,  2,  2,  2,  6,  3,  1,  2,  2,  2,  2,  1,  2,  2,  3, 12,\n",
+       "       10,  8,  1,  2,  2,  2, 12,  9,  2,  2,  2, -1, 12,  8,  2,  2,  9,\n",
+       "        9,  2,  3, 13,  2,  5, 13,  2,  2,  2,  6,  9,  8,  2,  0,  8, 12,\n",
+       "        2,  2,  5,  0,  2,  2,  8,  6,  6,  3,  2,  9,  7,  7,  1,  8,  2,\n",
+       "        6,  0,  2,  2,  6,  3,  2,  4,  5,  2, 10,  2,  2,  2,  5,  9,  2,\n",
+       "       -1, 11,  2,  9,  2,  3,  7,  2,  1,  2,  2,  2,  0,  2,  0,  2,  2,\n",
+       "        2, -1, -1,  8,  2,  4,  2,  3, -1,  4,  8,  2, 13,  3,  6,  1,  6,\n",
+       "        1,  2,  4,  3,  2,  3,  1,  2, 11,  2,  2,  7,  2,  8,  2,  2,  1,\n",
+       "        2,  2,  1,  8,  2, -1, -1, 13, 10, 12,  2,  2,  2,  2, -1,  2,  9,\n",
+       "        9,  2,  2,  6,  8,  2,  0,  2,  9,  2, 11,  8,  2,  3,  6,  2,  2,\n",
+       "        7,  0,  7,  0,  2,  2,  3,  6,  2,  8,  3,  2,  7, -1,  2,  1,  9,\n",
+       "        2,  2,  2,  2, -1,  2,  2,  2,  1,  2,  2,  9,  6,  2,  1,  9, -1,\n",
+       "        0,  2,  2,  2,  2,  2,  9,  2,  2,  2,  2,  2,  2,  7,  2,  0,  2,\n",
+       "        2,  6,  2,  2,  2,  2,  2,  3, -1, -1,  3,  0, -1,  2,  5,  2, 10,\n",
+       "        2,  3,  2,  2,  5,  2,  3,  2,  2, 11,  2,  2,  4, -1,  7,  2, -1,\n",
+       "        3,  9,  2,  2,  2,  2,  0,  8,  2,  3,  9, -1,  3,  2,  3,  2,  2,\n",
+       "        5,  5,  6,  2,  6, -1,  8,  6,  3,  2,  2,  0,  9,  6, 11,  2,  4,\n",
+       "        2,  2,  6,  5,  2,  2,  0,  2,  9,  2,  2,  3,  2, 13,  2,  9,  8,\n",
+       "       -1,  2,  8,  2,  2,  2,  2, -1,  1,  7,  2,  2,  2,  2,  2,  8,  2,\n",
+       "        2, -1,  7,  2,  2,  1,  6,  1,  6,  6,  2, -1,  7,  2,  2,  3,  2,\n",
+       "       -1,  6,  6,  2,  2,  3,  2,  6,  2,  4,  6,  2,  3,  0,  2,  3, -1,\n",
+       "        0,  2,  2,  8,  0,  9,  2,  3,  2,  2, -1,  7, 12,  6,  0, 12,  0,\n",
+       "        3,  2,  2,  0, -1,  2,  0,  2,  2,  2,  3,  1,  2,  2, -1,  2,  2,\n",
+       "       -1,  5,  2, 13,  1,  2,  2,  8,  2,  2,  2,  2,  8,  2,  9,  2,  2,\n",
+       "        2,  2,  9,  4, -1,  6,  6,  2,  2,  9,  8,  2,  8,  3,  0,  1,  2,\n",
+       "        2,  2,  5,  2,  2,  0,  2,  2,  2,  6,  0,  2,  2,  3,  2,  4,  2,\n",
+       "        3, 12, 12,  2,  2,  4, 10,  2,  2,  2,  4,  2,  5,  2,  0,  2,  8,\n",
+       "        8,  2,  2,  7,  2,  2,  6,  2,  9,  2, 13,  7,  2,  4,  3,  2,  2,\n",
+       "        7,  2,  2,  2,  2,  2,  8,  2,  2,  3, 13,  0,  2,  2, -1,  8,  0,\n",
+       "        2,  2,  8,  3,  2,  2,  2, -1,  6,  2,  2,  2,  1,  2,  3, 13, -1,\n",
+       "        6,  1,  2,  2,  0,  2,  2,  2,  2,  2,  3,  6,  3,  6,  2,  1, -1,\n",
+       "       -1,  2,  5,  2,  2,  2,  2,  2,  2,  2,  2, 12,  3, -1, 10,  3,  2,\n",
+       "        6,  2,  2,  3,  2,  0,  9,  2,  0,  2,  7,  2, 12,  3,  2,  2,  2,\n",
+       "        2,  2,  2,  2, -1,  2, -1,  4,  9,  6,  2,  0,  3,  0,  2,  3,  2,\n",
+       "        2,  2,  2,  2,  6,  4,  9,  2,  2, -1,  2,  2,  5,  3,  4,  2,  3,\n",
+       "        2,  2], dtype=int64)"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -215,22 +206,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fae2cb11db0>"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -240,27 +221,18 @@
     }
    ],
    "source": [
-    "plt.scatter(X[:,0],X[:,1],c=dbcan.labels_)"
+    "plt.scatter(X[:,0],X[:,1],c=dbcan.labels_)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fae2cb915d0>"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -270,7 +242,8 @@
     }
    ],
    "source": [
-    "plt.scatter(X[:,0],X[:,1],c=y)"
+    "plt.scatter(X[:,0],X[:,1],c=y)\n",
+    "plt.show()"
    ]
   },
   {
@@ -283,7 +256,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -297,7 +270,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/21-Anomaly Detection ML/Isolation Anamoly Detection.ipynb b/21-Anomaly Detection ML/Isolation Anamoly Detection.ipynb
index 7b074d80..af57a201 100644
--- a/21-Anomaly Detection ML/Isolation Anamoly Detection.ipynb	
+++ b/21-Anomaly Detection ML/Isolation Anamoly Detection.ipynb	
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "50e89513-a3aa-43df-b84b-3691e4a1588f",
    "metadata": {},
    "outputs": [
@@ -78,7 +78,7 @@
        "4 -3.329586  5.303160"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -91,23 +91,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "6061c71b-310c-4dd7-bce2-3bfdca55bba2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f5b9b43ea10>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -118,12 +108,13 @@
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "plt.scatter(df.iloc[:,0], df.iloc[:,1])"
+    "plt.scatter(df.iloc[:,0], df.iloc[:,1])\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "124a970f-bcfe-4081-95ed-3c37d094b76a",
    "metadata": {},
    "outputs": [],
@@ -133,19 +124,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "bb807655-f596-4796-97ec-a6430ff1f7d8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.10/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "clf = IsolationForest(contamination=0.2)\n",
     "clf.fit(df)\n",
@@ -154,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "3162132d-8be5-4299-8e76-35d2fd9d86d5",
    "metadata": {},
    "outputs": [
@@ -162,23 +144,23 @@
      "data": {
       "text/plain": [
        "array([ 1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
-       "        1,  1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
-       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1, -1, -1,  1,\n",
+       "        1,  1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1, -1,  1,  1,\n",
+       "        1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1, -1, -1,  1,\n",
        "        1,  1, -1,  1, -1,  1,  1,  1,  1,  1, -1, -1, -1,  1,  1,  1, -1,\n",
        "        1,  1,  1,  1, -1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,\n",
-       "       -1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
-       "        1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
+       "       -1,  1, -1,  1, -1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
+       "        1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
        "       -1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1, -1,  1, -1, -1,  1,  1,\n",
        "        1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,\n",
        "        1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1, -1, -1,  1,  1,\n",
        "        1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1, -1,  1,  1,  1,  1,\n",
-       "       -1,  1,  1, -1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1,  1,  1,\n",
-       "       -1,  1,  1,  1,  1, -1,  1,  1, -1,  1, -1,  1,  1, -1,  1,  1, -1,\n",
+       "       -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
+       "       -1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1, -1,\n",
        "       -1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
        "        1,  1,  1,  1, -1,  1,  1,  1,  1, -1, -1,  1])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -189,20 +171,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "6e8b9bfb-1ef3-463c-863b-741ec083bb07",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([  4,  20,  24,  45,  48,  49,  53,  55,  61,  62,  63,  67,  72,\n",
-       "         74,  78,  83,  85,  87,  92,  97, 108, 114, 119, 126, 130, 132,\n",
-       "        133, 141, 151, 160, 166, 167, 177, 179, 182, 187, 190, 197, 199,\n",
-       "        204, 209, 212, 214, 217, 220, 221, 227, 242, 247, 248]),)"
+       "(array([  4,  20,  24,  31,  40,  45,  48,  49,  53,  55,  61,  62,  63,\n",
+       "         67,  72,  74,  78,  83,  85,  87,  89,  92,  97, 104, 108, 114,\n",
+       "        119, 126, 130, 132, 133, 141, 151, 160, 166, 167, 177, 179, 182,\n",
+       "        187, 199, 204, 212, 217, 220, 221, 227, 242, 247, 248], dtype=int64),)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -215,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "176d346c-e2f1-493b-acb8-758345d07ec9",
    "metadata": {},
    "outputs": [],
@@ -232,7 +214,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f5b8ee40ac0>"
+       "<matplotlib.collections.PathCollection at 0x23554195010>"
       ]
      },
      "execution_count": 10,
@@ -241,7 +223,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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S06ZNw759+7B27VqsXbtWysNSiPV00/P2hu/NPj8c+g7DfWzfjkNVWPbuYVQ1tji3pSbEYOltQ3HLsFTntlDP/Ak1nT4qrHp6SD6sFExykjQwueqqq/DOO+8gPz8fy5cvR2ZmJgoLCzFjxgwpD0sy6O6m5+0N35t9mpMEr3o/HHYcqsLcTSXonEhV1diCBzaV4M+/zcYvr0wD0JYoukCAsLsB2um6rjkmeyyoZaIoqRyXfiC5sSQ9Sc6bJNnqmgRAFJGS0tTtPn8t2oU3S06huqk9SToxLgpPTx3uDDCcx7WJmLjqI5eeks40ABbeNAiZQi8kx8cgets7yH54DhNFKSKpNQGcQi+Q+zcDEwqJnmaGzJ36GABg9daVHvfJ+80T2Dbwao/HuP+6TOT/sj3Zeu/3Z3D3q5/51M7UhBj8z0+HMHH105LM/CFSsrLN25A1c6p9qry7nsuKVmD9eZRt2sphQeqWYmflUOgoPVHNU5JsdUI/LJt6H97/j2sAAHOnPoalH69F2vrTXfbZfcUE4KKHmikAXtl1HCPSE/HLK+15I7XnPPeUeFLd2IKHcTny3vgUKV99gV5n63DpsMsx9L+YKErqp+YEcAofvNKqQDgkqlltIlpum4p/T7wZp/7+PsoPfIvanyViX3oWbNr2i+D7/3ENPhg0DmN/LEPyT/Wu+3QTlDg8sfVrTB6WAp1Wg+T4GJ/b6eg+LPjkGIC+APoitSIGS4/WuSTKEqmR2hPAKTxwKCfMhcOS9u5mxUjpzTlXY2xmEj47dgbzNpeg4YIlKN83Z8JlmDQ0pdtkW6Jw5kvRRCX1yJLyMMckQoUiUS3QIaJ/fFWJ379R6tex/TU5y4jPj50NWkDSmbupxkRqwUrBFAwMTCKU1IlqjiGijkmgNYneDxH946sqzH+zBDbF/oX5xxH+rZ45isGJTJSeUxXu3P3vMwGcfMHk1wglZaJaoLUM3j1YiQVvhranJFQccdbj7xzCjUOMiI6SdC1M6iQccqrCHSsFk5z4VxbGpEpUs1pae1zbJnV5PqzzZrn9lLpiexnW7Tnh0zHD0Znmi7ja9CFW/no4e05ChMW/QoeVgkku/KgXxqRa0v7IlmIY6+vsybSeFrOrr8ORLcVd3jvnL19ERFDicLbZgrmbSrDjUJXcTVG9LgFzehQQrWkLmGOAwXp7wGxplbupRBQABiZhzLG6L8otsBW12HNKzCJQ0Wp/Xm5B1RKTz2Pvvg4RWW0i9n5/Bku2fo0PDtf6fT4aAH1ioyD3fBd/jr/s3cOwqi2ZRmECCZiJKHxwKCfMSbGkvS9DRMGaCuy4zdw7IRMFH34b0PcKxMKbBmHL/gqfzkeEfe2dfcfPYvzAvtI1LsKx+BdRZGBgogLBTlTzdjG7H4aOxjw3C+T5I6VtCq651RaE7+a/sZlJePCmQXjpo+9Q8GG5T+99r204h3VOpMHiX0SRgYGJSgQzUc0xRCTkzYatCG5rGZz640qs2HE0oKAkb9JgXNYvzmWF4L3fnwnKOfjr9E/2xQGLvjjp83v/svcH/GXvD6xzIhGu/kwUGZhjQm5l5+bgYMFrqKvrA6w/DzxzDlh/HrV1fXCw4DVcvP3XAQ3faGC/+f/nlWkYP7Cvs4dhbGYS+sTpg3MSfkiOj8G+42cDOrfqxhYmxEpAqpwqIlIW/geTR90NEW07cCqg7+3Iyyj44CgmXC4oYvgjNcHec7P9q8qAvo8Ie+C17N3DuHloiuznpSZS5FQRkbIwMKFueRoi8meBPHde+vh7vPTx90jqFY1xmYkwROnQcF6aUvI9WXrbUL8X/+uMCbHSYfEvInXjfzL5ZWxmElITYlDd2BKU5NezzRfx3qGaIHwn/8TotbhxiBGAd+cWF63DeS9WO649F5qFCyMNi38RqRdzTMgvOq0GS28bKnczgqbFYsPVpp3YcajK5dw6D8Jo2h73XzfQq+8brJ4lIqJIwcCE/HbLsFSsnjkKqQnquPmebb6IBzaV4H8/LIe51YbcSYNg7G1w2SclIQarZ47C/BsvR2pCjMdibBq056wQEZH3uLowBexiq7234WzzRbmbEnQpvWNw99j+XaY1A8COQ1WYu6kEAFyGfLj6MBFFukDu3+wxoYB9+UO9KoMSAKhpakHhh+UwRGldpjUD7T1GKZ16jBy9KgxKiIh8x+RXCpiaEzx7mvp7y7BU3Dw0BfuOn0XtuZYuvSpEROQbBibkF6ulFUfapmtG9UqE1vYz2LQe1jAJcz1N/dVpNZwSTEQUJAxMyGelheuQtjwfWfXtBa5GJfTDkzfch/f/4xoZWyYtTz1DVpvIHhMioiBhYEI+KS1chxF5s4HBUcAdcc41dIy7G7F660rMnfqYaoMTd1N/3a2uzLVyiIj8x+RX8prV0oq05fnA4Chop8faV3iN1gDpUdBOjwEG67H047XQ2toLj6UmxOD+6zIVOaVY42Wnhqepv45ZOZ3X1eFaOURE/mOPCXntyJZi+/DNHXFd7+oaDbQT9Uhbfxovpv+E1ut+7jKs8cgtV9gXx2u4gKXvHsK5lp6rpkrNMVH+vydchoTYaBR+WG7f3mEfx1k6ytU7WG0ilr172G1lWK6VQ0TkP/aYkNcunPzR/kWyhyTXtu3G5nrcPvISl+m1Oq0GjRcu4rl/HlVEUNLRe4eqMf/Gy32a+tvTCsQdE2aJiMh77DEhr8X2T7d/UWu1D+N0Vmt13a8Dx7CHEqv5OQIIX6b+ejtFWs1TqYmIpMDApAcdp8U6VjHVRegqpkOmTUHNAgHC7gZop+tch3NEEbY9FtQmChgybYrL+7ob9lAKRwDh7dRfb9fA4Vo5FAl4naRg4lBON0oL1+G0MQ1ZM6dizGPzkTVzKk4b01BauE7upslCp49C5RITUG6BragFqGgFzCJQ0Wp/Xm5B1RJTlwtST8MeSuBrAOFYgZhr5VCk43WSgo0hrQeepsUKuxsg5M1GKYDs3By5mxly2bk5KAWQtjwfxvXtdUxqEwVUFZjc/kyUPpzhTwDhWIF47qYSaOBdwiyR2vA6SVLgIn5uWC2tOG1MgyDU26fFdh6yKGpBbV0fCDWVEdtd6UvX7d7vz+DuVz8Laft6GXRoNnuXZLsmgHVtWMeEIhWvk9SdQO7f/Gtxw5tpsSnr61C2pRhZM26Xp5Ey0+mjvD53x7BHdWNLyPJMvA1KciZcFlAAwbVyKFLxOklSCVmOyTPPPAONRoPc3NxQHdJv3k6Lde5H3XIMewDwmJMhl0lDU9xut9pE7P3+DLYdOIW935+B1eY5pHIkzHaeIk2kZrxO2lktrSjbvA37TS+jbPM2WC2tcjcp7IWkx+SLL77AK6+8giuvvDIUhwtYINNiyb1bhqVi9cxRXYY9pJTUKxr1zRfd9tJoYK9R4i63hMMzRD3jddL9umE1CwRULnGfb0fekTzH5KeffsKoUaPw5z//GU899RRGjhyJwsJCr94rf45Jg73UOsdOg6bzgnf1zRexothzsNI5sdQbjqBj8ZShmPdGCQD3yanuCqd5qrfS3XuIIlGkXyc7Jv5qr412Jv7adluAcgsOFrwW0cFJIPdvyQOTWbNmISkpCQUFBbj++uu7DUzMZjPMZrPzeVNTEzIyMkIemAAd/+j00E7Ut//R7eEfXbA5gpXqphacPmdG/fmL0GqA8QP6ofGCBb9vCy680TmA8KX3w2oTMXHVR90GSSkJMdjz6I0criFC5F4nmfjbM8UmvxYVFaGkpARffPGFV/ubTCYsW7ZMyiZ5zZ9pseSfnoqardGOwqK/fY2G8xaX7XHROkRHaV22p3QKOnxJTvWlzLw3RdiI1C5Sr5NM/JWWZIFJRUUFFi5ciA8++AAxMd4Vr8rPz8dDDz3kfO7oMZFLdm4OrPNmoazTtNiUCI2A5eIILj47dgZ7vz8DQMT4Af1wdVtw0FPQ4W01V5aZJ/JdJF4nmfgrLcn+cr788kvU1tZi1KhRzm1WqxW7du3CSy+9BLPZDJ3O9ZdqMBhgMBikapJffJkWS9LRaTWYcHk/TLi8X5fXgtV7wTLzRP6JtOskE3+lJdl04Ztuuglff/01Dhw44HyMGTMGM2bMwIEDB7oEJURyY5l5IvLGkGlTUJMo2BNdO6dptq0bVu1m3TDyjmQ9JvHx8Rg2bJjLtl69eqFv375dthMFk78LirHMPBF5w7FumJA3G7YiuE38rSowqXo4S0r8qZGqBFpXwFO9lc5JtUQU2SI18TcUuFYOqUYw6wp0rrfCMvNE5I6/PbRqp+g6JoFgYELeYl0BIiLlCOT+HbK1coikdGRLMYz1dfaeEk91BerrcGRLsTwNJCIirzAwIVVgXQEiInVgYEKq4FJXwB3WFSAiCgsMTEgVWFeAiEgdGJiQKjjqCqDcAltRC1DRCphFoKLV/rzcgqolJia+EhEpHK/SpBqsK0BEFP44XZhUh3UFiIjkFcj9m1drUp1IW1CMiEhNGJgQUVhgTxhRZOB/NREpXqBrIBFR+GBgQkSK1nENJNwR51wDSdjdACFvNkoBBidhjD1h1BmTX4lIsbgGkro5esKMHXvCEtkTpgZcK4eIVIlrIKmXoydMEOqBnDggPx7IiYMgNGBE3myUFq6Tu4kkE37EIFIJX7rErTYR+46fRe25FiTHx2BsZhJ0Wk1Ar0nBnzWQQt1G8p3V0oq05fnA4CjXnrD0KGin62ArAlKX58M6bxZ7wiIQf+NEKuBLcuiOQ1VY9u5hVDW2OLelJsRg6W1DAcCn1/rE6nHvhEzMv/FySW7+Lmsgpbu5XHVaA6m7c7tlWGrQ20f+ObKl2P63ekec556w9XUo21LMqf8RiDkmRGGuY3Ko9tpoZ3KobbcFKLfgYMFrzuBkx6EqzN1Ugs7/9BqgyzZvXnPoE6fHM3cMd7n5B6Pnoj3HpAHa6THd5ph8cLTO47kBwOqZoxicKMR+08sY89h8+/BNtJu/CbMIPHMO+1e+hDH580LfQAoYc0yIIlSXLvH0KPuFPj3KfiMfrLd3iVtaYbWJWPbuYbdBRneBhzefXBrOW/DAphLsOFQFq03E/374LUav+AB3v/oZFhYdwN2vfoaJqz7CjkNVPp2ft2sgQafr8dyWvXsYVptiP4dFFK4GTt1hYEIUxnxJDt13/KzLEIcUFv3ta4xe8QEKPixHwwWLy2vVjS2Y2xa8+CI7NwcHC15DXV0fYP154JlzwPrzqK3r4+wN6uncRABVjS3Yd/ysH2dFwcbVwKk7zDEhCmO+JIfWZkkblAD2nhNPRNiHVZa9exg3D03xaVgnOzcH1nmzUNYpuTelLTGy9px35+btfiQtR0+YkDcbtiJAO1HfPgS5xz4EWVVgcv5+1Yj1WzzjT4EojPmSHJocHxPClrnXsedi/MC+Pr23uzWQvD03JfwMyC6SVwNnJePuMTAhCmNDpk1BzQIBwu4GaKfruiaH7rGg1tElrtMhNSEG1Y0tXuWNSKm6Kbg9F2Mzk7o9Nw2AlAR7Ai7AKcVK0VNPmBqxknHPOCuHKMy1X+j0brvE3c3KAVyTWr2ZeRNMSb30WPnr4UGdJeNpxpHD/ddlIv+XQzmlmGQTSZWMA7l/MzAhUgF3pb2rEwVU+VjHxGYD5r9ZglBNXsmbNAjzbxwUtN4K0z8O45Vdx92+pgFw33WZWLvruNspxVqbFauMTbjsYiPH/EkSZZu3IWvmVHulW3dDrxWtwPrzKNu0NezrtwRy/+Z/HZEK+NIlfsuwVNw8NMXtUMbe7894FZT0MujQbG6f6pnS24CWVhsaz1t86nkp+PBbvLmvAk/+KvDeCqtNxN8Pep7xIwJ4dXfXoAQAfnH031j68VqkNZ52buOYPwWbP5WMIxEDEyKV6C45tMu+Wo3b5FNvZ62s+NUw1J+/iB/OnselSXH43fjL8NGRGszdVOLzsFB1k30asT8F0Drmipw+Z+5xOrS7oGvy0X9j9daV9jH/33DMn6TjayXjSMWhHCJy2vv9Gdz96mc97pfUS4+zze1Tg7srW+8NR3Lqnkdv9HpYx92QlK+0Niv2rM1BirFB9WP+JD9fKhmH+98bK78SUVCMvjQRSb2ie9yvY1ACtBdPA4A9j96IN+dcjfk3DPT6uL4WQHMkugZaMG7sj2VIazzN1YspJLytZBzuQUmgIvvsicg5HPLB4WpsPVCJs80X3e7X3RBN5+Jp4wf2xdjMJLxdcsqn6cneDCV1V1q/J1qNvdCo473JP9W3fcExfwqNSK7f4q2IDExYcY8iVef6HfXNZqwo/sarnoekXtE44yFoAboWT9NpNVh621BnT4o3vCmA5k9pfUdfyJxr7bNyHEFW7c8S7S94GvOvbgUAWL4+hLLN23itoKCIxPotvoi4nwIr7lEwhGNwG0hORlIvPR775RV4+K8He9y3Y6/HLcNSsXrmKDz69ldovNDq8T0aAMbeBthEEdsOnOq26Jk/ZeVTOtQpGZHeB09sO4SzzRbsS89CZUI/pOxu7Fqg7vBFiNtaoAEw/s01wJtreK2goPElWT3SKPtKGmSsuEfBEI7BbU/Fx3pyttmChvOee0s6ctfr0V1QAth7L1pabZjx2ufObSm9Y3D32P64rF+cS6DibVn5xVOuQL94g8t7dxyqworib5w5MjatDs/+4gH86a9Pua7Zss8M8aOLwCAdcJ2B1wqiEIqYWTmRVHGPpNMxuNVeG91eZXV31yqrSmG1iZi46qOAE0ULpo3As+8f7TZnJKmXHp/lT0J0lNbrY3s7vdgx8+fmoSmYuOqjHsvPd57h4yk408Bex2TZJ2uR0tBWx0QLiAOjoLk78GtFOPauEQWKs3K84Mvy8ETuWC2tSFuebw9KpsfacxKiNUB6lH3q32A9Upfnw2rpvncg1PzJyXAnJSHWOSXY04Tes80W/Py5j7HjUJXXx/b2k5Fj5s8Hh6s9tsPxfOltQ12Cku4SZkUA//yPa3Dn/2zG1/+3FXvvfgCwAZrrAr9WlBauw2ljGrJmTsWYx+Yja+ZUnDamobRwnZdnTRR5JA1MTCYTrrrqKsTHxyM5ORlTp07F0aNHpTykR6y4R4GSMri12kTs/f4Mth04hb3fn4E1iDXh/cnJ6EgDe2VXmyjC3GpD7qRBMPY2eNzfEUDsOFQV8LE7cvxEnvx7GeJj9PjvCZchsZfeZZ+UhBi3hdp6CpBEAKfOWfDT+InQDx9m3xjgtcLRuyYI9fYS5PnxQE4cBKEBI/JmMzgh8kDS/sRPP/0U8+bNw1VXXYXW1lY89thj+MUvfoHDhw+jV69eUh66C1bco0BJFdxKvaictzkZ7jiGWTrnfxjjDfiZIQo/mbv2DnWcOvz8b0b4fWx3RADVTWaXtiT1isbUkWm4eWhKwAmztedacHkQrhVdetccgWx6FLTTdbAVwd67Nm8Wh3U64dAXSdpjsmPHDtxzzz3IysrCiBEj8Prrr+PkyZP48ssvpTysW0OmTUFNomDPBeicVtO2PHy1Y3l4Ijdcglt3/AhuPRUK69jrEKixmUnoZfAQTPXA8b6G864F1WrOmd0GJQ6OqcNfnDjj13F9Ud98ERv+dQKNFy56rBrrbXCWHB8TlGsFh479w6EvAkKcY9LY2AgASEpKcvu62WxGU1OTyyNYWHGPAhXs4LanvAfA3uvg67BOx2Ghf313Gp99fwaXCz/z6XsA9vvpT2YPQZiXXv/3DwG93xve/KzGZiYhNSHGY26MBvZeqrGZSUG5VnDo2Hcc+iKHkN2FbTYbcnNzMWHCBAwbNsztPiaTCcuWLZOsDay4R4Fw3LCEvNmuU0trrbDtsc/KqSoweV0kyZu8h44Fy7wRjPVjnMcPQppLwwVLzzsFQU8/K51Wg8VTrsDv3yjt8pq7hNlArxUcOvYNh76oo5BNF547dy7ee+897NmzB+np7v8ZzWYzzGaz83lTUxMyMjKCvogfxzApEI46JsYOdUyqEwVU+VjHZNuBU1hYdKDH/f53+kjcPvKSHvcLtFaJGnj6WXUXsHWXz+PvtSKSFmsLhrLN25A1c6q9p8RdIFfRCqw/j7JNW1mULEwEMl04JP8R8+fPx/bt27Fr1y6PQQkAGAwGGAyes/2DhRX3KBDBKiftS95DTwJZP0ZNTpxu7rKtp4Bt8RTPScb+XiuC3bumdhz6oo4k/a8QRRELFizAO++8g08++QSZmZlSHo4oZIIR3DryHnoqFDY2031OVkfBqlXiCw2AhDg9YqJ0qG4K7bE9KfjwW/xHSrwz0OgpYNMAWFF8GJOHpXhMnPUXh469x6Ev6kjS5Nd58+Zh06ZNeOONNxAfH4/q6mpUV1fjwoULUh6WKCw4FrkDvC8U5kkw64X4YuXU4fjjXSMwdWSaLMfvzDFN2ZEE60sejxSyc3PQr6YSZZu2Yv/Kl1C2aSuEmkoGJZ1w1iR1JGmPyerVqwEA119/vcv2DRs24J577pHy0ERhwbHIXef8hxQf65gEUqvEH6kJMfjViFQs316G6iZzz28Ikc5JsL7UL5EKh457FilDX8xv9I7kQzlE1L1bhqXi5qEp2Hf8LGrPtXS7sq4nPQ0LBdODN14OrQYo3PmdxEfynyPQCGYeD0lL7UNf4bj4p1wiZhE/IrVzJHkC3q8/4w+tBghixXxJvDnnaowf2Ne5iKCvC/6RfNTYqxCOi38GKpD7NwMTIhUJZh2TcOQu0PAUsDnCEHdr6xAFS6SubK/46cJEFBqdh4X6/cwAm1XEgqLSkBU7k5sjoXjv92ecQ2Mv/3YUVhQHlsdD5I8jW4rtwzd3xHlenmB9Hcq2FDMXqQ0DEyIV02o0uHpQXzxz5/CQDPPISasB5lxrL0kwcdVHXRZFXDzlCiT2Mvidx0PkD9Zo8R0DEyIV6W6lYnezf9REFIFXdh0HcLzLa9WNLZj3RilWzxzlVRVdomBhjRbfhXQRPyKSTk8rFQPAnkdvxObZ49AnVi9pWzr3WIdCdz1BgSyKSBQI1mjxHQMTIhXwdqViAJhweT88c+dwSdujxJR6qYupEbnDle19x58EkQp4W+H0s+/PQKvVYN/xs/iZIQo/mVtd9tNAvTkoDnJVyaXIpfYaLcHGwIRIBby92c57o6TH2Tl3jroEb5ecCkazFInF1EgOwVr8MxLwJ0KkAt7ebHsKSkQAfwtCUNIrWofzF62K6n3xZVFEIilweQLvMMeESAUcJemDkXMajGDilmEpIQ9KNB6+7vjc20URiUg+DEyIVKC7lYpDTaOB5ENBGgBx0a51IVISYrBm5iismTkKKQkxXV5jhVei8MCS9EQq4q6OSZ9Yveqqvl4/uB/W3TPW48KHVpsY0KKIRBQYrpVDRE6db8o2UcSM1z6Xu1lB9+ffjsIvr2QPCJESca0cUhU1ri4aSjqtBuMH9nU+t9pEpCbEeFxhN1wt3nYIk4Ya8eUP9ewZIVIR9piQopQWrrPP9a9vn+tfkyigcgnn+gfC0wq7HYVjDZOkXnqcbW4fpkr1YmE+DvMQSY9DOaQKpYXrMCJvNjA4Ctpro+2LW9Va7aWcyy04WPAag5MAuMs/Seqlx69HXoJJQ1Pw+bHTKNz5nYwtDJwjvPCU6NrdWkJMjCUKHgYmFPasllacNqZBEOqhnR7rutiKKMJW1ILauj4Qaio5rBOA7noLrDYRVz75PpovWmVtY2pCDFosrag/39rzzm446pXsefRGl54QR69R5wteT8EMEfkukPs3pwuTIhzZUgxjfZ29p6TzCnAaDbQT9Uipr8ORLcXyNFAlHPknt4+8BOMH9nW5ceu0Gtx33UAZW2f3qxGpeHqq/2v5uFsTx9u1hLjAH5H8GJiQIlw4+aP9i2Sd+x3atjv3I0nMv/Fy9InzfeVhTdvj5qHJAbfh7werMHlYKu6/LjOg79OxTL+3awlxgT8i+TEwIUWI7Z9u/6LWwzBC23bnfiQJnVaDZ+7wvbciIU6P1TNH4c5Rgf9+HAFC/i+H4s+/HYWkXtF+fZ+OZfq9XUuIC/wRyY+BCSnCkGlTUJMo2BNdO6c9iSJseyyoThQwZNoUeRoYQW4Zloo1M0chNcH7xe60Gg1+PjgZy949HJQ2OAKEX16Zii8en4Q351yN/zf+Uq/fn9ppTRxv1xLiAn9E8mMWISmCTh+FyiUmCHmzYSsCtBP17bNy9thn5VQVmLgSpwTcJcTeMiwVNw9Nwb7jZ/Gv7+rw0sffd/s9zjZfxNiVH+BcS3ASZzsGCI68GJso4i97f/Dq/Z3XxHGsJdTdcA4A1DebAbCWDpGc+J9GipGdm4NSwF7HZH17HZPaRAFVBaxjIoWeps+OH9jX6+GNYAUlfXtFu18B2Mu81IU3Xd5ldo1Oq8HiKUPx+zdKun3viuJvYPzwH0h/6jFkdayls4C1dIhChYEJKUp2bg6s82ahrNOnVfaUBJ+n6bNVjS14YFMJ1rRNnw318Mb4AX2x/avKLtOZT7f1ZvRkgPAzt9sTvchVuXLfR8jeuhIYHAXcEefstRN2N0DIm41SgMEJkcR4tSfF0emjkDXjdrmboWrdTZ91WPS3r3Hz0BTnMEigJe01APrE6VF/vvsFBbd/XYXtX1cBAFJ6G/Dkr7J8CpA87ddTz4/WZsXSj9faC/x1rKWTHgXtdB1sRUDq8nxY583isA6RhJj8ShSBepo+CwAN5y2Y/0YJ9h0/i8VThgZ0PEe2h+mO4fjzb7PhbQX46iYzHthUgh2HqpwBkqe3atA16bWjngKbsT+WIa3xNGvpEMmMgQlRBPI2b+S9Q9W4+9XPsKL4MO67LtPrqbud90tJiHFWVk3sZYCvdcwW/e1rAPakVgBdghPH885Jrx31FNgk/1Tf9gVr6RDJif2RRBHI17yR6sYWrN11HC9Oz8aSdw+5LJzXkaMc/Kd/uMHjqr/+1AppOG/BZ9+fwS3DUrF65qguCbspXqx3o9NqsPS2oZi7qaTLgoUaALU/S7Q/qbUC6W4ujaylQxQSDEyIItDYzCT0idWj4UL3+R4OIuw376ff+wZP3T4M894odW536NhrER2lxfiBfd1+L3+TafceO40Jg/q5TGX2dYXg7gKb//eH36FmRwGE3Q3QTtd1Xa9pjwW1rKVDJDkGJkQRSKfV4N4Jl6Hgw2+9fo+jbHtiL4PfvRYAAkimdV3Xx1Pg05PuAptS1tIhkh3/w4gi1PwbB2HDv0+goYdZMp3VnmvB7SMv8bvXorshle74G4h4aoO778daOkTy04hi5/rfyhHIsslE1DNPtUy68+acq4MSJLgr7uZJYpwe+5+42avAJxhY+ZUoMIHcvxmYEEW4f3xVhflvlvQ4U8aR2Lrn0RuDFiA4yuF/eLga6/51wuN+jmJvRBQeArl/h2S68Msvv4zLLrsMMTExGDduHPbt2xeKwxKRFxJ7RXcblGhtVlx98ivcdvhTPBxdBViDU3oeaB9SWXxbFtbMHIWU3q6JsakJMQxKiCKM5H2Tb731Fh566CGsWbMG48aNQ2FhISZPnoyjR48iOTlZ6sMTUQ+6m747+ei/sfTjtUhrPG3f8C5Q8/RDkqwbE8hsGyJSD8mHcsaNG4errroKL730EgDAZrMhIyMDCxYswKJFi7p9L4dyiKS39/szuPvVz7psn3z031jdtm6M9tro9hkqu+0zVA4WvMZkUCJyS7FDORcvXsSXX36JSZMmtR9Qq8WkSZOwd+/eLvubzWY0NTW5PIhIWu4qonZZNyY9CojWtK0bEwMM1tvXjbG0ytZuIlInSQOT06dPw2q1wmg0umw3Go2orq7usr/JZEJCQoLzkZGRIWXziAjt03eB9kohXDeGiOSiqLVy8vPz0djY6HxUVFTI3SSiiOCoiJqSYE8+5boxRCQXSZNf+/XrB51Oh5qaGpftNTU1SElJ6bK/wWCAwWCQsklE5EHH5NOTb58B3gXXjSGikJO0xyQ6OhqjR4/Gzp07ndtsNht27tyJ8ePHS3loIvKDY/rub/J+i5pEwZ7o2jk/vm3dmGquG0NEEpB8KOehhx7Cq6++io0bN+Kbb77B3Llz0dzcjHvvvVfqQxORn3T6KFQuMQHlFtiKWoCKVsAsAhWt9uflFlQtMbEaKhEFneRXlf/6r/9CXV0dlixZgurqaowcORI7duzokhBLRMrCdWOISA4sSU9E3eK6MUTkq0Du37y6EFG3dPooZM24Xe5mEFGEYGCiEvxUS0REasA7lwqUFq5D2vJ8ZNW35wHULBAkWc+EiIhISgxMwlxp4TqMyJsNDI4C7ohzrmci7G6AkDcbpQCDEyIiChtMfg1jVksrThvTIAj19vVMOpYOF0XYilpQW9cHQk0lh3WIiChkFLuIH0nryJZiGOvruJ4JERGpBj9GhzHnOiVczyRkmGRMRCQtXlHDmHOdEq5nEhJMMiaicBDuH6CYYxLG2nNMGqCdHsMcEwl1TDLWXhvtTDK27bYA5RYcLHiNwQkRyc7xAcrY8QNUYug/QDHHJEJxPZPQsFpakbY83x6UTI+1905Fa4D0KHtAOFiP1OX5sFpa5W4qEUUwxwcoQagHcuKA/HggJw6C0IARebNRWrhO7iZ6hYFJmMvOzcHBgtdQV9cHWH8eeOYcsP48auv68FN8kDDJmIiUTk0foBiYqEB2bg761VSibNNW7F/5Eso2bYVQU8mgJEiYZExESqemD1Ds41cJrmciHSYZE5HSqekDFHtMiHowZNoU1CQK9kTXzrniogjbHguqEwUMmTZFngYSUcRz+QDlThh9gGJgQtQDJhkTkdKp6QMUr6REXsjOzUEpYJ+Gt759Gl5tooCqAtYxCZZwr79AJBfHByghbzZsRYB2or69rMEee1mDqgITUsLg/4l1TIh8wBundJRSf4EonLn7P6pOFFAVRnVMGJgQkexYwI4oeJTwAYqBCRGFLa6STaQ+rPxKRGFLTfUXiChwDEyISFZqqr9ARIFjYEJEslJT/QUiChwDEyKSlZrqLxBR4BiYEJGsWMCOiDrifzoRyY4F7IjIgdOFiUgxlFB/gYgCF8j9m//xRKQYXCWbiJhjQkRERIrBwISIiIgUg4EJERERKQYDEyIiIlIMBiZERESkGJyVQ0REFIGUOj1f/hYQERGRUygChtLCdUhbno+s+vaChjULBFQukb+goWRDOSdOnEBOTg4yMzMRGxuLgQMHYunSpbh48aJUhyQiIgprpYXrcNqYhqyZUzHmsfnImjkVp41pKC1cF9RjjMibDUGoB3LigPx4ICcOgtCAEXmzg3osf0jWY3LkyBHYbDa88soruPzyy3Ho0CHMmTMHzc3NeP7556U6LBERUVhyBAwYHAXcEQck64BaK4TdDRDyZqMUCLg3w2ppRdryfGBwFLTTYwGNxv5CehS003WwFQGpy/NhnTdLtmGdkJakf+6557B69WocO3bMq/1Zkp6IiCKB1dKK08Y0CEK9a8AA2FfZLmpBbV0fCDWVAQUMZZu3IWvmVHtPSbqb71PRCqw/j7JNWwOqwhzI/Tuks3IaGxuRlJTk8XWz2YympiaXBxERkdod2VIMY30dtNdGuwYlAKDRQDtRj5T6OhzZUhzQcS6c/NH+RbLO/Q5t2537ySBkgcl3332HF198Effff7/HfUwmExISEpyPjIyMUDWPiIhINqEKGGL7p9u/qLW636Ftu3M/GfgcmCxatAgajabbx5EjR1zec+rUKdxyyy246667MGfOHI/fOz8/H42Njc5HRUWF72dEREQUZkIVMAyZNgU1iQJsuy1A50wOUYRtjwXViQKGTJsS0HEC4XOOSV1dHc6cOdPtPgMGDEB0dDQAoLKyEtdffz2uvvpqvP7669BqvY+FmGNCRESRoD3HpAHa6TGS5ZgAHZNs9dBO1DuTbG17LEC5BQcLXgs4yTaQ+7fPZycIAgRB8GrfU6dO4YYbbsDo0aOxYcMGn4ISIiKiSKHTR6FyiQlC3mzYiuA2YKgqMCElCDNlsnNzUAogbXk+jOvb65jUJgqoKpC/jolks3JOnTqF66+/Hpdeeik2btwIna593CwlJcWr78EeEyIiiiSOwmfGDoXPqhMFVElQ+EzKQm6B3L8lC0xef/113HvvvW5f8/aQDEyIpKHUUtREpI7/T0UGJsHAwIQo+Nx9IqtJVEYpaiJSh5DmmBBR+ApFZUkiokCwx4QoQoSqsiQRUdhUfiUi+YSqsiQRUSAYmBBFiHAoRU1ExMCEKEKEQylqIiIGJkQRIhxKURMRMTAhihCOypIot8BW1GJf3twsAhWt9uflFlQtMTHxlYhkxSsQUQRReilqIiJOFyaKQGqoLElEysUCa0TkE50+Clkzbpe7GUREXTDHhIiIiBSDPSZERKQaHKYMf/xtERGRKjgWqMzquEDlAi5QGW4YmBARUdjjApXqwVk5REQU1rhApfJwET8iIopYXKBSXRiYEBFRWOMClerCwISIiMIaF6hUFwYmREQU1rhApbowMCEiorDGBSrVhb8lIrAoE1G44wKV6sHpwhTxHEWZjB2LMiWyKBNROOKHDGXgIn5EfmJRJiJ14QKV4Y89JhSxWJSJiEgaLLBG5AcWZSIiUh4GJhSxWJSJiEh5GJhQxGJRJiIi5WFgQhGLRZmIiJSHgQlFLBZlIiJSHl5xKaKxKBMRkbJwujARWJSJiCiYWGCNKEAsykREpAzMMSEiIiLFYGBCREREihGSwMRsNmPkyJHQaDQ4cOBAKA5JREREYSgkgckjjzyCtLS0UByKiIiIwpjkya/vvfce/vnPf+Ltt9/Ge++9J/XhiMIWZwYREUkcmNTU1GDOnDnYunUr4uLietzfbDbDbDY7nzc1NUnZPCLFKC1ch7Tl+ciqb6+lUrNAQOUS1lIhosgiWWAiiiLuuecePPDAAxgzZgxOnDjR43tMJhOWLVsmVZOIgi4YvRylheswIm82MDgKuCPOvnhgrRXC7gYIebNRCjA4IaKI4XOBtUWLFmHVqlXd7vPNN9/gn//8J7Zs2YJPP/0UOp0OJ06cQGZmJkpLSzFy5Ei373PXY5KRkRHyAmvsUidvOHo5jB17ORJ96+WwWlpx2pgGQaiHdnosoNG0vyiKsBW1oLauD4SaSv4NElHYCKTAms+BSV1dHc6cOdPtPgMGDMC0adPw7rvvQtPhQmu1WqHT6TBjxgxs3Lixx2PJUfk1GDcbUr+OvRzaa6OdvRy23Rag3IKDBa959fdStnkbsmZOBXLigHQ3gUdFK7D+PMo2bWUBOCIKGyGt/CoIAgRB6HG/F154AU899ZTzeWVlJSZPnoy33noL48aN8/WwIcEudfKG1dKKtOX59qCkYy9HehS003WwFQGpy/NhnTerx16OCyd/tH+RrHO/Q9t2535ERCon2XTh/v37Y9iwYc7H4MGDAQADBw5Eenq6VIf1W5ebTXoUEK1pu9nEAIP19puNpVXuppLMjmwphrG+zt5T0nHoBQA0Gmgn6pFSX4cjW4p7/F6x/dv+F2qt7ndo2+7cj4hI5Vj5tU0wbzakbsHs5RgybQpqEgX7EFDnUVVRhG2PBdWJAoZMmxJIk4mIwkbIApPLLrsMoih6THyVG7vUyVvB7OXQ6aNQucQElFtgK2qx55SYRaCi1f683IKqJSYmvhJRxGCPSRt2qZO3gt3LkZ2bg4MFr6Gurg+w/jzwzDlg/XnU1vXxOomWiEgtfJ6VE0qhnJXTPm2zwZ5Twmmb1I32RGk9tBP17bNy9vg2K6cjTlMnIrUI6awctXJ0qQt5s2ErgtubTVWBCSm8URDsvRylgH1q+fr2qeW1iQKqCvybWq7TR3FKMBFFPPaYdOKujkl1ooAq1jEhN9jLQUTUVUgLrIWSHIEJwJsNERFRIDiUE2TsUpcXA0MiosjFqz0pClfZJYps/GBC/G2TYnBJAKLIxg8mBDDHhBSCq+wSRbZgLYxJyhDI/ZsF1kgRuCQAUeTiWmXUEQMTUgS1LQlgtbSibPM27De9jLLN23hBJeoGP5hQR+wTJ0VwWRIg3c2fZRgtCcBxciLfqO2DCQWGPSakCGpZZdcxTi4I9UBOHJAfD+TEQRAaMCJvNkoL18ndRCLF4Vpl1BEDE1IENayyy3FyIv+o5YMJBQcDE1KMcF9ll+PkRP5RwwcTCh7+lklRsnNzYJ03C2WdCiyFw+KJHCcn8p8UC2NSeFL+1Z4iTrguCaCmBF4iOYTzBxMKHhZYIwqS9iJxDfacEhaJI6IIxQJrRArAcXIiosDxCkkRLdgLhnGcnIgoMBzKoYjlKIRm7FgILTE4hdC4QioRRbJA7t+8UlJEknol43BN4CUikht7TCjicCVjIiJpMfmVyAcshEZEpFwMTCjisBAaEZFyMTChiMMFw4iIlIuBCUUcLhhGRKRcDEwo4rAQGhGRcvHKSxGJhdCIiJSJ04UporEQGhFR8LHAGpGfWAiNiEhZmGNCREREisHAhIiIiBSDgQkREREphqSBSXFxMcaNG4fY2FgkJiZi6tSpUh6OiIiIwpxkya9vv/025syZg5UrV+LGG29Ea2srDh06JNXhiIiISAUkCUxaW1uxcOFCPPfcc8jJaa8HMXToUCkOR0RERCohyVBOSUkJTp06Ba1Wi+zsbKSmpuLWW2/tscfEbDajqanJ5UFERESRQ5LA5NixYwCAJ598Ek888QS2b9+OxMREXH/99Th79qzH95lMJiQkJDgfGRkZUjSPiIiIFMqnwGTRokXQaDTdPo4cOQKbzQYAePzxx3HnnXdi9OjR2LBhAzQaDf761796/P75+flobGx0PioqKgI7OyIiIgorPuWYPPzww7jnnnu63WfAgAGoqqoC4JpTYjAYMGDAAJw8edLjew0GAwwGg/O5o1o+h3SIiIjCh+O+7c+qNz4FJoIgQBCEHvcbPXo0DAYDjh49iokTJwIALBYLTpw4gUsvvdTr4507dw4AOKRDREQUhs6dO4eEhASf3iPJrJzevXvjgQcewNKlS5GRkYFLL70Uzz33HADgrrvu8vr7pKWloaKiAvHx8dBoNAG1qampCRkZGaioqFD9goA8V3XiuaoTz1WdIv1cRVHEuXPnkJaW5vP3k6yOyXPPPYeoqCj87ne/w4ULFzBu3Dh89NFHSExM9Pp7aLVapKenB7VdvXv3Vv0fiQPPVZ14rurEc1WnSD5XX3tKHCQLTPR6PZ5//nk8//zzUh2CiIiIVIZr5RAREZFiRExgYjAYsHTpUpdZP2rFc1Unnqs68VzViefqP43oz1weIiIiIglETI8JERERKR8DEyIiIlIMBiZERESkGAxMiIiISDEiOjApLi7GuHHjEBsbi8TEREydOlXuJknKbDZj5MiR0Gg0OHDggNzNCboTJ04gJycHmZmZiI2NxcCBA7F06VJcvHhR7qYFxcsvv4zLLrsMMTExGDduHPbt2yd3k4LOZDLhqquuQnx8PJKTkzF16lQcPXpU7maFxDPPPAONRoPc3Fy5myKJU6dOYebMmejbty9iY2MxfPhw7N+/X+5mBZ3VasXixYtdrkMrVqzwa80Ypdm1axduu+02pKWlQaPRYOvWrS6vi6KIJUuWIDU1FbGxsZg0aRK+/fZbn48TsYHJ22+/jd/97ne49957cfDgQfzrX//Cb3/7W7mbJalHHnnEr/LA4cKxsvUrr7yCsrIyFBQUYM2aNXjsscfkblrA3nrrLTz00ENYunQpSkpKMGLECEyePBm1tbVyNy2oPv30U8ybNw+fffYZPvjgA1gsFvziF79Ac3Oz3E2T1BdffIFXXnkFV155pdxNkUR9fT0mTJgAvV6P9957D4cPH8Yf//hHnyqBh4tVq1Zh9erVeOmll/DNN99g1apVePbZZ/Hiiy/K3bSANTc3Y8SIEXj55Zfdvv7ss8/ihRdewJo1a/D555+jV69emDx5MlpaWnw7kBiBLBaLeMkll4ivvfaa3E0JmX/84x/ikCFDxLKyMhGAWFpaKneTQuLZZ58VMzMz5W5GwMaOHSvOmzfP+dxqtYppaWmiyWSSsVXSq62tFQGIn376qdxNkcy5c+fEQYMGiR988IH485//XFy4cKHcTQq6Rx99VJw4caLczQiJKVOmiP/93//tsu2OO+4QZ8yYIVOLpAFAfOedd5zPbTabmJKSIj733HPObQ0NDaLBYBDffPNNn753RPaYlJSU4NSpU9BqtcjOzkZqaipuvfVWHDp0SO6mSaKmpgZz5szB//3f/yEuLk7u5oRUY2MjkpKS5G5GQC5evIgvv/wSkyZNcm7TarWYNGkS9u7dK2PLpNfY2AgAYf877M68efMwZcoUl9+v2vz973/HmDFjcNdddyE5ORnZ2dl49dVX5W6WJK655hrs3LkT5eXlAICDBw9iz549uPXWW2VumbSOHz+O6upql7/jhIQEjBs3zufrVEQGJseOHQMAPPnkk3jiiSewfft2JCYm4vrrr8fZs2dlbl1wiaKIe+65Bw888ADGjBkjd3NC6rvvvsOLL76I+++/X+6mBOT06dOwWq0wGo0u241GI6qrq2VqlfRsNhtyc3MxYcIEDBs2TO7mSKKoqAglJSUwmUxyN0VSx44dw+rVqzFo0CC8//77mDt3Lh588EFs3LhR7qYF3aJFizB9+nQMGTIEer0e2dnZyM3NxYwZM+RumqQc16JgXKdUFZgsWrQIGo2m24cjDwEAHn/8cdx5550YPXo0NmzYAI1Gg7/+9a8yn4V3vD3XF198EefOnUN+fr7cTfabt+fa0alTp3DLLbfgrrvuwpw5c2RqOQVi3rx5OHToEIqKiuRuiiQqKiqwcOFCbN68GTExMXI3R1I2mw2jRo3CypUrkZ2djfvuuw9z5szBmjVr5G5a0G3ZsgWbN2/GG2+8gZKSEmzcuBHPP/+8KoMwqUi2urAcHn74Ydxzzz3d7jNgwABUVVUBAIYOHercbjAYMGDAAJw8eVLKJgaNt+f60UcfYe/evV3WMBgzZgxmzJgRFv8s3p6rQ2VlJW644QZcc801WLt2rcStk16/fv2g0+lQU1Pjsr2mpgYpKSkytUpa8+fPx/bt27Fr1y6kp6fL3RxJfPnll6itrcWoUaOc26xWK3bt2oWXXnoJZrMZOp1OxhYGT2pqqsv1FgCuuOIKvP322zK1SDp/+MMfnL0mADB8+HD88MMPMJlMmDVrlsytk47jWlRTU4PU1FTn9pqaGowcOdKn76WqwEQQBAiC0ON+o0ePhsFgwNGjRzFx4kQAgMViwYkTJ3DppZdK3cyg8PZcX3jhBTz11FPO55WVlZg8eTLeeustjBs3TsomBo235wrYe0puuOEGZy+YVhv+nYLR0dEYPXo0du7c6ZzSbrPZsHPnTsyfP1/exgWZKIpYsGAB3nnnHXzyySfIzMyUu0mSuemmm/D111+7bLv33nsxZMgQPProo6oJSgBgwoQJXaZ9l5eXh8311hfnz5/vct3R6XTOnnq1yszMREpKCnbu3OkMRJqamvD5559j7ty5vn2z4OTnhp+FCxeKl1xyifj++++LR44cEXNycsTk5GTx7NmzcjdNUsePH1ftrJwff/xRvPzyy8WbbrpJ/PHHH8WqqirnI9wVFRWJBoNBfP3118XDhw+L9913n9inTx+xurpa7qYF1dy5c8WEhATxk08+cfn9nT9/Xu6mhYRaZ+Xs27dPjIqKEp9++mnx22+/FTdv3izGxcWJmzZtkrtpQTdr1izxkksuEbdv3y4eP35c/Nvf/ib269dPfOSRR+RuWsDOnTsnlpaWiqWlpSIA8U9/+pNYWloq/vDDD6IoiuIzzzwj9unTR9y2bZv41VdfibfffruYmZkpXrhwwafjRGxgcvHiRfHhhx8Wk5OTxfj4eHHSpEnioUOH5G6W5NQcmGzYsEEE4PahBi+++KLYv39/MTo6Whw7dqz42Wefyd2koPP0+9uwYYPcTQsJtQYmoiiK7777rjhs2DDRYDCIQ4YMEdeuXSt3kyTR1NQkLly4UOzfv78YExMjDhgwQHz88cdFs9ksd9MC9vHHH7v9/5w1a5YoivYpw4sXLxaNRqNoMBjEm266STx69KjPx9GIogrK0REREZEqhP8APBEREakGAxMiIiJSDAYmREREpBgMTIiIiEgxGJgQERGRYjAwISIiIsVgYEJERESKwcCEiIiIFIOBCRERESkGAxMiIiJSDAYmREREpBgMTIiIiEgx/j97vcaIzFqvwwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -299,7 +281,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -313,7 +295,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/22-Dockers/Hello World.zip b/22-Dockers/Hello World.zip
deleted file mode 100644
index 8626959e..00000000
Binary files a/22-Dockers/Hello World.zip and /dev/null differ
diff --git a/22-Dockers/Hello World/Hello World/Dockerfile b/22-Dockers/Hello World/Hello World/Dockerfile
new file mode 100644
index 00000000..230058de
--- /dev/null
+++ b/22-Dockers/Hello World/Hello World/Dockerfile	
@@ -0,0 +1,5 @@
+FROM python:3.7
+COPY . /app
+WORKDIR /app
+RUN pip install -r requirements.txt
+CMD ["python","app.py"]
\ No newline at end of file
diff --git a/22-Dockers/Hello World/Hello World/app.py b/22-Dockers/Hello World/Hello World/app.py
new file mode 100644
index 00000000..7be8ede0
--- /dev/null
+++ b/22-Dockers/Hello World/Hello World/app.py	
@@ -0,0 +1,16 @@
+## flask app for hello world
+
+from flask import Flask
+import numpy as np
+import pandas as pd
+
+app=Flask(__name__)
+
+@app.route('/',methods=['GET'])
+def home():
+    return "Hello World"
+
+
+
+if __name__=="__main__":
+    app.run(host="0.0.0.0",port=5000)
\ No newline at end of file
diff --git a/22-Dockers/Hello World/Hello World/requirements.txt b/22-Dockers/Hello World/Hello World/requirements.txt
new file mode 100644
index 00000000..2f8a568b
--- /dev/null
+++ b/22-Dockers/Hello World/Hello World/requirements.txt	
@@ -0,0 +1,3 @@
+flask
+pandas
+numpy
\ No newline at end of file
diff --git a/22-Dockers/Hello World/Hello World/test.py b/22-Dockers/Hello World/Hello World/test.py
new file mode 100644
index 00000000..e69de29b
diff --git a/26-CompleteNLP For Machine Learning/Practicals/15-Bag Of Words Practical's.ipynb b/26-CompleteNLP For Machine Learning/Practicals/15-Bag Of Words Practical's.ipynb
index fa8c64ef..decf8da0 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/15-Bag Of Words Practical's.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/15-Bag Of Words Practical's.ipynb	
@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "messages=pd.read_csv('smsspamcollection/SMSSpamCollection',\n",
+    "messages=pd.read_csv(r'C:\\Users\\mudas\\Downloads\\Complete_ML_and_DL\\Complete-Data-Science-With-Machine-Learning-And-NLP-2024\\26-CompleteNLP For Machine Learning\\Practicals\\SpamClassifier-master\\smsspamcollection\\SMSSpamCollection',\n",
     "                    sep='\\t',names=[\"label\",\"message\"])"
    ]
   },
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +138,7 @@
      "output_type": "stream",
      "text": [
       "[nltk_data] Downloading package stopwords to\n",
-      "[nltk_data]     C:\\Users\\win10\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
       "[nltk_data]   Package stopwords is already up-to-date!\n"
      ]
     },
@@ -148,7 +148,7 @@
        "True"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1198,7 +1198,7 @@
        " ...]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1216,7 +1216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1228,7 +1228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1237,7 +1237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1303,10 +1303,10 @@
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
-       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int64)"
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], shape=(5572, 100))"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1327,115 +1327,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'go': 22,\n",
-       " 'great': 25,\n",
-       " 'got': 24,\n",
-       " 'wat': 90,\n",
-       " 'ok': 56,\n",
-       " 'free': 18,\n",
-       " 'win': 94,\n",
-       " 'text': 77,\n",
-       " 'txt': 85,\n",
-       " 'say': 67,\n",
-       " 'alreadi': 0,\n",
-       " 'think': 80,\n",
-       " 'hey': 28,\n",
-       " 'week': 92,\n",
-       " 'back': 3,\n",
-       " 'like': 38,\n",
-       " 'still': 73,\n",
-       " 'send': 69,\n",
-       " 'even': 15,\n",
-       " 'friend': 19,\n",
-       " 'prize': 62,\n",
-       " 'claim': 7,\n",
-       " 'call': 4,\n",
-       " 'mobil': 47,\n",
-       " 'co': 8,\n",
-       " 'home': 30,\n",
-       " 'want': 89,\n",
-       " 'today': 82,\n",
-       " 'cash': 6,\n",
-       " 'day': 12,\n",
-       " 'repli': 64,\n",
-       " 'www': 96,\n",
-       " 'right': 65,\n",
-       " 'thank': 78,\n",
-       " 'take': 75,\n",
-       " 'time': 81,\n",
-       " 'use': 87,\n",
-       " 'messag': 44,\n",
-       " 'oh': 55,\n",
-       " 'ye': 97,\n",
-       " 'make': 42,\n",
-       " 'way': 91,\n",
-       " 'feel': 16,\n",
-       " 'dont': 14,\n",
-       " 'miss': 46,\n",
-       " 'ur': 86,\n",
-       " 'tri': 84,\n",
-       " 'da': 11,\n",
-       " 'lor': 39,\n",
-       " 'meet': 43,\n",
-       " 'realli': 63,\n",
-       " 'get': 20,\n",
-       " 'know': 33,\n",
-       " 'love': 40,\n",
-       " 'let': 37,\n",
-       " 'work': 95,\n",
-       " 'wait': 88,\n",
-       " 'yeah': 98,\n",
-       " 'tell': 76,\n",
-       " 'pleas': 61,\n",
-       " 'msg': 49,\n",
-       " 'see': 68,\n",
-       " 'pl': 60,\n",
-       " 'need': 51,\n",
-       " 'tomorrow': 83,\n",
-       " 'hope': 31,\n",
-       " 'well': 93,\n",
-       " 'lt': 41,\n",
-       " 'gt': 26,\n",
-       " 'ask': 1,\n",
-       " 'morn': 48,\n",
-       " 'happi': 27,\n",
-       " 'sorri': 72,\n",
-       " 'give': 21,\n",
-       " 'new': 52,\n",
-       " 'find': 17,\n",
-       " 'year': 99,\n",
-       " 'later': 35,\n",
-       " 'pick': 59,\n",
-       " 'good': 23,\n",
-       " 'come': 9,\n",
-       " 'said': 66,\n",
-       " 'hi': 29,\n",
-       " 'babe': 2,\n",
-       " 'im': 32,\n",
-       " 'much': 50,\n",
-       " 'stop': 74,\n",
-       " 'one': 57,\n",
-       " 'night': 53,\n",
-       " 'servic': 70,\n",
-       " 'dear': 13,\n",
-       " 'thing': 79,\n",
-       " 'contact': 10,\n",
-       " 'last': 34,\n",
-       " 'min': 45,\n",
-       " 'number': 54,\n",
-       " 'leav': 36,\n",
-       " 'sleep': 71,\n",
-       " 'care': 5,\n",
-       " 'phone': 58}"
+       "{'go': np.int64(22),\n",
+       " 'great': np.int64(25),\n",
+       " 'got': np.int64(24),\n",
+       " 'wat': np.int64(90),\n",
+       " 'ok': np.int64(56),\n",
+       " 'free': np.int64(18),\n",
+       " 'win': np.int64(94),\n",
+       " 'text': np.int64(77),\n",
+       " 'txt': np.int64(85),\n",
+       " 'say': np.int64(67),\n",
+       " 'alreadi': np.int64(0),\n",
+       " 'think': np.int64(80),\n",
+       " 'hey': np.int64(28),\n",
+       " 'week': np.int64(92),\n",
+       " 'back': np.int64(3),\n",
+       " 'like': np.int64(38),\n",
+       " 'still': np.int64(73),\n",
+       " 'send': np.int64(69),\n",
+       " 'even': np.int64(15),\n",
+       " 'friend': np.int64(19),\n",
+       " 'prize': np.int64(62),\n",
+       " 'claim': np.int64(7),\n",
+       " 'call': np.int64(4),\n",
+       " 'mobil': np.int64(47),\n",
+       " 'co': np.int64(8),\n",
+       " 'home': np.int64(30),\n",
+       " 'want': np.int64(89),\n",
+       " 'today': np.int64(82),\n",
+       " 'cash': np.int64(6),\n",
+       " 'day': np.int64(12),\n",
+       " 'repli': np.int64(64),\n",
+       " 'www': np.int64(96),\n",
+       " 'right': np.int64(65),\n",
+       " 'thank': np.int64(78),\n",
+       " 'take': np.int64(75),\n",
+       " 'time': np.int64(81),\n",
+       " 'use': np.int64(87),\n",
+       " 'messag': np.int64(44),\n",
+       " 'oh': np.int64(55),\n",
+       " 'ye': np.int64(97),\n",
+       " 'make': np.int64(42),\n",
+       " 'way': np.int64(91),\n",
+       " 'feel': np.int64(16),\n",
+       " 'dont': np.int64(14),\n",
+       " 'miss': np.int64(46),\n",
+       " 'ur': np.int64(86),\n",
+       " 'tri': np.int64(84),\n",
+       " 'da': np.int64(11),\n",
+       " 'lor': np.int64(39),\n",
+       " 'meet': np.int64(43),\n",
+       " 'realli': np.int64(63),\n",
+       " 'get': np.int64(20),\n",
+       " 'know': np.int64(33),\n",
+       " 'love': np.int64(40),\n",
+       " 'let': np.int64(37),\n",
+       " 'work': np.int64(95),\n",
+       " 'wait': np.int64(88),\n",
+       " 'yeah': np.int64(98),\n",
+       " 'tell': np.int64(76),\n",
+       " 'pleas': np.int64(61),\n",
+       " 'msg': np.int64(49),\n",
+       " 'see': np.int64(68),\n",
+       " 'pl': np.int64(60),\n",
+       " 'need': np.int64(51),\n",
+       " 'tomorrow': np.int64(83),\n",
+       " 'hope': np.int64(31),\n",
+       " 'well': np.int64(93),\n",
+       " 'lt': np.int64(41),\n",
+       " 'gt': np.int64(26),\n",
+       " 'ask': np.int64(1),\n",
+       " 'morn': np.int64(48),\n",
+       " 'happi': np.int64(27),\n",
+       " 'sorri': np.int64(72),\n",
+       " 'give': np.int64(21),\n",
+       " 'new': np.int64(52),\n",
+       " 'find': np.int64(17),\n",
+       " 'year': np.int64(99),\n",
+       " 'later': np.int64(35),\n",
+       " 'pick': np.int64(59),\n",
+       " 'good': np.int64(23),\n",
+       " 'come': np.int64(9),\n",
+       " 'said': np.int64(66),\n",
+       " 'hi': np.int64(29),\n",
+       " 'babe': np.int64(2),\n",
+       " 'im': np.int64(32),\n",
+       " 'much': np.int64(50),\n",
+       " 'stop': np.int64(74),\n",
+       " 'one': np.int64(57),\n",
+       " 'night': np.int64(53),\n",
+       " 'servic': np.int64(70),\n",
+       " 'dear': np.int64(13),\n",
+       " 'thing': np.int64(79),\n",
+       " 'contact': np.int64(10),\n",
+       " 'last': np.int64(34),\n",
+       " 'min': np.int64(45),\n",
+       " 'number': np.int64(54),\n",
+       " 'leav': np.int64(36),\n",
+       " 'sleep': np.int64(71),\n",
+       " 'care': np.int64(5),\n",
+       " 'phone': np.int64(58)}"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1446,7 +1446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1459,115 +1459,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'free entri': 32,\n",
-       " 'claim call': 17,\n",
-       " 'call claim': 3,\n",
-       " 'free call': 31,\n",
-       " 'chanc win': 16,\n",
-       " 'txt word': 91,\n",
-       " 'let know': 55,\n",
-       " 'go home': 36,\n",
-       " 'pleas call': 70,\n",
-       " 'lt gt': 61,\n",
-       " 'want go': 97,\n",
-       " 'like lt': 56,\n",
-       " 'like lt gt': 57,\n",
-       " 'sorri call': 83,\n",
-       " 'call later': 11,\n",
-       " 'sorri call later': 84,\n",
-       " 'ur award': 92,\n",
-       " 'call custom': 4,\n",
-       " 'custom servic': 24,\n",
-       " 'cash prize': 15,\n",
-       " 'call custom servic': 5,\n",
-       " 'po box': 71,\n",
-       " 'tri contact': 89,\n",
-       " 'draw show': 28,\n",
-       " 'show prize': 81,\n",
-       " 'prize guarante': 75,\n",
-       " 'guarante call': 43,\n",
-       " 'valid hr': 95,\n",
-       " 'draw show prize': 29,\n",
-       " 'show prize guarante': 82,\n",
-       " 'prize guarante call': 76,\n",
-       " 'select receiv': 78,\n",
-       " 'privat account': 72,\n",
-       " 'account statement': 0,\n",
-       " 'call identifi': 6,\n",
-       " 'identifi code': 49,\n",
-       " 'code expir': 21,\n",
-       " 'privat account statement': 73,\n",
-       " 'call identifi code': 7,\n",
-       " 'identifi code expir': 50,\n",
-       " 'urgent mobil': 94,\n",
-       " 'call landlin': 10,\n",
-       " 'wat time': 98,\n",
-       " 'ur mob': 93,\n",
-       " 'gud ni': 45,\n",
-       " 'new year': 65,\n",
-       " 'send stop': 80,\n",
-       " 'get back': 34,\n",
-       " 'co uk': 20,\n",
-       " 'nice day': 66,\n",
-       " 'lt decim': 59,\n",
-       " 'decim gt': 26,\n",
-       " 'lt decim gt': 60,\n",
-       " 'good morn': 38,\n",
-       " 'good night': 39,\n",
-       " 'repli call': 77,\n",
-       " 'last night': 54,\n",
-       " 'pick phone': 68,\n",
-       " 'pl send': 69,\n",
-       " 'send messag': 79,\n",
-       " 'great day': 40,\n",
-       " 'suit land': 85,\n",
-       " 'land row': 53,\n",
-       " 'suit land row': 86,\n",
-       " 'good afternoon': 37,\n",
-       " 'take care': 87,\n",
-       " 'call mobileupd': 12,\n",
-       " 'call optout': 13,\n",
-       " 'gt min': 42,\n",
-       " 'lt gt min': 62,\n",
-       " 'txt stop': 90,\n",
-       " 'date servic': 25,\n",
-       " 'call land': 8,\n",
-       " 'land line': 51,\n",
-       " 'line claim': 58,\n",
-       " 'claim valid': 18,\n",
-       " 'guarante call land': 44,\n",
-       " 'call land line': 9,\n",
-       " 'land line claim': 52,\n",
-       " 'claim valid hr': 19,\n",
-       " 'gt lt': 41,\n",
-       " 'hope good': 48,\n",
-       " 'free text': 33,\n",
-       " 'prize claim': 74,\n",
-       " 'nd attempt': 64,\n",
-       " 'attempt contact': 1,\n",
-       " 'ok lor': 67,\n",
-       " 'want come': 96,\n",
-       " 'everi week': 30,\n",
-       " 'come home': 23,\n",
-       " 'happi new': 46,\n",
-       " 'happi new year': 47,\n",
-       " 'nation rate': 63,\n",
-       " 'week txt': 99,\n",
-       " 'tell ur': 88,\n",
-       " 'gift voucher': 35,\n",
-       " 'await collect': 2,\n",
-       " 'dont know': 27,\n",
-       " 'come back': 22,\n",
-       " 'call per': 14}"
+       "{'free entri': np.int64(32),\n",
+       " 'claim call': np.int64(17),\n",
+       " 'call claim': np.int64(3),\n",
+       " 'free call': np.int64(31),\n",
+       " 'chanc win': np.int64(16),\n",
+       " 'txt word': np.int64(91),\n",
+       " 'let know': np.int64(54),\n",
+       " 'go home': np.int64(35),\n",
+       " 'pleas call': np.int64(69),\n",
+       " 'lt gt': np.int64(60),\n",
+       " 'want go': np.int64(97),\n",
+       " 'like lt': np.int64(55),\n",
+       " 'like lt gt': np.int64(56),\n",
+       " 'sorri call': np.int64(82),\n",
+       " 'call later': np.int64(11),\n",
+       " 'sorri call later': np.int64(83),\n",
+       " 'ur award': np.int64(92),\n",
+       " 'call custom': np.int64(4),\n",
+       " 'custom servic': np.int64(24),\n",
+       " 'cash prize': np.int64(15),\n",
+       " 'call custom servic': np.int64(5),\n",
+       " 'po box': np.int64(70),\n",
+       " 'tri contact': np.int64(88),\n",
+       " 'draw show': np.int64(28),\n",
+       " 'show prize': np.int64(80),\n",
+       " 'prize guarante': np.int64(74),\n",
+       " 'guarante call': np.int64(41),\n",
+       " 'valid hr': np.int64(95),\n",
+       " 'draw show prize': np.int64(29),\n",
+       " 'show prize guarante': np.int64(81),\n",
+       " 'prize guarante call': np.int64(75),\n",
+       " 'select receiv': np.int64(77),\n",
+       " 'privat account': np.int64(71),\n",
+       " 'account statement': np.int64(0),\n",
+       " 'call identifi': np.int64(6),\n",
+       " 'identifi code': np.int64(48),\n",
+       " 'code expir': np.int64(21),\n",
+       " 'privat account statement': np.int64(72),\n",
+       " 'call identifi code': np.int64(7),\n",
+       " 'identifi code expir': np.int64(49),\n",
+       " 'urgent mobil': np.int64(94),\n",
+       " 'call landlin': np.int64(10),\n",
+       " 'wat time': np.int64(98),\n",
+       " 'ur mob': np.int64(93),\n",
+       " 'gud ni': np.int64(43),\n",
+       " 'new year': np.int64(64),\n",
+       " 'send stop': np.int64(79),\n",
+       " 'get back': np.int64(34),\n",
+       " 'co uk': np.int64(20),\n",
+       " 'nice day': np.int64(65),\n",
+       " 'lt decim': np.int64(58),\n",
+       " 'decim gt': np.int64(26),\n",
+       " 'lt decim gt': np.int64(59),\n",
+       " 'txt nokia': np.int64(89),\n",
+       " 'good morn': np.int64(36),\n",
+       " 'good night': np.int64(37),\n",
+       " 'repli call': np.int64(76),\n",
+       " 'last night': np.int64(53),\n",
+       " 'pick phone': np.int64(67),\n",
+       " 'pl send': np.int64(68),\n",
+       " 'send messag': np.int64(78),\n",
+       " 'great day': np.int64(38),\n",
+       " 'suit land': np.int64(84),\n",
+       " 'land row': np.int64(52),\n",
+       " 'suit land row': np.int64(85),\n",
+       " 'take care': np.int64(86),\n",
+       " 'call mobileupd': np.int64(12),\n",
+       " 'call optout': np.int64(13),\n",
+       " 'gt min': np.int64(40),\n",
+       " 'lt gt min': np.int64(61),\n",
+       " 'txt stop': np.int64(90),\n",
+       " 'date servic': np.int64(25),\n",
+       " 'call land': np.int64(8),\n",
+       " 'land line': np.int64(50),\n",
+       " 'line claim': np.int64(57),\n",
+       " 'claim valid': np.int64(18),\n",
+       " 'guarante call land': np.int64(42),\n",
+       " 'call land line': np.int64(9),\n",
+       " 'land line claim': np.int64(51),\n",
+       " 'claim valid hr': np.int64(19),\n",
+       " 'gt lt': np.int64(39),\n",
+       " 'hope good': np.int64(47),\n",
+       " 'free text': np.int64(33),\n",
+       " 'holiday cash': np.int64(46),\n",
+       " 'prize claim': np.int64(73),\n",
+       " 'nd attempt': np.int64(63),\n",
+       " 'attempt contact': np.int64(1),\n",
+       " 'ok lor': np.int64(66),\n",
+       " 'want come': np.int64(96),\n",
+       " 'everi week': np.int64(30),\n",
+       " 'come home': np.int64(23),\n",
+       " 'happi new': np.int64(44),\n",
+       " 'happi new year': np.int64(45),\n",
+       " 'nation rate': np.int64(62),\n",
+       " 'week txt': np.int64(99),\n",
+       " 'tell ur': np.int64(87),\n",
+       " 'await collect': np.int64(2),\n",
+       " 'dont know': np.int64(27),\n",
+       " 'come back': np.int64(22),\n",
+       " 'call per': np.int64(14)}"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1578,80 +1578,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int64)"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X"
    ]
@@ -1673,7 +1602,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1687,7 +1616,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/16-TF-IDF Practical.ipynb b/26-CompleteNLP For Machine Learning/Practicals/16-TF-IDF Practical.ipynb
index 085b594e..9978d777 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/16-TF-IDF Practical.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/16-TF-IDF Practical.ipynb	
@@ -138,7 +138,7 @@
      "output_type": "stream",
      "text": [
       "[nltk_data] Downloading package stopwords to\n",
-      "[nltk_data]     C:\\Users\\win10\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
       "[nltk_data]   Package stopwords is already up-to-date!\n"
      ]
     },
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -714,9 +714,9 @@
        " 'competition',\n",
        " 'boltblue tone p reply poly mono eg poly cha cha slide yeah slow jamz toxic come stop tone txt',\n",
        " 'credit topped http www bubbletext com renewal pin tgxxrz',\n",
-       " 'way transport le problematic sat night way u want ask n join bday feel free need know definite no booking fri',\n",
+       " 'way transport less problematic sat night way u want ask n join bday feel free need know definite no booking fri',\n",
        " 'usually person unconscious child adult may behave abnormally call',\n",
-       " 'ebay might le elsewhere',\n",
+       " 'ebay might less elsewhere',\n",
        " 'shall come get pickle',\n",
        " 'gonna go get taco',\n",
        " 'rude campus',\n",
@@ -1140,7 +1140,7 @@
        " 'theyre lot place hospital medical place safe',\n",
        " 'getting touch folk waiting company txt back name age opt enjoy community p sm',\n",
        " 'also sorta blown couple time recently id rather text blue looking weed',\n",
-       " 'sent score sophas secondary application school think thinking applying research cost also contact joke ogunrinde school one le expensive one',\n",
+       " 'sent score sophas secondary application school think thinking applying research cost also contact joke ogunrinde school one less expensive one',\n",
        " 'cant wait see photo useful',\n",
        " 'ur cash balance currently pound maximize ur cash send go p msg cc po box tcr w',\n",
        " 'hey booked kb sat already lesson going ah keep sat night free need meet confirm lodging',\n",
@@ -1152,7 +1152,7 @@
        " 'also remember get dobby bowl car',\n",
        " 'filthy story girl waiting',\n",
        " 'sorry c ur msg yar lor poor thing one night tmr u brand new room sleep',\n",
-       " 'love decision feeling could decide love life would much simpler le magical',\n",
+       " 'love decision feeling could decide love life would much simpler less magical',\n",
        " 'welp apparently retired',\n",
        " 'sort code acc bank natwest reply confirm sent right person',\n",
        " '',\n",
@@ -1198,7 +1198,7 @@
        " ...]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1216,7 +1216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1225,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1235,7 +1235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1246,76 +1246,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "       ...,\n",
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        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.707, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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-       "       [0, 0, 0, 0, 0, 0, 0, 0.369, 0, 0, 0.555, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.469, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.706, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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+       "       [0, 0, 0, 0, 0, 0, 0.369, 0, 0, 0.555, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.469, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.548, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.631, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.548, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.631, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], shape=(5572, 100))"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1333,7 +1333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1343,115 +1343,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'free entry': 31,\n",
-       " 'claim call': 15,\n",
-       " 'call claim': 3,\n",
-       " 'free call': 30,\n",
-       " 'chance win': 14,\n",
-       " 'txt word': 89,\n",
-       " 'let know': 54,\n",
-       " 'please call': 66,\n",
-       " 'lt gt': 58,\n",
-       " 'want go': 97,\n",
-       " 'like lt': 55,\n",
-       " 'sorry call': 79,\n",
-       " 'call later': 8,\n",
-       " 'ur awarded': 90,\n",
-       " 'hi hi': 47,\n",
-       " 'call customer': 4,\n",
-       " 'customer service': 22,\n",
-       " 'guaranteed cash': 42,\n",
-       " 'cash prize': 13,\n",
-       " 'po box': 68,\n",
-       " 'trying contact': 86,\n",
-       " 'draw show': 27,\n",
-       " 'show prize': 78,\n",
-       " 'prize guaranteed': 72,\n",
-       " 'guaranteed call': 41,\n",
-       " 'valid hr': 95,\n",
-       " 'selected receive': 75,\n",
-       " 'private account': 70,\n",
-       " 'account statement': 0,\n",
-       " 'statement show': 80,\n",
-       " 'call identifier': 5,\n",
-       " 'identifier code': 50,\n",
-       " 'code expires': 19,\n",
-       " 'urgent mobile': 94,\n",
-       " 'call landline': 7,\n",
-       " 'wat time': 98,\n",
-       " 'give call': 34,\n",
-       " 'ur mob': 93,\n",
-       " 'gud ni': 44,\n",
-       " 'new year': 62,\n",
-       " 'send stop': 77,\n",
-       " 'co uk': 18,\n",
-       " 'gud mrng': 43,\n",
-       " 'nice day': 63,\n",
-       " 'lt decimal': 57,\n",
-       " 'decimal gt': 24,\n",
-       " 'txt nokia': 87,\n",
-       " 'good morning': 36,\n",
-       " 'ur friend': 92,\n",
-       " 'good night': 37,\n",
-       " 'reply call': 74,\n",
-       " 'last night': 53,\n",
-       " 'camera phone': 12,\n",
-       " 'pick phone': 65,\n",
-       " 'pls send': 67,\n",
-       " 'send message': 76,\n",
-       " 'great day': 38,\n",
-       " 'ur cash': 91,\n",
-       " 'suite land': 81,\n",
-       " 'land row': 52,\n",
-       " 'good afternoon': 35,\n",
-       " 'take care': 82,\n",
-       " 'double min': 26,\n",
-       " 'call mobileupd': 9,\n",
-       " 'call optout': 10,\n",
-       " 'gt min': 40,\n",
-       " 'half price': 45,\n",
-       " 'txt stop': 88,\n",
-       " 'dating service': 23,\n",
-       " 'pobox wq': 69,\n",
-       " 'mobile number': 59,\n",
-       " 'call land': 6,\n",
-       " 'land line': 51,\n",
-       " 'line claim': 56,\n",
-       " 'claim valid': 17,\n",
-       " 'gt lt': 39,\n",
-       " 'hope good': 49,\n",
-       " 'free text': 32,\n",
-       " 'holiday cash': 48,\n",
-       " 'prize claim': 71,\n",
-       " 'nd attempt': 61,\n",
-       " 'attempt contact': 1,\n",
-       " 'claim ur': 16,\n",
-       " 'redeemed point': 73,\n",
-       " 'ok lor': 64,\n",
-       " 'want come': 96,\n",
-       " 'every week': 28,\n",
-       " 'come home': 21,\n",
-       " 'happy new': 46,\n",
-       " 'every wk': 29,\n",
-       " 'national rate': 60,\n",
-       " 'tone ur': 85,\n",
-       " 'week txt': 99,\n",
-       " 'tell ur': 83,\n",
-       " 'gift voucher': 33,\n",
-       " 'await collection': 2,\n",
-       " 'dont know': 25,\n",
-       " 'come back': 20,\n",
-       " 'call per': 11,\n",
-       " 'th ur': 84}"
+       "{'free entry': np.int64(31),\n",
+       " 'claim call': np.int64(15),\n",
+       " 'call claim': np.int64(3),\n",
+       " 'free call': np.int64(30),\n",
+       " 'chance win': np.int64(14),\n",
+       " 'txt word': np.int64(89),\n",
+       " 'let know': np.int64(52),\n",
+       " 'mobile free': np.int64(57),\n",
+       " 'please call': np.int64(67),\n",
+       " 'lt gt': np.int64(56),\n",
+       " 'want go': np.int64(97),\n",
+       " 'like lt': np.int64(53),\n",
+       " 'sorry call': np.int64(80),\n",
+       " 'call later': np.int64(8),\n",
+       " 'ur awarded': np.int64(90),\n",
+       " 'hi hi': np.int64(45),\n",
+       " 'call customer': np.int64(4),\n",
+       " 'customer service': np.int64(22),\n",
+       " 'guaranteed cash': np.int64(41),\n",
+       " 'cash prize': np.int64(13),\n",
+       " 'po box': np.int64(69),\n",
+       " 'trying contact': np.int64(86),\n",
+       " 'draw show': np.int64(27),\n",
+       " 'show prize': np.int64(79),\n",
+       " 'prize guaranteed': np.int64(73),\n",
+       " 'guaranteed call': np.int64(40),\n",
+       " 'valid hr': np.int64(95),\n",
+       " 'selected receive': np.int64(76),\n",
+       " 'private account': np.int64(71),\n",
+       " 'account statement': np.int64(0),\n",
+       " 'statement show': np.int64(81),\n",
+       " 'call identifier': np.int64(5),\n",
+       " 'identifier code': np.int64(48),\n",
+       " 'code expires': np.int64(19),\n",
+       " 'urgent mobile': np.int64(94),\n",
+       " 'call landline': np.int64(7),\n",
+       " 'wat time': np.int64(98),\n",
+       " 'ur mob': np.int64(93),\n",
+       " 'gud ni': np.int64(43),\n",
+       " 'new year': np.int64(62),\n",
+       " 'send stop': np.int64(78),\n",
+       " 'co uk': np.int64(18),\n",
+       " 'gud mrng': np.int64(42),\n",
+       " 'nice day': np.int64(63),\n",
+       " 'lt decimal': np.int64(55),\n",
+       " 'decimal gt': np.int64(24),\n",
+       " 'txt nokia': np.int64(87),\n",
+       " 'good morning': np.int64(35),\n",
+       " 'ur friend': np.int64(92),\n",
+       " 'good night': np.int64(36),\n",
+       " 'network min': np.int64(61),\n",
+       " 'reply call': np.int64(75),\n",
+       " 'last night': np.int64(51),\n",
+       " 'camera phone': np.int64(12),\n",
+       " 'pick phone': np.int64(66),\n",
+       " 'pls send': np.int64(68),\n",
+       " 'send message': np.int64(77),\n",
+       " 'great day': np.int64(37),\n",
+       " 'ur cash': np.int64(91),\n",
+       " 'suite land': np.int64(83),\n",
+       " 'land row': np.int64(50),\n",
+       " 'good afternoon': np.int64(34),\n",
+       " 'take care': np.int64(84),\n",
+       " 'double min': np.int64(26),\n",
+       " 'call mobileupd': np.int64(9),\n",
+       " 'call optout': np.int64(10),\n",
+       " 'gt min': np.int64(39),\n",
+       " 'txt stop': np.int64(88),\n",
+       " 'dating service': np.int64(23),\n",
+       " 'pobox wq': np.int64(70),\n",
+       " 'mobile number': np.int64(58),\n",
+       " 'call land': np.int64(6),\n",
+       " 'land line': np.int64(49),\n",
+       " 'line claim': np.int64(54),\n",
+       " 'claim valid': np.int64(17),\n",
+       " 'gt lt': np.int64(38),\n",
+       " 'hope good': np.int64(47),\n",
+       " 'free text': np.int64(32),\n",
+       " 'holiday cash': np.int64(46),\n",
+       " 'prize claim': np.int64(72),\n",
+       " 'nd attempt': np.int64(60),\n",
+       " 'attempt contact': np.int64(1),\n",
+       " 'claim ur': np.int64(16),\n",
+       " 'redeemed point': np.int64(74),\n",
+       " 'ok lor': np.int64(64),\n",
+       " 'want come': np.int64(96),\n",
+       " 'stop text': np.int64(82),\n",
+       " 'every week': np.int64(29),\n",
+       " 'come home': np.int64(21),\n",
+       " 'happy new': np.int64(44),\n",
+       " 'national rate': np.int64(59),\n",
+       " 'week txt': np.int64(99),\n",
+       " 'tell ur': np.int64(85),\n",
+       " 'gift voucher': np.int64(33),\n",
+       " 'await collection': np.int64(2),\n",
+       " 'dont know': np.int64(25),\n",
+       " 'come back': np.int64(20),\n",
+       " 'call per': np.int64(11),\n",
+       " 'per min': np.int64(65),\n",
+       " 'dun wan': np.int64(28)}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1462,7 +1462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1515,7 +1515,7 @@
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        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       "       [0, 0, 0, 0, 0, 0, 0, 0, 0.688, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.726, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0.688, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.726, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
@@ -1524,14 +1524,14 @@
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        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.588, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0.578, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.588, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0.578, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], shape=(5572, 100))"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1540,6 +1540,13 @@
     "X"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1550,7 +1557,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1564,7 +1571,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/27.2-Spam Ham Classification Project Using BOW And TFIDF And ML.ipynb b/26-CompleteNLP For Machine Learning/Practicals/27.2-Spam Ham Classification Project Using BOW And TFIDF And ML.ipynb
index 61eadee2..62db27bb 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/27.2-Spam Ham Classification Project Using BOW And TFIDF And ML.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/27.2-Spam Ham Classification Project Using BOW And TFIDF And ML.ipynb	
@@ -3678,6 +3678,1634 @@
     "print(classification_report(y_pred,y_test))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BOW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(r'C:\\Users\\mudas\\Downloads\\Complete_ML_and_DL\\Complete-Data-Science-With-Machine-Learning-And-NLP-2024\\26-CompleteNLP For Machine Learning\\Practicals\\SpamClassifier-master\\smsspamcollection\\SMSSpamCollection',sep='\\t',names=['label','message'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>message</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Ok lar... Joking wif u oni...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>U dun say so early hor... U c already then say...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5567</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5568</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Will ü b going to esplanade fr home?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5569</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5570</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5571</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Rofl. Its true to its name</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5572 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     label                                            message\n",
+       "0      ham  Go until jurong point, crazy.. Available only ...\n",
+       "1      ham                      Ok lar... Joking wif u oni...\n",
+       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
+       "3      ham  U dun say so early hor... U c already then say...\n",
+       "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
+       "...    ...                                                ...\n",
+       "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
+       "5568   ham               Will ü b going to esplanade fr home?\n",
+       "5569   ham  Pity, * was in mood for that. So...any other s...\n",
+       "5570   ham  The guy did some bitching but I acted like i'd...\n",
+       "5571   ham                         Rofl. Its true to its name\n",
+       "\n",
+       "[5572 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>message</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>778</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Hi its Kate it was lovely to see you tonight a...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5388</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>NOT MUCH NO FIGHTS. IT WAS A GOOD NITE!!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2729</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>Urgent Please call 09066612661 from landline. ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2618</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>I cant pick the phone right now. Pls send a me...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3474</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>You getting back any time soon?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1620</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Friends that u can stay on fb chat with</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4112</th>\n",
+       "      <td>spam</td>\n",
+       "      <td>URGENT! Your Mobile number has been awarded a ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5453</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Except theres a chick with huge boobs.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4140</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Ever green quote ever told by Jerry in cartoon...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2052</th>\n",
+       "      <td>ham</td>\n",
+       "      <td>Hey darlin.. i can pick u up at college if u t...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     label                                            message\n",
+       "778    ham  Hi its Kate it was lovely to see you tonight a...\n",
+       "5388   ham           NOT MUCH NO FIGHTS. IT WAS A GOOD NITE!!\n",
+       "2729  spam  Urgent Please call 09066612661 from landline. ...\n",
+       "2618   ham  I cant pick the phone right now. Pls send a me...\n",
+       "3474   ham                    You getting back any time soon?\n",
+       "1620   ham            Friends that u can stay on fb chat with\n",
+       "4112  spam  URGENT! Your Mobile number has been awarded a ...\n",
+       "5453   ham             Except theres a chick with huge boobs.\n",
+       "4140   ham  Ever green quote ever told by Jerry in cartoon...\n",
+       "2052   ham  Hey darlin.. i can pick u up at college if u t..."
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5572, 2)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import nltk\n",
+    "nltk.download('stopwords')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.stem import PorterStemmer\n",
+    "from nltk.corpus import stopwords\n",
+    "ps=PorterStemmer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['go jurong point crazi avail bugi n great world la e buffet cine got amor wat',\n",
+       " 'ok lar joke wif u oni',\n",
+       " 'free entri wkli comp win fa cup final tkt st may text fa receiv entri question std txt rate c appli',\n",
+       " 'u dun say earli hor u c alreadi say',\n",
+       " 'nah think goe usf live around though',\n",
+       " 'freemsg hey darl week word back like fun still tb ok xxx std chg send rcv',\n",
+       " 'even brother like speak treat like aid patent',\n",
+       " 'per request mell mell oru minnaminungint nurungu vettam set callertun caller press copi friend callertun',\n",
+       " 'winner valu network custom select receivea prize reward claim call claim code kl valid hour',\n",
+       " 'mobil month u r entitl updat latest colour mobil camera free call mobil updat co free',\n",
+       " 'gonna home soon want talk stuff anymor tonight k cri enough today',\n",
+       " 'six chanc win cash pound txt csh send cost p day day tsandc appli repli hl info',\n",
+       " 'urgent week free membership prize jackpot txt word claim c www dbuk net lccltd pobox ldnw rw',\n",
+       " 'search right word thank breather promis wont take help grant fulfil promis wonder bless time',\n",
+       " 'date sunday',\n",
+       " 'xxxmobilemovieclub use credit click wap link next txt messag click http wap xxxmobilemovieclub com n qjkgighjjgcbl',\n",
+       " 'oh k watch',\n",
+       " 'eh u rememb spell name ye v naughti make v wet',\n",
+       " 'fine way u feel way gota b',\n",
+       " 'england v macedonia dont miss goal team news txt ur nation team eg england tri wale scotland txt poboxox w wq',\n",
+       " 'serious spell name',\n",
+       " 'go tri month ha ha joke',\n",
+       " 'pay first lar da stock comin',\n",
+       " 'aft finish lunch go str lor ard smth lor u finish ur lunch alreadi',\n",
+       " 'ffffffffff alright way meet sooner',\n",
+       " 'forc eat slice realli hungri tho suck mark get worri know sick turn pizza lol',\n",
+       " 'lol alway convinc',\n",
+       " 'catch bu fri egg make tea eat mom left dinner feel love',\n",
+       " 'back amp pack car let know room',\n",
+       " 'ahhh work vagu rememb feel like lol',\n",
+       " 'wait still clear sure sarcast x want live us',\n",
+       " 'yeah got v apologet n fallen actin like spoilt child got caught till go badli cheer',\n",
+       " 'k tell anyth',\n",
+       " 'fear faint housework quick cuppa',\n",
+       " 'thank subscript rington uk mobil charg month pleas confirm repli ye repli charg',\n",
+       " 'yup ok go home look time msg xuhui go learn nd may lesson',\n",
+       " 'oop let know roommat done',\n",
+       " 'see letter b car',\n",
+       " 'anyth lor u decid',\n",
+       " 'hello saturday go text see decid anyth tomo tri invit anyth',\n",
+       " 'pl go ahead watt want sure great weekend abiola',\n",
+       " 'forget tell want need crave love sweet arabian steed mmmmmm yummi',\n",
+       " 'rodger burn msg tri call repli sm free nokia mobil free camcord pleas call deliveri tomorrow',\n",
+       " 'see',\n",
+       " 'great hope like man well endow lt gt inch',\n",
+       " 'call messag miss call',\n",
+       " 'get hep b immunis nigeria',\n",
+       " 'fair enough anyth go',\n",
+       " 'yeah hope tyler could mayb ask around bit',\n",
+       " 'u know stubborn even want go hospit kept tell mark weak sucker hospit weak sucker',\n",
+       " 'think first time saw class',\n",
+       " 'gram usual run like lt gt half eighth smarter though get almost whole second gram lt gt',\n",
+       " 'k fyi x ride earli tomorrow morn crash place tonight',\n",
+       " 'wow never realiz embarass accomod thought like sinc best could alway seem happi cave sorri give sorri offer sorri room embarass',\n",
+       " 'sm ac sptv new jersey devil detroit red wing play ice hockey correct incorrect end repli end sptv',\n",
+       " 'know mallika sherawat yesterday find lt url gt',\n",
+       " 'congrat year special cinema pass call c suprman v matrix starwar etc free bx ip pm dont miss',\n",
+       " 'sorri call later meet',\n",
+       " 'tell reach',\n",
+       " 'ye gauti sehwag odi seri',\n",
+       " 'gonna pick burger way home even move pain kill',\n",
+       " 'ha ha ha good joke girl situat seeker',\n",
+       " 'part check iq',\n",
+       " 'sorri roommat took forev ok come',\n",
+       " 'ok lar doubl check wif da hair dresser alreadi said wun cut v short said cut look nice',\n",
+       " 'valu custom pleas advis follow recent review mob award bonu prize call',\n",
+       " 'today song dedic day song u dedic send ur valuabl frnd first rpli',\n",
+       " 'urgent ur award complimentari trip eurodisinc trav aco entri claim txt di morefrmmob shracomorsglsuplt ls aj',\n",
+       " 'hear new divorc barbi come ken stuff',\n",
+       " 'plane give month end',\n",
+       " 'wah lucki man save money hee',\n",
+       " 'finish class',\n",
+       " 'hi babe im home wanna someth xx',\n",
+       " 'k k perform',\n",
+       " 'u call',\n",
+       " 'wait machan call free',\n",
+       " 'that cool gentleman treat digniti respect',\n",
+       " 'like peopl much shi pa',\n",
+       " 'oper lt gt',\n",
+       " 'still look job much ta earn',\n",
+       " 'sorri call later',\n",
+       " 'k call ah',\n",
+       " 'ok way home hi hi',\n",
+       " 'place man',\n",
+       " 'yup next stop',\n",
+       " 'call later network urgnt sm',\n",
+       " 'real u get yo need ticket one jacket done alreadi use multi',\n",
+       " 'ye start send request make pain came back back bed doubl coin factori gotta cash nitro',\n",
+       " 'realli still tonight babe',\n",
+       " 'ela kano il download come wen ur free',\n",
+       " 'yeah stand close tho catch someth',\n",
+       " 'sorri pain ok meet anoth night spent late afternoon casualti mean done stuff moro includ time sheet sorri',\n",
+       " 'smile pleasur smile pain smile troubl pour like rain smile sum hurt u smile becoz someon still love see u smile',\n",
+       " 'pleas call custom servic repres pm guarante cash prize',\n",
+       " 'havent plan buy later check alreadi lido got show e afternoon u finish work alreadi',\n",
+       " 'free rington wait collect simpli text password mix verifi get usher britney fml po box mk h ppw',\n",
+       " 'watch telugu movi wat abt u',\n",
+       " 'see finish load loan pay',\n",
+       " 'hi wk ok hol ye bit run forgot hairdress appoint four need get home n shower beforehand caus prob u',\n",
+       " 'see cup coffe anim',\n",
+       " 'pleas text anymor noth els say',\n",
+       " 'okay name ur price long legal wen pick u ave x am xx',\n",
+       " 'still look car buy gone drive test yet',\n",
+       " 'per request mell mell oru minnaminungint nurungu vettam set callertun caller press copi friend callertun',\n",
+       " 'wow right mean guess gave boston men chang search locat nyc someth chang cuz signin page still say boston',\n",
+       " 'umma life vava umma love lot dear',\n",
+       " 'thank lot wish birthday thank make birthday truli memor',\n",
+       " 'aight hit get cash',\n",
+       " 'would ip address test consid comput minecraft server',\n",
+       " 'know grumpi old peopl mom like better lie alway one play joke',\n",
+       " 'dont worri guess busi',\n",
+       " 'plural noun research',\n",
+       " 'go dinner msg',\n",
+       " 'ok wif co like tri new thing scare u dun like mah co u said loud',\n",
+       " 'gent tri contact last weekend draw show prize guarante call claim code k valid hr ppm',\n",
+       " 'wa ur openin sentenc formal anyway fine juz tt eatin much n puttin weight haha anythin special happen',\n",
+       " 'enter cabin pa said happi b day boss felt special askd lunch lunch invit apart went',\n",
+       " 'winner u special select receiv holiday flight inc speak live oper claim p min',\n",
+       " 'goodo ye must speak friday egg potato ratio tortilla need',\n",
+       " 'hmm uncl inform pay school directli pl buy food',\n",
+       " 'privat account statement show unredeem bonu point claim call identifi code expir',\n",
+       " 'urgent mobil award bonu caller prize final tri contact u call landlin box wr c ppm',\n",
+       " 'new address appl pair malarki',\n",
+       " 'today voda number end select receiv award match pleas call quot claim code standard rate app',\n",
+       " 'go sao mu today done',\n",
+       " 'predict wat time finish buy',\n",
+       " 'good stuff',\n",
+       " 'know yetund sent money yet sent text bother send dont involv anyth impos anyth first place apologis',\n",
+       " 'room',\n",
+       " 'hey girl r u hope u r well del r bak long time c give call sum time lucyxx',\n",
+       " 'k k much cost',\n",
+       " 'home',\n",
+       " 'dear call tmorrow pl accomod',\n",
+       " 'first answer question',\n",
+       " 'sunshin quiz wkli q win top soni dvd player u know countri algarv txt ansr sp tyron',\n",
+       " 'want get laid tonight want real dog locat sent direct ur mob join uk largest dog network bt txting gravel nt ec p msg p',\n",
+       " 'haf msn yiju hotmail com',\n",
+       " 'call meet',\n",
+       " 'check room befor activ',\n",
+       " 'rcv msg chat svc free hardcor servic text go u get noth u must age verifi yr network tri',\n",
+       " 'got c lazi type forgot lect saw pouch like v nice',\n",
+       " 'k text way',\n",
+       " 'sir wait mail',\n",
+       " 'swt thought nver get tire littl thing lovabl person coz somtim littl thing occupi biggest part heart gud ni',\n",
+       " 'know pl open back',\n",
+       " 'ye see ya dot',\n",
+       " 'what staff name take class us',\n",
+       " 'freemsg repli text randi sexi femal live local luv hear u netcollex ltd p per msg repli stop end',\n",
+       " 'ummma call check life begin qatar pl pray hard',\n",
+       " 'k delet contact',\n",
+       " 'sindu got job birla soft',\n",
+       " 'wine flow never',\n",
+       " 'yup thk cine better co need go plaza mah',\n",
+       " 'ok ur typic repli',\n",
+       " 'per request mell mell oru minnaminungint nurungu vettam set callertun caller press copi friend callertun',\n",
+       " 'everywher dirt floor window even shirt sometim open mouth come flow dream world without half chore time joy lot tv show see guess like thing must exist like rain hail mist time done becom one',\n",
+       " 'aaooooright work',\n",
+       " 'leav hous',\n",
+       " 'hello love get interview today happi good boy think miss',\n",
+       " 'custom servic annonc new year deliveri wait pleas call arrang deliveri',\n",
+       " 'winner u special select receiv cash holiday flight inc speak live oper claim',\n",
+       " 'keep safe need miss alreadi envi everyon see real life',\n",
+       " 'new car hous parent new job hand',\n",
+       " 'love excit day spend make happi',\n",
+       " 'pl stop bootydeli f invit friend repli ye see www sm ac u bootydeli stop send stop frnd',\n",
+       " 'bangbab ur order way u receiv servic msg download ur content u goto wap bangb tv ur mobil internet servic menu',\n",
+       " 'place ur point e cultur modul alreadi',\n",
+       " 'urgent tri contact last weekend draw show prize guarante call claim code valid hr',\n",
+       " 'hi frnd best way avoid missunderstd wit belov one',\n",
+       " 'great escap fanci bridg need lager see tomo',\n",
+       " 'ye complet form clark also utter wast',\n",
+       " 'sir need axi bank account bank address',\n",
+       " 'hmmm thk sure got time hop ard ya go free abt muz call u discuss liao',\n",
+       " 'time come later',\n",
+       " 'bloodi hell cant believ forgot surnam mr ill give u clue spanish begin',\n",
+       " 'well gonna finish bath good fine night',\n",
+       " 'let know got money carlo make call',\n",
+       " 'u still go mall',\n",
+       " 'turn friend stay whole show back til lt gt feel free go ahead smoke lt gt worth',\n",
+       " 'text doesnt repli let know log',\n",
+       " 'hi spoke maneesha v like know satisfi experi repli toll free ye',\n",
+       " 'lift hope offer money need especi end month approach hurt studi anyway gr weekend',\n",
+       " 'lol u trust',\n",
+       " 'ok gentleman treat digniti respect',\n",
+       " 'guy close',\n",
+       " 'go noth great bye',\n",
+       " 'hello handsom find job lazi work toward get back net mummi boytoy miss',\n",
+       " 'haha awesom minut',\n",
+       " 'pleas call custom servic repres freephon pm guarante cash prize',\n",
+       " 'got xma radio time get',\n",
+       " 'ju reach home go bath first si use net tell u finish k',\n",
+       " 'uniqu enough find th august www areyouuniqu co uk',\n",
+       " 'sorri join leagu peopl dont keep touch mean great deal friend time even great person cost great week',\n",
+       " 'hi final complet cours',\n",
+       " 'stop howev suggest stay someon abl give or everi stool',\n",
+       " 'hope settl new school year wishin gr day',\n",
+       " 'gud mrng dear hav nice day',\n",
+       " 'u got person stori',\n",
+       " 'hamster dead hey tmr meet pm orchard mrt',\n",
+       " 'hi kate even hope see tomorrow bit bloodi babyjontet txt back u xxx',\n",
+       " 'found enc lt gt',\n",
+       " 'sent lt gt buck',\n",
+       " 'hello darlin ive finish colleg txt u finish u love kate xxx',\n",
+       " 'account refil success inr lt decim gt keralacircl prepaid account balanc rs lt decim gt transact id kr lt gt',\n",
+       " 'goodmorn sleep ga',\n",
+       " 'u call alter ok',\n",
+       " 'say like dat dun buy ericsson oso cannot oredi lar',\n",
+       " 'enter cabin pa said happi b day boss felt special askd lunch lunch invit apart went',\n",
+       " 'aight yo dat straight dogg',\n",
+       " 'pleas give us connect today lt decim gt refund bill',\n",
+       " 'shoot big load get readi',\n",
+       " 'bruv hope great break reward semest',\n",
+       " 'home alway chat',\n",
+       " 'k k good studi well',\n",
+       " 'yup noe leh',\n",
+       " 'sound great home',\n",
+       " 'final match head toward draw predict',\n",
+       " 'tire slept well past night',\n",
+       " 'easi ah sen got select mean good',\n",
+       " 'take exam march',\n",
+       " 'yeah think use gt atm regist sure anyway help let know sure readi',\n",
+       " 'ok prob take ur time',\n",
+       " 'os call ubandu run without instal hard disk use os copi import file system give repair shop',\n",
+       " 'sorri call later',\n",
+       " 'u say leh cours noth happen lar say v romant ju bit lor thk e nite sceneri nice leh',\n",
+       " 'new mobil must go txt nokia collect today www tc biz optout gbp mtmsg',\n",
+       " 'would realli appreci call need someon talk',\n",
+       " 'u meet ur dream partner soon ur career flyng start find free txt horo follow ur star sign e g horo ari',\n",
+       " 'hey compani elama po mudyadhu',\n",
+       " 'life strict teacher bcoz teacher teach lesson amp conduct exam life first conduct exam amp teach lesson happi morn',\n",
+       " 'dear good morn',\n",
+       " 'get gandhipuram walk cross cut road right side lt gt street road turn first right',\n",
+       " 'dear go rubber place',\n",
+       " 'sorri batteri die yeah',\n",
+       " 'ye tv alway avail work place',\n",
+       " 'text meet someon sexi today u find date even flirt u join p repli name age eg sam msg recd thirtyeight penc',\n",
+       " 'print oh lt gt come upstair',\n",
+       " 'ill littl closer like bu stop street',\n",
+       " 'wil reach',\n",
+       " 'new theori argument win situat lose person dont argu ur friend kick amp say alway correct',\n",
+       " 'u secret admir look make contact u find r reveal think ur special call',\n",
+       " 'tomarrow final hear laptop case cant',\n",
+       " 'pleassssssseeeee tel v avent done sportsx',\n",
+       " 'okay shine meant sign sound better',\n",
+       " 'although told u dat baig face watch realli like e watch u gave co fr u thanx everyth dat u done today touch',\n",
+       " 'u rememb old commerci',\n",
+       " 'late said websit dont slipper',\n",
+       " 'ask call ok',\n",
+       " 'kalli wont bat nd inning',\n",
+       " 'didnt work oh ok goodnight fix readi time wake dearli miss good night sleep',\n",
+       " 'congratul ur award cd voucher gift guarante free entri wkli draw txt music tnc www ldew com win ppmx age',\n",
+       " 'ranjith cal drpd deeraj deepak min hold',\n",
+       " 'wen ur lovabl bcum angri wid u dnt take serious coz angri childish n true way show deep affect care n luv kettoda manda nice day da',\n",
+       " '',\n",
+       " 'up day also ship compani take wk way usp take week get lag may bribe nipost get stuff',\n",
+       " 'back lemm know readi',\n",
+       " 'necessarili expect done get back though headin',\n",
+       " 'mmm yummi babe nice jolt suzi',\n",
+       " 'lover need',\n",
+       " 'tri contact repli offer video handset anytim network min unlimit text camcord repli call',\n",
+       " 'park next mini come today think',\n",
+       " 'yup',\n",
+       " 'anyway go shop co si done yet dun disturb u liao',\n",
+       " 'luton ring ur around h',\n",
+       " 'hey realli horni want chat see nake text hot text charg pm unsubscrib text stop',\n",
+       " 'dint come us',\n",
+       " 'wana plan trip sometm',\n",
+       " 'sure yet still tri get hold',\n",
+       " 'ur rington servic chang free credit go club mobil com choos content stop txt club stop p wk club po box mk wt',\n",
+       " 'evo download flash jealou',\n",
+       " 'rington club get uk singl chart mobil week choos top qualiti rington messag free charg',\n",
+       " 'come mu sort narcot situat',\n",
+       " 'night end anoth day morn come special way may smile like sunni ray leav worri blue blue bay',\n",
+       " 'hmv bonu special pound genuin hmv voucher answer easi question play send hmv info www percent real com',\n",
+       " 'usf guess might well take car',\n",
+       " 'object bf come',\n",
+       " 'thanx',\n",
+       " 'tell rob mack gf theater',\n",
+       " 'awesom see bit',\n",
+       " 'sent type food like',\n",
+       " 'done hand celebr full swing yet',\n",
+       " 'got call tool',\n",
+       " 'wen u miss someon person definit special u person special miss keep touch gdeve',\n",
+       " 'ok ask money far',\n",
+       " 'oki',\n",
+       " 'yeah think usual guy still pass last night get ahold anybodi let know throw',\n",
+       " 'k might come tonight class let earli',\n",
+       " 'ok',\n",
+       " 'hi babi im cruisin girl friend r u give call hour home that alright fone fone love jenni xxx',\n",
+       " 'life mean lot love life love peopl life world call friend call world ge',\n",
+       " 'dear shall mail tonit busi street shall updat tonit thing look ok varunnathu edukkukaye raksha ollu good one real sens',\n",
+       " 'hey told name gautham ah',\n",
+       " 'haf u found feel stupid da v cam work',\n",
+       " 'oop got bit',\n",
+       " 'much buzi',\n",
+       " 'accident delet messag resend pleas',\n",
+       " 'mobil custom may claim free camera phone upgrad pay go sim card loyalti call offer end thfeb c appli',\n",
+       " 'unless situat go gurl would appropri',\n",
+       " 'hurt teas make cri end life die plz keep one rose grave say stupid miss u nice day bslvyl',\n",
+       " 'cant pick phone right pl send messag',\n",
+       " 'need coffe run tomo believ time week alreadi',\n",
+       " 'awesom rememb last time got somebodi high first time diesel v',\n",
+       " 'shit realli shock scari cant imagin second def night u think somewher could crash night save taxi',\n",
+       " 'oh way food fridg want go meal tonight',\n",
+       " 'womdarful actor',\n",
+       " 'sm ac blind date u rodd aberdeen unit kingdom check http img sm ac w icmb cktz r blind date send hide',\n",
+       " 'yup remb think book',\n",
+       " 'jo ask u wana meet',\n",
+       " 'lol ye friendship hang thread caus u buy stuff',\n",
+       " 'themob check newest select content game tone gossip babe sport keep mobil fit funki text wap',\n",
+       " 'garag key bookshelf',\n",
+       " 'today accept day u accept brother sister lover dear best clo lvblefrnd jstfrnd cutefrnd lifpartnr belovd swtheart bstfrnd rpli mean enemi',\n",
+       " 'think ur smart win week weekli quiz text play cs winnersclub po box uz gbp week',\n",
+       " 'say give call friend got money definit buy end week',\n",
+       " 'hi way u day normal way real ur uniqu hope know u rest mylif hope u find wot lost',\n",
+       " 'made day great day',\n",
+       " 'k k advanc happi pongal',\n",
+       " 'hmmm guess go kb n power yoga haha dunno tahan power yoga anot thk got lo oso forgot liao',\n",
+       " 'realli dude friend afraid',\n",
+       " 'decemb mobil mth entitl updat latest colour camera mobil free call mobil updat co free',\n",
+       " 'coffe cake guess',\n",
+       " 'merri christma babe love ya kiss',\n",
+       " 'hey dont go watch x men lunch haha',\n",
+       " 'cud u tell ppl im gona b bit l co buse hav gon past co full im still waitin pete x',\n",
+       " 'would great guild could meet bristol road somewher get touch weekend plan take flight good week',\n",
+       " 'problem',\n",
+       " 'call messag miss call',\n",
+       " 'hi da today class',\n",
+       " 'say good sign well know track record read women',\n",
+       " 'cool text park',\n",
+       " 'read text sent meant joke read light',\n",
+       " 'k k apo k good movi',\n",
+       " 'mayb could get book tomo return immedi someth',\n",
+       " 'call germani penc per minut call fix line via access number prepay direct access',\n",
+       " 'chanc might evapor soon violat privaci steal phone number employ paperwork cool pleas contact report supervisor',\n",
+       " 'valentin day special win quiz take partner trip lifetim send go p msg rcvd custcar',\n",
+       " 'ta daaaaa home babe still',\n",
+       " 'cool come havent wine dine',\n",
+       " 'sleep surf',\n",
+       " 'sorri call later',\n",
+       " 'u call right call hand phone',\n",
+       " 'ok great thanx lot',\n",
+       " 'take post come must text happi read one wiv hello carolin end favourit bless',\n",
+       " 'u hide stranger',\n",
+       " 'interest like',\n",
+       " 'sister clear two round birla soft yesterday',\n",
+       " 'gudnit tc practic go',\n",
+       " 'di yiju ju saw ur mail case huim havent sent u num di num',\n",
+       " 'one small prestig problem',\n",
+       " 'fanci shag interest sextextuk com txt xxuk suzi txt cost per msg tnc websit x',\n",
+       " 'check realli miss see jeremiah great month',\n",
+       " 'nah help never iphon',\n",
+       " 'car hour half go apeshit',\n",
+       " 'today sorri day ever angri ever misbehav hurt plz plz slap urself bcoz ur fault basic good',\n",
+       " 'yo guy ever figur much need alcohol jay tri figur much safe spend weed',\n",
+       " 'lt gt ish minut minut ago wtf',\n",
+       " 'thank call forgot say happi onam sirji fine rememb met insur person meet qatar insha allah rakhesh ex tata aig join tissco tayseer',\n",
+       " 'congratul ur award cd voucher gift guarante free entri wkli draw txt music tnc www ldew com win ppmx age',\n",
+       " 'ur cash balanc current pound maxim ur cash send cash p msg cc hg suit land row w j hl',\n",
+       " 'actor work work even sleep late sinc unemploy moment alway sleep late unemploy everi day saturday',\n",
+       " 'hello got st andrew boy long way cold keep post',\n",
+       " 'ha ha cool cool chikku chikku db',\n",
+       " 'oh ok prob',\n",
+       " 'check audrey statu right',\n",
+       " 'busi tri finish new year look forward final meet',\n",
+       " 'good afternoon sunshin dawn day refresh happi aliv breath air smile think love alway',\n",
+       " 'well know z take care worri',\n",
+       " 'update_now xma offer latest motorola sonyericsson nokia free bluetooth doubl min txt orang call mobileupd call optout f q',\n",
+       " 'discount code rp stop messag repli stop www regalportfolio co uk custom servic',\n",
+       " 'wat uniform get',\n",
+       " 'cool text readi',\n",
+       " 'hello boytoy geeee miss alreadi woke wish bed cuddl love',\n",
+       " 'spoil bed well',\n",
+       " 'go bath msg next lt gt min',\n",
+       " 'cant keep talk peopl sure pay agre price pl tell want realli buy much will pay',\n",
+       " 'thank rington order refer charg gbp per week unsubscrib anytim call custom servic',\n",
+       " 'say happen',\n",
+       " 'could seen recognis face',\n",
+       " 'well lot thing happen lindsay new year sigh bar ptbo blue heron someth go',\n",
+       " 'keep payasam rinu bring',\n",
+       " 'taught ranjith sir call sm like becau he verifi project prabu told today pa dont mistak',\n",
+       " 'guess worri must know way bodi repair quit sure worri take slow first test guid ovul relax noth said reason worri keep followin',\n",
+       " 'yeah sure give coupl minut track wallet',\n",
+       " 'hey leav big deal take care',\n",
+       " 'hey late ah meet',\n",
+       " 'doubl min txt month free bluetooth orang avail soni nokia motorola phone call mobileupd call optout n dx',\n",
+       " 'took mr owl lick',\n",
+       " 'custom place call',\n",
+       " 'mm time dont like fun',\n",
+       " 'mth half price orang line rental latest camera phone free phone mth call mobilesdirect free updat stoptxt',\n",
+       " 'yup lunch buffet u eat alreadi',\n",
+       " 'huh late fr dinner',\n",
+       " 'hey sat go intro pilat kickbox',\n",
+       " 'morn ok',\n",
+       " 'ye think offic lap room think that last day didnt shut',\n",
+       " 'pick bout ish time go',\n",
+       " 'perform award calcul everi two month current one month period',\n",
+       " 'actual sleep still might u call back text gr rock si send u text wen wake',\n",
+       " 'alway put busi put pictur ass facebook one open peopl ever met would think pictur room would hurt make feel violat',\n",
+       " 'good even sir al salam wahleykkum share happi news grace god got offer tayseer tissco join hope fine inshah allah meet sometim rakhesh visitor india',\n",
+       " 'hmmm k want chang field quickli da wanna get system administr network administr',\n",
+       " 'free rington text first poli text get true tone help st free tone x pw e nd txt stop',\n",
+       " 'dear chechi talk',\n",
+       " 'hair cream ship',\n",
+       " 'none happen til get though',\n",
+       " 'yep great loxahatche xma tree burn lt gt start hour',\n",
+       " 'haha get use drive usf man know lot stoner',\n",
+       " 'well slightli disastr class pm fav darl hope day ok coffe wld good stay late tomorrow time place alway',\n",
+       " 'hello good week fanci drink someth later',\n",
+       " 'headin toward busetop',\n",
+       " 'messag text miss sender name miss number miss sent date miss miss u lot that everyth miss sent via fullonsm com',\n",
+       " 'come room point iron plan weekend',\n",
+       " 'co want thing',\n",
+       " 'oki go yan jiu skip ard oso go cine den go mrt one blah blah blah',\n",
+       " 'bring home wendi',\n",
+       " 'date servic cal l box sk ch',\n",
+       " 'whatsup dont u want sleep',\n",
+       " 'alright new goal',\n",
+       " 'free entri weekli competit text word win c www txttowin co uk',\n",
+       " 'alright head minut text meet',\n",
+       " 'send logo ur lover name join heart txt love name name mobno eg love adam eve yahoo pobox w wq txtno ad p',\n",
+       " 'ye last week take live call',\n",
+       " 'someon contact date servic enter phone fanci find call landlin pobox n tf p',\n",
+       " 'siva hostel aha',\n",
+       " 'urgent mobil number award prize guarante call land line claim valid hr',\n",
+       " 'send ur friend receiv someth ur voic speak express childish naughti sentiment rowdi ful attitud romant shi attract funni lt gt irrit lt gt lovabl repli',\n",
+       " 'ok ok guess',\n",
+       " 'aathi dear',\n",
+       " 'pain urin thing els',\n",
+       " 'esplanad mind give lift co got car today',\n",
+       " 'wnt buy bmw car urgent vri urgent hv shortag lt gt lac sourc arng di amt lt gt lac that prob',\n",
+       " 'home watch tv lor',\n",
+       " 'usual take fifteen fuck minut respond ye question',\n",
+       " 'congrat nokia video camera phone call call cost ppm ave call min vari mobil close post bcm ldn wc n xx',\n",
+       " 'book ticket pongal',\n",
+       " 'avail like right around hillsborough amp lt gt th',\n",
+       " 'messag sent askin lt gt dollar shoul pay lt gt lt gt',\n",
+       " 'ask g iouri told stori like ten time alreadi',\n",
+       " 'long applebe fuck take',\n",
+       " 'hi hope u get txt journey hasnt gd min late think',\n",
+       " 'like love arrang',\n",
+       " 'ye realli great bhaji told kalli best cricket sachin world tough get',\n",
+       " 'suppos wake gt',\n",
+       " 'oic saw tot din c found group liao',\n",
+       " 'sorri call later',\n",
+       " 'hey hey wereth monkeespeopl say monkeyaround howdi gorgeou howu doin foundurself jobyet sausag love jen xxx',\n",
+       " 'sorri batteri die come get gram place',\n",
+       " 'well done blimey exercis yeah kinda rememb wot hmm',\n",
+       " 'wont get concentr dear know mind everyth',\n",
+       " 'lol made plan new year',\n",
+       " 'min later k',\n",
+       " 'hank lotsli',\n",
+       " 'thank hope good day today',\n",
+       " 'k k detail want transfer acc enough',\n",
+       " 'ok tell stay yeah tough optimist thing improv month',\n",
+       " 'loan purpos homeown tenant welcom previous refus still help call free text back help',\n",
+       " 'si si think ill go make oreo truffl',\n",
+       " 'look ami ure beauti intellig woman like u lot know u like like worri',\n",
+       " 'hope result consist intellig kind start ask practicum link keep ear open best ttyl',\n",
+       " 'call cost guess isnt bad miss ya need ya want ya love ya',\n",
+       " 'go thru differ feel waver decis cope individu time heal everyth believ',\n",
+       " 'u go phone gonna die stay',\n",
+       " 'great never better day give even reason thank god',\n",
+       " 'upgrdcentr orang custom may claim free camera phone upgrad loyalti call offer end th juli c appli opt avail',\n",
+       " 'sorri call later ok bye',\n",
+       " 'ok way railway',\n",
+       " 'great princess love give receiv oral doggi style fave posit enjoy make love lt gt time per night',\n",
+       " 'put stuff road keep get slipperi',\n",
+       " 'go ride bike',\n",
+       " 'yup need ju wait e rain stop',\n",
+       " 'mani compani tell languag',\n",
+       " 'okmail dear dave final notic collect tenerif holiday cash award call landlin tc sae box cw wx ppm',\n",
+       " 'long sinc scream princess',\n",
+       " 'noth meant money enter account bank remov flat rate someon transfer lt gt account lt gt dollar got remov bank differ charg also differ sure trust ja person send account detail co',\n",
+       " 'want get laid tonight want real dog locat sent direct ur mob join uk largest dog network txting moan nyt ec p msg p',\n",
+       " 'nice line said broken heart plz cum time infront wise trust u good',\n",
+       " 'ok gonna head usf like fifteen minut',\n",
+       " 'love aathi love u lot',\n",
+       " 'tension ah machi problem',\n",
+       " 'k pick anoth th done',\n",
+       " 'guy get back g said think stay mcr',\n",
+       " 'almost see u sec',\n",
+       " 'yo carlo friend alreadi ask work weekend',\n",
+       " 'watch tv lor',\n",
+       " 'thank babi cant wait tast real thing',\n",
+       " 'chang fb jaykwon thuglyf falconerf',\n",
+       " 'win realli side long time',\n",
+       " 'free messag activ free text messag repli messag word free term condit visit www com',\n",
+       " 'dear reach railway happen',\n",
+       " 'depend qualiti want type sent boy fade glori want ralph mayb',\n",
+       " 'think fix send test messag',\n",
+       " 'sorri man account dri would want could trade back half could buy shit credit card',\n",
+       " 'congrat year special cinema pass call c suprman v matrix starwar etc free bx ip pm dont miss',\n",
+       " 'sorri meet call later',\n",
+       " 'class lt gt reunion',\n",
+       " 'free call',\n",
+       " 'got meh',\n",
+       " 'nope think go monday sorri repli late',\n",
+       " 'told accentur confirm true',\n",
+       " 'kate jackson rec center ish right',\n",
+       " 'dear reach room',\n",
+       " 'fight world easi u either win lose bt fightng close u dificult u lose u lose u win u still lose',\n",
+       " 'come',\n",
+       " 'check nuerologist',\n",
+       " 'lolnic went fish water',\n",
+       " 'congratul week competit draw u prize claim call b cs stop sm ppm',\n",
+       " 'wait e car dat bore wat co wait outsid got noth home stuff watch tv wat',\n",
+       " 'mayb westshor hyde park villag place near hous',\n",
+       " 'know anthoni bring money school fee pay rent stuff like that need help friend need',\n",
+       " 'signific',\n",
+       " 'opinion jada kusruthi lovabl silent spl charact matur stylish simpl pl repli',\n",
+       " 'latest g still scroung ammo want give new ak tri',\n",
+       " 'prabha soryda reali frm heart sori',\n",
+       " 'lol ok forgiven',\n",
+       " 'jst chang tat',\n",
+       " 'guarante latest nokia phone gb ipod mp player prize txt word collect ibhltd ldnw h p mtmsgrcvd',\n",
+       " 'competit',\n",
+       " 'boltblu tone p repli poli mono eg poli cha cha slide yeah slow jamz toxic come stop tone txt',\n",
+       " 'credit top http www bubbletext com renew pin tgxxrz',\n",
+       " 'way transport less problemat sat night way u want ask n join bday feel free need know definit no book fri',\n",
+       " 'usual person unconsci children adult may behav abnorm call',\n",
+       " 'ebay might less elsewher',\n",
+       " 'shall come get pickl',\n",
+       " 'gonna go get taco',\n",
+       " 'rude campu',\n",
+       " 'urgent mobil award bonu caller prize nd attempt contact call box qu',\n",
+       " 'hi b ard christma enjoy n merri x ma',\n",
+       " 'today offer claim ur worth discount voucher text ye savamob member offer mobil cs sub unsub repli x',\n",
+       " 'ye pretti ladi like singl',\n",
+       " 'reciev tone within next hr term condit pleas see channel u teletext pg',\n",
+       " 'jay say doubl faggot',\n",
+       " 'privat account statement show un redeem point call identifi code expir',\n",
+       " 'today sunday sunday holiday work',\n",
+       " 'gudnit tc practic go',\n",
+       " 'late',\n",
+       " 'call hope l r malaria know miss guy miss bani big pl give love especi great day',\n",
+       " 'good afternoon love goe day hope mayb got lead job think boytoy send passion kiss across sea',\n",
+       " 'probabl gonna see later tonight lt',\n",
+       " 'mayb fat finger press button know',\n",
+       " 'ummmmmaah mani mani happi return day dear sweet heart happi birthday dear',\n",
+       " 'tirupur da start offic call',\n",
+       " 'www applausestor com monthlysubscript p msg max month csc web age stop txt stop',\n",
+       " 'famou quot develop abil listen anyth uncondit without lose temper self confid mean marri',\n",
+       " 'go colleg pa els ill come self pa',\n",
+       " 'oclock mine bash flat plan',\n",
+       " 'girl stay bed girl need recoveri time id rather pass fun coop bed',\n",
+       " 'special',\n",
+       " 'know need get hotel got invit apologis cali sweet come english bloke weddin',\n",
+       " 'sorri took long omw',\n",
+       " 'wait lt gt min',\n",
+       " 'ok give minut think see btw alibi cut hair whole time',\n",
+       " 'imagin final get sink bath put pace mayb even eat left also imagin feel cage cock surround bath water remind alway own enjoy cuck',\n",
+       " 'hurri weed defici like three day',\n",
+       " 'sure get acknowledg astoundingli tactless gener faggi demand blood oath fo',\n",
+       " 'ok everi night take warm bath drink cup milk see work magic still need loos weight know',\n",
+       " 'look fri pan case cheap book perhap silli fri pan like book',\n",
+       " 'well uv caus mutat sunscreen like essenti theseday',\n",
+       " 'lunch onlin',\n",
+       " 'know friend alreadi told',\n",
+       " 'hi princess thank pic pretti',\n",
+       " 'aiyo u alway c ex one dunno abt mei repli first time u repli fast lucki workin huh got bao ur sugardad ah gee',\n",
+       " 'hi msg offic',\n",
+       " 'thanx e browni v nice',\n",
+       " 'geeeee love much bare stand',\n",
+       " 'gent tri contact last weekend draw show prize guarante call claim code k valid hr ppm',\n",
+       " 'fuck babe miss alreadi know let send money toward net need want crave',\n",
+       " 'ill call u mrw ninish address icki american freek wont stop callin bad jen k eh',\n",
+       " 'oooh bed ridden ey think',\n",
+       " 'anyway go gym whatev love smile hope ok good day babe miss much alreadi',\n",
+       " 'love daddi make scream pleasur go slap ass dick',\n",
+       " 'wot u wanna missi',\n",
+       " 'yar lor wait mum finish sch lunch lor whole morn stay home clean room room quit clean hee',\n",
+       " 'know lab goggl went',\n",
+       " 'open door',\n",
+       " 'wait call',\n",
+       " 'nope wait sch daddi',\n",
+       " 'cash prize claim call',\n",
+       " 'tire argu week week want',\n",
+       " 'wait sch finish ard',\n",
+       " 'mobil number claim call us back ring claim hot line',\n",
+       " 'arngd marriag u r walkin unfortuntli snake bite u bt love marriag danc frnt snake amp sayin bite bite',\n",
+       " 'huh earli dinner outsid izzit',\n",
+       " 'ok anyway need chang said',\n",
+       " 'tri contact repli offer min textand new video phone call repli free deliveri tomorrow',\n",
+       " 'ex wife abl kid want kid one day',\n",
+       " 'scotland hope show jjc tendenc take care live dream',\n",
+       " 'tell u headach want use hour sick time',\n",
+       " 'dun thk quit yet hmmm go jazz yogasana oso go meet em lesson den',\n",
+       " 'pete pleas ring meiv hardli gotani credit',\n",
+       " 'ya srsli better yi tho',\n",
+       " 'meet call later',\n",
+       " 'ur chanc win wkli shop spree txt shop c www txt shop com custcar x p wk',\n",
+       " 'special select receiv pound award call line close cost ppm cs appli ag promo',\n",
+       " 'privat account statement show un redeem point call identifi code expir',\n",
+       " 'still grand prix',\n",
+       " 'met stranger choos friend long world stand friendship never end let friend forev gud nitz',\n",
+       " 'great',\n",
+       " 'gud mrng dear nice day',\n",
+       " 'import custom servic announc call freephon',\n",
+       " 'exhaust train morn much wine pie sleep well',\n",
+       " 'go buy mum present ar',\n",
+       " 'mind blastin tsunami occur rajnik stop swim indian ocean',\n",
+       " 'u send home first ok lor readi yet',\n",
+       " 'speak cash yet',\n",
+       " 'happi come noon',\n",
+       " 'meet lunch la',\n",
+       " 'take care n get well soon',\n",
+       " 'xclusiv clubsaisai morow soire special zouk nichol pari free rose ladi info',\n",
+       " 'meant say cant wait see u get bore bridgwat banter',\n",
+       " 'neva mind ok',\n",
+       " 'fine imma get drink somethin want come find',\n",
+       " 'day kick euro u kept date latest news result daili remov send get txt stop',\n",
+       " 'valentin game send di msg ur friend answer r someon realli love u que colour suit best rpli',\n",
+       " 'mani depend',\n",
+       " 'thanx today cer nice catch ave find time often oh well take care c u soon c',\n",
+       " 'call said choos futur',\n",
+       " 'happi valentin day know earli hundr handsom beauti wish thought finish aunti uncl st',\n",
+       " 'like v shock leh co tell shuhui like tell leona also like dat almost know liao got ask abt ur reaction lor',\n",
+       " 'famili happi',\n",
+       " 'come n pick come immedi aft ur lesson',\n",
+       " 'let snow let snow kind weather bring ppl togeth friendship grow',\n",
+       " 'dear got lt gt dollar hi hi',\n",
+       " 'good word word may leav u dismay mani time',\n",
+       " 'make sure alex know birthday fifteen minut far concern',\n",
+       " 'sorri got thing may pub later',\n",
+       " 'nah straight bring bud drink someth actual littl use straight cash',\n",
+       " 'haha good hear offici paid market th',\n",
+       " 'mani lick take get center tootsi pop',\n",
+       " 'yup thk r e teacher said make face look longer darren ask cut short',\n",
+       " 'new textbuddi chat horni guy ur area p free receiv search postcod gaytextbuddi com txt one name',\n",
+       " 'today vodafon number end select receiv award number match call receiv award',\n",
+       " 'pleas dont say like hi hi hi',\n",
+       " 'thank u',\n",
+       " 'oh forward messag thought send',\n",
+       " 'got seventeen pound seven hundr ml hope ok',\n",
+       " 'dear voucher holder claim week offer pc go http www e tlp co uk expressoff ts cs appli stop text txt stop',\n",
+       " 'n funni',\n",
+       " 'sweetheart hope kind day one load reason smile biola',\n",
+       " 'login dat time dad fetch home',\n",
+       " 'shower babi',\n",
+       " 'askd u question hour answer',\n",
+       " 'well imma definit need restock thanksgiv let know',\n",
+       " 'said kiss kiss sound effect gorgeou man kind person need smile brighten day',\n",
+       " 'probabl gonna swing wee bit',\n",
+       " 'ya nice readi thursday',\n",
+       " 'allo brave buse taken train triumph mean b ham jolli good rest week',\n",
+       " 'watch cartoon listen music amp eve go templ amp church u',\n",
+       " 'mind ask happen dont say uncomfort',\n",
+       " 'privat account statement show un redeem point call identifi code expir',\n",
+       " 'prob send email',\n",
+       " 'cash prize claim call c rstm sw ss ppm',\n",
+       " 'that cool sometim slow gentl sonetim rough hard',\n",
+       " 'gonna say sorri would normal start panic time sorri see tuesday',\n",
+       " 'wait know wesley town bet hella drug',\n",
+       " 'fine miss much',\n",
+       " 'u got person stori',\n",
+       " 'tell drug dealer get impati',\n",
+       " 'sun cant come earth send luv ray cloud cant come river send luv rain cant come meet u send care msg u gud evng',\n",
+       " 'place man',\n",
+       " 'doesnt make sens take unless free need know wikipedia com',\n",
+       " 'premium phone servic call',\n",
+       " 'sea lay rock rock envelop envelop paper paper word',\n",
+       " 'mum repent',\n",
+       " 'sorri go home first daddi come fetch later',\n",
+       " 'leav de start prepar next',\n",
+       " 'ye babi studi posit kama sutra',\n",
+       " 'en chikku nang bakra msg kalstiya tea coffe',\n",
+       " 'carlo minut still need buy',\n",
+       " 'pay lt decim gt lakh',\n",
+       " 'good even ttyl',\n",
+       " 'u receiv msg',\n",
+       " 'ho ho big belli laugh see ya tomo',\n",
+       " 'sm ac sun post hello seem cool want say hi hi stop send stop',\n",
+       " 'get ur st rington free repli msg tone gr top tone phone everi week per wk opt send stop',\n",
+       " 'ditto worri say anyth anymor like said last night whatev want peac',\n",
+       " 'got lt gt way could pick',\n",
+       " 'dont knw pa drink milk',\n",
+       " 'mayb say hi find got card great escap wetherspoon',\n",
+       " 'piggi r u awak bet u still sleep go lunch',\n",
+       " 'caus freaki lol',\n",
+       " 'miss call caus yell scrappi miss u wait u come home lone today',\n",
+       " 'hex place talk explain',\n",
+       " 'log wat sdryb',\n",
+       " 'xy go e lunch',\n",
+       " 'hi sue year old work lapdanc love sex text live bedroom text sue textoper g da ppmsg',\n",
+       " 'want ask wait finish lect co lect finish hour anyway',\n",
+       " 'finish work yet',\n",
+       " 'everi king cri babi everi great build map imprtant u r today u wil reach tomorw gud ni',\n",
+       " 'dear cherthala case u r come cochin pl call bfore u start shall also reach accordingli tell day u r come tmorow engag an holiday',\n",
+       " 'thank love torch bold',\n",
+       " 'forward pleas call immedi urgent messag wait',\n",
+       " 'farm open',\n",
+       " 'sorri troubl u buy dad big small sat n sun thanx',\n",
+       " 'sister law hope great month say hey abiola',\n",
+       " 'purchas stuff today mail po box number',\n",
+       " 'ah poop look like ill prob send laptop get fix cuz gpu problem',\n",
+       " 'good good job like entrepreneur',\n",
+       " 'aight close still around alex place',\n",
+       " 'meet corpor st outsid gap see mind work',\n",
+       " 'mum ask buy food home',\n",
+       " 'k u also dont msg repli msg',\n",
+       " 'much r will pay',\n",
+       " 'sorri call later',\n",
+       " 'import prevent dehydr give enough fluid',\n",
+       " 'that bit weird even suppos happen good idea sure pub',\n",
+       " 'true dear sat pray even felt sm time',\n",
+       " 'think get away trek long famili town sorri',\n",
+       " 'wanna gym harri',\n",
+       " 'quit late lar ard anyway wun b drivin',\n",
+       " 'review keep fantast nokia n gage game deck club nokia go www cnupdat com newslett unsubscrib alert repli word',\n",
+       " 'mth half price orang line rental latest camera phone free phone mth call mobilesdirect free updat stoptxt cs',\n",
+       " 'height confid aeronaut professor wer calld amp wer askd sit aeroplan aftr sat wer told dat plane ws made student dey hurri plane bt didnt move said made student wont even start datz confid',\n",
+       " 'seem like weird time night g want come smoke day shitstorm attribut alway come make everyon smoke',\n",
+       " 'pm cost p',\n",
+       " 'save stress person dorm account send account detail money sent',\n",
+       " 'also know lunch menu da know',\n",
+       " 'stuff sell tell',\n",
+       " 'urgent nd attempt contact u u call b csbcm wc n xx callcost ppm mobilesvari max',\n",
+       " 'book lesson msg call work sth go get spec membership px',\n",
+       " 'guarante cash prize claim yr prize call custom servic repres pm',\n",
+       " 'macha dont feel upset assum mindset believ one even wonder plan us let life begin call anytim',\n",
+       " 'oh send address',\n",
+       " 'fine anytim best',\n",
+       " 'wondar full flim',\n",
+       " 'ya even cooki jelli',\n",
+       " 'world run still mayb feel admit mad correct let call life keep run world may u r also run let run',\n",
+       " 'got look scrumptiou daddi want eat night long',\n",
+       " 'co lar ba dao ok pm lor u never ask go ah said u would ask fri said u ask today',\n",
+       " 'alright omw gotta chang order half th',\n",
+       " 'exactli anyway far jide studi visit',\n",
+       " 'dunno u ask',\n",
+       " 'email alertfrom jeri stewarts kbsubject low cost prescripiton drvgsto listen email call',\n",
+       " 'spring come earli yay',\n",
+       " 'lol feel bad use money take steak dinner',\n",
+       " 'even u dont get troubl convinc tel twice tel neglect msg dont c read dont repli',\n",
+       " 'leav qatar tonit search opportun went fast pl add ur prayer dear rakhesh',\n",
+       " 'one talk',\n",
+       " 'thank look realli appreci',\n",
+       " 'hi custom loyalti offer new nokia mobil txtauction txt word start get ctxt tc p mtmsg',\n",
+       " 'wish',\n",
+       " 'haha mayb u rite u know well da feel like someon gd lor u faster go find one gal group attach liao',\n",
+       " 'ye glad made',\n",
+       " 'well littl time thing good time ahead',\n",
+       " 'got room soon put clock back til shout everyon get realis wahay anoth hour bed',\n",
+       " 'ok may free gym',\n",
+       " 'men like shorter ladi gaze eye',\n",
+       " 'dunno ju say go lido time',\n",
+       " 'promis take good care princess run pleas send pic get chanc ttyl',\n",
+       " 'u subscrib best mobil content servic uk per day send stop helplin',\n",
+       " 'reason spoken year anyway great week best exam',\n",
+       " 'monday next week give full gist',\n",
+       " 'realiz year thousand old ladi run around tattoo',\n",
+       " 'import custom servic announc premier',\n",
+       " 'dont gimm lip caveboy',\n",
+       " 'get librari',\n",
+       " 'reali sorri recognis number confus r u pleas',\n",
+       " 'didnt holla',\n",
+       " 'cant think anyon spare room top head',\n",
+       " 'faith make thing possibl hope make thing work love make thing beauti may three christma merri christma',\n",
+       " 'u made appoint',\n",
+       " 'call carlo phone vibrat act might hear text',\n",
+       " 'romant pari night flight book next year call ts cs appli',\n",
+       " 'grandma oh dear u still ill felt shit morn think hungov anoth night leav sat',\n",
+       " 'urgent ur guarante award still unclaim call closingd claimcod pmmorefrommobil bremov mobypobox ls yf',\n",
+       " 'noth ju tot u would ask co u ba gua went mt faber yest yest ju went alreadi mah today go ju call lor',\n",
+       " 'wish famili merri x ma happi new year advanc',\n",
+       " 'ur award citi break could win summer shop spree everi wk txt store skilgm tsc winawk age perwksub',\n",
+       " 'nt goin got somethin unless meetin dinner lor haha wonder go ti time',\n",
+       " 'sorri call later',\n",
+       " 'cant pick phone right pl send messag',\n",
+       " 'lol know dramat school alreadi close tomorrow appar drive inch snow suppos get',\n",
+       " 'get anywher damn job hunt',\n",
+       " 'lol u drunkard hair moment yeah still tonight wat plan',\n",
+       " 'idc get weasel way shit twice row',\n",
+       " 'wil lt gt minut got space',\n",
+       " 'sleep surf',\n",
+       " 'thank pick trash',\n",
+       " 'go tell friend sure want live smoke much spend hour beg come smoke',\n",
+       " 'hi kate love see tonight ill phone tomorrow got sing guy gave card xxx',\n",
+       " 'happi new year dear brother realli miss got number decid send text wish happi abiola',\n",
+       " 'mean get door',\n",
+       " 'opinion jada kusruthi lovabl silent spl charact matur stylish simpl pl repli',\n",
+       " 'hmmm thought said hour slave late punish',\n",
+       " 'beerag',\n",
+       " 'import custom servic announc premier call freephon',\n",
+       " 'dont think turn like randomlli within min open',\n",
+       " 'suppos make still town though',\n",
+       " 'time fix spell sometim get complet diff word go figur',\n",
+       " 'ever thought live good life perfect partner txt back name age join mobil commun p sm',\n",
+       " 'free top polyphon tone call nation rate get toppoli tune sent everi week text subpoli per pole unsub',\n",
+       " 'gud mrng dear hav nice day',\n",
+       " 'hope enjoy game yesterday sorri touch pl know fondli bein thot great week abiola',\n",
+       " 'e best ur drive tmr',\n",
+       " 'u dogbreath sound like jan c al',\n",
+       " 'omg want scream weigh lost weight woohoo',\n",
+       " 'gener one uncount noun u dictionari piec research',\n",
+       " 'realli get hang around',\n",
+       " 'orang custom may claim free camera phone upgrad loyalti call offer end thmarch c appli opt availa',\n",
+       " 'petey boy wherear friendsar thekingshead come canlov nic',\n",
+       " 'ok msg u b leav hous',\n",
+       " 'gimm lt gt minut ago',\n",
+       " 'last chanc claim ur worth discount voucher today text shop savamob offer mobil cs savamob pobox uz sub',\n",
+       " 'appt lt time gt fault u listen told u twice',\n",
+       " 'free st week nokia tone ur mobil everi week txt nokia get txting tell ur mate www getz co uk pobox w wq norm p tone',\n",
+       " 'guarante award even cashto claim ur award call free stop getstop php rg jx',\n",
+       " 'k',\n",
+       " 'dled imp',\n",
+       " 'sure make sure know smokin yet',\n",
+       " 'boooo alway work quit',\n",
+       " 'take half day leav bec well',\n",
+       " 'ugh wanna get bed warm',\n",
+       " 'nervou lt gt',\n",
+       " 'ring come guy costum gift futur yowif hint hint',\n",
+       " 'congratul ur award either cd gift voucher free entri weekli draw txt music tnc www ldew com win ppmx age',\n",
+       " 'borrow ur bag ok',\n",
+       " 'u outbid simonwatson shinco dvd plyr bid visit sm ac smsreward end bid notif repli end',\n",
+       " 'boytoy miss happen',\n",
+       " 'lot use one babe model help youi bring match',\n",
+       " 'also bring galileo dobbi',\n",
+       " 'respond',\n",
+       " 'boo babe u enjoyin yourjob u seem b gettin well hunni hope ure ok take care llspeak u soonlot lovem xxxx',\n",
+       " 'good afternoon starshin boytoy crave yet ach fuck sip cappuccino miss babe teas kiss',\n",
+       " 'road cant txt',\n",
+       " 'smsservic yourinclus text credit pl goto www comuk net login qxj unsubscrib stop extra charg help comuk cm ae',\n",
+       " 'p alfi moon children need song ur mob tell ur txt tone chariti nokia poli chariti poli zed profit chariti',\n",
+       " 'good even ttyl',\n",
+       " 'hmm bit piec lol sigh',\n",
+       " 'hahaha use brain dear',\n",
+       " 'hey got mail',\n",
+       " 'sorri light turn green meant anoth friend want lt gt worth may around',\n",
+       " 'thank yesterday sir wonder hope enjoy burial mojibiola',\n",
+       " 'u secret admir reveal think u r special call opt repli reveal stop per msg recd cust care',\n",
+       " 'hi mate rv u hav nice hol messag say hello coz sent u age start drive stay road rvx',\n",
+       " 'dear voucher holder claim week offer pc pleas go http www e tlp co uk expressoff ts cs appli stop text txt stop',\n",
+       " 'thank much skype wit kz sura didnt get pleasur compani hope good given ultimatum oh countin aburo enjoy messag sent day ago',\n",
+       " 'sure result offer',\n",
+       " 'good morn dear great amp success day',\n",
+       " 'want anytim network min text new video phone five pound per week call repli deliveri tomorrow',\n",
+       " 'sir late pay rent past month pay lt gt charg felt would inconsider nag someth give great cost didnt speak howev recess wont abl pay charg month henc askin well ahead month end pleas help thank',\n",
+       " 'tri contact offer new video phone anytim network min half price rental camcord call repli deliveri wed',\n",
+       " 'last chanc claim ur worth discount voucher text ye savamob member offer mobil cs sub remov txt x stop',\n",
+       " 'luv u soo much u understand special u r ring u morrow luv u xxx',\n",
+       " 'pl send comprehens mail pay much',\n",
+       " 'prashanthettan mother pass away last night pray famili',\n",
+       " 'urgent call landlin complimentari ibiza holiday cash await collect sae cs po box sk wp ppm',\n",
+       " 'k k go',\n",
+       " 'meanwhil shit suit xavier decid give us lt gt second warn samantha come play jay guitar impress shit also think doug realiz live anymor',\n",
+       " 'stomach thru much trauma swear eat better lose weight',\n",
+       " 'offic what matter msg call break',\n",
+       " 'yeah bare enough room two us x mani fuck shoe sorri man see later',\n",
+       " 'today offer claim ur worth discount voucher text ye savamob member offer mobil cs sub unsub repli x',\n",
+       " 'u reach orchard alreadi u wan go buy ticket first',\n",
+       " 'real babi want bring inner tigress',\n",
+       " 'da run activ full version da',\n",
+       " 'ah poor babi hope urfeel bettersn luv probthat overdos work hey go care spk u sn lot lovejen xxx',\n",
+       " 'stop stori told return say order',\n",
+       " 'talk sexi make new friend fall love world discreet text date servic text vip see could meet',\n",
+       " 'go take babe',\n",
+       " 'hai ana tomarrow come morn lt decim gt ill sathi go rto offic repli came home',\n",
+       " 'spoon okay',\n",
+       " 'say somebodi name tampa',\n",
+       " 'work go min',\n",
+       " 'brother geniu',\n",
+       " 'sorri guess whenev get hold connect mayb hour two text',\n",
+       " 'u find time bu coz need sort stuff',\n",
+       " 'dude ive see lotta corvett late',\n",
+       " 'congratul ur award either yr suppli cd virgin record mysteri gift guarante call ts cs www smsco net pm approx min',\n",
+       " 'consid wall bunker shit import never play peac guess place high enough matter',\n",
+       " 'privat account statement xxxxxx show un redeem point call identifi code expir',\n",
+       " 'hello need posh bird chap user trial prod champney put need address dob asap ta r',\n",
+       " 'u want xma free text messag new video phone half price line rental call free find',\n",
+       " 'well offici philosoph hole u wanna call home readi save',\n",
+       " 'go good problem still need littl experi understand american custom voic',\n",
+       " 'text drop x',\n",
+       " 'ugh long day exhaust want cuddl take nap',\n",
+       " 'talk atleast day otherwis miss best friend world shakespear shesil lt gt',\n",
+       " 'shop till u drop either k k cash travel voucher call ntt po box cr bt fixedlin cost ppm mobil vari',\n",
+       " 'castor need see someth',\n",
+       " 'sunshin quiz wkli q win top soni dvd player u know countri liverpool play mid week txt ansr sp tyron',\n",
+       " 'u secret admir look make contact u find r reveal think ur special call',\n",
+       " 'u secret admir look make contact u find r reveal think ur special call stopsm',\n",
+       " 'remind download content alreadi paid goto http doit mymobi tv collect content',\n",
+       " 'see knew give break time woul lead alway want miss curfew gonna gibe til one midnight movi gonna get til need come home need getsleep anyth need b studdi ear train',\n",
+       " 'love give massag use lot babi oil fave posit',\n",
+       " 'dude go sup',\n",
+       " 'yoyyooo u know chang permiss drive mac usb flash drive',\n",
+       " 'gibb unsold mike hussey',\n",
+       " 'like talk pa abl dont know',\n",
+       " 'dun cut short leh u dun like ah fail quit sad',\n",
+       " 'unbeliev faglord',\n",
+       " 'wife knew time murder exactli',\n",
+       " 'ask princess',\n",
+       " 'great princess think',\n",
+       " 'nutter cutter ctter cttergg cttargg ctargg ctagg ie',\n",
+       " 'ok noe u busi realli bore msg u oso dunno wat colour choos one',\n",
+       " 'g class earli tomorrow thu tri smoke lt gt',\n",
+       " 'superb thought grate u dont everyth u want mean u still opportun happier tomorrow u today',\n",
+       " 'hope good week check',\n",
+       " 'use hope agent drop sinc book thing year whole boston nyc experi',\n",
+       " 'thursday night yeah sure thing work',\n",
+       " 'free rington wait collect simpli text password mix verifi get usher britney fml po box mk h ppw',\n",
+       " 'probabl money worri thing come due sever outstand invoic work two three month ago',\n",
+       " 'possibl teach',\n",
+       " 'wonder phone batteri went dead tell love babe',\n",
+       " 'love smell bu tobacco',\n",
+       " 'get worri derek taylor alreadi assum worst',\n",
+       " 'hey charl sorri late repli',\n",
+       " 'lastest stereophon marley dizze racal libertin stroke win nookii game flirt click themob wap bookmark text wap',\n",
+       " 'give plu said grinul greet whenev speak',\n",
+       " 'white fudg oreo store',\n",
+       " 'januari male sale hot gay chat cheaper call nation rate p min cheap p min peak stop text call p min',\n",
+       " 'love come took long leav zaher got word ym happi see sad left miss',\n",
+       " 'sorri hurt',\n",
+       " 'feel nauseou piss eat sweet week caus today plan pig diet week hungri',\n",
+       " 'ok lor earli still project meet',\n",
+       " 'call da wait call',\n",
+       " 'could ask carlo could get anybodi els chip',\n",
+       " 'actual send remind today wonder weekend',\n",
+       " 'peopl see msg think iam addict msging wrong bcoz don\\\\ know iam addict sweet friend bslvyl',\n",
+       " 'hey gave photo regist drive ah tmr wanna meet yck',\n",
+       " 'dont talk ever ok word',\n",
+       " 'u wana see',\n",
+       " 'way school pl send ashley number',\n",
+       " 'shall fine avalarr hollalat',\n",
+       " 'went attend anoth two round today still reach home',\n",
+       " 'actual delet old websit blog magicalsong blogspot com',\n",
+       " 'k wait chikku il send aftr lt gt min',\n",
+       " 'diet ate mani slice pizza yesterday ugh alway diet',\n",
+       " 'k give kvb acc detail',\n",
+       " 'oh come ah',\n",
+       " 'money r lucki winner claim prize text money million give away ppt x normal text rate box w jy',\n",
+       " 'realli sorri b abl friday hope u find altern hope yr term go ok',\n",
+       " 'congratul ore mo owo wa enjoy wish mani happi moment fro wherev go',\n",
+       " 'samu shoulder yet',\n",
+       " 'time think need know near campu',\n",
+       " 'dear matthew pleas call landlin complimentari lux tenerif holiday cash await collect ppm sae cs box sk xh',\n",
+       " 'dun wear jean lor',\n",
+       " 'sinc side fever vomitin',\n",
+       " 'k k colleg',\n",
+       " 'urgent call landlin complimentari tenerif holiday cash await collect sae cs box hp yf ppm',\n",
+       " 'better made friday stuf like pig yesterday feel bleh least writh pain kind bleh',\n",
+       " 'sell ton coin sell coin someon thru paypal voila money back life pocket',\n",
+       " 'theyr lot place hospit medic place safe',\n",
+       " 'get touch folk wait compani txt back name age opt enjoy commun p sm',\n",
+       " 'also sorta blown coupl time recent id rather text blue look weed',\n",
+       " 'sent score sopha secondari applic school think think appli research cost also contact joke ogunrind school one less expens one',\n",
+       " 'cant wait see photo use',\n",
+       " 'ur cash balanc current pound maxim ur cash send go p msg cc po box tcr w',\n",
+       " 'hey book kb sat alreadi lesson go ah keep sat night free need meet confirm lodg',\n",
+       " 'chk ur belovd ms dict',\n",
+       " 'time want come',\n",
+       " 'awesom lemm know whenev around',\n",
+       " 'shb b ok lor thanx',\n",
+       " 'beauti truth graviti read care heart feel light someon feel heavi someon leav good night',\n",
+       " 'also rememb get dobbi bowl car',\n",
+       " 'filthi stori girl wait',\n",
+       " 'sorri c ur msg yar lor poor thing one night tmr u brand new room sleep',\n",
+       " 'love decis feel could decid love life would much simpler less magic',\n",
+       " 'welp appar retir',\n",
+       " 'sort code acc bank natwest repli confirm sent right person',\n",
+       " '',\n",
+       " 'u sure u take sick time',\n",
+       " 'urgent tri contact u today draw show prize guarante call land line claim valid hr',\n",
+       " 'watch cartoon listen music amp eve go templ amp church u',\n",
+       " 'yo chad gymnast class wanna take site say christian class full',\n",
+       " 'much buzi',\n",
+       " 'better still catch let ask sell lt gt',\n",
+       " 'sure night menu know noon menu',\n",
+       " 'u want come back beauti necklac token heart that give wife like see one give dont call wait till come',\n",
+       " 'will go aptitud class',\n",
+       " 'wont b tri sort hous ok',\n",
+       " 'yar lor wan go c hors race today mah eat earlier lor ate chicken rice u',\n",
+       " 'haha awesom omw back',\n",
+       " 'yup thk e shop close lor',\n",
+       " 'account number',\n",
+       " 'eh u send wrongli lar',\n",
+       " 'hey ad crap nite borin without ya boggi u bore biatch thanx u wait til nxt time il ave ya',\n",
+       " 'ok shall talk',\n",
+       " 'dont hesit know second time weak like keep notebook eat day anyth chang day sure noth',\n",
+       " 'hey pay salari de lt gt',\n",
+       " 'anoth month need chocol weed alcohol',\n",
+       " 'start search get job day great potenti talent',\n",
+       " 'reckon need town eightish walk carpark',\n",
+       " 'congrat mobil g videophon r call videochat wid mate play java game dload polyph music nolin rentl',\n",
+       " 'look fuckin time fuck think',\n",
+       " 'yo guess drop',\n",
+       " 'carlo say mu lt gt minut',\n",
+       " 'offic call lt gt min',\n",
+       " 'geeee miss alreadi know think fuck wait till next year togeth love kiss',\n",
+       " 'yun ah ubi one say wan call tomorrow call look iren ere got bu ubi cre ubi tech park ph st wkg day n',\n",
+       " 'ugh gotta drive back sd la butt sore',\n",
+       " 'th juli',\n",
+       " 'hi im relax time ever get everi day parti good night get home tomorrow ish',\n",
+       " 'wan come come lor din c stripe skirt',\n",
+       " 'xma stori peac xma msg love xma miracl jesu hav bless month ahead amp wish u merri xma',\n",
+       " 'number',\n",
+       " 'chang e one next escal',\n",
+       " 'yetund class run water make ok pl',\n",
+       " 'lot happen feel quiet beth aunt charli work lot helen mo',\n",
+       " 'wait bu stop aft ur lect lar dun c go get car come back n pick',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus=[]\n",
+    "for i in range(len(df)):\n",
+    "    review=re.sub('[^a-zA-z]',' ',df['message'][i])\n",
+    "    review=review.lower()\n",
+    "    review=review.split()\n",
+    "    review=[ps.stem(word) for word in review if not word in stopwords.words('english')]\n",
+    "    review=' '.join(review)\n",
+    "    corpus.append(review)\n",
+    "corpus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "vectorizer = CountVectorizer(max_features=100,ngram_range=(1,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X=vectorizer.fit_transform(corpus).toarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       ...,\n",
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+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], shape=(5572, 100))"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.set_printoptions(edgeitems=30, linewidth=100000,\n",
+    "    formatter=dict(float=lambda x: \"%.3g\" % x))     \n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'go': np.int64(22),\n",
+       " 'great': np.int64(25),\n",
+       " 'got': np.int64(24),\n",
+       " 'wat': np.int64(91),\n",
+       " 'ok': np.int64(58),\n",
+       " 'free': np.int64(18),\n",
+       " 'win': np.int64(95),\n",
+       " 'text': np.int64(78),\n",
+       " 'txt': np.int64(87),\n",
+       " 'say': np.int64(69),\n",
+       " 'alreadi': np.int64(0),\n",
+       " 'think': np.int64(81),\n",
+       " 'hey': np.int64(28),\n",
+       " 'week': np.int64(93),\n",
+       " 'back': np.int64(4),\n",
+       " 'like': np.int64(39),\n",
+       " 'still': np.int64(74),\n",
+       " 'send': np.int64(71),\n",
+       " 'even': np.int64(15),\n",
+       " 'friend': np.int64(19),\n",
+       " 'prize': np.int64(64),\n",
+       " 'claim': np.int64(8),\n",
+       " 'call': np.int64(5),\n",
+       " 'mobil': np.int64(49),\n",
+       " 'co': np.int64(9),\n",
+       " 'home': np.int64(30),\n",
+       " 'want': np.int64(90),\n",
+       " 'today': np.int64(83),\n",
+       " 'cash': np.int64(7),\n",
+       " 'day': np.int64(12),\n",
+       " 'repli': np.int64(66),\n",
+       " 'www': np.int64(97),\n",
+       " 'right': np.int64(67),\n",
+       " 'thank': np.int64(79),\n",
+       " 'take': np.int64(76),\n",
+       " 'time': np.int64(82),\n",
+       " 'messag': np.int64(46),\n",
+       " 'oh': np.int64(57),\n",
+       " 'ye': np.int64(98),\n",
+       " 'make': np.int64(44),\n",
+       " 'way': np.int64(92),\n",
+       " 'feel': np.int64(16),\n",
+       " 'dont': np.int64(14),\n",
+       " 'miss': np.int64(48),\n",
+       " 'ur': np.int64(88),\n",
+       " 'tri': np.int64(86),\n",
+       " 'da': np.int64(11),\n",
+       " 'lor': np.int64(40),\n",
+       " 'meet': np.int64(45),\n",
+       " 'realli': np.int64(65),\n",
+       " 'get': np.int64(20),\n",
+       " 'know': np.int64(33),\n",
+       " 'love': np.int64(41),\n",
+       " 'amp': np.int64(1),\n",
+       " 'let': np.int64(37),\n",
+       " 'work': np.int64(96),\n",
+       " 'wait': np.int64(89),\n",
+       " 'yeah': np.int64(99),\n",
+       " 'tell': np.int64(77),\n",
+       " 'pleas': np.int64(63),\n",
+       " 'msg': np.int64(51),\n",
+       " 'see': np.int64(70),\n",
+       " 'pl': np.int64(62),\n",
+       " 'need': np.int64(53),\n",
+       " 'tomorrow': np.int64(84),\n",
+       " 'hope': np.int64(31),\n",
+       " 'well': np.int64(94),\n",
+       " 'lt': np.int64(42),\n",
+       " 'gt': np.int64(26),\n",
+       " 'lt gt': np.int64(43),\n",
+       " 'ask': np.int64(2),\n",
+       " 'morn': np.int64(50),\n",
+       " 'happi': np.int64(27),\n",
+       " 'sorri': np.int64(73),\n",
+       " 'give': np.int64(21),\n",
+       " 'new': np.int64(54),\n",
+       " 'find': np.int64(17),\n",
+       " 'later': np.int64(35),\n",
+       " 'pick': np.int64(61),\n",
+       " 'good': np.int64(23),\n",
+       " 'come': np.int64(10),\n",
+       " 'said': np.int64(68),\n",
+       " 'hi': np.int64(29),\n",
+       " 'babe': np.int64(3),\n",
+       " 'im': np.int64(32),\n",
+       " 'much': np.int64(52),\n",
+       " 'stop': np.int64(75),\n",
+       " 'one': np.int64(59),\n",
+       " 'night': np.int64(55),\n",
+       " 'life': np.int64(38),\n",
+       " 'dear': np.int64(13),\n",
+       " 'thing': np.int64(80),\n",
+       " 'last': np.int64(34),\n",
+       " 'min': np.int64(47),\n",
+       " 'number': np.int64(56),\n",
+       " 'leav': np.int64(36),\n",
+       " 'sleep': np.int64(72),\n",
+       " 'care': np.int64(6),\n",
+       " 'phone': np.int64(60),\n",
+       " 'tone': np.int64(85)}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vectorizer.vocabulary_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y=pd.get_dummies(df['label'])\n",
+    "y=y.iloc[:,0].astype(int).values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import  MultinomialNB\n",
+    "spam_detect_model=MultinomialNB().fit(X_train,y_train)\n",
+    "y_pred=spam_detect_model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.87      0.80      0.84       143\n",
+      "           1       0.97      0.98      0.98       972\n",
+      "\n",
+      "    accuracy                           0.96      1115\n",
+      "   macro avg       0.92      0.89      0.91      1115\n",
+      "weighted avg       0.96      0.96      0.96      1115\n",
+      "\n",
+      "0.9596412556053812\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test,y_pred))\n",
+    "from sklearn.metrics import accuracy_score \n",
+    "score=accuracy_score(y_test,y_pred)\n",
+    "print(score)\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -3688,7 +5316,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -3702,7 +5330,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/28 And 29 -Spam Ham Projects Using Word2vec,AvgWord2vec.ipynb b/26-CompleteNLP For Machine Learning/Practicals/28 And 29 -Spam Ham Projects Using Word2vec,AvgWord2vec.ipynb
index 6fe09a21..d9875f97 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/28 And 29 -Spam Ham Projects Using Word2vec,AvgWord2vec.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/28 And 29 -Spam Ham Projects Using Word2vec,AvgWord2vec.ipynb	
@@ -2,27 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting gensim\n",
-      "  Using cached gensim-4.2.0-cp38-cp38-win_amd64.whl (24.0 MB)\n",
-      "Requirement already satisfied: numpy>=1.17.0 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from gensim) (1.19.2)\n",
-      "Requirement already satisfied: scipy>=0.18.1 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from gensim) (1.5.2)\n",
-      "Collecting Cython==0.29.28\n",
-      "  Using cached Cython-0.29.28-py2.py3-none-any.whl (983 kB)\n",
-      "Collecting smart-open>=1.8.1\n",
-      "  Using cached smart_open-6.2.0-py3-none-any.whl (58 kB)\n",
-      "Installing collected packages: Cython, smart-open, gensim\n",
-      "  Attempting uninstall: Cython\n",
-      "    Found existing installation: Cython 0.29.21\n",
-      "    Uninstalling Cython-0.29.21:\n",
-      "      Successfully uninstalled Cython-0.29.21\n",
-      "Successfully installed Cython-0.29.28 gensim-4.2.0 smart-open-6.2.0\n"
+      "Requirement already satisfied: gensim in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (4.3.3)\n",
+      "Requirement already satisfied: numpy<2.0,>=1.18.5 in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from gensim) (1.26.4)\n",
+      "Requirement already satisfied: scipy<1.14.0,>=1.7.0 in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from gensim) (1.13.1)\n",
+      "Requirement already satisfied: smart-open>=1.8.1 in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from gensim) (7.1.0)\n",
+      "Requirement already satisfied: wrapt in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from smart-open>=1.8.1->gensim) (1.17.2)\n"
      ]
     }
    ],
@@ -32,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,17 +33,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import gensim.downloader as api\n",
     "\n",
@@ -63,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -147,7 +130,7 @@
        "      dtype=float32)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,18 +141,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "messages=pd.read_csv('smsspamcollection/SMSSpamCollection',\n",
-    "                    sep='\\t',names=[\"label\",\"message\"])"
+    "messages = pd.read_csv(r\"C:\\Users\\mudas\\Downloads\\Complete_ML_and_DL\\Complete-Data-Science-With-Machine-Learning-And-NLP-2024\\26-CompleteNLP For Machine Learning\\Practicals\\SpamClassifier-master\\smsspamcollection\\SMSSpamCollection\", sep='\\t', names=['label', 'message'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +160,7 @@
        "(5572, 2)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -189,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -199,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -207,7 +189,7 @@
      "output_type": "stream",
      "text": [
       "[nltk_data] Downloading package stopwords to\n",
-      "[nltk_data]     C:\\Users\\win10\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
       "[nltk_data]   Package stopwords is already up-to-date!\n"
      ]
     },
@@ -217,7 +199,7 @@
        "True"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -230,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -247,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -256,7 +238,7 @@
        "[[0, '', '645'], [0, '', ':) '], [0, '', ':-) :-)']]"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -267,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -792,9 +774,9 @@
        " 's no competition for him',\n",
        " 'boltblue tone for p reply poly or mono eg poly cha cha slide yeah slow jamz toxic come with me or stop more tone txt more',\n",
        " 'your credit have been topped up for http www bubbletext com your renewal pin is tgxxrz',\n",
-       " 'that way transport is le problematic than on sat night by the way if u want to ask n to join my bday feel free but need to know definite no a booking on fri',\n",
+       " 'that way transport is less problematic than on sat night by the way if u want to ask n to join my bday feel free but need to know definite no a booking on fri',\n",
        " 'usually the person is unconscious that s in child but in adult they may just behave abnormally i ll call you now',\n",
-       " 'but that s on ebay it might be le elsewhere',\n",
+       " 'but that s on ebay it might be less elsewhere',\n",
        " 'shall i come to get pickle',\n",
        " 'were gonna go get some taco',\n",
        " 'that s very rude you on campus',\n",
@@ -1218,7 +1200,7 @@
        " 'theyre doing it to lot of place only hospital and medical place are safe',\n",
        " 'how about getting in touch with folk waiting for company just txt back your name and age to opt in enjoy the community p sm',\n",
        " 'and also i ve sorta blown him off a couple time recently so id rather not text him out of the blue looking for weed',\n",
-       " 'i sent my score to sophas and i had to do secondary application for a few school i think if you are thinking of applying do a research on cost also contact joke ogunrinde her school is one me the le expensive one',\n",
+       " 'i sent my score to sophas and i had to do secondary application for a few school i think if you are thinking of applying do a research on cost also contact joke ogunrinde her school is one me the less expensive one',\n",
        " 'i cant wait to see you how were the photo were useful',\n",
        " 'ur cash balance is currently pound to maximize ur cash in now send go to only p msg cc po box tcr w',\n",
        " 'hey i booked the kb on sat already what other lesson are we going for ah keep your sat night free we need to meet and confirm our lodging',\n",
@@ -1230,7 +1212,7 @@
        " 'also remember to get dobby s bowl from your car',\n",
        " 'filthy story and girl waiting for your',\n",
        " 'sorry i now then c ur msg yar lor so poor thing but only one night tmr u ll have a brand new room sleep in',\n",
-       " 'love isn t a decision it s a feeling if we could decide who to love then life would be much simpler but then le magical',\n",
+       " 'love isn t a decision it s a feeling if we could decide who to love then life would be much simpler but then less magical',\n",
        " 'welp apparently he retired',\n",
        " 'my sort code is and acc no is the bank is natwest can you reply to confirm i ve sent this to the right person',\n",
        " 'where',\n",
@@ -1276,7 +1258,7 @@
        " ...]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1287,7 +1269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1297,7 +1279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1310,7 +1292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -7537,7 +7519,7 @@
        "  'way',\n",
        "  'transport',\n",
        "  'is',\n",
-       "  'le',\n",
+       "  'less',\n",
        "  'problematic',\n",
        "  'than',\n",
        "  'on',\n",
@@ -7585,7 +7567,7 @@
        "  'call',\n",
        "  'you',\n",
        "  'now'],\n",
-       " ['but', 'that', 'on', 'ebay', 'it', 'might', 'be', 'le', 'elsewhere'],\n",
+       " ['but', 'that', 'on', 'ebay', 'it', 'might', 'be', 'less', 'elsewhere'],\n",
        " ['shall', 'come', 'to', 'get', 'pickle'],\n",
        " ['were', 'gonna', 'go', 'get', 'some', 'taco'],\n",
        " ['that', 'very', 'rude', 'you', 'on', 'campus'],\n",
@@ -12855,7 +12837,7 @@
        "  'one',\n",
        "  'me',\n",
        "  'the',\n",
-       "  'le',\n",
+       "  'less',\n",
        "  'expensive',\n",
        "  'one'],\n",
        " ['cant',\n",
@@ -12992,7 +12974,7 @@
        "  'simpler',\n",
        "  'but',\n",
        "  'then',\n",
-       "  'le',\n",
+       "  'less',\n",
        "  'magical'],\n",
        " ['welp', 'apparently', 'he', 'retired'],\n",
        " ['my',\n",
@@ -13503,7 +13485,7 @@
        " ...]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -13514,7 +13496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13523,7 +13505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13533,7 +13515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -14239,95 +14221,94 @@
        " 'tel',\n",
        " 'sea',\n",
        " 'wit',\n",
+       " 'project',\n",
+       " 'pretty',\n",
+       " 'outside',\n",
+       " 'nope',\n",
+       " 'term',\n",
        " 'used',\n",
+       " 'drug',\n",
+       " 'fucking',\n",
+       " 'wonder',\n",
        " 'camcorder',\n",
+       " 'lovely',\n",
        " 'wrong',\n",
        " 'least',\n",
-       " 'wonder',\n",
-       " 'fucking',\n",
-       " 'drug',\n",
        " 'chennai',\n",
-       " 'project',\n",
-       " 'lovely',\n",
-       " 'crazy',\n",
        " 'fri',\n",
-       " 'term',\n",
-       " 'nope',\n",
-       " 'outside',\n",
-       " 'pretty',\n",
-       " 'fone',\n",
-       " 'meant',\n",
-       " 'frm',\n",
-       " 'hmm',\n",
+       " 'crazy',\n",
+       " 'ten',\n",
+       " 'log',\n",
+       " 'cum',\n",
+       " 'listen',\n",
+       " 'frnds',\n",
+       " 'freemsg',\n",
        " 'seeing',\n",
-       " 'savamob',\n",
+       " 'blue',\n",
        " 'telling',\n",
+       " 'fone',\n",
        " 'case',\n",
-       " 'fr',\n",
-       " 'ten',\n",
+       " 'meant',\n",
+       " 'jay',\n",
        " 'whole',\n",
-       " 'cum',\n",
+       " 'fr',\n",
+       " 'unlimited',\n",
        " 'cd',\n",
        " 'their',\n",
-       " 'unlimited',\n",
-       " 'blue',\n",
-       " 'frnds',\n",
-       " 'listen',\n",
-       " 'snow',\n",
-       " 'le',\n",
-       " 'freemsg',\n",
-       " 'support',\n",
-       " 'wkly',\n",
-       " 'wq',\n",
-       " 'earlier',\n",
+       " 'wasn',\n",
        " 'isn',\n",
+       " 'support',\n",
        " 'course',\n",
-       " 'hungry',\n",
-       " 'jay',\n",
-       " 'wasn',\n",
+       " 'frm',\n",
        " 'sunday',\n",
-       " 'log',\n",
-       " 'john',\n",
-       " 'understand',\n",
-       " 'enter',\n",
-       " 'eh',\n",
-       " 'single',\n",
-       " 'cut',\n",
-       " 'gas',\n",
-       " 'un',\n",
-       " 'father',\n",
-       " 'valued',\n",
-       " 'within',\n",
+       " 'hmm',\n",
+       " 'wq',\n",
+       " 'savamob',\n",
+       " 'snow',\n",
+       " 'hungry',\n",
+       " 'earlier',\n",
+       " 'wkly',\n",
        " 'stupid',\n",
-       " 'press',\n",
-       " 'mr',\n",
-       " 'hee',\n",
        " 'die',\n",
-       " 'hmmm',\n",
-       " 'yar',\n",
        " 'happiness',\n",
+       " 'un',\n",
+       " 'dnt',\n",
        " 'move',\n",
-       " 'almost',\n",
-       " 'knew',\n",
+       " 'etc',\n",
+       " 'hee',\n",
+       " 'within',\n",
+       " 'single',\n",
+       " 'yar',\n",
+       " 'hmmm',\n",
+       " 'cut',\n",
+       " 'mr',\n",
+       " 'eh',\n",
+       " 'moment',\n",
+       " 'march',\n",
+       " 'enter',\n",
+       " 'joy',\n",
+       " 'luck',\n",
+       " 'film',\n",
+       " 'na',\n",
+       " 'balance',\n",
+       " 'gn',\n",
+       " 'john',\n",
+       " 'gas',\n",
        " 'child',\n",
+       " 'knew',\n",
+       " 'understand',\n",
+       " 'pas',\n",
+       " 'father',\n",
+       " 'valued',\n",
        " 'store',\n",
        " 'tired',\n",
        " 'bslvyl',\n",
        " 'mayb',\n",
        " 'sell',\n",
-       " 'gn',\n",
-       " 'balance',\n",
-       " 'na',\n",
-       " 'dnt',\n",
-       " 'film',\n",
+       " 'almost',\n",
        " 'sex',\n",
        " 'paper',\n",
-       " 'luck',\n",
-       " 'joy',\n",
-       " 'pas',\n",
-       " 'moment',\n",
-       " 'etc',\n",
-       " 'march',\n",
+       " 'press',\n",
        " 'side',\n",
        " 'area',\n",
        " 'sk',\n",
@@ -14342,173 +14323,174 @@
        " 'ago',\n",
        " 'download',\n",
        " 'india',\n",
-       " 'talking',\n",
+       " 'lost',\n",
        " 'mah',\n",
        " 'christmas',\n",
-       " 'lost',\n",
-       " 'park',\n",
-       " 'motorola',\n",
-       " 'load',\n",
+       " 'talking',\n",
        " 'reading',\n",
+       " 'load',\n",
+       " 'motorola',\n",
+       " 'park',\n",
        " 'shower',\n",
-       " 'hospital',\n",
        " 'bill',\n",
-       " 'picking',\n",
+       " 'hospital',\n",
        " 'askd',\n",
-       " 'ipod',\n",
-       " 'darren',\n",
-       " 'eye',\n",
+       " 'picking',\n",
+       " 'charged',\n",
+       " 'photo',\n",
        " 'direct',\n",
-       " 'return',\n",
        " 'heard',\n",
+       " 'return',\n",
        " 'rental',\n",
-       " 'photo',\n",
-       " 'extra',\n",
+       " 'eye',\n",
+       " 'via',\n",
+       " 'darren',\n",
        " 'confirm',\n",
        " 'semester',\n",
        " 'correct',\n",
-       " 'via',\n",
+       " 'reveal',\n",
        " 'red',\n",
        " 'doin',\n",
        " 'ac',\n",
-       " 'information',\n",
+       " 'laptop',\n",
+       " 'xy',\n",
        " 'supposed',\n",
-       " 'reveal',\n",
        " 'wow',\n",
        " 'sort',\n",
-       " 'laptop',\n",
        " 'ugh',\n",
-       " 'charged',\n",
-       " 'xy',\n",
+       " 'extra',\n",
+       " 'information',\n",
        " 'bcoz',\n",
-       " 'sending',\n",
-       " 'ish',\n",
+       " 'kid',\n",
+       " 'gym',\n",
        " 'swing',\n",
-       " 'seen',\n",
        " 'redeemed',\n",
+       " 'surprise',\n",
+       " 'seen',\n",
        " 'difficult',\n",
-       " 'ge',\n",
        " 'through',\n",
+       " 'ge',\n",
        " 'promise',\n",
        " 'met',\n",
        " 'max',\n",
+       " 'sending',\n",
        " 'lady',\n",
-       " 'gym',\n",
        " 'complimentary',\n",
-       " 'kid',\n",
-       " 'gotta',\n",
        " 'comp',\n",
-       " 'grin',\n",
-       " 'surprise',\n",
        " 'figure',\n",
-       " 'police',\n",
-       " 'whenever',\n",
-       " 'discount',\n",
+       " 'ipod',\n",
+       " 'gotta',\n",
+       " 'grin',\n",
+       " 'ish',\n",
+       " 'abiola',\n",
        " 'slowly',\n",
        " 'ex',\n",
+       " 'whenever',\n",
+       " 'discount',\n",
+       " 'lovable',\n",
        " 'yep',\n",
        " 'muz',\n",
-       " 'lovable',\n",
        " 'request',\n",
+       " 'std',\n",
        " 'bath',\n",
+       " 'police',\n",
        " 'hg',\n",
-       " 'std',\n",
-       " 'abiola',\n",
        " 'crave',\n",
        " 'usf',\n",
-       " 'eg',\n",
-       " 'loan',\n",
-       " 'reward',\n",
        " 'safe',\n",
-       " 'small',\n",
+       " 'reward',\n",
+       " 'nobody',\n",
+       " 'eg',\n",
+       " 'orchard',\n",
        " 'road',\n",
+       " 'kate',\n",
        " 'wine',\n",
        " 'comin',\n",
        " 'slow',\n",
        " 'weed',\n",
-       " 'nobody',\n",
-       " 'truth',\n",
        " 'link',\n",
+       " 'asap',\n",
+       " 'truth',\n",
        " 'wap',\n",
-       " 'study',\n",
        " 'fantasy',\n",
+       " 'study',\n",
        " 'fact',\n",
        " 'loved',\n",
-       " 'orchard',\n",
+       " 'loan',\n",
        " 'cheer',\n",
-       " 'asap',\n",
-       " 'kate',\n",
+       " 'small',\n",
        " 'somebody',\n",
        " 'page',\n",
-       " 'noon',\n",
-       " 'remove',\n",
+       " 'rest',\n",
+       " 'laugh',\n",
        " 'rply',\n",
        " 'hmv',\n",
+       " 'joke',\n",
        " 'leaf',\n",
        " 'entered',\n",
        " 'txting',\n",
        " 'blood',\n",
-       " 'rest',\n",
        " 'wana',\n",
        " 'idea',\n",
+       " 'noon',\n",
        " 'clean',\n",
        " 'dogging',\n",
        " 'door',\n",
-       " 'asking',\n",
        " 'checking',\n",
+       " 'asking',\n",
        " 'train',\n",
        " 'own',\n",
+       " 'remove',\n",
        " 'lover',\n",
-       " 'laugh',\n",
-       " 'joke',\n",
+       " 'monday',\n",
        " 'save',\n",
        " 'rent',\n",
-       " 'member',\n",
-       " 'monday',\n",
        " 'pete',\n",
-       " 'copy',\n",
-       " 'empty',\n",
-       " 'deal',\n",
+       " 'member',\n",
+       " 'energy',\n",
        " 'nah',\n",
+       " 'deal',\n",
        " 'near',\n",
        " 'del',\n",
        " 'forever',\n",
        " 'mistake',\n",
-       " 'energy',\n",
        " 'cup',\n",
+       " 'copy',\n",
        " 'normal',\n",
        " 'somewhere',\n",
        " 'men',\n",
        " 'england',\n",
        " 'la',\n",
-       " 'situation',\n",
        " 'opinion',\n",
+       " 'situation',\n",
+       " 'em',\n",
        " 'cheap',\n",
        " 'warm',\n",
        " 'hoping',\n",
-       " 'water',\n",
+       " 'empty',\n",
        " 'across',\n",
-       " 'woke',\n",
        " 'hw',\n",
+       " 'woke',\n",
        " 'usual',\n",
        " 'rakhesh',\n",
        " 'callertune',\n",
        " 'spend',\n",
        " 'med',\n",
-       " 'em',\n",
+       " 'gave',\n",
        " 'cover',\n",
        " 'write',\n",
        " 'short',\n",
-       " 'gave',\n",
        " 'bb',\n",
-       " 'ringtones',\n",
        " 'tonite',\n",
+       " 'ringtones',\n",
        " 'bathe',\n",
+       " 'water',\n",
        " 'different',\n",
-       " 'voice',\n",
-       " 'teach',\n",
+       " 'representative',\n",
+       " 'sony',\n",
        " 'ho',\n",
-       " 'otherwise',\n",
+       " 'ntt',\n",
+       " 'voice',\n",
        " 'king',\n",
        " 'merry',\n",
        " 'gap',\n",
@@ -14523,26 +14505,26 @@
        " 'getzed',\n",
        " 'ldn',\n",
        " 'kick',\n",
-       " 'glad',\n",
+       " 'less',\n",
        " 'immediately',\n",
-       " 'street',\n",
+       " 'glad',\n",
        " 'summer',\n",
        " 'wishing',\n",
-       " 'representative',\n",
+       " 'street',\n",
+       " 'teach',\n",
        " 'cr',\n",
-       " 'ntt',\n",
-       " 'sony',\n",
-       " 'rd',\n",
-       " 'deep',\n",
+       " 'otherwise',\n",
        " 'worried',\n",
-       " 'turn',\n",
-       " 'catch',\n",
-       " 'reached',\n",
-       " 'pray',\n",
+       " 'doctor',\n",
+       " 'sale',\n",
+       " 'il',\n",
+       " 'convey',\n",
+       " 'custcare',\n",
+       " 'indian',\n",
        " ...]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14554,7 +14536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -14563,7 +14545,7 @@
        "5569"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14574,7 +14556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -14583,7 +14565,7 @@
        "5"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14594,25 +14576,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[('my', 0.9986233711242676),\n",
-       " ('day', 0.9984341263771057),\n",
-       " ('morning', 0.9983787536621094),\n",
-       " ('wa', 0.9983707666397095),\n",
-       " ('not', 0.9983642101287842),\n",
-       " ('well', 0.9983597993850708),\n",
-       " ('happy', 0.9982820749282837),\n",
-       " ('night', 0.9982796907424927),\n",
-       " ('very', 0.9981509447097778),\n",
-       " ('dear', 0.9981385469436646)]"
+       "[('wa', 0.9986920952796936),\n",
+       " ('night', 0.9985600113868713),\n",
+       " ('not', 0.9984620809555054),\n",
+       " ('day', 0.9983150959014893),\n",
+       " ('of', 0.9983053207397461),\n",
+       " ('there', 0.9982918500900269),\n",
+       " ('at', 0.9982302784919739),\n",
+       " ('oh', 0.9982110857963562),\n",
+       " ('morning', 0.998174250125885),\n",
+       " ('in', 0.9981483817100525)]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14623,7 +14605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -14632,7 +14614,7 @@
        "(100,)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14643,7 +14625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -14669,7 +14651,7 @@
        " 'wat']"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14680,7 +14662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14695,14 +14677,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: tqdm in c:\\users\\win10\\anaconda3\\lib\\site-packages (4.50.2)\n"
+      "Requirement already satisfied: tqdm in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (4.67.1)\n",
+      "Requirement already satisfied: colorama in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from tqdm) (0.4.6)\n"
      ]
     }
    ],
@@ -14712,7 +14695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14721,18 +14704,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                                                         | 0/5569 [00:00<?, ?it/s]C:\\Users\\win10\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3372: RuntimeWarning: Mean of empty slice.\n",
+      "  0%|          | 0/5569 [00:00<?, ?it/s]c:\\Users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "c:\\Users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages\\numpy\\core\\_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|███████████████████████████████████████████████████████████████████████████| 5569/5569 [00:00<00:00, 10193.26it/s]\n"
+      "100%|██████████| 5569/5569 [00:00<00:00, 9033.31it/s]\n"
      ]
     }
    ],
@@ -14746,7 +14729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -14755,7 +14738,7 @@
        "5569"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14766,15 +14749,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-146-c24378517c84>:2: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
-      "  X_new=np.array(X)\n"
+     "ename": "NameError",
+     "evalue": "name 'np' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m##independent Features\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m X_new\u001b[38;5;241m=\u001b[39m\u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39marray(X)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
      ]
     }
    ],
@@ -14785,7 +14771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -14794,7 +14780,7 @@
        "(5572, 2)"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14805,41 +14791,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-1.93356231e-01,  2.06870615e-01,  8.65866244e-02,  9.58224237e-02,\n",
-       "        7.69399405e-02, -3.98698807e-01,  1.05395772e-01,  4.14924890e-01,\n",
-       "       -1.91718236e-01, -1.09027036e-01, -1.47230983e-01, -3.41823012e-01,\n",
-       "       -6.78465441e-02,  7.88864195e-02,  1.66462719e-01, -1.24658264e-01,\n",
-       "        9.31969061e-02, -2.84715623e-01, -3.52087431e-03, -4.92967844e-01,\n",
-       "        1.51998132e-01,  8.75003263e-02,  9.82758403e-02, -1.58014327e-01,\n",
-       "       -2.43601371e-02,  2.52882279e-02, -1.66070610e-01, -1.58925757e-01,\n",
-       "       -1.97879627e-01,  4.81840223e-02,  2.71549881e-01,  1.90760940e-02,\n",
-       "        8.39922428e-02, -1.51190266e-01, -9.77698565e-02,  3.23482841e-01,\n",
-       "        6.03131205e-02, -1.12584829e-01, -9.79556665e-02, -4.00274873e-01,\n",
-       "        7.30916634e-02, -1.94243461e-01, -1.80711836e-01,  9.73581523e-03,\n",
-       "        1.34885401e-01, -2.19938867e-02, -1.17072053e-01, -7.56110102e-02,\n",
-       "        1.87373236e-01,  8.83920640e-02,  1.53641552e-01, -1.86086714e-01,\n",
-       "       -4.09735888e-02,  6.52891472e-02, -1.11653253e-01,  5.94264977e-02,\n",
-       "        1.51845068e-01,  6.50782604e-05, -3.66079718e-01,  1.11160830e-01,\n",
-       "       -2.00572684e-02,  1.31564200e-01,  2.28943885e-03, -7.61395097e-02,\n",
-       "       -2.45968997e-01,  2.20238850e-01,  5.04133813e-02,  1.87917173e-01,\n",
-       "       -2.64686793e-01,  3.19347739e-01, -2.22753942e-01,  1.54231340e-01,\n",
-       "        3.31943005e-01, -9.06851366e-02,  2.73179471e-01,  3.11135929e-02,\n",
-       "        5.29456995e-02, -9.35524926e-02, -1.48092657e-01,  6.22706711e-02,\n",
-       "       -1.89699516e-01, -7.74405822e-02, -1.95555031e-01,  3.77129048e-01,\n",
-       "       -1.06729805e-01, -1.81981213e-02,  5.22396378e-02,  2.01196045e-01,\n",
-       "        2.87859976e-01,  6.73278570e-02,  2.99241841e-01,  1.57048851e-01,\n",
-       "        5.43779917e-02,  5.20608798e-02,  3.67087603e-01,  1.57485634e-01,\n",
-       "        1.63157001e-01, -1.70741916e-01,  1.44291580e-01,  1.90210640e-02],\n",
+       "array([-0.18480636,  0.18316269,  0.09118807,  0.1139645 ,  0.03595163,\n",
+       "       -0.3991524 ,  0.14694929,  0.46549007, -0.20502023, -0.12690903,\n",
+       "       -0.1966387 , -0.3329585 , -0.07086463,  0.08169955,  0.15547924,\n",
+       "       -0.162067  ,  0.11110888, -0.32169992, -0.04541419, -0.48556036,\n",
+       "        0.1983112 ,  0.09149661,  0.11322919, -0.15046224,  0.01177698,\n",
+       "       -0.01741295, -0.20103002, -0.16081962, -0.24040928,  0.0587237 ,\n",
+       "        0.25879654, -0.00920143,  0.10696919, -0.1837695 , -0.1168717 ,\n",
+       "        0.31165698,  0.02019083, -0.13293494, -0.1519759 , -0.44220412,\n",
+       "        0.02736169, -0.14530359, -0.16655359, -0.00790542,  0.16069794,\n",
+       "       -0.03415268, -0.13337614, -0.07375044,  0.2073419 ,  0.12187356,\n",
+       "        0.11137906, -0.16782337, -0.07148591,  0.03214823, -0.05451738,\n",
+       "        0.06880517,  0.15428478,  0.06105424, -0.312397  ,  0.13531536,\n",
+       "        0.00497902,  0.07616561, -0.04959057, -0.08006502, -0.26267064,\n",
+       "        0.19824982,  0.03523917,  0.1634273 , -0.25553447,  0.29963797,\n",
+       "       -0.16751207,  0.18134095,  0.30730742, -0.09766024,  0.2976629 ,\n",
+       "        0.00919708,  0.03785983, -0.12995084, -0.16999678,  0.055443  ,\n",
+       "       -0.19767673, -0.04333981, -0.2201936 ,  0.3957232 , -0.18512511,\n",
+       "       -0.09036708,  0.07965185,  0.20018464,  0.3243936 ,  0.02815875,\n",
+       "        0.30565718,  0.10039792,  0.05692359,  0.04609526,  0.45629278,\n",
+       "        0.16558234,  0.1290138 , -0.212688  ,  0.16530378, -0.0112021 ],\n",
        "      dtype=float32)"
       ]
      },
-     "execution_count": 154,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14850,18 +14831,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(5569,)"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'X_new' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[59], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mX_new\u001b[49m\u001b[38;5;241m.\u001b[39mshape\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'X_new' is not defined"
+     ]
     }
    ],
    "source": [
@@ -14870,18 +14852,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(100,)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'X_new' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[60], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mX_new\u001b[49m[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mshape\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'X_new' is not defined"
+     ]
     }
    ],
    "source": [
@@ -14890,7 +14873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14903,7 +14886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -14912,7 +14895,7 @@
        "(5569,)"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14923,7 +14906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -14932,7 +14915,7 @@
        "(1, 100)"
       ]
      },
-     "execution_count": 158,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14943,9 +14926,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 81,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'append'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_15072\\285459965.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m## this is the final independent features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   6314\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6315\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6316\u001b[0m         ):\n\u001b[0;32m   6317\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6318\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'"
+     ]
+    }
+   ],
    "source": [
     "## this is the final independent features\n",
     "df=pd.DataFrame()\n",
@@ -14956,7 +14952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -14980,181 +14976,20 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>90</th>\n",
-       "      <th>91</th>\n",
-       "      <th>92</th>\n",
-       "      <th>93</th>\n",
-       "      <th>94</th>\n",
-       "      <th>95</th>\n",
-       "      <th>96</th>\n",
-       "      <th>97</th>\n",
-       "      <th>98</th>\n",
-       "      <th>99</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-0.216207</td>\n",
-       "      <td>0.239545</td>\n",
-       "      <td>0.104032</td>\n",
-       "      <td>0.112374</td>\n",
-       "      <td>0.082477</td>\n",
-       "      <td>-0.466196</td>\n",
-       "      <td>0.137822</td>\n",
-       "      <td>0.476254</td>\n",
-       "      <td>-0.222950</td>\n",
-       "      <td>-0.137382</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.339254</td>\n",
-       "      <td>0.194080</td>\n",
-       "      <td>0.071031</td>\n",
-       "      <td>0.070812</td>\n",
-       "      <td>0.438865</td>\n",
-       "      <td>0.190742</td>\n",
-       "      <td>0.190763</td>\n",
-       "      <td>-0.187745</td>\n",
-       "      <td>0.162193</td>\n",
-       "      <td>0.028564</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.193356</td>\n",
-       "      <td>0.206871</td>\n",
-       "      <td>0.086587</td>\n",
-       "      <td>0.095822</td>\n",
-       "      <td>0.076940</td>\n",
-       "      <td>-0.398699</td>\n",
-       "      <td>0.105396</td>\n",
-       "      <td>0.414925</td>\n",
-       "      <td>-0.191718</td>\n",
-       "      <td>-0.109027</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.299242</td>\n",
-       "      <td>0.157049</td>\n",
-       "      <td>0.054378</td>\n",
-       "      <td>0.052061</td>\n",
-       "      <td>0.367088</td>\n",
-       "      <td>0.157486</td>\n",
-       "      <td>0.163157</td>\n",
-       "      <td>-0.170742</td>\n",
-       "      <td>0.144292</td>\n",
-       "      <td>0.019021</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.224004</td>\n",
-       "      <td>0.258399</td>\n",
-       "      <td>0.109422</td>\n",
-       "      <td>0.132594</td>\n",
-       "      <td>0.072034</td>\n",
-       "      <td>-0.512921</td>\n",
-       "      <td>0.139317</td>\n",
-       "      <td>0.468239</td>\n",
-       "      <td>-0.235252</td>\n",
-       "      <td>-0.170547</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.337649</td>\n",
-       "      <td>0.206957</td>\n",
-       "      <td>0.062190</td>\n",
-       "      <td>0.053607</td>\n",
-       "      <td>0.447795</td>\n",
-       "      <td>0.179290</td>\n",
-       "      <td>0.148134</td>\n",
-       "      <td>-0.218474</td>\n",
-       "      <td>0.189056</td>\n",
-       "      <td>0.040700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.292759</td>\n",
-       "      <td>0.325016</td>\n",
-       "      <td>0.135531</td>\n",
-       "      <td>0.154963</td>\n",
-       "      <td>0.110923</td>\n",
-       "      <td>-0.622693</td>\n",
-       "      <td>0.179386</td>\n",
-       "      <td>0.650676</td>\n",
-       "      <td>-0.304797</td>\n",
-       "      <td>-0.172119</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.460873</td>\n",
-       "      <td>0.255784</td>\n",
-       "      <td>0.096641</td>\n",
-       "      <td>0.101251</td>\n",
-       "      <td>0.582463</td>\n",
-       "      <td>0.258556</td>\n",
-       "      <td>0.274167</td>\n",
-       "      <td>-0.251514</td>\n",
-       "      <td>0.216341</td>\n",
-       "      <td>0.030516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.257790</td>\n",
-       "      <td>0.268228</td>\n",
-       "      <td>0.123055</td>\n",
-       "      <td>0.129405</td>\n",
-       "      <td>0.104291</td>\n",
-       "      <td>-0.530266</td>\n",
-       "      <td>0.150534</td>\n",
-       "      <td>0.557835</td>\n",
-       "      <td>-0.263921</td>\n",
-       "      <td>-0.155187</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.399028</td>\n",
-       "      <td>0.217131</td>\n",
-       "      <td>0.087345</td>\n",
-       "      <td>0.086943</td>\n",
-       "      <td>0.497969</td>\n",
-       "      <td>0.224949</td>\n",
-       "      <td>0.225521</td>\n",
-       "      <td>-0.226079</td>\n",
-       "      <td>0.180644</td>\n",
-       "      <td>0.029771</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 100 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         0         1         2         3         4         5         6   \\\n",
-       "0 -0.216207  0.239545  0.104032  0.112374  0.082477 -0.466196  0.137822   \n",
-       "1 -0.193356  0.206871  0.086587  0.095822  0.076940 -0.398699  0.105396   \n",
-       "2 -0.224004  0.258399  0.109422  0.132594  0.072034 -0.512921  0.139317   \n",
-       "3 -0.292759  0.325016  0.135531  0.154963  0.110923 -0.622693  0.179386   \n",
-       "4 -0.257790  0.268228  0.123055  0.129405  0.104291 -0.530266  0.150534   \n",
-       "\n",
-       "         7         8         9   ...        90        91        92        93  \\\n",
-       "0  0.476254 -0.222950 -0.137382  ...  0.339254  0.194080  0.071031  0.070812   \n",
-       "1  0.414925 -0.191718 -0.109027  ...  0.299242  0.157049  0.054378  0.052061   \n",
-       "2  0.468239 -0.235252 -0.170547  ...  0.337649  0.206957  0.062190  0.053607   \n",
-       "3  0.650676 -0.304797 -0.172119  ...  0.460873  0.255784  0.096641  0.101251   \n",
-       "4  0.557835 -0.263921 -0.155187  ...  0.399028  0.217131  0.087345  0.086943   \n",
-       "\n",
-       "         94        95        96        97        98        99  \n",
-       "0  0.438865  0.190742  0.190763 -0.187745  0.162193  0.028564  \n",
-       "1  0.367088  0.157486  0.163157 -0.170742  0.144292  0.019021  \n",
-       "2  0.447795  0.179290  0.148134 -0.218474  0.189056  0.040700  \n",
-       "3  0.582463  0.258556  0.274167 -0.251514  0.216341  0.030516  \n",
-       "4  0.497969  0.224949  0.225521 -0.226079  0.180644  0.029771  \n",
-       "\n",
-       "[5 rows x 100 columns]"
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: []"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -15165,7 +15000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15174,7 +15009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -15198,181 +15033,44 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>91</th>\n",
-       "      <th>92</th>\n",
-       "      <th>93</th>\n",
-       "      <th>94</th>\n",
-       "      <th>95</th>\n",
-       "      <th>96</th>\n",
-       "      <th>97</th>\n",
-       "      <th>98</th>\n",
-       "      <th>99</th>\n",
        "      <th>Output</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.216207</td>\n",
-       "      <td>0.239545</td>\n",
-       "      <td>0.104032</td>\n",
-       "      <td>0.112374</td>\n",
-       "      <td>0.082477</td>\n",
-       "      <td>-0.466196</td>\n",
-       "      <td>0.137822</td>\n",
-       "      <td>0.476254</td>\n",
-       "      <td>-0.222950</td>\n",
-       "      <td>-0.137382</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.194080</td>\n",
-       "      <td>0.071031</td>\n",
-       "      <td>0.070812</td>\n",
-       "      <td>0.438865</td>\n",
-       "      <td>0.190742</td>\n",
-       "      <td>0.190763</td>\n",
-       "      <td>-0.187745</td>\n",
-       "      <td>0.162193</td>\n",
-       "      <td>0.028564</td>\n",
-       "      <td>1</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-0.193356</td>\n",
-       "      <td>0.206871</td>\n",
-       "      <td>0.086587</td>\n",
-       "      <td>0.095822</td>\n",
-       "      <td>0.076940</td>\n",
-       "      <td>-0.398699</td>\n",
-       "      <td>0.105396</td>\n",
-       "      <td>0.414925</td>\n",
-       "      <td>-0.191718</td>\n",
-       "      <td>-0.109027</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.157049</td>\n",
-       "      <td>0.054378</td>\n",
-       "      <td>0.052061</td>\n",
-       "      <td>0.367088</td>\n",
-       "      <td>0.157486</td>\n",
-       "      <td>0.163157</td>\n",
-       "      <td>-0.170742</td>\n",
-       "      <td>0.144292</td>\n",
-       "      <td>0.019021</td>\n",
-       "      <td>1</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.224004</td>\n",
-       "      <td>0.258399</td>\n",
-       "      <td>0.109422</td>\n",
-       "      <td>0.132594</td>\n",
-       "      <td>0.072034</td>\n",
-       "      <td>-0.512921</td>\n",
-       "      <td>0.139317</td>\n",
-       "      <td>0.468239</td>\n",
-       "      <td>-0.235252</td>\n",
-       "      <td>-0.170547</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.206957</td>\n",
-       "      <td>0.062190</td>\n",
-       "      <td>0.053607</td>\n",
-       "      <td>0.447795</td>\n",
-       "      <td>0.179290</td>\n",
-       "      <td>0.148134</td>\n",
-       "      <td>-0.218474</td>\n",
-       "      <td>0.189056</td>\n",
-       "      <td>0.040700</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.292759</td>\n",
-       "      <td>0.325016</td>\n",
-       "      <td>0.135531</td>\n",
-       "      <td>0.154963</td>\n",
-       "      <td>0.110923</td>\n",
-       "      <td>-0.622693</td>\n",
-       "      <td>0.179386</td>\n",
-       "      <td>0.650676</td>\n",
-       "      <td>-0.304797</td>\n",
-       "      <td>-0.172119</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.255784</td>\n",
-       "      <td>0.096641</td>\n",
-       "      <td>0.101251</td>\n",
-       "      <td>0.582463</td>\n",
-       "      <td>0.258556</td>\n",
-       "      <td>0.274167</td>\n",
-       "      <td>-0.251514</td>\n",
-       "      <td>0.216341</td>\n",
-       "      <td>0.030516</td>\n",
-       "      <td>1</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.257790</td>\n",
-       "      <td>0.268228</td>\n",
-       "      <td>0.123055</td>\n",
-       "      <td>0.129405</td>\n",
-       "      <td>0.104291</td>\n",
-       "      <td>-0.530266</td>\n",
-       "      <td>0.150534</td>\n",
-       "      <td>0.557835</td>\n",
-       "      <td>-0.263921</td>\n",
-       "      <td>-0.155187</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.217131</td>\n",
-       "      <td>0.087345</td>\n",
-       "      <td>0.086943</td>\n",
-       "      <td>0.497969</td>\n",
-       "      <td>0.224949</td>\n",
-       "      <td>0.225521</td>\n",
-       "      <td>-0.226079</td>\n",
-       "      <td>0.180644</td>\n",
-       "      <td>0.029771</td>\n",
-       "      <td>1</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 101 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "          0         1         2         3         4         5         6  \\\n",
-       "0 -0.216207  0.239545  0.104032  0.112374  0.082477 -0.466196  0.137822   \n",
-       "1 -0.193356  0.206871  0.086587  0.095822  0.076940 -0.398699  0.105396   \n",
-       "2 -0.224004  0.258399  0.109422  0.132594  0.072034 -0.512921  0.139317   \n",
-       "3 -0.292759  0.325016  0.135531  0.154963  0.110923 -0.622693  0.179386   \n",
-       "4 -0.257790  0.268228  0.123055  0.129405  0.104291 -0.530266  0.150534   \n",
-       "\n",
-       "          7         8         9  ...        91        92        93        94  \\\n",
-       "0  0.476254 -0.222950 -0.137382  ...  0.194080  0.071031  0.070812  0.438865   \n",
-       "1  0.414925 -0.191718 -0.109027  ...  0.157049  0.054378  0.052061  0.367088   \n",
-       "2  0.468239 -0.235252 -0.170547  ...  0.206957  0.062190  0.053607  0.447795   \n",
-       "3  0.650676 -0.304797 -0.172119  ...  0.255784  0.096641  0.101251  0.582463   \n",
-       "4  0.557835 -0.263921 -0.155187  ...  0.217131  0.087345  0.086943  0.497969   \n",
-       "\n",
-       "         95        96        97        98        99  Output  \n",
-       "0  0.190742  0.190763 -0.187745  0.162193  0.028564       1  \n",
-       "1  0.157486  0.163157 -0.170742  0.144292  0.019021       1  \n",
-       "2  0.179290  0.148134 -0.218474  0.189056  0.040700       0  \n",
-       "3  0.258556  0.274167 -0.251514  0.216341  0.030516       1  \n",
-       "4  0.224949  0.225521 -0.226079  0.180644  0.029771       1  \n",
-       "\n",
-       "[5 rows x 101 columns]"
+       "   Output\n",
+       "0    True\n",
+       "1    True\n",
+       "2   False\n",
+       "3    True\n",
+       "4    True"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -15383,7 +15081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15392,27 +15090,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0         0\n",
-       "1         0\n",
-       "2         0\n",
-       "3         0\n",
-       "4         0\n",
-       "         ..\n",
-       "96        0\n",
-       "97        0\n",
-       "98        0\n",
-       "99        0\n",
        "Output    0\n",
-       "Length: 101, dtype: int64"
+       "dtype: int64"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -15423,7 +15111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15433,27 +15121,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0         0\n",
-       "1         0\n",
-       "2         0\n",
-       "3         0\n",
-       "4         0\n",
-       "         ..\n",
-       "96        0\n",
-       "97        0\n",
-       "98        0\n",
-       "99        0\n",
        "Output    0\n",
-       "Length: 101, dtype: int64"
+       "dtype: int64"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -15464,7 +15142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15473,7 +15151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15484,7 +15162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -15508,181 +15186,44 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>91</th>\n",
-       "      <th>92</th>\n",
-       "      <th>93</th>\n",
-       "      <th>94</th>\n",
-       "      <th>95</th>\n",
-       "      <th>96</th>\n",
-       "      <th>97</th>\n",
-       "      <th>98</th>\n",
-       "      <th>99</th>\n",
        "      <th>Output</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>4464</th>\n",
-       "      <td>-0.217020</td>\n",
-       "      <td>0.255512</td>\n",
-       "      <td>0.106173</td>\n",
-       "      <td>0.119504</td>\n",
-       "      <td>0.074694</td>\n",
-       "      <td>-0.487429</td>\n",
-       "      <td>0.139651</td>\n",
-       "      <td>0.487703</td>\n",
-       "      <td>-0.226025</td>\n",
-       "      <td>-0.155531</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.207455</td>\n",
-       "      <td>0.071450</td>\n",
-       "      <td>0.063290</td>\n",
-       "      <td>0.447863</td>\n",
-       "      <td>0.190330</td>\n",
-       "      <td>0.184317</td>\n",
-       "      <td>-0.198362</td>\n",
-       "      <td>0.172240</td>\n",
-       "      <td>0.025990</td>\n",
-       "      <td>1</td>\n",
+       "      <th>1509</th>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>672</th>\n",
-       "      <td>-0.244875</td>\n",
-       "      <td>0.271410</td>\n",
-       "      <td>0.114304</td>\n",
-       "      <td>0.134989</td>\n",
-       "      <td>0.089957</td>\n",
-       "      <td>-0.544205</td>\n",
-       "      <td>0.156727</td>\n",
-       "      <td>0.523276</td>\n",
-       "      <td>-0.257568</td>\n",
-       "      <td>-0.178775</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.220620</td>\n",
-       "      <td>0.075331</td>\n",
-       "      <td>0.068684</td>\n",
-       "      <td>0.480091</td>\n",
-       "      <td>0.212190</td>\n",
-       "      <td>0.184667</td>\n",
-       "      <td>-0.235810</td>\n",
-       "      <td>0.193756</td>\n",
-       "      <td>0.035351</td>\n",
-       "      <td>0</td>\n",
+       "      <th>3051</th>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>359</th>\n",
-       "      <td>-0.267663</td>\n",
-       "      <td>0.287808</td>\n",
-       "      <td>0.125663</td>\n",
-       "      <td>0.134431</td>\n",
-       "      <td>0.103460</td>\n",
-       "      <td>-0.555823</td>\n",
-       "      <td>0.162521</td>\n",
-       "      <td>0.577144</td>\n",
-       "      <td>-0.270206</td>\n",
-       "      <td>-0.162406</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.238139</td>\n",
-       "      <td>0.095809</td>\n",
-       "      <td>0.087063</td>\n",
-       "      <td>0.528695</td>\n",
-       "      <td>0.240875</td>\n",
-       "      <td>0.241080</td>\n",
-       "      <td>-0.220694</td>\n",
-       "      <td>0.186851</td>\n",
-       "      <td>0.029009</td>\n",
-       "      <td>1</td>\n",
+       "      <th>2109</th>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>-0.200889</td>\n",
-       "      <td>0.218030</td>\n",
-       "      <td>0.097211</td>\n",
-       "      <td>0.105852</td>\n",
-       "      <td>0.079384</td>\n",
-       "      <td>-0.435910</td>\n",
-       "      <td>0.127253</td>\n",
-       "      <td>0.451032</td>\n",
-       "      <td>-0.213273</td>\n",
-       "      <td>-0.126732</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.175443</td>\n",
-       "      <td>0.068785</td>\n",
-       "      <td>0.063010</td>\n",
-       "      <td>0.401926</td>\n",
-       "      <td>0.175555</td>\n",
-       "      <td>0.182444</td>\n",
-       "      <td>-0.174961</td>\n",
-       "      <td>0.151151</td>\n",
-       "      <td>0.017436</td>\n",
-       "      <td>1</td>\n",
+       "      <th>488</th>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3148</th>\n",
-       "      <td>-0.196464</td>\n",
-       "      <td>0.213178</td>\n",
-       "      <td>0.091318</td>\n",
-       "      <td>0.099782</td>\n",
-       "      <td>0.073367</td>\n",
-       "      <td>-0.412015</td>\n",
-       "      <td>0.116882</td>\n",
-       "      <td>0.409727</td>\n",
-       "      <td>-0.193720</td>\n",
-       "      <td>-0.120134</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.172275</td>\n",
-       "      <td>0.066354</td>\n",
-       "      <td>0.058961</td>\n",
-       "      <td>0.380369</td>\n",
-       "      <td>0.168739</td>\n",
-       "      <td>0.166854</td>\n",
-       "      <td>-0.162263</td>\n",
-       "      <td>0.142802</td>\n",
-       "      <td>0.028292</td>\n",
-       "      <td>1</td>\n",
+       "      <th>2428</th>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 101 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "             0         1         2         3         4         5         6  \\\n",
-       "4464 -0.217020  0.255512  0.106173  0.119504  0.074694 -0.487429  0.139651   \n",
-       "672  -0.244875  0.271410  0.114304  0.134989  0.089957 -0.544205  0.156727   \n",
-       "359  -0.267663  0.287808  0.125663  0.134431  0.103460 -0.555823  0.162521   \n",
-       "70   -0.200889  0.218030  0.097211  0.105852  0.079384 -0.435910  0.127253   \n",
-       "3148 -0.196464  0.213178  0.091318  0.099782  0.073367 -0.412015  0.116882   \n",
-       "\n",
-       "             7         8         9  ...        91        92        93  \\\n",
-       "4464  0.487703 -0.226025 -0.155531  ...  0.207455  0.071450  0.063290   \n",
-       "672   0.523276 -0.257568 -0.178775  ...  0.220620  0.075331  0.068684   \n",
-       "359   0.577144 -0.270206 -0.162406  ...  0.238139  0.095809  0.087063   \n",
-       "70    0.451032 -0.213273 -0.126732  ...  0.175443  0.068785  0.063010   \n",
-       "3148  0.409727 -0.193720 -0.120134  ...  0.172275  0.066354  0.058961   \n",
-       "\n",
-       "            94        95        96        97        98        99  Output  \n",
-       "4464  0.447863  0.190330  0.184317 -0.198362  0.172240  0.025990       1  \n",
-       "672   0.480091  0.212190  0.184667 -0.235810  0.193756  0.035351       0  \n",
-       "359   0.528695  0.240875  0.241080 -0.220694  0.186851  0.029009       1  \n",
-       "70    0.401926  0.175555  0.182444 -0.174961  0.151151  0.017436       1  \n",
-       "3148  0.380369  0.168739  0.166854 -0.162263  0.142802  0.028292       1  \n",
-       "\n",
-       "[5 rows x 101 columns]"
+       "      Output\n",
+       "1509    True\n",
+       "3051    True\n",
+       "2109    True\n",
+       "488     True\n",
+       "2428    True"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -15693,27 +15234,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4464    1\n",
-       "672     0\n",
-       "359     1\n",
-       "70      1\n",
-       "3148    1\n",
-       "       ..\n",
-       "1758    1\n",
-       "290     1\n",
-       "1399    1\n",
-       "4683    1\n",
-       "2172    1\n",
-       "Name: Output, Length: 4445, dtype: uint8"
+       "1509     True\n",
+       "3051     True\n",
+       "2109     True\n",
+       "488      True\n",
+       "2428     True\n",
+       "        ...  \n",
+       "2177     True\n",
+       "650     False\n",
+       "2937     True\n",
+       "5222     True\n",
+       "2119     True\n",
+       "Name: Output, Length: 4455, dtype: bool"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -15724,7 +15265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15734,16 +15275,434 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "RandomForestClassifier()"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -15754,7 +15713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15763,14 +15722,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9973021582733813\n"
+      "1.0\n"
      ]
     }
    ],
@@ -15781,7 +15740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -15790,12 +15749,12 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       1.00      0.98      0.99       147\n",
-      "           1       1.00      1.00      1.00       965\n",
+      "       False       1.00      1.00      1.00       158\n",
+      "        True       1.00      1.00      1.00       956\n",
       "\n",
-      "    accuracy                           1.00      1112\n",
-      "   macro avg       1.00      0.99      0.99      1112\n",
-      "weighted avg       1.00      1.00      1.00      1112\n",
+      "    accuracy                           1.00      1114\n",
+      "   macro avg       1.00      1.00      1.00      1114\n",
+      "weighted avg       1.00      1.00      1.00      1114\n",
       "\n"
      ]
     }
@@ -15804,6 +15763,20 @@
     "print(classification_report(y_test,y_pred))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -15814,7 +15787,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -15828,7 +15801,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/30 and 31-Project 2- Kindle Review Sentiment Analyis.ipynb b/26-CompleteNLP For Machine Learning/Practicals/30 and 31-Project 2- Kindle Review Sentiment Analyis.ipynb
index c139dfab..bee04fbc 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/30 and 31-Project 2- Kindle Review Sentiment Analyis.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/30 and 31-Project 2- Kindle Review Sentiment Analyis.ipynb	
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -72,8 +72,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
        "      <th>Unnamed: 0.1</th>\n",
+       "      <th>Unnamed: 0</th>\n",
        "      <th>asin</th>\n",
        "      <th>helpful</th>\n",
        "      <th>rating</th>\n",
@@ -161,12 +161,12 @@
        "</div>"
       ],
       "text/plain": [
-       "   Unnamed: 0  Unnamed: 0.1        asin  helpful  rating  \\\n",
-       "0           0         11539  B0033UV8HI  [8, 10]       3   \n",
-       "1           1          5957  B002HJV4DE   [1, 1]       5   \n",
-       "2           2          9146  B002ZG96I4   [0, 0]       3   \n",
-       "3           3          7038  B002QHWOEU   [1, 3]       3   \n",
-       "4           4          1776  B001A06VJ8   [0, 1]       4   \n",
+       "   Unnamed: 0.1  Unnamed: 0        asin  helpful  rating  \\\n",
+       "0             0       11539  B0033UV8HI  [8, 10]       3   \n",
+       "1             1        5957  B002HJV4DE   [1, 1]       5   \n",
+       "2             2        9146  B002ZG96I4   [0, 0]       3   \n",
+       "3             3        7038  B002QHWOEU   [1, 3]       3   \n",
+       "4             4        1776  B001A06VJ8   [0, 1]       4   \n",
        "\n",
        "                                          reviewText   reviewTime  \\\n",
        "0  Jace Rankin may be short, but he's nothing to ...   09 2, 2010   \n",
@@ -183,7 +183,7 @@
        "4  A3C9V987IQHOQD      Rjostler                      Book      1356912000  "
       ]
      },
-     "execution_count": 65,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +264,7 @@
        "4  I did not expect this type of book to be in li...       4"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -285,7 +285,7 @@
        "(12000, 2)"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -307,7 +307,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -328,7 +328,7 @@
        "array([3, 5, 4, 2, 1], dtype=int64)"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -339,21 +339,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "rating\n",
        "5    3000\n",
        "4    3000\n",
        "3    2000\n",
        "2    2000\n",
        "1    2000\n",
-       "Name: rating, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -373,14 +374,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-86-1e5aedecae4a>:2: SettingWithCopyWarning: \n",
+      "C:\\Users\\mudas\\AppData\\Local\\Temp\\ipykernel_3452\\3208188810.py:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -396,18 +397,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "rating\n",
        "1    8000\n",
        "0    4000\n",
-       "Name: rating, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -418,14 +420,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-88-26e5fcb41736>:2: SettingWithCopyWarning: \n",
+      "C:\\Users\\mudas\\AppData\\Local\\Temp\\ipykernel_3452\\1654308119.py:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -441,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -508,7 +510,7 @@
        "4  i did not expect this type of book to be in li...       1"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -527,7 +529,7 @@
      "output_type": "stream",
      "text": [
       "[nltk_data] Downloading package stopwords to\n",
-      "[nltk_data]     C:\\Users\\win10\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
       "[nltk_data]   Package stopwords is already up-to-date!\n"
      ]
     },
@@ -537,7 +539,7 @@
        "True"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -560,38 +562,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-92-86b4aeb70b16>:2: SettingWithCopyWarning: \n",
+      "C:\\Users\\mudas\\AppData\\Local\\Temp\\ipykernel_3452\\2810722791.py:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  df['reviewText']=df['reviewText'].apply(lambda x:re.sub('[^a-z A-z 0-9-]+', '',x))\n",
-      "<ipython-input-92-86b4aeb70b16>:4: SettingWithCopyWarning: \n",
+      "C:\\Users\\mudas\\AppData\\Local\\Temp\\ipykernel_3452\\2810722791.py:4: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  df['reviewText']=df['reviewText'].apply(lambda x:\" \".join([y for y in x.split() if y not in stopwords.words('english')]))\n",
-      "<ipython-input-92-86b4aeb70b16>:6: SettingWithCopyWarning: \n",
+      "C:\\Users\\mudas\\AppData\\Local\\Temp\\ipykernel_3452\\2810722791.py:6: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  df['reviewText']=df['reviewText'].apply(lambda x: re.sub(r'(http|https|ftp|ssh)://([\\w_-]+(?:(?:\\.[\\w_-]+)+))([\\w.,@?^=%&:/~+#-]*[\\w@?^=%&/~+#-])?', '' , str(x)))\n",
-      "<ipython-input-92-86b4aeb70b16>:8: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df['reviewText']=df['reviewText'].apply(lambda x: BeautifulSoup(x, 'lxml').get_text())\n",
-      "<ipython-input-92-86b4aeb70b16>:10: SettingWithCopyWarning: \n",
+      "C:\\Users\\mudas\\AppData\\Local\\Temp\\ipykernel_3452\\2810722791.py:10: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -608,14 +604,14 @@
     "## Remove url \n",
     "df['reviewText']=df['reviewText'].apply(lambda x: re.sub(r'(http|https|ftp|ssh)://([\\w_-]+(?:(?:\\.[\\w_-]+)+))([\\w.,@?^=%&:/~+#-]*[\\w@?^=%&/~+#-])?', '' , str(x)))\n",
     "## Remove html tags\n",
-    "df['reviewText']=df['reviewText'].apply(lambda x: BeautifulSoup(x, 'lxml').get_text())\n",
+    "# df['reviewText']=df['reviewText'].apply(lambda x: BeautifulSoup(x, 'lxml').get_text())\n",
     "## Remove any additional spaces\n",
     "df['reviewText']=df['reviewText'].apply(lambda x: \" \".join(x.split()))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -682,7 +678,7 @@
        "4  expect type book library pleased find price right       1"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -693,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -703,7 +699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -712,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -722,14 +718,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-98-7fa46fdadeba>:1: SettingWithCopyWarning: \n",
+      "C:\\Users\\mudas\\AppData\\Local\\Temp\\ipykernel_3452\\1959687590.py:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -744,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -811,7 +807,7 @@
        "4  expect type book library pleased find price right       1"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -822,7 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -834,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -846,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -858,7 +854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -873,7 +869,7 @@
        "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -884,7 +880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -895,7 +891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -904,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -913,7 +909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -922,17 +918,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[511, 308],\n",
-       "       [692, 889]], dtype=int64)"
+       "array([[540, 262],\n",
+       "       [734, 864]], dtype=int64)"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -943,14 +939,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "BOW accuracy:  0.5833333333333334\n"
+      "BOW accuracy:  0.585\n"
      ]
     }
    ],
@@ -960,17 +956,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[502, 317],\n",
-       "       [687, 894]], dtype=int64)"
+       "array([[530, 272],\n",
+       "       [727, 871]], dtype=int64)"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -981,14 +977,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "TFIDF accuracy:  0.5816666666666667\n"
+      "TFIDF accuracy:  0.58375\n"
      ]
     }
    ],
@@ -996,38 +992,585 @@
     "print(\"TFIDF accuracy: \",accuracy_score(y_test,y_pred_tfidf))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### using word2vec as the dataset is bigger"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from gensim.models import Word2Vec,keyedvectors\n",
+    "\n",
+    "# Load pre-trained Word2Vec model\n",
+    "import gensim.downloader as api\n",
+    "\n",
+    "# Load Google News Word2Vec (300-dimensional vectors)\n",
+    "model = api.load(\"word2vec-google-news-300\")\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def sentence_to_vector(tokens, model=model, dim=300):\n",
+    "    \"\"\"Average Word2Vec vector for a list of tokens.\"\"\"\n",
+    "    valid_tokens = [token for token in tokens if token in model]\n",
+    "    if not valid_tokens:\n",
+    "        return np.zeros(dim)\n",
+    "    return np.mean([model[token] for token in valid_tokens], axis=0)\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Convert train and test texts into vectors\n",
+    "X_train_vec = np.vstack([sentence_to_vector(tokens) for tokens in X_train])\n",
+    "X_test_vec = np.vstack([sentence_to_vector(tokens) for tokens in X_test])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.17123772,  0.10068347,  0.00308287, ..., -0.01851041,\n",
+       "        -0.12023208,  0.14137597],\n",
+       "       [-0.16554852,  0.11662091,  0.00165173, ..., -0.03151945,\n",
+       "        -0.11110669,  0.15965755],\n",
+       "       [-0.17680272,  0.11143753,  0.01199744, ..., -0.0204295 ,\n",
+       "        -0.09648161,  0.19121394],\n",
+       "       ...,\n",
+       "       [-0.15994614,  0.11755606, -0.02857854, ..., -0.0434406 ,\n",
+       "        -0.1073937 ,  0.15836746],\n",
+       "       [-0.13955688,  0.12349134,  0.00980749, ..., -0.01935597,\n",
+       "        -0.12610412,  0.15859047],\n",
+       "       [-0.20481262,  0.14030609, -0.05043793, ..., -0.04925537,\n",
+       "        -0.1113678 ,  0.16055985]], dtype=float32)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train_vec"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier()"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "nb_model_w2v = RandomForestClassifier().fit(X_train_vec, y_train)\n",
+    "nb_model_w2v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "y_pred_w2v = nb_model_w2v.predict(X_test_vec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w2v Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.48      0.14      0.22       802\n",
+      "           1       0.68      0.92      0.78      1598\n",
+      "\n",
+      "    accuracy                           0.66      2400\n",
+      "   macro avg       0.58      0.53      0.50      2400\n",
+      "weighted avg       0.61      0.66      0.60      2400\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(\"w2v Classification Report:\\n\", classification_report(y_test, y_pred_w2v))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w2v accuracy:  0.6608333333333334\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "print(\"w2v accuracy: \", accuracy_score(y_test, y_pred_w2v))"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1041,7 +1584,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/4-Tokenization Example Using NLTK.ipynb b/26-CompleteNLP For Machine Learning/Practicals/4-Tokenization Example Using NLTK.ipynb
index af4350ad..b5d1c9e2 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/4-Tokenization Example Using NLTK.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/4-Tokenization Example Using NLTK.ipynb	
@@ -2,28 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting nltk\n",
-      "  Downloading nltk-3.7-py3-none-any.whl (1.5 MB)\n",
-      "     ---------------------------------------- 1.5/1.5 MB 10.5 MB/s eta 0:00:00\n",
-      "Collecting regex>=2021.8.3\n",
-      "  Downloading regex-2022.9.13-cp38-cp38-win_amd64.whl (267 kB)\n",
-      "     ------------------------------------- 267.7/267.7 kB 16.1 MB/s eta 0:00:00\n",
-      "Requirement already satisfied: tqdm in c:\\users\\win10\\anaconda3\\envs\\development\\lib\\site-packages (from nltk) (4.62.3)\n",
-      "Requirement already satisfied: click in c:\\users\\win10\\anaconda3\\envs\\development\\lib\\site-packages (from nltk) (8.0.3)\n",
-      "Requirement already satisfied: joblib in c:\\users\\win10\\anaconda3\\envs\\development\\lib\\site-packages (from nltk) (1.1.0)\n",
-      "Requirement already satisfied: colorama in c:\\users\\win10\\anaconda3\\envs\\development\\lib\\site-packages (from click->nltk) (0.4.4)\n",
-      "Installing collected packages: regex, nltk\n",
-      "Successfully installed nltk-3.7 regex-2022.9.13\n",
-      "\n",
-      "[notice] A new release of pip available: 22.1.2 -> 22.3\n",
-      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+      "Requirement already satisfied: nltk in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (3.9.1)\n",
+      "Requirement already satisfied: click in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from nltk) (8.1.8)\n",
+      "Requirement already satisfied: joblib in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from nltk) (1.5.0)\n",
+      "Requirement already satisfied: regex>=2021.8.3 in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from nltk) (2024.11.6)\n",
+      "Requirement already satisfied: tqdm in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from nltk) (4.67.1)\n",
+      "Requirement already satisfied: colorama in c:\\users\\mudas\\anaconda3\\envs\\env\\lib\\site-packages (from click->nltk) (0.4.6)\n"
      ]
     }
    ],
@@ -33,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -63,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +83,7 @@
        "list"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -103,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -123,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -166,7 +157,7 @@
        " '.']"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -177,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -197,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -238,7 +229,7 @@
        " '.']"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -249,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -297,7 +288,7 @@
        " '.']"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -308,17 +299,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello INTJ ROOT\n",
+      "Welcome INTJ intj\n",
+      ", PUNCT punct\n",
+      "to ADP prep\n",
+      "Krish PROPN compound\n",
+      "Naik PROPN poss\n",
+      "'s PART case\n",
+      "NLP PROPN compound\n",
+      "Tutorials PROPN pobj\n",
+      ". PUNCT punct\n",
+      "\n",
+      " SPACE dep\n",
+      "Please INTJ intj\n",
+      "do AUX aux\n",
+      "watch VERB ROOT\n",
+      "the DET det\n",
+      "entire ADJ amod\n",
+      "course NOUN dobj\n",
+      "! PUNCT punct\n",
+      "to PART aux\n",
+      "become VERB ROOT\n",
+      "expert NOUN attr\n",
+      "in ADP prep\n",
+      "NLP PROPN pobj\n",
+      ". PUNCT punct\n"
+     ]
+    }
+   ],
+   "source": [
+    "# using spacy\n",
+    "\n",
+    "import spacy\n",
+    "nlp = spacy.load(\"en_core_web_sm\")\n",
+    "doc = nlp('''Hello Welcome,to Krish Naik's NLP Tutorials.\n",
+    "Please do watch the entire course! to become expert in NLP.''')\n",
+    "\n",
+    "for token in doc:\n",
+    "    print(token.text, token.pos_, token.dep_)"
+   ]
   },
   {
    "cell_type": "code",
@@ -330,7 +356,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -344,7 +370,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/5-Stemming And Its Types- Text Preprocessing.ipynb b/26-CompleteNLP For Machine Learning/Practicals/5-Stemming And Its Types- Text Preprocessing.ipynb
index 43f4ae04..834821ac 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/5-Stemming And Its Types- Text Preprocessing.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/5-Stemming And Its Types- Text Preprocessing.ipynb	
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -84,7 +84,7 @@
        "'congratul'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +104,7 @@
        "'sit'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "metadata": {
     "scrolled": true
    },
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,7 @@
        "'eat'"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -172,7 +172,7 @@
        "'ingeat'"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,13 +181,6 @@
     "reg_stemmer.stem('ingeating')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -198,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -243,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -252,7 +245,7 @@
        "('fairli', 'sportingli')"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -272,7 +265,7 @@
        "('fair', 'sport')"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -283,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -292,7 +285,7 @@
        "'goe'"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -303,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -312,7 +305,7 @@
        "'goe'"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -320,39 +313,11 @@
    "source": [
     "stemming.stem('goes')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -366,7 +331,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/6-Lemmatization- Text Preprocessing.ipynb b/26-CompleteNLP For Machine Learning/Practicals/6-Lemmatization- Text Preprocessing.ipynb
index 8a0f8917..b1535c94 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/6-Lemmatization- Text Preprocessing.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/6-Lemmatization- Text Preprocessing.ipynb	
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package wordnet to\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "nltk.download('wordnet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.download('averaged_perceptron_tagger')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -40,14 +100,15 @@
        "'go'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "'''\n",
-    "POS- Noun-n\n",
+    "POS- \n",
+    "Noun-n\n",
     "verb-v\n",
     "adjective-a\n",
     "adverb-r\n",
@@ -57,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +163,7 @@
        "'go'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -113,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -122,29 +183,15 @@
        "('fairly', 'sportingly')"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "lemmatizer.lemmatize(\"fairly\",pos='v'),lemmatizer.lemmatize(\"sportingly\")"
+    "lemmatizer.lemmatize(\"fairly\",pos='a'),lemmatizer.lemmatize(\"sportingly\",pos='a')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -155,7 +202,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -169,7 +216,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/7-Text Preprocessing-Stopwords With NLTK.ipynb b/26-CompleteNLP For Machine Learning/Practicals/7-Text Preprocessing-Stopwords With NLTK.ipynb
index 83efd412..a22a1071 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/7-Text Preprocessing-Stopwords With NLTK.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/7-Text Preprocessing-Stopwords With NLTK.ipynb	
@@ -34,16 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.stem import PorterStemmer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -60,7 +51,7 @@
      "output_type": "stream",
      "text": [
       "[nltk_data] Downloading package stopwords to\n",
-      "[nltk_data]     C:\\Users\\win10\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
       "[nltk_data]   Package stopwords is already up-to-date!\n"
      ]
     },
@@ -70,7 +61,7 @@
        "True"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -82,194 +73,213 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['i',\n",
-       " 'me',\n",
-       " 'my',\n",
-       " 'myself',\n",
-       " 'we',\n",
-       " 'our',\n",
-       " 'ours',\n",
-       " 'ourselves',\n",
-       " 'you',\n",
-       " \"you're\",\n",
-       " \"you've\",\n",
-       " \"you'll\",\n",
-       " \"you'd\",\n",
-       " 'your',\n",
-       " 'yours',\n",
-       " 'yourself',\n",
-       " 'yourselves',\n",
-       " 'he',\n",
-       " 'him',\n",
-       " 'his',\n",
-       " 'himself',\n",
-       " 'she',\n",
-       " \"she's\",\n",
-       " 'her',\n",
-       " 'hers',\n",
-       " 'herself',\n",
-       " 'it',\n",
-       " \"it's\",\n",
-       " 'its',\n",
-       " 'itself',\n",
-       " 'they',\n",
-       " 'them',\n",
-       " 'their',\n",
-       " 'theirs',\n",
-       " 'themselves',\n",
-       " 'what',\n",
-       " 'which',\n",
-       " 'who',\n",
-       " 'whom',\n",
-       " 'this',\n",
-       " 'that',\n",
-       " \"that'll\",\n",
-       " 'these',\n",
-       " 'those',\n",
+       "['a',\n",
+       " 'about',\n",
+       " 'above',\n",
+       " 'after',\n",
+       " 'again',\n",
+       " 'against',\n",
+       " 'ain',\n",
+       " 'all',\n",
        " 'am',\n",
-       " 'is',\n",
-       " 'are',\n",
-       " 'was',\n",
-       " 'were',\n",
-       " 'be',\n",
-       " 'been',\n",
-       " 'being',\n",
-       " 'have',\n",
-       " 'has',\n",
-       " 'had',\n",
-       " 'having',\n",
-       " 'do',\n",
-       " 'does',\n",
-       " 'did',\n",
-       " 'doing',\n",
-       " 'a',\n",
        " 'an',\n",
-       " 'the',\n",
        " 'and',\n",
-       " 'but',\n",
-       " 'if',\n",
-       " 'or',\n",
-       " 'because',\n",
+       " 'any',\n",
+       " 'are',\n",
+       " 'aren',\n",
+       " \"aren't\",\n",
        " 'as',\n",
-       " 'until',\n",
-       " 'while',\n",
-       " 'of',\n",
        " 'at',\n",
-       " 'by',\n",
-       " 'for',\n",
-       " 'with',\n",
-       " 'about',\n",
-       " 'against',\n",
-       " 'between',\n",
-       " 'into',\n",
-       " 'through',\n",
-       " 'during',\n",
+       " 'be',\n",
+       " 'because',\n",
+       " 'been',\n",
        " 'before',\n",
-       " 'after',\n",
-       " 'above',\n",
+       " 'being',\n",
        " 'below',\n",
-       " 'to',\n",
-       " 'from',\n",
-       " 'up',\n",
-       " 'down',\n",
-       " 'in',\n",
-       " 'out',\n",
-       " 'on',\n",
-       " 'off',\n",
-       " 'over',\n",
-       " 'under',\n",
-       " 'again',\n",
-       " 'further',\n",
-       " 'then',\n",
-       " 'once',\n",
-       " 'here',\n",
-       " 'there',\n",
-       " 'when',\n",
-       " 'where',\n",
-       " 'why',\n",
-       " 'how',\n",
-       " 'all',\n",
-       " 'any',\n",
+       " 'between',\n",
        " 'both',\n",
-       " 'each',\n",
-       " 'few',\n",
-       " 'more',\n",
-       " 'most',\n",
-       " 'other',\n",
-       " 'some',\n",
-       " 'such',\n",
-       " 'no',\n",
-       " 'nor',\n",
-       " 'not',\n",
-       " 'only',\n",
-       " 'own',\n",
-       " 'same',\n",
-       " 'so',\n",
-       " 'than',\n",
-       " 'too',\n",
-       " 'very',\n",
-       " 's',\n",
-       " 't',\n",
+       " 'but',\n",
+       " 'by',\n",
        " 'can',\n",
-       " 'will',\n",
-       " 'just',\n",
-       " 'don',\n",
-       " \"don't\",\n",
-       " 'should',\n",
-       " \"should've\",\n",
-       " 'now',\n",
-       " 'd',\n",
-       " 'll',\n",
-       " 'm',\n",
-       " 'o',\n",
-       " 're',\n",
-       " 've',\n",
-       " 'y',\n",
-       " 'ain',\n",
-       " 'aren',\n",
-       " \"aren't\",\n",
        " 'couldn',\n",
        " \"couldn't\",\n",
+       " 'd',\n",
+       " 'did',\n",
        " 'didn',\n",
        " \"didn't\",\n",
+       " 'do',\n",
+       " 'does',\n",
        " 'doesn',\n",
        " \"doesn't\",\n",
+       " 'doing',\n",
+       " 'don',\n",
+       " \"don't\",\n",
+       " 'down',\n",
+       " 'during',\n",
+       " 'each',\n",
+       " 'few',\n",
+       " 'for',\n",
+       " 'from',\n",
+       " 'further',\n",
+       " 'had',\n",
        " 'hadn',\n",
        " \"hadn't\",\n",
+       " 'has',\n",
        " 'hasn',\n",
        " \"hasn't\",\n",
+       " 'have',\n",
        " 'haven',\n",
        " \"haven't\",\n",
+       " 'having',\n",
+       " 'he',\n",
+       " \"he'd\",\n",
+       " \"he'll\",\n",
+       " 'her',\n",
+       " 'here',\n",
+       " 'hers',\n",
+       " 'herself',\n",
+       " \"he's\",\n",
+       " 'him',\n",
+       " 'himself',\n",
+       " 'his',\n",
+       " 'how',\n",
+       " 'i',\n",
+       " \"i'd\",\n",
+       " 'if',\n",
+       " \"i'll\",\n",
+       " \"i'm\",\n",
+       " 'in',\n",
+       " 'into',\n",
+       " 'is',\n",
        " 'isn',\n",
        " \"isn't\",\n",
+       " 'it',\n",
+       " \"it'd\",\n",
+       " \"it'll\",\n",
+       " \"it's\",\n",
+       " 'its',\n",
+       " 'itself',\n",
+       " \"i've\",\n",
+       " 'just',\n",
+       " 'll',\n",
+       " 'm',\n",
        " 'ma',\n",
+       " 'me',\n",
        " 'mightn',\n",
        " \"mightn't\",\n",
+       " 'more',\n",
+       " 'most',\n",
        " 'mustn',\n",
        " \"mustn't\",\n",
+       " 'my',\n",
+       " 'myself',\n",
        " 'needn',\n",
        " \"needn't\",\n",
+       " 'no',\n",
+       " 'nor',\n",
+       " 'not',\n",
+       " 'now',\n",
+       " 'o',\n",
+       " 'of',\n",
+       " 'off',\n",
+       " 'on',\n",
+       " 'once',\n",
+       " 'only',\n",
+       " 'or',\n",
+       " 'other',\n",
+       " 'our',\n",
+       " 'ours',\n",
+       " 'ourselves',\n",
+       " 'out',\n",
+       " 'over',\n",
+       " 'own',\n",
+       " 're',\n",
+       " 's',\n",
+       " 'same',\n",
        " 'shan',\n",
        " \"shan't\",\n",
+       " 'she',\n",
+       " \"she'd\",\n",
+       " \"she'll\",\n",
+       " \"she's\",\n",
+       " 'should',\n",
        " 'shouldn',\n",
        " \"shouldn't\",\n",
+       " \"should've\",\n",
+       " 'so',\n",
+       " 'some',\n",
+       " 'such',\n",
+       " 't',\n",
+       " 'than',\n",
+       " 'that',\n",
+       " \"that'll\",\n",
+       " 'the',\n",
+       " 'their',\n",
+       " 'theirs',\n",
+       " 'them',\n",
+       " 'themselves',\n",
+       " 'then',\n",
+       " 'there',\n",
+       " 'these',\n",
+       " 'they',\n",
+       " \"they'd\",\n",
+       " \"they'll\",\n",
+       " \"they're\",\n",
+       " \"they've\",\n",
+       " 'this',\n",
+       " 'those',\n",
+       " 'through',\n",
+       " 'to',\n",
+       " 'too',\n",
+       " 'under',\n",
+       " 'until',\n",
+       " 'up',\n",
+       " 've',\n",
+       " 'very',\n",
+       " 'was',\n",
        " 'wasn',\n",
        " \"wasn't\",\n",
+       " 'we',\n",
+       " \"we'd\",\n",
+       " \"we'll\",\n",
+       " \"we're\",\n",
+       " 'were',\n",
        " 'weren',\n",
        " \"weren't\",\n",
+       " \"we've\",\n",
+       " 'what',\n",
+       " 'when',\n",
+       " 'where',\n",
+       " 'which',\n",
+       " 'while',\n",
+       " 'who',\n",
+       " 'whom',\n",
+       " 'why',\n",
+       " 'will',\n",
+       " 'with',\n",
        " 'won',\n",
        " \"won't\",\n",
        " 'wouldn',\n",
-       " \"wouldn't\"]"
+       " \"wouldn't\",\n",
+       " 'y',\n",
+       " 'you',\n",
+       " \"you'd\",\n",
+       " \"you'll\",\n",
+       " 'your',\n",
+       " \"you're\",\n",
+       " 'yours',\n",
+       " 'yourself',\n",
+       " 'yourselves',\n",
+       " \"you've\"]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +1052,7 @@
        " 'هبّ']"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1053,7 +1063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1062,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1071,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1080,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1089,7 +1099,7 @@
        "list"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1100,7 +1110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1114,46 +1124,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['I three vision india .',\n",
-       " 'In 3000 year histori , peopl world come invad us , captur land , conquer mind .',\n",
+       "['i three vision india .',\n",
+       " 'in 3000 year histori , peopl world come invad us , captur land , conquer mind .',\n",
        " 'from alexand onward , greek , turk , mogul , portugues , british , french , dutch , came loot us , took .',\n",
        " 'yet done nation .',\n",
-       " 'We conquer anyon .',\n",
-       " 'We grab land , cultur , histori tri enforc way life .',\n",
+       " 'we conquer anyon .',\n",
+       " 'we grab land , cultur , histori tri enforc way life .',\n",
        " 'whi ?',\n",
        " 'becaus respect freedom others.that first vision freedom .',\n",
-       " 'I believ india got first vision 1857 , start war independ .',\n",
-       " 'It freedom must protect nurtur build .',\n",
-       " 'If free , one respect us .',\n",
-       " 'My second vision india ’ develop .',\n",
+       " 'i believ india got first vision 1857 , start war independ .',\n",
+       " 'it freedom must protect nurtur build .',\n",
+       " 'if free , one respect us .',\n",
+       " 'my second vision india ’ develop .',\n",
        " 'for fifti year develop nation .',\n",
-       " 'It time see develop nation .',\n",
-       " 'We among top 5 nation world term gdp .',\n",
-       " 'We 10 percent growth rate area .',\n",
+       " 'it time see develop nation .',\n",
+       " 'we among top 5 nation world term gdp .',\n",
+       " 'we 10 percent growth rate area .',\n",
        " 'our poverti level fall .',\n",
        " 'our achiev global recognis today .',\n",
        " 'yet lack self-confid see develop nation , self-reli self-assur .',\n",
        " 'isn ’ incorrect ?',\n",
-       " 'I third vision .',\n",
+       " 'i third vision .',\n",
        " 'india must stand world .',\n",
-       " 'becaus I believ unless india stand world , one respect us .',\n",
+       " 'becaus i believ unless india stand world , one respect us .',\n",
        " 'onli strength respect strength .',\n",
-       " 'We must strong militari power also econom power .',\n",
+       " 'we must strong militari power also econom power .',\n",
        " 'both must go hand-in-hand .',\n",
-       " 'My good fortun work three great mind .',\n",
+       " 'my good fortun work three great mind .',\n",
        " 'dr. vikram sarabhai dept .',\n",
        " 'space , professor satish dhawan , succeed dr. brahm prakash , father nuclear materi .',\n",
-       " 'I lucki work three close consid great opportun life .',\n",
-       " 'I see four mileston career']"
+       " 'i lucki work three close consid great opportun life .',\n",
+       " 'i see four mileston career']"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1164,7 +1174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1174,7 +1184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,46 +1198,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['i three vision india .',\n",
-       " 'in 3000 year histori , peopl world come invad us , captur land , conquer mind .',\n",
-       " 'from alexand onward , greek , turk , mogul , portugues , british , french , dutch , came loot us , took .',\n",
+       "['three vision india .',\n",
+       " '3000 year histori , peopl world come invad us , captur land , conquer mind .',\n",
+       " 'alexand onward , greek , turk , mogul , portugu , british , french , dutch , came loot us , took .',\n",
        " 'yet done nation .',\n",
-       " 'we conquer anyon .',\n",
-       " 'we grab land , cultur , histori tri enforc way life .',\n",
+       " 'conquer anyon .',\n",
+       " 'grab land , cultur , histori tri enforc way life .',\n",
        " 'whi ?',\n",
        " 'becaus respect freedom others.that first vision freedom .',\n",
-       " 'i believ india got first vision 1857 , start war independ .',\n",
-       " 'it freedom must protect nurtur build .',\n",
-       " 'if free , one respect us .',\n",
-       " 'my second vision india ’ develop .',\n",
-       " 'for fifti year develop nation .',\n",
-       " 'it time see develop nation .',\n",
-       " 'we among top 5 nation world term gdp .',\n",
-       " 'we 10 percent growth rate area .',\n",
-       " 'our poverti level fall .',\n",
-       " 'our achiev global recognis today .',\n",
-       " 'yet lack self-confid see develop nation , self-reli self-assur .',\n",
-       " 'isn ’ incorrect ?',\n",
-       " 'i third vision .',\n",
+       " 'believ india got first vision 1857 , start war independ .',\n",
+       " 'freedom must protect nurtur build .',\n",
+       " 'free , one respect us .',\n",
+       " 'second vision india ’ develop .',\n",
+       " 'fifti year develop nation .',\n",
+       " 'time see develop nation .',\n",
+       " 'among top 5 nation world term gdp .',\n",
+       " '10 percent growth rate area .',\n",
+       " 'poverti level fall .',\n",
+       " 'achiev global recogni today .',\n",
+       " 'yet lack self-confid see develop nation , self-r self-assur .',\n",
+       " '’ incorrect ?',\n",
+       " 'third vision .',\n",
        " 'india must stand world .',\n",
-       " 'becaus i believ unless india stand world , one respect us .',\n",
-       " 'onli strength respect strength .',\n",
-       " 'we must strong militari power also econom power .',\n",
-       " 'both must go hand-in-hand .',\n",
-       " 'my good fortun work three great mind .',\n",
+       " 'becaus believ unless india stand world , one respect us .',\n",
+       " 'on strength respect strength .',\n",
+       " 'must strong militari power also econom power .',\n",
+       " 'must go hand-in-hand .',\n",
+       " 'good fortun work three great mind .',\n",
        " 'dr. vikram sarabhai dept .',\n",
        " 'space , professor satish dhawan , succeed dr. brahm prakash , father nuclear materi .',\n",
-       " 'i lucki work three close consid great opportun life .',\n",
-       " 'i see four mileston career']"
+       " 'lucki work three close consid great opportun life .',\n",
+       " 'see four mileston career']"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1238,7 +1248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1248,7 +1258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1263,46 +1273,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['i three visions india .',\n",
-       " 'in 3000 years history , people world come invade us , capture land , conquer mind .',\n",
-       " 'from alexander onwards , greeks , turks , moguls , portuguese , british , french , dutch , come loot us , take .',\n",
+       "['three vision india .',\n",
+       " '3000 year histori , peopl world come invad us , captur land , conquer mind .',\n",
+       " 'alexand onward , greek , turk , mogul , portugu , british , french , dutch , come loot us , take .',\n",
        " 'yet do nation .',\n",
-       " 'we conquer anyone .',\n",
-       " 'we grab land , culture , history try enforce way life .',\n",
-       " 'why ?',\n",
-       " 'because respect freedom others.that first vision freedom .',\n",
-       " 'i believe india get first vision 1857 , start war independence .',\n",
-       " 'it freedom must protect nurture build .',\n",
-       " 'if free , one respect us .',\n",
-       " 'my second vision india ’ development .',\n",
-       " 'for fifty years develop nation .',\n",
-       " 'it time see develop nation .',\n",
-       " 'we among top 5 nations world term gdp .',\n",
-       " 'we 10 percent growth rate areas .',\n",
-       " 'our poverty level fall .',\n",
-       " 'our achievements globally recognise today .',\n",
-       " 'yet lack self-confidence see develop nation , self-reliant self-assured .',\n",
-       " 'isn ’ incorrect ?',\n",
-       " 'i third vision .',\n",
+       " 'conquer anyon .',\n",
+       " 'grab land , cultur , histori tri enforc way life .',\n",
+       " 'whi ?',\n",
+       " 'becaus respect freedom others.that first vision freedom .',\n",
+       " 'believ india get first vision 1857 , start war independ .',\n",
+       " 'freedom must protect nurtur build .',\n",
+       " 'free , one respect us .',\n",
+       " 'second vision india ’ develop .',\n",
+       " 'fifti year develop nation .',\n",
+       " 'time see develop nation .',\n",
+       " 'among top 5 nation world term gdp .',\n",
+       " '10 percent growth rate area .',\n",
+       " 'poverti level fall .',\n",
+       " 'achiev global recogni today .',\n",
+       " 'yet lack self-confid see develop nation , self-r self-assur .',\n",
+       " '’ incorrect ?',\n",
+       " 'third vision .',\n",
        " 'india must stand world .',\n",
-       " 'because i believe unless india stand world , one respect us .',\n",
-       " 'only strength respect strength .',\n",
-       " 'we must strong military power also economic power .',\n",
-       " 'both must go hand-in-hand .',\n",
-       " 'my good fortune work three great mind .',\n",
+       " 'becaus believ unless india stand world , one respect us .',\n",
+       " 'strength respect strength .',\n",
+       " 'must strong militari power also econom power .',\n",
+       " 'must go hand-in-hand .',\n",
+       " 'good fortun work three great mind .',\n",
        " 'dr. vikram sarabhai dept .',\n",
-       " 'space , professor satish dhawan , succeed dr. brahm prakash , father nuclear material .',\n",
-       " 'i lucky work three closely consider great opportunity life .',\n",
-       " 'i see four milestones career']"
+       " 'space , professor satish dhawan , succeed dr. brahm prakash , father nuclear materi .',\n",
+       " 'lucki work three close consid great opportun life .',\n",
+       " 'see four mileston career']"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1321,7 +1331,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1335,7 +1345,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/8-Parts Of Speech Tagging.ipynb b/26-CompleteNLP For Machine Learning/Practicals/8-Parts Of Speech Tagging.ipynb
index 9626cd8a..c740c8f5 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/8-Parts Of Speech Tagging.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/8-Parts Of Speech Tagging.ipynb	
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,18 +89,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
+    "import nltk\n",
     "from nltk.corpus import stopwords\n",
     "sentences=nltk.sent_tokenize(paragraph)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -139,7 +139,7 @@
        " 'I see four milestones in my career']"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,16 +150,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
-      "[nltk_data]     C:\\Users\\win10\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Unzipping taggers\\averaged_perceptron_tagger.zip.\n"
+      "[nltk_data] Downloading package averaged_perceptron_tagger_eng to\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger_eng is already up-to-\n",
+      "[nltk_data]       date!\n"
      ]
     },
     {
@@ -168,18 +169,19 @@
        "True"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "nltk.download('averaged_perceptron_tagger')"
+    "import nltk\n",
+    "nltk.download('averaged_perceptron_tagger_eng')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -253,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -265,17 +267,9 @@
     }
    ],
    "source": [
-    "\n",
     "print(nltk.pos_tag(\"Taj Mahal is a beautiful Monument\".split()))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -286,7 +280,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -300,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/26-CompleteNLP For Machine Learning/Practicals/9-Named Entity Recognition.ipynb b/26-CompleteNLP For Machine Learning/Practicals/9-Named Entity Recognition.ipynb
index 4214923e..f76928a9 100644
--- a/26-CompleteNLP For Machine Learning/Practicals/9-Named Entity Recognition.ipynb	
+++ b/26-CompleteNLP For Machine Learning/Practicals/9-Named Entity Recognition.ipynb	
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,16 +53,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[nltk_data] Downloading package maxent_ne_chunker to\n",
-      "[nltk_data]     C:\\Users\\win10\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Unzipping chunkers\\maxent_ne_chunker.zip.\n"
+      "[nltk_data] Downloading package maxent_ne_chunker_tab to\n",
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package maxent_ne_chunker_tab is already up-to-date!\n"
      ]
     },
     {
@@ -71,18 +71,19 @@
        "True"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "nltk.download('maxent_ne_chunker')"
+    "import nltk\n",
+    "nltk.download('maxent_ne_chunker_tab')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -90,8 +91,8 @@
      "output_type": "stream",
      "text": [
       "[nltk_data] Downloading package words to\n",
-      "[nltk_data]     C:\\Users\\win10\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Unzipping corpora\\words.zip.\n"
+      "[nltk_data]     C:\\Users\\mudas\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package words is already up-to-date!\n"
      ]
     },
     {
@@ -100,7 +101,7 @@
        "True"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -111,59 +112,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "nltk.ne_chunk(tag_elements).draw()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -177,7 +136,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/28-RNN( Simple RNN, LSTM, GRU)/1-2 Simple RNN.pdf b/28-RNN( Simple RNN, LSTM, GRU)/1-2 Simple RNN.pdf
new file mode 100644
index 00000000..a1b7fe64
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diff --git a/28-RNN( Simple RNN, LSTM, GRU)/3-4 backwardpropogation.pdf b/28-RNN( Simple RNN, LSTM, GRU)/3-4 backwardpropogation.pdf
new file mode 100644
index 00000000..a57a2511
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diff --git a/28-RNN( Simple RNN, LSTM, GRU)/5-Problems With RNN.pdf b/28-RNN( Simple RNN, LSTM, GRU)/5-Problems With RNN.pdf
new file mode 100644
index 00000000..75730e02
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diff --git a/28-RNN( Simple RNN, LSTM, GRU)/Bidirection+RNN.pdf b/28-RNN( Simple RNN, LSTM, GRU)/Bidirection+RNN.pdf
new file mode 100644
index 00000000..1689a61b
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diff --git a/28-RNN( Simple RNN, LSTM, GRU)/Complete+LSTM+And+GRU.pdf b/28-RNN( Simple RNN, LSTM, GRU)/Complete+LSTM+And+GRU.pdf
new file mode 100644
index 00000000..5097e5ce
Binary files /dev/null and b/28-RNN( Simple RNN, LSTM, GRU)/Complete+LSTM+And+GRU.pdf differ
diff --git a/29-Encoder-decoder/EncoderDecoderSeq2SEq.pdf b/29-Encoder-decoder/EncoderDecoderSeq2SEq.pdf
new file mode 100644
index 00000000..4d7d9152
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diff --git a/3-Complete Linear Regression/Practicals/Multiple Linear Regression- Economics Dataset.ipynb b/3-Complete Linear Regression/Practicals/Multiple Linear Regression- Economics Dataset.ipynb
index 7e182a91..b51d7848 100644
--- a/3-Complete Linear Regression/Practicals/Multiple Linear Regression- Economics Dataset.ipynb	
+++ b/3-Complete Linear Regression/Practicals/Multiple Linear Regression- Economics Dataset.ipynb	
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +208,7 @@
        "4           2.50                5.4         1256"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -231,7 +231,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -243,30 +243,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x276860727c0>"
+       "<seaborn.axisgrid.PairGrid at 0x1e0144f4680>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 540x540 with 12 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -277,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -336,7 +324,7 @@
        "index_price             0.935793          -0.922338     1.000000"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -347,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -356,21 +344,9 @@
        "Text(0, 0.5, 'unemployment rate')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -382,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -393,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -460,7 +436,7 @@
        "4           2.50                5.4"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -471,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -504,7 +480,7 @@
        "Name: index_price, dtype: int64"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -515,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -535,38 +511,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
-      "  warnings.warn(\n"
+     "ename": "TypeError",
+     "evalue": "regplot() takes from 0 to 1 positional arguments but 2 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[15], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m sns\u001b[38;5;241m.\u001b[39mregplot(df_index[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minterest_rate\u001b[39m\u001b[38;5;124m'\u001b[39m],df_index[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex_price\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
+      "\u001b[1;31mTypeError\u001b[0m: regplot() takes from 0 to 1 positional arguments but 2 were given"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='interest_rate', ylabel='index_price'>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -575,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -598,7 +555,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -615,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -638,7 +595,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -655,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -664,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -675,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -712,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -722,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -742,7 +699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -754,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -774,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -784,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -805,7 +762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -831,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -860,7 +817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -875,7 +832,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -892,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -916,7 +873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -931,7 +888,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -949,7 +906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -964,7 +921,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -982,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -993,7 +950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1106,7 +1063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1157,7 +1114,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -1171,7 +1128,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/3-Complete Linear Regression/Practicals/Polynomial Regression Implementation.ipynb b/3-Complete Linear Regression/Practicals/Polynomial Regression Implementation.ipynb
index 9be4ab16..2d105768 100644
--- a/3-Complete Linear Regression/Practicals/Polynomial Regression Implementation.ipynb	
+++ b/3-Complete Linear Regression/Practicals/Polynomial Regression Implementation.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -24,21 +24,9 @@
        "Text(0, 0.5, 'Y dataset')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -52,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,16 +68,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "LinearRegression()"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -100,14 +495,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.6258604495084354\n"
+      "0.771760060471031\n"
      ]
     }
    ],
@@ -119,30 +514,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0, 0.5, 'Y')"
+       "Text(47.097222222222214, 0.5, 'Y')"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -155,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,95 +559,95 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
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-       "       [ 1.00000000e+00, -1.45924666e+00,  2.12940082e+00],\n",
-       "       [ 1.00000000e+00,  1.57421615e+00,  2.47815648e+00],\n",
-       "       [ 1.00000000e+00, -2.18117707e+00,  4.75753342e+00],\n",
-       "       [ 1.00000000e+00,  1.05998407e+00,  1.12356623e+00],\n",
-       "       [ 1.00000000e+00, -6.51568696e-01,  4.24541765e-01],\n",
-       "       [ 1.00000000e+00,  1.67352100e-01,  2.80067254e-02],\n",
-       "       [ 1.00000000e+00,  2.66287718e+00,  7.09091488e+00],\n",
-       "       [ 1.00000000e+00, -2.02883126e+00,  4.11615630e+00],\n",
-       "       [ 1.00000000e+00, -1.51118982e+00,  2.28369468e+00],\n",
-       "       [ 1.00000000e+00, -1.84252834e+00,  3.39491068e+00],\n",
-       "       [ 1.00000000e+00, -1.07136262e+00,  1.14781785e+00],\n",
-       "       [ 1.00000000e+00,  9.97213919e-01,  9.94435599e-01],\n",
-       "       [ 1.00000000e+00,  1.51736778e+00,  2.30240497e+00],\n",
-       "       [ 1.00000000e+00,  2.39182148e+00,  5.72081002e+00],\n",
-       "       [ 1.00000000e+00, -1.86558636e+00,  3.48041246e+00],\n",
-       "       [ 1.00000000e+00, -4.57496373e-01,  2.09302931e-01],\n",
-       "       [ 1.00000000e+00, -1.68216491e-01,  2.82967880e-02],\n",
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-       "       [ 1.00000000e+00,  2.98594938e+00,  8.91589371e+00],\n",
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-       "       [ 1.00000000e+00, -2.93675059e+00,  8.62450403e+00],\n",
-       "       [ 1.00000000e+00, -2.42876800e+00,  5.89891398e+00],\n",
-       "       [ 1.00000000e+00,  6.02873835e-01,  3.63456861e-01],\n",
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-       "       [ 1.00000000e+00, -1.59039281e-01,  2.52934931e-02],\n",
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-       "       [ 1.00000000e+00,  2.62214178e+00,  6.87562751e+00]])"
+       "array([[ 1.        , -2.16429241,  4.68416163],\n",
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+       "       [ 1.        ,  1.05370059,  1.11028493],\n",
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+       "       [ 1.        , -1.60507499,  2.57626572],\n",
+       "       [ 1.        ,  0.45277029,  0.20500094],\n",
+       "       [ 1.        , -2.60905884,  6.80718805],\n",
+       "       [ 1.        , -2.7548485 ,  7.58919026],\n",
+       "       [ 1.        ,  2.94109405,  8.65003418],\n",
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+       "       [ 1.        , -0.20947648,  0.0438804 ],\n",
+       "       [ 1.        , -0.22447809,  0.05039041],\n",
+       "       [ 1.        , -0.81570595,  0.6653762 ],\n",
+       "       [ 1.        , -0.92615202,  0.85775757],\n",
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+       "       [ 1.        ,  2.20168676,  4.84742457],\n",
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+       "       [ 1.        ,  1.03942347,  1.08040116],\n",
+       "       [ 1.        , -2.9629022 ,  8.77878943],\n",
+       "       [ 1.        , -1.95032687,  3.80377489],\n",
+       "       [ 1.        , -0.19152104,  0.03668031],\n",
+       "       [ 1.        ,  1.19119699,  1.41895027],\n",
+       "       [ 1.        , -0.35361984,  0.12504699],\n",
+       "       [ 1.        ,  0.48205459,  0.23237663],\n",
+       "       [ 1.        ,  2.70887968,  7.33802914],\n",
+       "       [ 1.        , -0.92631045,  0.85805105],\n",
+       "       [ 1.        ,  2.26363275,  5.12403323],\n",
+       "       [ 1.        , -2.84165918,  8.0750269 ],\n",
+       "       [ 1.        ,  2.51018176,  6.30101245],\n",
+       "       [ 1.        , -0.42541237,  0.18097569],\n",
+       "       [ 1.        , -2.36623148,  5.59905143],\n",
+       "       [ 1.        , -1.83394387,  3.36335013],\n",
+       "       [ 1.        ,  0.96483233,  0.93090143],\n",
+       "       [ 1.        , -2.45265194,  6.01550153],\n",
+       "       [ 1.        ,  2.82915872,  8.00413905],\n",
+       "       [ 1.        ,  2.14836475,  4.61547108],\n",
+       "       [ 1.        ,  2.88410814,  8.31807976],\n",
+       "       [ 1.        ,  0.14417303,  0.02078586],\n",
+       "       [ 1.        ,  0.5649689 ,  0.31918986],\n",
+       "       [ 1.        ,  1.48590502,  2.20791374],\n",
+       "       [ 1.        , -1.68523143,  2.84000496],\n",
+       "       [ 1.        , -2.20445522,  4.8596228 ],\n",
+       "       [ 1.        , -2.39407104,  5.73157616],\n",
+       "       [ 1.        , -1.76653622,  3.12065021]])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -275,35 +658,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.00000000e+00, -1.67504154e+00,  2.80576417e+00],\n",
-       "       [ 1.00000000e+00,  2.04814890e+00,  4.19491392e+00],\n",
-       "       [ 1.00000000e+00, -7.77663328e-01,  6.04760251e-01],\n",
-       "       [ 1.00000000e+00, -2.82542840e+00,  7.98304562e+00],\n",
-       "       [ 1.00000000e+00,  1.95122921e+00,  3.80729543e+00],\n",
-       "       [ 1.00000000e+00, -1.63621585e+00,  2.67720230e+00],\n",
-       "       [ 1.00000000e+00, -8.15172988e-01,  6.64507000e-01],\n",
-       "       [ 1.00000000e+00,  6.74398175e-01,  4.54812899e-01],\n",
-       "       [ 1.00000000e+00, -2.63726054e+00,  6.95514315e+00],\n",
-       "       [ 1.00000000e+00, -1.86307438e-01,  3.47104614e-02],\n",
-       "       [ 1.00000000e+00, -2.62472160e+00,  6.88916348e+00],\n",
-       "       [ 1.00000000e+00, -2.93340573e+00,  8.60486920e+00],\n",
-       "       [ 1.00000000e+00, -1.55858596e+00,  2.42919020e+00],\n",
-       "       [ 1.00000000e+00,  2.72476472e+00,  7.42434278e+00],\n",
-       "       [ 1.00000000e+00,  1.69168227e-01,  2.86178890e-02],\n",
-       "       [ 1.00000000e+00,  4.44943540e-02,  1.97974754e-03],\n",
-       "       [ 1.00000000e+00,  1.81130598e+00,  3.28082936e+00],\n",
-       "       [ 1.00000000e+00,  1.34073538e+00,  1.79757135e+00],\n",
-       "       [ 1.00000000e+00, -2.15652733e+00,  4.65061014e+00],\n",
-       "       [ 1.00000000e+00,  1.07671762e+00,  1.15932084e+00]])"
+       "array([[ 1.        , -1.98839939,  3.95373212],\n",
+       "       [ 1.        , -1.87976362,  3.53351128],\n",
+       "       [ 1.        ,  2.15280888,  4.63458607],\n",
+       "       [ 1.        ,  0.94440164,  0.89189445],\n",
+       "       [ 1.        ,  0.99451666,  0.98906339],\n",
+       "       [ 1.        , -0.67479264,  0.45534511],\n",
+       "       [ 1.        , -1.32349225,  1.75163173],\n",
+       "       [ 1.        , -2.95499972,  8.73202333],\n",
+       "       [ 1.        , -2.37836824,  5.6566355 ],\n",
+       "       [ 1.        , -2.65610446,  7.05489093],\n",
+       "       [ 1.        , -2.28461676,  5.21947373],\n",
+       "       [ 1.        , -1.21914208,  1.48630742],\n",
+       "       [ 1.        ,  0.36271545,  0.1315625 ],\n",
+       "       [ 1.        ,  2.07719368,  4.31473359],\n",
+       "       [ 1.        ,  1.00109053,  1.00218224],\n",
+       "       [ 1.        ,  2.83403193,  8.03173696],\n",
+       "       [ 1.        , -2.44560328,  5.98097538],\n",
+       "       [ 1.        ,  0.82267015,  0.67678617],\n",
+       "       [ 1.        , -0.40186126,  0.16149247],\n",
+       "       [ 1.        ,  0.84669432,  0.71689128]])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -314,14 +697,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9393239901567215\n"
+      "0.875161581521316\n"
      ]
     }
    ],
@@ -337,14 +720,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.         1.5819644  0.50905021]]\n"
+      "[[0.         1.51487096 0.54109208]]\n"
      ]
     }
    ],
@@ -354,14 +737,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1.84676477]\n"
+      "[1.74464688]\n"
      ]
     }
    ],
@@ -371,30 +754,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x2504c06b610>"
+       "<matplotlib.collections.PathCollection at 0x26f06b86b10>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -404,7 +775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,175 +786,175 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.00000000e+00, -2.74888188e+00,  7.55635156e+00,\n",
-       "        -2.07715179e+01],\n",
-       "       [ 1.00000000e+00,  1.43055517e+00,  2.04648809e+00,\n",
-       "         2.92761412e+00],\n",
-       "       [ 1.00000000e+00,  5.90335028e-01,  3.48495446e-01,\n",
-       "         2.05729069e-01],\n",
-       "       [ 1.00000000e+00,  8.12149214e-01,  6.59586345e-01,\n",
-       "         5.35682531e-01],\n",
-       "       [ 1.00000000e+00,  3.66109686e-01,  1.34036303e-01,\n",
-       "         4.90719887e-02],\n",
-       "       [ 1.00000000e+00,  1.16597431e+00,  1.35949609e+00,\n",
-       "         1.58513752e+00],\n",
-       "       [ 1.00000000e+00, -3.31206334e-01,  1.09697636e-01,\n",
-       "        -3.63325517e-02],\n",
-       "       [ 1.00000000e+00, -2.72438731e+00,  7.42228621e+00,\n",
-       "        -2.02211824e+01],\n",
-       "       [ 1.00000000e+00, -1.75407407e+00,  3.07677584e+00,\n",
-       "        -5.39689271e+00],\n",
-       "       [ 1.00000000e+00, -2.33894500e+00,  5.47066371e+00,\n",
-       "        -1.27955815e+01],\n",
-       "       [ 1.00000000e+00,  2.90177215e+00,  8.42028158e+00,\n",
-       "         2.44337385e+01],\n",
-       "       [ 1.00000000e+00, -6.90515463e-01,  4.76811605e-01,\n",
-       "        -3.29245786e-01],\n",
-       "       [ 1.00000000e+00, -1.16779767e+00,  1.36375141e+00,\n",
-       "        -1.59258572e+00],\n",
-       "       [ 1.00000000e+00, -9.74366994e-01,  9.49391039e-01,\n",
-       "        -9.25055292e-01],\n",
-       "       [ 1.00000000e+00, -1.84913689e+00,  3.41930725e+00,\n",
-       "        -6.32276718e+00],\n",
-       "       [ 1.00000000e+00, -2.22897028e+00,  4.96830852e+00,\n",
-       "        -1.10742120e+01],\n",
-       "       [ 1.00000000e+00,  2.90475311e+00,  8.43759060e+00,\n",
-       "         2.45091175e+01],\n",
-       "       [ 1.00000000e+00,  2.76540282e+00,  7.64745273e+00,\n",
-       "         2.11482873e+01],\n",
-       "       [ 1.00000000e+00,  1.81653747e-01,  3.29980837e-02,\n",
-       "         5.99422555e-03],\n",
-       "       [ 1.00000000e+00,  2.98247392e+00,  8.89515071e+00,\n",
-       "         2.65295550e+01],\n",
-       "       [ 1.00000000e+00,  6.20439630e-02,  3.84945335e-03,\n",
-       "         2.38835341e-04],\n",
-       "       [ 1.00000000e+00,  2.72378506e+00,  7.41900504e+00,\n",
-       "         2.02077751e+01],\n",
-       "       [ 1.00000000e+00,  1.57689605e+00,  2.48660115e+00,\n",
-       "         3.92111154e+00],\n",
-       "       [ 1.00000000e+00,  2.38227522e+00,  5.67523522e+00,\n",
-       "         1.35199722e+01],\n",
-       "       [ 1.00000000e+00, -1.31982776e+00,  1.74194531e+00,\n",
-       "        -2.29906777e+00],\n",
-       "       [ 1.00000000e+00,  2.14015731e+00,  4.58027331e+00,\n",
-       "         9.80250542e+00],\n",
-       "       [ 1.00000000e+00,  4.04438649e-01,  1.63570621e-01,\n",
-       "         6.61542809e-02],\n",
-       "       [ 1.00000000e+00,  6.41599445e-01,  4.11649847e-01,\n",
-       "         2.64114313e-01],\n",
-       "       [ 1.00000000e+00, -1.54898329e+00,  2.39934922e+00,\n",
-       "        -3.71655183e+00],\n",
-       "       [ 1.00000000e+00, -1.34560552e+00,  1.81065422e+00,\n",
-       "        -2.43642632e+00],\n",
-       "       [ 1.00000000e+00,  2.88459805e+00,  8.32090592e+00,\n",
-       "         2.40024690e+01],\n",
-       "       [ 1.00000000e+00, -1.53502308e+00,  2.35629585e+00,\n",
-       "        -3.61696850e+00],\n",
-       "       [ 1.00000000e+00, -2.74044436e+00,  7.51003530e+00,\n",
-       "        -2.05808339e+01],\n",
-       "       [ 1.00000000e+00, -2.37748636e+00,  5.65244141e+00,\n",
-       "        -1.34386024e+01],\n",
-       "       [ 1.00000000e+00,  1.48646577e+00,  2.20958049e+00,\n",
-       "         3.28446577e+00],\n",
-       "       [ 1.00000000e+00, -1.71238020e+00,  2.93224597e+00,\n",
-       "        -5.02111995e+00],\n",
-       "       [ 1.00000000e+00,  2.03548332e+00,  4.14319233e+00,\n",
-       "         8.43339885e+00],\n",
-       "       [ 1.00000000e+00, -1.17633641e+00,  1.38376736e+00,\n",
-       "        -1.62777594e+00],\n",
-       "       [ 1.00000000e+00, -2.17206826e+00,  4.71788052e+00,\n",
-       "        -1.02475585e+01],\n",
-       "       [ 1.00000000e+00,  2.29770897e+00,  5.27946650e+00,\n",
-       "         1.21306775e+01],\n",
-       "       [ 1.00000000e+00, -3.28405762e-01,  1.07850344e-01,\n",
-       "        -3.54186744e-02],\n",
-       "       [ 1.00000000e+00,  9.48361078e-01,  8.99388734e-01,\n",
-       "         8.52945270e-01],\n",
-       "       [ 1.00000000e+00,  6.53633998e-02,  4.27237404e-03,\n",
-       "         2.79256892e-04],\n",
-       "       [ 1.00000000e+00, -8.42291468e-01,  7.09454917e-01,\n",
-       "        -5.97567824e-01],\n",
-       "       [ 1.00000000e+00,  1.32384602e+00,  1.75256828e+00,\n",
-       "         2.32013053e+00],\n",
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+       "         2.26449798e+01],\n",
+       "       [ 1.00000000e+00,  2.14836475e+00,  4.61547108e+00,\n",
+       "         9.91571535e+00],\n",
+       "       [ 1.00000000e+00,  2.88410814e+00,  8.31807976e+00,\n",
+       "         2.39902415e+01],\n",
+       "       [ 1.00000000e+00,  1.44173025e-01,  2.07858612e-02,\n",
+       "         2.99676049e-03],\n",
+       "       [ 1.00000000e+00,  5.64968904e-01,  3.19189863e-01,\n",
+       "         1.80332347e-01],\n",
+       "       [ 1.00000000e+00,  1.48590502e+00,  2.20791374e+00,\n",
+       "         3.28075012e+00],\n",
+       "       [ 1.00000000e+00, -1.68523143e+00,  2.84000496e+00,\n",
+       "        -4.78606562e+00],\n",
+       "       [ 1.00000000e+00, -2.20445522e+00,  4.85962280e+00,\n",
+       "        -1.07128208e+01],\n",
+       "       [ 1.00000000e+00, -2.39407104e+00,  5.73157616e+00,\n",
+       "        -1.37218005e+01],\n",
+       "       [ 1.00000000e+00, -1.76653622e+00,  3.12065021e+00,\n",
+       "        -5.51274163e+00]])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -594,14 +965,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9343611155722256\n"
+      "0.874331952151268\n"
      ]
     }
    ],
@@ -617,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1025,7 +1396,7 @@
        "         2.70000000e+01]])"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1039,19 +1410,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1075,7 +1444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1084,7 +1453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1113,37 +1482,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "poly_regression(6)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -1157,7 +1517,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/3-Complete Linear Regression/Practicals/Practical Simple Linear Regression.ipynb b/3-Complete Linear Regression/Practicals/Practical Simple Linear Regression.ipynb
index de6e3f46..1da63959 100644
--- a/3-Complete Linear Regression/Practicals/Practical Simple Linear Regression.ipynb	
+++ b/3-Complete Linear Regression/Practicals/Practical Simple Linear Regression.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -90,7 +90,7 @@
        "4      70     160"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -101,29 +101,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "Text(0, 0.5, 'Height')"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
@@ -131,12 +119,13 @@
     "##scatter plot\n",
     "plt.scatter(df['Weight'],df['Height'])\n",
     "plt.xlabel(\"Weight\")\n",
-    "plt.ylabel(\"Height\")"
+    "plt.ylabel(\"Height\")\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -185,7 +174,7 @@
        "Height  0.931142  1.000000"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,41 +186,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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bt2/H//zP/+CPf/wjQkNDsWLFCjz99NN45ZVXDPs8//zzWLJkCRITEzFmzBhotVocO3aMz4ATEdmQBx8dq9bdw9unCvD7v2TjrU/zuxzepl6IRAwLm7T7Cjw8PNzot+6CggL4+fmhf//+cHIyHnBw9epVkxTXs2dPbN26FVu3bm1xH4lEgvXr12P9+vUm+c7WFBUVQaVq/tEHc/Px8UFgYKBVvpssj3/XyN6Y+9ExUx/f1h91AzoQ4N39lnRRURFCQkJRU2Peuw4tcXNzx9df5/E/rN0A/66RPTL3o2OmPr4YHnVrd4C/9NJL5qzD5qlUKtTUVCNi/kuQ+ve36Hdrbn+L7L+ug0ql4n9UuwH+XSN7ZO5Hx0x9fDE86tapQWzdmdS/P+SBQ6xdBnUD/LtGpmILI6nN/eiYqY8vhkfdOjWIrXEU+oMvb29v9OnTB9HR0RabrY2IiFpWUlGDpA+vYdKWM5jxznlMeuMMlnx4DSUVNRato3GhkAdX+zLVQiGmPr656zWFTl2Br127Fq+++iri4uLw61//GgBw8eJFHDt2DIsXL0ZhYSGeeeYZ1NfXY+HChSYtmIiI2sfWFg0x90Ihpj6+rS9s0qkAP3fuHDZs2IBFixYZbf+///s//Oc//8GBAwcQFhaGbdu2McCJiKzEFkdSm3uhEFMf35YXNunULfTjx48brQDWaNKkSTh+/DgAYOrUqfjmm2+6Vh0REXWaGEZSU+d1KsDlcjk++eSTJts/+eQTwwxtVVVV6NmzZ9eqIyKiThPDSGrqvE7dQl+zZg2eeeYZZGRkGH4Dv3TpEv7f//t/SEtLAwCcOHEC0dHRpquUiIg6RAwjqanzOhXgCxcuxNChQ/H222/j448/BgAMGTIEZ86cwdixYwEAzz33nOmqJCKiDmscSb36QK5RiNvSSGrqvE4/Bz5u3Dizr0ZGRERdY+sjqanz2h3gGo0GUqnU8M+tadyPiIisz5ZHUlPntTvAe/Xqhdu3b8PX1xdeXl5GC5s0EgQBEokE9+7dM2mRREREZKzdAX7q1CnDCPOMjAyzFURERERta3eA3z+inKPLiYiIrKtTz4EDwNmzZzFnzhyMHTsWP/zwAwDgb3/7G86dO2ey4oiISJzU1TrcKtfiWtFd3PpRC3W1ztol2Z1OBfiBAwcwefJkuLm54erVq6irqwMAqNVqvPbaayYtkIiIxMVWFlCxd50K8A0bNiAtLQ3vvfcenJx+mcln3LhxuHr1qsmKIyIicWlrARVeiZtOpwL8xo0biIqKarJdJpOhoqKiqzUREZFItWcBFTKNTgW4QqFAQUFBk+3nzp3DgAEDulwUERGJExdQsZxOBfjChQvx7LPPIjs7GxKJBCUlJdi3bx9WrFiBZ555xtQ1EhGRSHABFcvpUIAXFhYCAFavXo3f//73mDRpErRaLaKiovDUU0/h6aefxpIlS0xaYP/+/SGRSJq8Fi9eDACora3F4sWL4e3tDU9PT8THx6OsrMykNRARUfs0LqDSHC6gYlodmgt94MCB6NevH2JiYhATE4O8vDxUVlZCq9Vi6NCh8PT0NHmBly5dMprZ7csvv8TDDz+M3/3udwCA5cuX48iRI/joo48gk8mQlJSEmTNn4rPPPjN5LURE1DqZuzM2PPZf+NPBL3Cu4CfD9vGDvLHhsf/ilK4m1KEAP3XqFE6fPo3Tp0/jww8/hE6nw4ABAzBx4kRMnDgREyZMgJ+fn0kL7N27t9H7jRs3YuDAgYiOjoZarcauXbuwf/9+TJw4EQCwe/duhIaG4sKFC3jooYdMWgsREbVOXa3D+sPXMTKwF+aNC0JdfQNcHB1wrbgCrxy+jtd/N4IhbiIdCvAJEyZgwoQJAH6+dX3+/HlDoO/duxd6vR4hISH46quvzFErdDodPvjgAyQnJ0MikeDKlSvQ6/WIjY017BMSEoLAwEBkZWW1GOB1dXWGZ9eBthdnIaL24/nVvam0OnyaV45P88pbbGeAm0anZ2JzdXXFxIkT8eKLL2LdunVYunQpPD098fXXX5uyPiPp6emoqKjAH/7wBwBAaWkpnJ2d4eXlZbSfn58fSktLWzxOamoqZDKZ4RUQEGC2mom6G55f3RtHoVtOhwNcp9MhMzMT69atQ0xMDLy8vLBo0SLcvXsXb7/9tmGgmzns2rULcXFxUCqVXTpOSkoK1Gq14VVcXGyiComI51f3xlHoltOhW+gTJ05EdnY2goKCEB0djaeffhr79++Hv7+/ueoz+O677/Dpp5/i448/NmxTKBTQ6XSoqKgwugovKyuDQqFo8VguLi5wcXExZ7lmkZeXZ5Xvraurs8qfl7X6a+0abKHfXSHW84tMo3EUemYzk7lwFLppdSjAz549C39/f8OAtejoaHh7e5urNiO7d++Gr68vHnnkEcO2UaNGwcnJCSdPnkR8fDyAn2eJKyoqQmRkpEXqsoQa9U8AJJgzZ451CpBIAEGwzncD0NdZfuYmq/+Zwzr9JuoqmbszNsaHYfWBXKMQjwr2wab4MP7+bUIdCvCKigqcPXsWp0+fxqZNmzB79mwMHjwY0dHRhkB/cNS4KTQ0NGD37t2YO3cuHB1/KVkmk2HBggVITk6GXC6HVCrFkiVLEBkZaVcj0PXVlQAEjPz9KvQOCrHod9/+IgtfHtpp1e+ur6+36PcCtvFnbo1+E5mC0ssN22eHQ6XVobJWj56uTvDxdGZ4m1iHAtzDwwNTpkzBlClTAACVlZU4d+4cMjIysHnzZiQkJCA4OBhffvmlSYv89NNPUVRUhPnz5zdpe/PNN+Hg4ID4+HjU1dVh8uTJeOedd0z6/bbC0zcQ8sAhFv1Oze1vrf7d1tRd+03UVTJ3Bra5dSjAH+Th4QG5XA65XI5evXrB0dHRLL/f/fa3v4XQwi1cV1dX7NixAzt27DD59xIREdmqDgV4Q0MDLl++jNOnTyMjIwOfffYZqqqq0KdPH8TExGDHjh2IiYkxV61ERET0/+tQgHt5eaGqqgoKhQIxMTF48803MWHCBAwcONBc9REREVEzOhTgf/7znxETE4PBgwebqx4iIiJqhw4F+NNPP22uOoiIiKgDOj2VKhEREVlPl0ahExERNVJX66DS6qCp1UPq5gQfDz5KZk4McCIi6rKSihqsOpCLsw/MvrYxPgxKLzcrVma/eAudiIi6RF2taxLeAJCZr8LqA7lQV3NaYHNggBMRUZeotLom4d0oM18FlZYBbg4McCIi6hKuAW4dDHAiIuoSrgFuHQxwIiLqksY1wJvDNcDNhwFORERd0rgG+IMhzjXAzYuPkRERUZdxDXDLY4ATEZFJcA1wy+ItdCIiIhFigBMREYkQA5yIiEiEGOBEREQixAAnIiISIQY4ERGRCNl8gP/www+YM2cOvL294ebmhuHDh+Py5cuGdkEQsHbtWvj7+8PNzQ2xsbHIz8+3YsVERKanrtbhVrkW14ru4taPWq7wRbb9HPjdu3cxbtw4xMTE4OjRo+jduzfy8/PRq1cvwz6bN2/Gtm3bsHfvXgQFBWHNmjWYPHkyrl+/DldXVytWT0RkGlxrm5pj0wG+adMmBAQEYPfu3YZtQUFBhn8WBAFbt27Fiy++iOnTpwMA3n//ffj5+SE9PR2zZs2yeM1ERKbU1lrb22eHc/KUbsqmb6EfOnQIo0ePxu9+9zv4+voiPDwc7733nqG9sLAQpaWliI2NNWyTyWSIiIhAVlZWi8etq6uDRqMxehGRafD8Mi2utU0tsekA/+abb/Duu+8iODgYx48fxzPPPIOlS5di7969AIDS0lIAgJ+fn9Hn/Pz8DG3NSU1NhUwmM7wCAgLM1wmibobnl2lxrW1qiU0HeENDA371q1/htddeQ3h4OBITE7Fw4UKkpaV16bgpKSlQq9WGV3FxsYkqJiKeX6bFtbapJTYd4P7+/hg6dKjRttDQUBQVFQEAFAoFAKCsrMxon7KyMkNbc1xcXCCVSo1eRGQa9n5+WXo0ONfappbYdICPGzcON27cMNp28+ZN9OvXD8DPA9oUCgVOnjxpaNdoNMjOzkZkZKRFayUi+1dSUYOkD69h0pYzmPHOeUx64wyWfHgNJRU1ZvtOrrVNLbHpUejLly/H2LFj8dprr+Hxxx/HxYsXsXPnTuzcuRMAIJFIsGzZMmzYsAHBwcGGx8iUSiUee+wx6xZPRHbFmqPBudY2NcemA3zMmDE4ePAgUlJSsH79egQFBWHr1q1ISEgw7PP888+jqqoKiYmJqKiowPjx43Hs2DE+A05EJtWe0eDmDFSutU0PsukAB4D//u//xn//93+32C6RSLB+/XqsX7/eglURUXfD0eBka2z6N3AiIlvB0eBkaxjgRETtwNHgZGsY4ERE7WCK0eBckIRMyeZ/AycishVdGQ3OBUnI1HgFTkTUATJ3Zwz09cTIwF4Y6OvZ7ivv1h5B45U4dQYDnIjIzLggCZkDA5yIyMz4CBqZAwOciMjM+AgamQMDnIjIzPgIGpkDA5yIyMy4IAmZAx8jIyKyAC5IQqbGACcishAuSEKmxFvoREREIsQAJyIiEiEGOBERkQgxwImIiESIAU5ERCRCDHAiIiIRYoATERGJEAOciIhIhGw+wF9++WVIJBKjV0hIiKG9trYWixcvhre3Nzw9PREfH4+ysjIrVkxERGR+opiJbdiwYfj0008N7x0dfyl7+fLlOHLkCD766CPIZDIkJSVh5syZ+Oyzz6xRKhFRi9TVOqi0Omhq9ZC6OcHHgzOzUeeJIsAdHR2hUCiabFer1di1axf279+PiRMnAgB2796N0NBQXLhwAQ899JClSyUialZJRQ1WHcjF2XyVYVtUsA82xodB6eVmxcpIrGz+FjoA5OfnQ6lUYsCAAUhISEBRUREA4MqVK9Dr9YiNjTXsGxISgsDAQGRlZVmrXCIiI+pqXZPwBoDMfBVWH8iFulpnpcpIzGz+CjwiIgJ79uzBkCFDcPv2baxbtw6/+c1v8OWXX6K0tBTOzs7w8vIy+oyfnx9KS0tbPGZdXR3q6uoM7zUajbnKJ+p2eH41pdLqmoR3o8x8FVRaHW+lU4fZfIDHxcUZ/jksLAwRERHo168f/vnPf8LNrXO3nVJTU7Fu3TpTlUhE9+H51ZSmVt9qe2Ub7UTNEcUt9Pt5eXlh8ODBKCgogEKhgE6nQ0VFhdE+ZWVlzf5m3iglJQVqtdrwKi4uNnPVRN0Hz6+mpK5Orbb3bKOdqDmiC3CtVotbt27B398fo0aNgpOTE06ePGlov3HjBoqKihAZGdniMVxcXCCVSo1eRGQa5ji/1NU63CrX4lrRXdz6USu634x9PJ0RFezTbFtUsA98PHn7nDrO5m+hr1ixAtOmTUO/fv1QUlKCl156CT169MDs2bMhk8mwYMECJCcnQy6XQyqVYsmSJYiMjOQIdCI7YQ+jt2XuztgYH4bVB3KR+UA/NsWH8fdv6hSbD/Dvv/8es2fPxk8//YTevXtj/PjxuHDhAnr37g0AePPNN+Hg4ID4+HjU1dVh8uTJeOedd6xcNRGZQlujt7fPDhdN+Cm93LB9djhUWh0qa/Xo6eoEH08+B06dZ/MB/ve//73VdldXV+zYsQM7duywUEVEZCn2Nnpb5s7AJtMR3W/gRNR9cPQ2UcsY4ERkszh6m6hlDHAislkcvU3UMgY4EdmsxtHbD4Y4R28TiWAQGxF1bxy9TdQ8BjgR2TyO3iZqigFORFZlyTWyuR432RMGOBFZjSVnWbOHGd2I7sdBbERkFZZcI5vrcZM9YoATkVW0Z5Y1MX4XkaUwwInIKiw5yxpndCN7xAAnIquw5CxrnNGN7BEDnIiswpKzrHFGN7JHDHAisgpLzrLGGd3IHvExMiKyGkvOssYZ3cjeMMCJyKosOcsaZ3Qje8Jb6ERERCLEACciIhIh3kIHIAgCAECj0bS4j1arBQCov/8GDfX3LFKX4bvLfwAAaEq/hYuLC7+b3202laXf/VyDVtvq+QAAPXv2hEQiafOY7Tm/iMhYe84vidB4dnVj33//PQICAqxdBpGoqNVqSKXSNvfj+UXUce05vxjgABoaGlBSUtLuKwox0Wg0CAgIQHFxcbv+Y2sv2G/z97u950t7zq/u9O+LfbVPpu5re84v3kIH4ODggL59+1q7DLOSSqV2fwI1h/22vo6cX7ZUt7mxr/bJkn3lIDYiIiIRYoATERGJEAPczrm4uOCll16y+Ghma2O/xdVvsdbdGeyrfbJGXzmIjYiISIR4BU5ERCRCDHAiIiIRYoATERGJEAOciIhIhBjg+HmuZo1GA47nIzI9nl9E5sEAB1BZWQmZTIbKykprl0Jkd3h+EZkHA5yIiEiEGOBEREQixAAnIiISIQY4ERGRCDHAiYiIRIjrgRPZCXW1DiqtDppaPaRuTvDxcIbM3dnaZRGRmTDAiexASUUNVh3Ixdl8lWFbVLAPNsaHQenlZsXKiMhceAudSOTU1bom4Q0AmfkqrD6QC3W1zkqVEZE58QqcyIaUaWpxt0oHTW09pG6O6OXuDD+pa6ufUWl1TcK7UWa+CiqtjrfSie5TVFQElar5c8bcfHx8EBgYaJJjMcCJbETRT1VIOfgFPiv4ybBt/CBvvDZjOAK9PVr8nKZW3+pxK9toJ+pOioqKEBISipqaaqt8v5ubO77+Os8kIc4AJ7IBZZraJuENAOcKfsKfDn6BNx4f2eKVuNTVqdVj92yjnag7UalUqKmpRsT8lyD172/R79bc/hbZf10HlUrFACeyF3erdE3Cu9G5gp9wt0rXYoD7eDojKtgHmc3cRo8K9oGPJ2+fEz1I6t8f8sAh1i6jSziIjcgGaGrrO90uc3fGxvgwRAX7GG2PCvbBpvgw/v5NZKd4BU5kA6SurZ+KbbUrvdywfXY4VFodKmv16OnqBB9PPgdOZM+segWemZmJadOmQalUQiKRID093ahdq9UiKSkJffv2hZubG4YOHYq0tDSjfWpra7F48WJ4e3vD09MT8fHxKCsrs2AviLqul4czxg/ybrZt/CBv9PJoO4hl7s4Y6OuJkYG9MNDXk+FNZOesGuBVVVUYMWIEduzY0Wx7cnIyjh07hg8++AB5eXlYtmwZkpKScOjQIcM+y5cvxyeffIKPPvoIZ86cQUlJCWbOnGmpLhCZhJ/UFa/NGN4kxBtHobf1KBkRdT9WvYUeFxeHuLi4FtvPnz+PuXPnYsKECQCAxMRE/N///R8uXryIRx99FGq1Grt27cL+/fsxceJEAMDu3bsRGhqKCxcu4KGHHrJEN4hMItDbA288PvKX58BdHdHLo+3nwImoe7LpQWxjx47FoUOH8MMPP0AQBGRkZODmzZv47W9/CwC4cuUK9Ho9YmNjDZ8JCQlBYGAgsrKyWjxuXV0dNBqN0YvIFvhJXRHiL8Wvg+QI8ZeKMrx5fhFZhk0H+Pbt2zF06FD07dsXzs7OmDJlCnbs2IGoqCgAQGlpKZydneHl5WX0OT8/P5SWlrZ43NTUVMhkMsMrICDAnN0g6lZ4fhFZhs0H+IULF3Do0CFcuXIFb7zxBhYvXoxPP/20S8dNSUmBWq02vIqLi01UMRHx/CKyDJt9jKympgZ/+tOfcPDgQTzyyCMAgLCwMOTk5OD1119HbGwsFAoFdDodKioqjK7Cy8rKoFAoWjy2i4sLXFxczN0Fom6J5xeRZdjsFbher4der4eDg3GJPXr0QENDAwBg1KhRcHJywsmTJw3tN27cQFFRESIjIy1aLxERkSVZ9Qpcq9WioKDA8L6wsBA5OTmQy+UIDAxEdHQ0Vq5cCTc3N/Tr1w9nzpzB+++/jy1btgAAZDIZFixYgOTkZMjlckilUixZsgSRkZEcgU5ERHbNqgF++fJlxMTEGN4nJycDAObOnYs9e/bg73//O1JSUpCQkIA7d+6gX79+ePXVV7Fo0SLDZ9588004ODggPj4edXV1mDx5Mt555x2L94UsT12tg0qrg6ZWD6mbE3w8OPMYEXUfVg3wCRMmQBCEFtsVCgV2797d6jFcXV2xY8eOFieDIftUUlGDVQdyjdbBjgr2wcb4MCi93KxYGRGRZdjsb+BELVFX65qENwBk5quw+kAu1NU6K1VGRGQ5NjsKnaglKq2uSXg3ysxXQaXVtXgrnbfdicheMMBJdDS1+lbbK1to5213IrInvIVOoiN1dWq1vWcz7bztTkT2hlfgJDo+ns6ICvZBZjO30aOCfeDj2fSWeFduu3eEmG/Ri7l2ou6IAU6iI3N3xsb4MKw+kGsU4lHBPtgUH9Zs6HT2tntHiPkWvZhrJ+quGOAkSkovN2yfHQ6VVofKWj16ujrBx7PlK8bO3HbviLZu0W+fHW6zV7Nirp2oO2OAk2jJ3Nt/i7czt907wlK36M1BzLUTdWccxEbdQuNt96hgH6Ptrd127whL3KI3FzHXTtSd8Qqcuo2O3nbvCHPfojcnMddO1J3xCpy6FZm7Mwb6emJkYC8M9PU02a3hxlv0zTHFLXpzEnPtRN0ZA5zIBMx9i96cxFw7UXfGW+hEJmLOW/TmJubaiborBjiRCXVkZLytEXPtRN0Rb6ETERGJEAOciIhIhBjgREREIsQAJyIiEiEGOBERkQhZNcAzMzMxbdo0KJVKSCQSpKenG7VLJJJmX3/+858N+9y5cwcJCQmQSqXw8vLCggULoNVqLdwTsgZ1tQ63yrW4VnQXt37UmmxNb3Mdl4jIlKz6GFlVVRVGjBiB+fPnY+bMmU3ab9++bfT+6NGjWLBgAeLj4w3bEhIScPv2bZw4cQJ6vR7z5s1DYmIi9u/fb/b6yXrMtfwll9UkIrGwaoDHxcUhLi6uxXaFQmH0/t///jdiYmIwYMAAAEBeXh6OHTuGS5cuYfTo0QCA7du3Y+rUqXj99dehVCrNVzxZjbmWv+SymkQkJqL5DbysrAxHjhzBggULDNuysrLg5eVlCG8AiI2NhYODA7Kzs1s8Vl1dHTQajdGLxKM9y1/a0nG7G55fRJYhmgDfu3cvevbsaXSrvbS0FL6+vkb7OTo6Qi6Xo7S0tMVjpaamQiaTGV4BAQFmq5tMz1zLX3JZTdPg+UVkGaIJ8L/+9a9ISEiAq6trl4+VkpICtVpteBUXF5ugQrIUcy1/yWU1TYPnF5FliGIu9LNnz+LGjRv4xz/+YbRdoVCgvLzcaFt9fT3u3LnT5Pfz+7m4uMDFxcUstZL5NS5/mdnM7e6uLH9pruN2Nzy/iCxDFFfgu3btwqhRozBixAij7ZGRkaioqMCVK1cM206dOoWGhgZERERYukyyEHMtf8llNYlITKx6Ba7ValFQUGB4X1hYiJycHMjlcgQGBgIANBoNPvroI7zxxhtNPh8aGoopU6Zg4cKFSEtLg16vR1JSEmbNmsUR6HbOXMtfcllNIhILqwb45cuXERMTY3ifnJwMAJg7dy727NkDAPj73/8OQRAwe/bsZo+xb98+JCUlYdKkSXBwcEB8fDy2bdtm9trJ+sy1/GVLx1VX66DS6qCp1UPq5gQfDwY7EVmPVQN8woQJEASh1X0SExORmJjYYrtcLuekLWR2nOCFiGyNKH4DJ7KmtiZ44VSrRGQNDHCiNnCCFyKyRQxwojZwghciskUMcKI2cIIXIrJFDHCiNjRO8NIcTvBCRNbCACdqAyd4ISJbJIqpVImsjRO8EJGtYYATtZO5Jo4hIuoM3kInIiISIQY4ERGRCDHAiYiIRIi/gZPd4aIjRLavqKgIKlXzMxyaU15ensW/01wY4GRXuOgIke0rKipCSEgoamqqrVaDvk78UyAzwMlutLXoyPbZ4bwSJ7IBKpUKNTXViJj/EqT+/S363be/yMKXh3aivr7eot9rDgxwEq0Hb5U3NAhtLjrCACeyHVL//pAHDrHod2puf2vR7zMnBjiJUnO3ynfNHd3qZ7joCBHZE45CJ9Fp6VZ5W7joCBHZEwY4iU5L63NfK67AuEHezX6Gi44Qkb1hgJPotLQ+91/PFWLeuCD8houOEFE3YNUAz8zMxLRp06BUKiGRSJCent5kn7y8PDz66KOQyWTw8PDAmDFjUFRUZGivra3F4sWL4e3tDU9PT8THx6OsrMyCvSBLa2l97mrdPSz98Bo2TP8vnEyORvofx+JkcjS2zw6HPx8hIyI7Y9UAr6qqwogRI7Bjx45m22/duoXx48cjJCQEp0+fRm5uLtasWQNXV1fDPsuXL8cnn3yCjz76CGfOnEFJSQlmzpxpqS5QC9TVOtwq1+Ja0V3c+lELdbWuXW3t0dr63KP79YKXuxMG+npiZGAvDPT15JU3Edklq45Cj4uLQ1xcXIvtL7zwAqZOnYrNmzcbtg0cONDwz2q1Grt27cL+/fsxceJEAMDu3bsRGhqKCxcu4KGHHjJf8dSiliZT2RQfBgHo8kQrjetzrz6Qi8xmvoOBTUTdgc3+Bt7Q0IAjR45g8ODBmDx5Mnx9fREREWF0m/3KlSvQ6/WIjY01bAsJCUFgYCCysrKsUDW1NpnK6Zs/YtW/Wp5opSNX4o3rc/NWORF1Vzb7HHh5eTm0Wi02btyIDRs2YNOmTTh27BhmzpyJjIwMREdHo7S0FM7OzvDy8jL6rJ+fH0pLS1s8dl1dHerq6gzvNRqNubohap2ZU7ylEeIA4NvTBWcLTDfRCtfntk08v4gsw2YDvKGhAQAwffp0LF++HAAwcuRInD9/HmlpaYiOju70sVNTU7Fu3TqT1GmvOjuneEsjxAGgrr6h1e/kRCv2gecXkWXY7C10Hx8fODo6YujQoUbbQ0NDDaPQFQoFdDodKioqjPYpKyuDQqFo8dgpKSlQq9WGV3FxscnrF7O25hRv7VZ3SyPEAcDFsfW/bpxoxT7w/CKyDJsNcGdnZ4wZMwY3btww2n7z5k3069cPADBq1Cg4OTnh5MmThvYbN26gqKgIkZGRLR7bxcUFUqnU6EW/aO02eOOt7pa0NkK8vLKuxTZOtGI/eH4RWYZVb6FrtVoUFBQY3hcWFiInJwdyuRyBgYFYuXIlnnjiCURFRSEmJgbHjh3DJ598gtOnTwMAZDIZFixYgOTkZMjlckilUixZsgSRkZEcgd4Frd0GB1q/1d3aCPGYwb0RPbg3R48TEZmAVQP88uXLiImJMbxPTk4GAMydOxd79uzBjBkzkJaWhtTUVCxduhRDhgzBgQMHMH78eMNn3nzzTTg4OCA+Ph51dXWYPHky3nnnHYv3xZ60dhscaPtWd+MIcZVWh8paPXq6OsHH85cBZ621ERFR+1g1wCdMmABBEFrdZ/78+Zg/f36L7a6urtixY0eLk8FQxzXeBs9s5jZ6e291tzZCnKPHiYi6rlO/ga9fvx7V1dVNttfU1GD9+vVdLoqsq/E2+IO/V/NWNxGR7ejUFfi6deuwaNEiuLu7G22vrq7GunXrsHbtWpMUR9bT1m1wIiKyrk4FuCAIkEgkTbZ//vnnkMvlXS6KbANvdRMR2a4OBXivXr0gkUggkUgwePBgoxC/d+8etFotFi1aZPIiiYiIyFiHAnzr1q0QBAHz58/HunXrIJPJDG3Ozs7o379/q89fExERkWl0KMDnzp0LAAgKCsLYsWPh5MSZs4iIiKyhU7+BR0dHo6GhATdv3kR5eblh3vJGUVFRJimOiIiImtepAL9w4QJ+//vf47vvvmvyHLdEIsG9e/dMUhwRERE1r1MBvmjRIowePRpHjhyBv79/syPSiYiIyHw6FeD5+fn417/+hUGDBpm6HiIiImqHTs3EFhERYbQICREREVlWu6/Ac3NzDf+8ZMkSPPfccygtLcXw4cObjEYPCwszXYVERETURLsDfOTIkZBIJEaD1u5fZKSxjYPYiIiIzK/dAV5YWGjOOoiIiKgD2h3g/fr1M2cdRERE1AGdGoV+6NChZrdLJBK4urpi0KBBCAoK6lJhRERE1LJOBfhjjz3W5PdwwPh38PHjxyM9PR29evUySaFERET0i049RnbixAmMGTMGJ06cgFqthlqtxokTJxAREYHDhw8jMzMTP/30E1asWGHqeomIiAidvAJ/9tlnsXPnTowdO9awbdKkSXB1dUViYiK++uorbN261WiUOhEREZlOp67Ab926BalU2mS7VCrFN998AwAIDg6GSqXqWnVERETUrE4F+KhRo7By5Ur8+OOPhm0//vgjnn/+eYwZMwbAz9OtBgQEtHqczMxMTJs2DUqlEhKJBOnp6Ubtf/jDHyCRSIxeU6ZMMdrnzp07SEhIgFQqhZeXFxYsWACtVtuZbpGNUlfrcKtci2tFd3HrRy3U1Tprl0REZHWduoW+a9cuTJ8+HX379jWEdHFxMQYMGIB///vfAACtVosXX3yx1eNUVVVhxIgRmD9/PmbOnNnsPlOmTMHu3bsN711cXIzaExIScPv2bZw4cQJ6vR7z5s1DYmIi9u/f35mukY0pqajBqgO5OJv/y92cqGAfbIwPg9LLzYqVERFZV6cCfMiQIbh+/Tr+85//4ObNm4ZtDz/8MBwcfr6of+yxx9o8TlxcHOLi4lrdx8XFBQqFotm2vLw8HDt2DJcuXcLo0aMBANu3b8fUqVPx+uuvQ6lUdqBXZGvU1bom4Q0AmfkqrD6Qi+2zwyFzd7ZSdURE1tWpAAcABwcHTJkypcktbVM7ffo0fH190atXL0ycOBEbNmyAt7c3ACArKwteXl6G8AaA2NhYODg4IDs7GzNmzGj2mHV1dairqzO812g0Zu0DdY5Kq2sS3o0y81VQaXUMcBvE84vIMtod4Nu2bUNiYiJcXV2xbdu2VvddunRplwsDfr59PnPmTAQFBeHWrVv405/+hLi4OGRlZaFHjx4oLS2Fr6+v0WccHR0hl8tRWlra4nFTU1Oxbt06k9RI5qOp1bfaXtlGO1kHzy8iy2h3gL/55ptISEiAq6sr3nzzzRb3k0gkJgvwWbNmGf55+PDhCAsLw8CBA3H69GlMmjSp08dNSUlBcnKy4b1Go2lzwB1ZntTVqdX2nm20k3Xw/CKyjE4tZmKthU0GDBgAHx8fFBQUYNKkSVAoFCgvLzfap76+Hnfu3Gnxd3Pg59/VHxwMR7bHx9MZUcE+yGzmNnpUsA98PHn73Bbx/CKyjE49RtZIp9Phxo0bqK+vN1U9rfr+++/x008/wd/fHwAQGRmJiooKXLlyxbDPqVOn0NDQgIiICIvUROYjc3fGxvgwRAX7GG2PCvbBpvgw/v5NRN1apwaxVVdXY8mSJdi7dy8A4ObNmxgwYACWLFmCPn36YPXq1e06jlarRUFBgeF9YWEhcnJyIJfLIZfLsW7dOsTHx0OhUODWrVt4/vnnMWjQIEyePBkAEBoaiilTpmDhwoVIS0uDXq9HUlISZs2axRHodkLp5Ybts8Oh0upQWatHT1cn+Hg6M7yJqNvr1BV4SkoKPv/8c5w+fRqurq6G7bGxsfjHP/7R7uNcvnwZ4eHhCA8PBwAkJycjPDwca9euRY8ePZCbm4tHH30UgwcPxoIFCzBq1CicPXvW6Pbcvn37EBISgkmTJmHq1KkYP348du7c2ZlukY2SuTtjoK8nRgb2wkBfT4Y3ERE6eQWenp6Of/zjH3jooYcgkUgM24cNG4Zbt261+zgTJkxosqLZ/Y4fP97mMeRyOSdtISKibqdTV+A//vhjk8e3gJ9nVrs/0ImIiMg8OhXgo0ePxpEjRwzvG0P7L3/5CyIjI01TGREREbWoU7fQX3vtNcTFxeH69euor6/HW2+9hevXr+P8+fM4c+aMqWskIiKiB3TqCnz8+PHIyclBfX09hg8fjv/85z/w9fVFVlYWRo0aZeoaiYiI6AEdugK/f07j3r1744033mh2n+bWCifLU1froNLqoKnVQ+rmBB8PPn5FRGQvOhTgXl5erQ5SEwQBEokE9+7d63Jh1DVchpOIyL51KMAzMjIM/ywIAqZOnYq//OUv6NOnj8kLo87jMpxERPavQwEeHR1t9L5Hjx546KGHMGDAAJMWRV3DZThJjIqKiqBSNf/31tzq6uqsNn+7j48PAgMDrfLd1vozz8vLs/h32qNOrwdOtovLcJLYFBUVISQkFDU11dYpQCIBWplUypzc3Nzx9dd5Fg9xq/+ZA9DX6az23faAAW6HuAwniY1KpUJNTTUi5r8EqX9/i3737S+y8OWhnRj5+1XoHRRi0e/W3P4W2X9dB5VKZfEAt4U/c0sthGWvuhzgnHnN9nAZThIrqX9/yAOHWPQ7Nbe/BQB4+gZa/LttgTX/zKlrOhTgM2fONHpfW1uLRYsWwcPDw2j7xx9/3PXKqNMal+FcfSDXKMS5DCcRkf3oUIDLZDKj93PmzDFpMWQ6XIaTiMi+dSjAd+/eba46yAxk7gxsIiJ71ampVImIiMi6GOBEREQixAAnIiISIQY4ERGRCDHAiYiIRMiqAZ6ZmYlp06ZBqVRCIpEgPT29xX0XLVoEiUSCrVu3Gm2/c+cOEhISIJVK4eXlhQULFkCr1Zq38A5SV+twq1yLa0V3cetHLdTVnZs+0FTHISIi8bPqVKpVVVUYMWIE5s+f32SSmPsdPHgQFy5cgFKpbNKWkJCA27dv48SJE9Dr9Zg3bx4SExOxf/9+c5bebqZa1pPLgxIR0f2segUeFxeHDRs2YMaMGS3u88MPP2DJkiXYt28fnJyM5/DOy8vDsWPH8Je//AUREREYP348tm/fjr///e8oKSkxd/ltamtZz/ZeQZvqOEREZD9s+jfwhoYGPPnkk1i5ciWGDRvWpD0rKwteXl4YPXq0YVtsbCwcHByQnZ3d4nHr6uqg0WiMXubQnmU9LXkcIkuw1PlF1N3ZdIBv2rQJjo6OWLp0abPtpaWl8PX1Ndrm6OgIuVyO0tLSFo+bmpoKmUxmeAUEBJi07kamWtaTy4OSmFjq/CLq7mw2wK9cuYK33noLe/bsMfmKZykpKVCr1YZXcXGxSY/fyFTLenJ5UBITS51fRN2dzQb42bNnUV5ejsDAQDg6OsLR0RHfffcdnnvuOfTv3x8AoFAoUF5ebvS5+vp63LlzBwqFosVju7i4QCqVGr3MoXFZz+Z0ZFlPUx2HyBIsdX4RdXc2G+BPPvkkcnNzkZOTY3gplUqsXLkSx48fBwBERkaioqICV65cMXzu1KlTaGhoQEREhLVKN2hc1vPB8O3osp6mOg4REdkPqz5GptVqUVBQYHhfWFiInJwcyOVyBAYGwtvb22h/JycnKBQKDBny8+LzoaGhmDJlChYuXIi0tDTo9XokJSVh1qxZzT5yZg2mWtaTy4MSEdH9rBrgly9fRkxMjOF9cnIyAGDu3LnYs2dPu46xb98+JCUlYdKkSXBwcEB8fDy2bdtmjnI7zVTLenJ5UCIiamTVAJ8wYQIEQWj3/t9++22TbXK53GYmbSEiIrIUm/0NnIiIiFrGACciIhIhBjgREZEIMcCJiIhEiAFOREQkQlYdhU6moa7WQaXVQVOrh9TNCT4efNyMSEzy8vK6xXeSaTHARY7rhBOJV436JwASzJkzx2o16Ou4mqFYMcBFrK11wrfPDueVOJEN01dXAhAw8ver0DsoxKLfffuLLHx5aCfq6+st+r1kOgxwEWvPOuEMcCLb5+kbCHngEIt+p+b2txb9PjI9DmITMa4TTkTUfTHARYzrhBMRdV8McBHjOuFERN0XA1zEuE44EVH3xUFsIsd1womIuicGuB3gOuFERN0Pb6ETERGJEAOciIhIhBjgREREIsQAJyIiEiGrBnhmZiamTZsGpVIJiUSC9PR0o/aXX34ZISEh8PDwQK9evRAbG4vs7Gyjfe7cuYOEhARIpVJ4eXlhwYIF0Gq1FuwFERGR5Vk1wKuqqjBixAjs2LGj2fbBgwfj7bffxhdffIFz586hf//++O1vf4sff/zRsE9CQgK++uornDhxAocPH0ZmZiYSExMt1QUiIiKrsOpjZHFxcYiLi2ux/fe//73R+y1btmDXrl3Izc3FpEmTkJeXh2PHjuHSpUsYPXo0AGD79u2YOnUqXn/9dSiVSrPWT0REZC2i+Q1cp9Nh586dkMlkGDFiBAAgKysLXl5ehvAGgNjYWDg4ODS51U5ERGRPbH4il8OHD2PWrFmorq6Gv78/Tpw4AR+fn6cOLS0tha+vr9H+jo6OkMvlKC0tbfGYdXV1qKurM7zXaDSt1qCu1kGl1UFTq4fUzQk+Hpw4haglHT2/iKhzbD7AY2JikJOTA5VKhffeew+PP/44srOzmwR3R6SmpmLdunXt2rekogarDuQarbsdFeyDjfFhUHq5dboGInvVkfOLiDrP5m+he3h4YNCgQXjooYewa9cuODo6YteuXQAAhUKB8vJyo/3r6+tx584dKBSKFo+ZkpICtVpteBUXFze7n7pa1yS8ASAzX4XVB3KhrtZ1sXdE9qe95xcRdY3NX4E/qKGhwXB7LjIyEhUVFbhy5QpGjRoFADh16hQaGhoQERHR4jFcXFzg4uLS5neptLom4d0oM18FlVbHW+lED2jv+UVEXWPVANdqtSgoKDC8LywsRE5ODuRyOby9vfHqq6/i0Ucfhb+/P1QqFXbs2IEffvgBv/vd7wAAoaGhmDJlChYuXIi0tDTo9XokJSVh1qxZJhmBrqnVt9pe2UY7ERGRuVg1wC9fvoyYmBjD++TkZADA3LlzkZaWhq+//hp79+6FSqWCt7c3xowZg7Nnz2LYsGGGz+zbtw9JSUmYNGkSHBwcEB8fj23btpmkPqmrU6vtPdtoJyIiMherBviECRMgCEKL7R9//HGbx5DL5di/f78pyzLw8XRGVLAPMpu5jR4V7AMfT94+JyIi67D5QWzWJHN3xsb4MEQF+xhtjwr2wab4MP7+TUREViO6QWyWpvRyw/bZ4VBpdais1aOnqxN8PPkcOBERWRcDvB1k7gxsIiKyLbyFTkREJEIMcCIiIhFigBMREYkQA5yIiEiEGOBEREQixAAnIiISIQY4ERGRCDHAiYiIRIgTuZiIuloHlVYHTa0eUjcn+Hhw8hciIjIfBrgJlFTUYNWBXKO1w6OCfbAxPgxKLzcrVkZERPaKt9C7SF2taxLeAJCZr8LqA7lQV+usVBkREdkzBngXqbS6JuHdKDNfBZWWAU5ERKbHAO8iTa2+1fbKNtqJiIg6gwHeRVJXp1bbe7bRTkRE1BkM8C7y8XRGVLBPs21RwT7w8eRIdCIiMj0GeBfJ3J2xMT6sSYhHBftgU3wYHyUjIiKz4GNkJqD0csP22eFQaXWorNWjp6sTfDz5HDgREZmPVa/AMzMzMW3aNCiVSkgkEqSnpxva9Ho9Vq1aheHDh8PDwwNKpRL/+7//i5KSEqNj3LlzBwkJCZBKpfDy8sKCBQug1WpNWqe6Wodb5VpcK7qLWz9qm300TObujIG+nhgZ2AsDfT0Z3kREZFZWDfCqqiqMGDECO3bsaNJWXV2Nq1evYs2aNbh69So+/vhj3LhxA48++qjRfgkJCfjqq69w4sQJHD58GJmZmUhMTDRZjSUVNUj68BombTmDGe+cx6Q3zmDJh9dQUlFjsu8gIiLqKKveQo+Li0NcXFyzbTKZDCdOnDDa9vbbb+PXv/41ioqKEBgYiLy8PBw7dgyXLl3C6NGjAQDbt2/H1KlT8frrr0OpVHapvrYmadk+O5xX2kREZBWiGsSmVqshkUjg5eUFAMjKyoKXl5chvAEgNjYWDg4OyM7ObvE4dXV10Gg0Rq/mcJIWoo5r7/lFRF0jmgCvra3FqlWrMHv2bEilUgBAaWkpfH19jfZzdHSEXC5HaWlpi8dKTU2FTCYzvAICAprdj5O0EHVce88vIuoaUQS4Xq/H448/DkEQ8O6773b5eCkpKVCr1YZXcXFxs/txkhaijmvv+UVEXWPzj5E1hvd3332HU6dOGa6+AUChUKC8vNxo//r6ety5cwcKhaLFY7q4uMDFxaXN726cpCWzmdvonKSFqHntPb+IqGts+gq8Mbzz8/Px6aefwtvb26g9MjISFRUVuHLlimHbqVOn0NDQgIiIiC5/PydpISIiW2XVK3CtVouCggLD+8LCQuTk5EAul8Pf3x//8z//g6tXr+Lw4cO4d++e4XdtuVwOZ2dnhIaGYsqUKVi4cCHS0tKg1+uRlJSEWbNmdXkEeiNO0kJERLbIqgF++fJlxMTEGN4nJycDAObOnYuXX34Zhw4dAgCMHDnS6HMZGRmYMGECAGDfvn1ISkrCpEmT4ODggPj4eGzbts2kdcrcGdhERGRbrBrgEyZMgCAILba31tZILpdj//79piyLiIjI5tn0b+BERETUPAY4ERGRCNn8Y2SW0HirnjNGEbVfz549IZFI2tyvPedX4wJE6u+/QUP9PdMU2E7a8h8AAJrSby3++Bu/u3t9d2Xpdz/XoNW2mTftOb8kQnt+aLZz33//PWeLIuogtVptNC9DS3h+EXVce84vBjiAhoYGlJSUtPuKQkw0Gg0CAgJQXFzcrv/Y2gv22/z9bu/50p7zqzv9+2Jf7ZOp+9qe84u30AE4ODigb9++1i7DrKRSqd2fQM1hv62vI+eXLdVtbuyrfbJkXzmIjYiISIQY4ERERCLEALdzLi4ueOmll7rd4hLst7j6Lda6O4N9tU/W6CsHsREREYkQr8CJiIhEiAFOREQkQgxwIiIiEWKAExERiRAD3I788MMPmDNnDry9veHm5obhw4fj8uXLhnZBELB27Vr4+/vDzc0NsbGxyM/Pt2LFXXfv3j2sWbMGQUFBcHNzw8CBA/HKK68YLUVrD/3OzMzEtGnToFQqIZFIkJ6ebtTenj7euXMHCQkJkEql8PLywoIFCwxzkFujbr1ej1WrVmH48OHw8PCAUqnE//7v/6KkpMTqdXdUW/9+7rdo0SJIJBJs3brVaLsY+gm0r695eXl49NFHIZPJ4OHhgTFjxqCoqMjQXltbi8WLF8Pb2xuenp6Ij49HWVmZBXvRPm31VavVIikpCX379oWbmxuGDh2KtLQ0o33M2VcGuJ24e/cuxo0bBycnJxw9ehTXr1/HG2+8gV69ehn22bx5M7Zt24a0tDRkZ2fDw8MDkydPRm1trRUr75pNmzbh3Xffxdtvv428vDxs2rQJmzdvxvbt2w372EO/q6qqMGLECOzYsaPZ9vb0MSEhAV999RVOnDiBw4cPIzMzE4mJiVaru7q6GlevXsWaNWtw9epVfPzxx7hx4wYeffRRo/2sUXdHtfXvp9HBgwdx4cIFKJXKJm1i6CfQdl9v3bqF8ePHIyQkBKdPn0Zubi7WrFkDV1dXwz7Lly/HJ598go8++ghnzpxBSUkJZs6caakutFtbfU1OTsaxY8fwwQcfIC8vD8uWLUNSUhIOHTpk2MesfRXILqxatUoYP358i+0NDQ2CQqEQ/vznPxu2VVRUCC4uLsKHH35oiRLN4pFHHhHmz59vtG3mzJlCQkKCIAj22W8AwsGDBw3v29PH69evCwCES5cuGfY5evSoIJFIhB9++MEqdTfn4sWLAgDhu+++EwTBNuruqJb6+f333wt9+vQRvvzyS6Ffv37Cm2++aWgTYz8Fofm+PvHEE8KcOXNa/ExFRYXg5OQkfPTRR4ZteXl5AgAhKyvLXKV2WXN9HTZsmLB+/Xqjbb/61a+EF154QRAE8/eVV+B24tChQxg9ejR+97vfwdfXF+Hh4XjvvfcM7YWFhSgtLUVsbKxhm0wmQ0REBLKysqxRskmMHTsWJ0+exM2bNwEAn3/+Oc6dO4e4uDgA9tvv+7Wnj1lZWfDy8sLo0aMN+8TGxsLBwQHZ2dkWr7klarUaEokEXl5eAMRTd1saGhrw5JNPYuXKlRg2bFiTdnvq55EjRzB48GBMnjwZvr6+iIiIMLr1fOXKFej1eqO/ryEhIQgMDBTdOTl27FgcOnQIP/zwAwRBQEZGBm7evInf/va3AMzfVwa4nfjmm2/w7rvvIjg4GMePH8czzzyDpUuXYu/evQCA0tJSAICfn5/R5/z8/AxtYrR69WrMmjULISEhcHJyQnh4OJYtW4aEhAQA9tvv+7Wnj6WlpfD19TVqd3R0hFwut5k/h9raWqxatQqzZ882LAYhhrrbY9OmTXB0dMTSpUubbbeXfpaXl0Or1WLjxo2YMmUK/vOf/2DGjBmYOXMmzpw5A+Dnvjo7Oxv+J62RGM/J7du3Y+jQoejbty+cnZ0xZcoU7NixA1FRUQDM31euRmYnGhoaMHr0aLz22msAgPDwcHz55ZdIS0vD3LlzrVyd+fzzn//Evn37sH//fgwbNgw5OTlYtmwZlEqlXffb3uj1ejz++OMQBAHvvvuutcsxqStXruCtt97C1atX7W654gc1NDQAAKZPn47ly5cDAEaOHInz588jLS0N0dHR1izP5LZv344LFy7g0KFD6NevHzIzM7F48WIolUqjq25z4RW4nfD398fQoUONtoWGhhpGfioUCgBoMvqxrKzM0CZGK1euNFyFDx8+HE8++SSWL1+O1NRUAPbb7/u1p48KhQLl5eVG7fX19bhz547V/xwaw/u7777DiRMnjJZitOW62+vs2bMoLy9HYGAgHB0d4ejoiO+++w7PPfcc+vfvD8A++gkAPj4+cHR0bPO/RTqdDhUVFUb7iO2crKmpwZ/+9Cds2bIF06ZNQ1hYGJKSkvDEE0/g9ddfB2D+vjLA7cS4ceNw48YNo203b95Ev379AABBQUFQKBQ4efKkoV2j0SA7OxuRkZEWrdWUqqur4eBg/Ne4R48ehisBe+33/drTx8jISFRUVODKlSuGfU6dOoWGhgZERERYvOZGjeGdn5+PTz/9FN7e3kbttlp3Rzz55JPIzc1FTk6O4aVUKrFy5UocP34cgH30EwCcnZ0xZsyYVv9bNGrUKDg5ORn9fb1x4waKiopEdU7q9Xro9fpW//tj9r52eRgc2YSLFy8Kjo6Owquvvirk5+cL+/btE9zd3YUPPvjAsM/GjRsFLy8v4d///reQm5srTJ8+XQgKChJqamqsWHnXzJ07V+jTp49w+PBhobCwUPj4448FHx8f4fnnnzfsYw/9rqysFK5duyZcu3ZNACBs2bJFuHbtmmG0dnv6OGXKFCE8PFzIzs4Wzp07JwQHBwuzZ8+2Wt06nU549NFHhb59+wo5OTnC7du3Da+6ujqr1t1Rbf37edCDo9AFQRz9FIS2+/rxxx8LTk5Ows6dO4X8/Hxh+/btQo8ePYSzZ88ajrFo0SIhMDBQOHXqlHD58mUhMjJSiIyMtFaXWtRWX6Ojo4Vhw4YJGRkZwjfffCPs3r1bcHV1Fd555x3DMczZVwa4Hfnkk0+E//qv/xJcXFyEkJAQYefOnUbtDQ0Nwpo1awQ/Pz/BxcVFmDRpknDjxg0rVWsaGo1GePbZZ4XAwEDB1dVVGDBggPDCCy8YBYA99DsjI0MA0OQ1d+5cQRDa18effvpJmD17tuDp6SlIpVJh3rx5QmVlpdXqLiwsbLYNgJCRkWHVujuqrX8/D2ouwMXQT0FoX1937dolDBo0SHB1dRVGjBghpKenGx2jpqZG+OMf/yj06tVLcHd3F2bMmCHcvn3bwj1pW1t9vX37tvCHP/xBUCqVgqurqzBkyBDhjTfeEBoaGgzHMGdfuZwoERGRCPE3cCIiIhFigBMREYkQA5yIiEiEGOBEREQixAAnIiISIQY4ERGRCDHAiYiIRIgBTjbn9OnTkEgkTeYPbs3LL7+MkSNHmq0mInvQv39/bN26td37f/vtt5BIJMjJyTFbTdR5DHDqkrS0NPTs2RP19fWGbVqtFk5OTpgwYYLRvo3BfOvWrVaPOXbsWNy+fRsymcyktU6YMAHLli0z6TGJLOEPf/gDHnvssSbbO/o/u5cuXUJiYqJJa9uzZ0+T5TLJMhjg1CUxMTHQarW4fPmyYdvZs2ehUCiQnZ2N2tpaw/aMjAwEBgZi4MCBrR7T2dkZCoXC7pdeJLK03r17w93d3dplkIkwwKlLhgwZAn9/f5w+fdqw7fTp05g+fTqCgoJw4cIFo+0xMTFoaGhAamoqgoKC4ObmhhEjRuBf//qX0X4PXlW89957CAgIgLu7O2bMmIEtW7Y0+3/9f/vb39C/f3/IZDLMmjULlZWVAH6+gjlz5gzeeustSCQSSCQSfPvtt6b+4yCyqnPnzuE3v/kN3NzcEBAQgKVLl6KqqsrQ/uAt9K+//hrjx4+Hq6srhg4dik8//RQSiQTp6elGx/3mm28QExMDd3d3jBgxAllZWQB+PlfnzZsHtVptOK9efvllC/SUAAY4mUBMTAwyMjIM7zMyMjBhwgRER0cbttfU1CA7OxsxMTFITU3F+++/j7S0NHz11VdYvnw55syZgzNnzjR7/M8++wyLFi3Cs88+i5ycHDz88MN49dVXm+x369YtpKen4/Dhwzh8+DDOnDmDjRs3AgDeeustREZGYuHChbh9+zZu376NgIAAM/xpEFnHrVu3MGXKFMTHxyM3Nxf/+Mc/cO7cOSQlJTW7/7179/DYY4/B3d0d2dnZ2LlzJ1544YVm933hhRewYsUK5OTkYPDgwZg9ezbq6+sxduxYbN26FVKp1HBerVixwpzdpPuZZEkU6tbee+89wcPDQ9Dr9YJGoxEcHR2F8vJyYf/+/UJUVJQgCIJw8uRJAYDw7bffCu7u7sL58+eNjrFgwQLD0omNKwDdvXtXEARBeOKJJ4RHHnnEaP+EhARBJpMZ3r/00kuCu7u7oNFoDNtWrlwpREREGN5HR0cLzz77rAl7TmQZc+fOFXr06CF4eHgYvVxdXQ3nyoIFC4TExESjz509e1ZwcHAwLCt7/ypoR48eFRwdHY1Wxjpx4oQAQDh48KAgCIJhxbi//OUvhn2++uorAYCQl5cnCIIg7N692+hcJMtxtOb/PJB9mDBhAqqqqnDp0iXcvXsXgwcPRu/evREdHY158+ahtrYWp0+fxoABA6DValFdXY2HH37Y6Bg6nQ7h4eHNHv/GjRuYMWOG0bZf//rXOHz4sNG2/v37o2fPnob3/v7+KC8vN1EviawrJiYG7777rtG27OxszJkzBwDw+eefIzc3F/v27TO0C4KAhoYGFBYWIjQ01OizN27cQEBAABQKhWHbr3/962a/OywszPDP/v7+AIDy8nKEhIR0rVPUJQxw6rJBgwahb9++yMjIwN27dxEdHQ0AUCqVCAgIwPnz55GRkYGJEydCq9UCAI4cOYI+ffoYHcfFxaVLdTg5ORm9l0gkaGho6NIxiWyFh4cHBg0aZLTt+++/N/yzVqvF008/jaVLlzb5bGBgYJe++/5zq3FwKc8t62OAk0nExMTg9OnTuHv3LlauXGnYHhUVhaNHj+LixYt45plnMHToULi4uKCoqMgQ9G0ZMmQILl26ZLTtwfft4ezsjHv37nX4c0Ri8Ktf/QrXr19vEvItGTJkCIqLi1FWVgY/Pz8APK/EhgFOJhETE4PFixdDr9cbBXN0dDSSkpKg0+kQExODnj17YsWKFVi+fDkaGhowfvx4qNVqfPbZZ5BKpZg7d26TYy9ZsgRRUVHYsmULpk2bhlOnTuHo0aMdfsysf//+yM7OxrfffgtPT0/I5XI4OHAcJ9mHVatW4aGHHkJSUhKeeuopeHh44Pr16zhx4gTefvvtJvs//PDDGDhwIObOnYvNmzejsrISL774IgB06Nzq378/tFotTp48iREjRsDd3Z2PqlkI/+tFJhETE4OamhoMGjTI8H/zwM8BXllZaXjcDABeeeUVrFmzBqmpqQgNDcWUKVNw5MgRBAUFNXvscePGIS0tDVu2bMGIESNw7NgxLF++HK6urh2qccWKFejRoweGDh2K3r17o6ioqPMdJrIxYWFhOHPmDG7evInf/OY3CA8Px9q1a6FUKpvdv0ePHkhPT4dWq8WYMWPw1FNPGUahd+TcGjt2LBYtWoQnnngCvXv3xubNm03SH2qbRBAEwdpFEHXUwoUL8fXXX+Ps2bPWLoXIbnz22WcYP348CgoK2pxwiayPt9BJFF5//XU8/PDD8PDwwNGjR7F3716888471i6LSNQOHjwIT09PBAcHo6CgAM8++yzGjRvH8BYJBjiJwsWLFw2/0w0YMADbtm3DU089Ze2yiEStsrISq1atQlFREXx8fBAbG4s33njD2mVRO/EWOhERkQhxEBsREZEIMcCJiIhEiAFOREQkQgxwIiIiEWKAExERiRADnIiISIQY4ERERCLEACciIhIhBjgREZEI/X9Z5IIDUBvmYAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x2a2d47d9370>"
+       "<Figure size 500x500 with 6 Axes>"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "## Seaborn for visualization\n",
     "import seaborn as sns\n",
-    "sns.pairplot(df)"
+    "sns.pairplot(df)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -251,7 +229,7 @@
        "(23,)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -272,7 +250,7 @@
        "(23,)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -283,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -322,30 +300,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "X_test=scaler.transform(X_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.33497168],\n",
-       "       [ 0.33497168],\n",
-       "       [-1.6641678 ],\n",
-       "       [ 1.36483141],\n",
-       "       [-0.45256812],\n",
-       "       [ 1.97063125]])"
+       "array([[ 78.],\n",
+       "       [ 78.],\n",
+       "       [ 45.],\n",
+       "       [ 95.],\n",
+       "       [ 65.],\n",
+       "       [105.]])"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -356,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,16 +362,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression(n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression(n_jobs=-1)</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "LinearRegression(n_jobs=-1)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -395,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -414,36 +808,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2a2d7cc5a30>]"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 46,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "## plot Training data plot best fit line\n",
     "plt.scatter(X_train,y_train)\n",
-    "plt.plot(X_train,regression.predict(X_train))"
+    "plt.plot(X_train,regression.predict(X_train))\n",
+    "plt.show()"
    ]
   },
   {
@@ -457,7 +840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -467,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -477,16 +860,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "114.84069295228699\n",
-      "9.665125886795005\n",
-      "10.716374991212605\n"
+      "1886933.5397307763\n",
+      "1336.1312312741586\n",
+      "1373.6569949338796\n"
      ]
     }
    ],
@@ -516,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -525,14 +908,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.7360826717981276\n"
+      "-4335.39370764813\n"
      ]
     }
    ],
@@ -556,16 +939,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.6701033397476595"
+       "-5419.492134560162"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -577,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -587,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -596,15 +979,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[  5.79440897   5.79440897 -28.78711691  23.60913442  -7.82861638\n",
-      "  34.08838469]\n"
+      "[1349.26004494 1349.26004494  778.4192567  1643.32954191 1124.38337078\n",
+      " 1816.31159896]\n"
      ]
     }
    ],
@@ -615,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -627,8 +1010,8 @@
       "Dep. Variable:                 Height   R-squared (uncentered):                   0.012\n",
       "Model:                            OLS   Adj. R-squared (uncentered):             -0.050\n",
       "Method:                 Least Squares   F-statistic:                             0.1953\n",
-      "Date:                Mon, 01 Aug 2022   Prob (F-statistic):                       0.664\n",
-      "Time:                        19:51:00   Log-Likelihood:                         -110.03\n",
+      "Date:                Wed, 09 Apr 2025   Prob (F-statistic):                       0.664\n",
+      "Time:                        21:25:52   Log-Likelihood:                         -110.03\n",
       "No. Observations:                  17   AIC:                                      222.1\n",
       "Df Residuals:                      16   BIC:                                      222.9\n",
       "Df Model:                           1                                                  \n",
@@ -653,8 +1036,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17\n",
-      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:531: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17\n",
+      "  res = hypotest_fun_out(*samples, **kwds)\n"
      ]
     }
    ],
@@ -664,23 +1047,488 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([155.97744705])"
+       "array([156.47058824])"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "## Prediction For new data\n",
-    "regression.predict(scaler.transform([[72]]))"
+    "regression.predict(scaler.fit_transform([[72]]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using gradient descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import SGDRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SGDRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.SGDRegressor.html\">?<span>Documentation for SGDRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SGDRegressor(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SGDRegressor(random_state=42)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = SGDRegressor(max_iter=1000, tol=1e-3, random_state=42)\n",
+    "model.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[156.28350531]\n",
+      "[17.2758141]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(model.intercept_)\n",
+    "print(model.coef_)"
    ]
   },
   {
@@ -693,7 +1541,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -707,7 +1555,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/30-Attenttion mechanism/Attention+Mechanism.pdf b/30-Attenttion mechanism/Attention+Mechanism.pdf
new file mode 100644
index 00000000..84fe24ed
Binary files /dev/null and b/30-Attenttion mechanism/Attention+Mechanism.pdf differ
diff --git a/31-Transformers/best+final+transformers.pdf b/31-Transformers/best+final+transformers.pdf
new file mode 100644
index 00000000..3e644a77
Binary files /dev/null and b/31-Transformers/best+final+transformers.pdf differ
diff --git a/4-Ridge Lasso And Elasticnet/Ridge Lassso Elastic Regression Practicals/Model Training.ipynb b/4-Ridge Lasso And Elasticnet/Ridge Lassso Elastic Regression Practicals/Model Training.ipynb
index 2b72cd16..2c871d3a 100644
--- a/4-Ridge Lasso And Elasticnet/Ridge Lassso Elastic Regression Practicals/Model Training.ipynb	
+++ b/4-Ridge Lasso And Elasticnet/Ridge Lassso Elastic Regression Practicals/Model Training.ipynb	
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -176,7 +176,7 @@
        "4  0.5  not fire          0  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +198,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -353,7 +353,7 @@
        "4       0  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,24 +364,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "Classes\n",
        "fire             131\n",
        "not fire         101\n",
        "fire               4\n",
-       "not fire           2\n",
        "fire               2\n",
+       "not fire           2\n",
        "not fire           1\n",
        "not fire           1\n",
        "not fire           1\n",
-       "Name: Classes, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -392,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -536,7 +537,7 @@
        "242       1  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -547,18 +548,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "Classes\n",
        "1    137\n",
        "0    106\n",
-       "Name: Classes, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -569,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -580,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -701,7 +703,7 @@
        "4           27  77  16   0.0  64.8  3.0  14.2  1.2  3.9        0       0"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -712,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -732,7 +734,7 @@
        "Name: FWI, Length: 243, dtype: float64"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -743,7 +745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -754,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -763,7 +765,7 @@
        "((182, 11), (61, 11))"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -774,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -998,7 +1000,7 @@
        "Region      -0.060838  0.296441  0.114897  0.188837  1.000000  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1010,30 +1012,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:>"
+       "<Axes: >"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1045,7 +1035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1062,7 +1052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1072,27 +1062,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4163: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  return super().drop(\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "{'BUI', 'DC'}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/plain": [
        "((182, 9), (61, 9))"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1113,7 +1112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1125,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1146,7 +1145,7 @@
        "        -1.10431526, -0.98907071]])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1164,7 +1163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1173,21 +1172,9 @@
        "Text(0.5, 1.0, 'X_train After Scaling')"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1209,38 +1196,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean absolute error 0.5468236465249988\n",
+      "Mean absolute error 0.5468236465249977\n",
       "R2 Score 0.9847657384266951\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1a558dd8c70>"
+       "<matplotlib.collections.PathCollection at 0x14f03c90830>"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1266,7 +1241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1280,24 +1255,12 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1a558e84250>"
+       "<matplotlib.collections.PathCollection at 0x14f03e17b60>"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1323,16 +1286,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LassoCV(cv=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LassoCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LassoCV.html\">?<span>Documentation for LassoCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LassoCV(cv=5)</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "LassoCV(cv=5)"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1345,7 +1715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1354,7 +1724,7 @@
        "0.05725391318234405"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1365,40 +1735,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([7.05853002e+00, 6.58280872e+00, 6.13914944e+00, 5.72539132e+00,\n",
-       "       5.33951911e+00, 4.97965339e+00, 4.64404142e+00, 4.33104857e+00,\n",
-       "       4.03915039e+00, 3.76692517e+00, 3.51304702e+00, 3.27627941e+00,\n",
-       "       3.05546914e+00, 2.84954075e+00, 2.65749124e+00, 2.47838523e+00,\n",
-       "       2.31135036e+00, 2.15557308e+00, 2.01029467e+00, 1.87480753e+00,\n",
-       "       1.74845178e+00, 1.63061198e+00, 1.52071419e+00, 1.41822315e+00,\n",
-       "       1.32263965e+00, 1.23349817e+00, 1.15036452e+00, 1.07283380e+00,\n",
-       "       1.00052839e+00, 9.33096128e-01, 8.70208572e-01, 8.11559427e-01,\n",
-       "       7.56863037e-01, 7.05853002e-01, 6.58280872e-01, 6.13914944e-01,\n",
-       "       5.72539132e-01, 5.33951911e-01, 4.97965339e-01, 4.64404142e-01,\n",
-       "       4.33104857e-01, 4.03915039e-01, 3.76692517e-01, 3.51304702e-01,\n",
-       "       3.27627941e-01, 3.05546914e-01, 2.84954075e-01, 2.65749124e-01,\n",
-       "       2.47838523e-01, 2.31135036e-01, 2.15557308e-01, 2.01029467e-01,\n",
-       "       1.87480753e-01, 1.74845178e-01, 1.63061198e-01, 1.52071419e-01,\n",
-       "       1.41822315e-01, 1.32263965e-01, 1.23349817e-01, 1.15036452e-01,\n",
-       "       1.07283380e-01, 1.00052839e-01, 9.33096128e-02, 8.70208572e-02,\n",
-       "       8.11559427e-02, 7.56863037e-02, 7.05853002e-02, 6.58280872e-02,\n",
-       "       6.13914944e-02, 5.72539132e-02, 5.33951911e-02, 4.97965339e-02,\n",
-       "       4.64404142e-02, 4.33104857e-02, 4.03915039e-02, 3.76692517e-02,\n",
-       "       3.51304702e-02, 3.27627941e-02, 3.05546914e-02, 2.84954075e-02,\n",
-       "       2.65749124e-02, 2.47838523e-02, 2.31135036e-02, 2.15557308e-02,\n",
-       "       2.01029467e-02, 1.87480753e-02, 1.74845178e-02, 1.63061198e-02,\n",
-       "       1.52071419e-02, 1.41822315e-02, 1.32263965e-02, 1.23349817e-02,\n",
-       "       1.15036452e-02, 1.07283380e-02, 1.00052839e-02, 9.33096128e-03,\n",
-       "       8.70208572e-03, 8.11559427e-03, 7.56863037e-03, 7.05853002e-03])"
+       "array([7.05853002, 6.58280872, 6.13914944, 5.72539132, 5.33951911,\n",
+       "       4.97965339, 4.64404142, 4.33104857, 4.03915039, 3.76692517,\n",
+       "       3.51304702, 3.27627941, 3.05546914, 2.84954075, 2.65749124,\n",
+       "       2.47838523, 2.31135036, 2.15557308, 2.01029467, 1.87480753,\n",
+       "       1.74845178, 1.63061198, 1.52071419, 1.41822315, 1.32263965,\n",
+       "       1.23349817, 1.15036452, 1.0728338 , 1.00052839, 0.93309613,\n",
+       "       0.87020857, 0.81155943, 0.75686304, 0.705853  , 0.65828087,\n",
+       "       0.61391494, 0.57253913, 0.53395191, 0.49796534, 0.46440414,\n",
+       "       0.43310486, 0.40391504, 0.37669252, 0.3513047 , 0.32762794,\n",
+       "       0.30554691, 0.28495408, 0.26574912, 0.24783852, 0.23113504,\n",
+       "       0.21555731, 0.20102947, 0.18748075, 0.17484518, 0.1630612 ,\n",
+       "       0.15207142, 0.14182231, 0.13226397, 0.12334982, 0.11503645,\n",
+       "       0.10728338, 0.10005284, 0.09330961, 0.08702086, 0.08115594,\n",
+       "       0.0756863 , 0.0705853 , 0.06582809, 0.06139149, 0.05725391,\n",
+       "       0.05339519, 0.04979653, 0.04644041, 0.04331049, 0.0403915 ,\n",
+       "       0.03766925, 0.03513047, 0.03276279, 0.03055469, 0.02849541,\n",
+       "       0.02657491, 0.02478385, 0.0231135 , 0.02155573, 0.02010295,\n",
+       "       0.01874808, 0.01748452, 0.01630612, 0.01520714, 0.01418223,\n",
+       "       0.0132264 , 0.01233498, 0.01150365, 0.01072834, 0.01000528,\n",
+       "       0.00933096, 0.00870209, 0.00811559, 0.00756863, 0.00705853])"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1409,7 +1774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1517,7 +1882,7 @@
        "       [ 0.70711086,  1.50300182,  8.01992921,  1.90919915,  0.82842365]])"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1528,28 +1893,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean absolute error 0.6199701158263436\n",
+      "Mean absolute error 0.6199701158263433\n",
       "R2 Score 0.9820946715928275\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1570,38 +1923,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean absolute error 0.5642305340105719\n",
+      "Mean absolute error 0.5642305340105715\n",
       "R2 Score 0.9842993364555513\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1a558ef4be0>"
+       "<matplotlib.collections.PathCollection at 0x14f015852b0>"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1620,28 +1961,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean absolute error 0.5642305340105719\n",
+      "Mean absolute error 0.5642305340105715\n",
       "R2 Score 0.9842993364555513\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1658,22 +1987,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'alphas': array([ 0.1,  1. , 10. ]),\n",
+       "{'alpha_per_target': False,\n",
+       " 'alphas': (0.1, 1.0, 10.0),\n",
        " 'cv': 5,\n",
        " 'fit_intercept': True,\n",
        " 'gcv_mode': None,\n",
-       " 'normalize': False,\n",
        " 'scoring': None,\n",
-       " 'store_cv_values': False}"
+       " 'store_cv_results': None,\n",
+       " 'store_cv_values': 'deprecated'}"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1691,7 +2021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1705,24 +2035,12 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1a558f5bd60>"
+       "<matplotlib.collections.PathCollection at 0x14f03cae4b0>"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUSUlEQVR4nO3df2zc9X3H8dc7xtAjVHUyDEoMIXRC6QZZ4+7EkDJNQEdDpxbcTKVF3ZRp1dI/igSoipYgJMLaKdEyOv6ZqqUqaqbSNGgEk2poAQETKxsMB4caGjIQCiGXKDECtzCsYZz3/vD3wuX8/d6d777fu/t87/mQkO2vz77PV1/yyifvzy9zdwEAwrOo0w0AADSHAAeAQBHgABAoAhwAAkWAA0Cgzmnnm1144YW+cuXKdr4lAATvwIEDb7v7YPX1tgb4ypUrNTY21s63BIDgmdmbcdcpoQBAoAhwAAgUAQ4AgSLAASBQBDgABKqts1AAoNeMjpe0Y/9hHZ+a1vKBgjatW6WR4aFUfjcBDgAZGR0vacveCU3PzEqSSlPT2rJ3QpJSCXFKKACQkR37D58J77LpmVnt2H84ld9PgANARo5PTS/o+kIR4ACQkeUDhQVdXygCHAAysmndKhX6+866Vujv06Z1q1L5/QxiAkBGygOVzEIBgACNDA+lFtjVKKEAQKAIcAAIFAEOAIEiwAEgUAQ4AASKAAeAQBHgABAoAhwAAkWAA0CgCHAACBQBDgCBIsABIFB1A9zMLjWzp83skJm9Yma3R9eXmtkTZvZa9HFJ9s0FAJQ10gP/SNJ33P13JF0j6dtm9ruSNkt60t2vkPRk9DUAoE3qBri7n3D3F6PP35N0SNKQpJsl7YpetkvSSEZtBADEWFAN3MxWShqW9Lyki939hDQX8pIuSviZjWY2ZmZjk5OTLTYXAFDWcICb2QWSHpZ0h7v/ptGfc/ed7l509+Lg4GAzbQQAxGgowM2sX3Ph/aC7740unzSzZdH3l0k6lU0TAQBxGpmFYpJ+JOmQu3+/4lv7JG2IPt8g6dH0mwcASNLImZhrJf25pAkzOxhdu0vSdkkPmdk3JR2V9NVMWggAiFU3wN39F5Is4dufT7c5AIBGsRITAAJFgANAoAhwAAgUAQ4AgSLAASBQBDgABIoAB4BAEeAAECgCHAACRYADQKAIcAAIFAEOAIEiwAEgUAQ4AASKAAeAQDVyoAOAwI2Ol7Rj/2Edn5rW8oGCNq1bpZHhoU43Cy0iwIGcGx0vacveCU3PzEqSSlPT2rJ3QpII8cBRQgFybsf+w2fCu2x6ZlY79h/uUIuQFgIcyLnjU9MLuo5wEOBAzi0fKCzoOsJBgAM5t2ndKhX6+866Vujv06Z1qzrUIqSFQUwg58oDlcxCyR8CHOgBI8NDBHYOUUIBgEAR4AAQKAIcAAJFgANAoAhwAAgUAQ4AgWIaIdAD2I0wnwhwIOfYjTC/KKEAOcduhPlVtwduZg9I+pKkU+5+VXRtq6S/kjQZvewud38sq0YCaFx1uaTEboS51UgP/MeSboy5/g/uvib6j/AGukC5XFKampZrrlxiCa9lN8Lw1e2Bu/szZrayDW0BcqNTg4Zx5RKXZNHHMnYjzIdWauC3mdkvzewBM1uS9CIz22hmY2Y2Njk5mfQyIDfiesFb9k5odLyU+XsnlUVc0tBAQRZ93LZ+NQOYOdDsLJQfSPqu5v6/+K6k+yT9ZdwL3X2npJ2SVCwWPe41QJ7UGjTMOjSTat5DAwU9u/n6TN8b7ddUD9zdT7r7rLuflvRDSVen2ywgXJ08wozDG3pLUwFuZssqvvyKpJfTaQ4Qvk4eYTYyPKRt61dTLukRjUwj3C3pWkkXmtkxSfdIutbM1miuhHJE0reyayLQveIGKzetW3XWwhmpvb1gDm/oHebevrJ0sVj0sbGxtr0fkKXqFY7SXFBvW79aEkeYIT1mdsDdi9XXWUoPxGhkGmCtwcpnN19PYCNzBDgQKYd2efFL+d+mSXuHdHKwEpDYCwWQdPbcbensRS9S/N4hnRysBCQCHJAUXw6pVt2zZsoeOo0SCqDGyh7VPetyOYXBSnQKAY5caHXvkVq79knJPWum7KGTKKEgeGnsPRJXDinv4sdiGHQreuAIXhp7j1AOQYgIcAQvrel8lEMQGkooCB7T+dCrCHAEj+l86FWUUBC8LOrXnTpRB1gIAhy5kGb9unqTqqSl9ECnUUIBqtSa1QJ0E3rgCE7W5Q02qUIo6IEjKO04MJhZLQgFPXAEJasDgyt79QPn96t/kWnm9Md7EjKrBd2IAEdQsihvVA9avvvBjPr7TAOFfv16eoZZKOhaBDiCkrTpVCvljbhe/cysa/F55+jgPV9o+vcCWaMGjqBksWiHQUuEigBHUEaGh7Rt/WoNDRRkSmenQAYtESpKKAhO2ptObVq3KvZ0eQYt0e0IcPQ8tpJFqAhwQGwlizBRAweAQBHgABAoAhwAAkWAA0CgCHAACBQBDgCBIsABIFAEOAAEqu5CHjN7QNKXJJ1y96uia0sl7ZG0UtIRSbe4+7vZNRPdgsN+ge7RSA/8x5JurLq2WdKT7n6FpCejr5Fzd49O6M49BzM9DQdA4+oGuLs/I+mdqss3S9oVfb5L0ki6zUKjRsdLWrv9KV2++V+1dvtTmYXp6HhJDz53VF51ncN+gc5pdi+Ui939hCS5+wkzuyjphWa2UdJGSVqxYkWTb4c41SfJlHvEkpoqa9Qqj+zYf3heeJeVpqa1dvtTuu4zg3r61UnKK0CbZD6I6e473b3o7sXBwcGs366n1DofcqHqHRZc73CD0tS0fvLcUcorQBs1G+AnzWyZJEUfT6XXJDQqzZNk6v1l0MzhBpRXgGw1G+D7JG2IPt8g6dF0mgOp8bp2mifJ1PvLIO4os1Z+L4DW1Q1wM9st6b8krTKzY2b2TUnbJd1gZq9JuiH6GimoV8qolOb5kPX+Mqg+yqzPrKXfC6B1dQcx3f3WhG99PuW2QLVLGdUDgs2cJJM0UNnIsWKVhx5UD6DG4VgyIFucyNNlFlrXXshJMo3MWmn0L4O41zMLBWgvArzLLB8oqBQT1mmUIur17hd6rBjHkAGdRYB3mbROSI8rlaQ5awVA5xHgXSaNE9KTSiWfKvRranpm3usZaATCRIB3UNKA4kLr2tW/I6lUMj0zK5POWlFpkq77DAusgBAR4G1WDtzS1PRZYdrMMviknnatmSHVy+Fd0sMHSipetpR6NhAY9gNvo8o53tL8MF3oysWknnajc7SbfV8A3YEeeIaqyxsffPhRzd6xdPaAYr29t5MGH2fdVejvq/teSe8LIAz0wDMSt6Ly3Q/mDyBWKw8oxv38HXsOas29j59ZlZk0+Dg0UDizarJRDGQC4SHAMxJX3qincrpg0s9PTc+cWVpfayn9yPCQnt18ve7/2pq6e5iwYhIIEwGekUZLEuVqdbnXXC6R1Pr5ysU3lfuTVP8Oaf4eJkMDBf3ZNStq/gyAMJh70jb96SsWiz42Nta29+uktdufil1ROVDo1+Lzzomta1fWvBeZabbOsxliuTrQE8zsgLsXq68ziJmRpBWVW2+6MjZwq6cE1gtvqfUTeACEjQDPSNKKSmmud17dA0+qeS8y6XSNLE/aqRBA/hHgGapeUVlrN8Ckmre7dP/X1pxZ/BOHKYBAb2IQs42SFt5856GXEg8MXj5QODOjJGlaIFMAgd5EgLdRrYU3caqn96V5Ag+A8FFCaaOkvb7jxM0wSWOnQgD5QYC3UdzMlDgm6dnN15853Dhut0IAIMDbqLoHnTTXe/lAoaHjzwD0NgK8zeodDFyuaS/kcGMAvYkAr6HeboCtqlXTvnPPwdifYcoggDICPEG7ShhJNe0sDzcGkA9MI0xQq4TRDkwZBFAPPfAEjZzgnmWJhSmDAOohwBPUK2G0o8TClEEAtVBCSVCvhHHvz1/paIkFAOiBJ6hVwhgdLyUej8YsEQDtQoDXkFTCqNXLZpYIgHahhNKEWr1sZokAaBcCvAlJveyBQj+DjgDahgBvQtIA59abruxQiwD0opZq4GZ2RNJ7kmYlfRR36GYeMUcbQDdIYxDzOnd/O4XfExTmaAPoNEooABCoVnvgLulxM3NJ/+TuO6tfYGYbJW2UpBUrVrT4dtnIetdBAMhCqwG+1t2Pm9lFkp4ws1fd/ZnKF0ShvlOSisVi0tm9HcPBCQBC1VIJxd2PRx9PSXpE0tVpNKqdknYdvGPPQa3d/pRGx0sdahkA1NZ0gJvZYjP7ZPlzSV+Q9HJaDWuXWotyyr1xQhxAN2qlhHKxpEfMrPx7furu/5ZKq1JWq8Zd76R4jjED0K2aDnB3f0PSZ1NsSybq1bgbOSmeDaoAdKPcTyOsd7LOyPCQtq1fraEam1CxQRWAbhT0boRJpZHK60nTXip71eVFObVOiQeAbhNsgCeVRsbefEcPHyjVLIlI8b1qlsgDCEmwAZ5UGtn9/Fua9drTzfsXWWKvmiXyAEIRbA08aWCxXnhLkizlxgBABwQb4EkDi31WP51nZp2zKwEEL9gAj9uT2yRd8+kl867HYWoggNAFG+Ajw0P6098fOqsa4pJePPprfW7Fp+pWSZgaCCB0wQ1iVk4RXGQ2b5rg9Mys/vONdxKnD0pMDQSQD0EFePXUwaQBy1rjmENMDQSQE0GVUOKmDgJArwoqwNMYeGSHQQB5EVSAL2Tgccn5/Yn7m1TuhQIAoQoqwOOmDsbp7zPd8+Ur9ezm6xNnozCNEEDoggrw8s6BA4X+2i+sGMRM6rUzjRBA6IIKcGkuxBefV3vyzMzpj1daxvXamUYIIA+CmkZY1kj5o/wadhgEkFdBBni9Y9DKryljh0EAeRRcCWV0vKT//b+Par6GEgmAXhBUD3x0vKRN//KSZmaTl1r2mWnb+tX0uAHkXlA98Ht//krN8C709+m+Wz5LeAPoCUH1wN/9YCbxe0vO79c9X76S8AbQM4Lqgddy/rnnEN4AekpQAV5rAQ8rKwH0mqACfOtNVyZ+j5WVAHpNUAEuSX2L5u9uUuuUeQDIq6ACfMf+w5o9PX8WygWfoP4NoPcEFeBJqy9rzU4BgLwKKsD7LH5z2KTrAJBnQQV40hmYSdcBIM+CCvCkE3aSrgNAngUV4OztDQAfaynAzexGMztsZq+b2ea0GpWkfCLP0EBBprmeNxtXAehVTe+FYmZ9kv5R0g2Sjkl6wcz2ufuv0mpc2eh4iQMZAKBKKz3wqyW97u5vuPuHkn4m6eZ0mvWx0fGStuydUGlqWq65qYR37jmou0cn0n4rAAhKKwE+JOmtiq+PRddStWP/YU3PzJ51zSU9+NxRjY6X0n47AAhGKwEeN/l63nw+M9toZmNmNjY5ObngN0napMqlMwcXA0AvaiXAj0m6tOLrSyQdr36Ru+9096K7FwcHBxf8JrU2qWIHQgC9rJUAf0HSFWZ2uZmdK+nrkval06yPbVq3KrarL7EDIYDe1nSAu/tHkm6TtF/SIUkPufsraTWsbGR4SN+4ZsW8EGf+N4Be19KRau7+mKTHUmpLou+NrFbxsqVMJQSACl1/JubdoxPa/fxbmnVXn5m+cc0KfW9kdaebBQAd19UBfvfohH7y3NEzX8+6n/maEAfQ67p6L5Tdz7+1oOsA0Eu6OsDZPhYAknV1gHOAAwAk6+oAv/UPLl3QdQDoJV0d4MXLlqr6EPpFNncdAHpdVwf4jv2HVX0I/WlnDxQAkLo8wJP2OmEPFADo8gBP2uuEPVAAoMsDnDMwASBZV6/ELO91wh4oADBfVwe4NBfiBDYAzNfVJRQAQDICHAACRYADQKAIcAAIFAEOAIEyb+PWrGY2KenNJn/8Qklvp9icTsnLfUj5uRfuo7vk5T6k9O7lMncfrL7Y1gBvhZmNuXux0+1oVV7uQ8rPvXAf3SUv9yFlfy+UUAAgUAQ4AAQqpADf2ekGpCQv9yHl5164j+6Sl/uQMr6XYGrgAICzhdQDBwBUIMABIFBBBLiZ3Whmh83sdTPb3On2NMvMjpjZhJkdNLOxTrenUWb2gJmdMrOXK64tNbMnzOy16OOSTraxUQn3stXMStFzOWhmf9LJNtZjZpea2dNmdsjMXjGz26PrwT2TGvcS2jP5hJn9t5m9FN3HvdH1TJ9J19fAzaxP0v9IukHSMUkvSLrV3X/V0YY1wcyOSCq6e1CLFMzsjyS9L+mf3f2q6NrfSXrH3bdHf6kucfe/7mQ7G5FwL1slve/uf9/JtjXKzJZJWubuL5rZJyUdkDQi6S8U2DOpcS+3KKxnYpIWu/v7ZtYv6ReSbpe0Xhk+kxB64FdLet3d33D3DyX9TNLNHW5TT3H3ZyS9U3X5Zkm7os93ae4PXddLuJeguPsJd38x+vw9SYckDSnAZ1LjXoLic96PvuyP/nNl/ExCCPAhSW9VfH1MAT7giEt63MwOmNnGTjemRRe7+wlp7g+hpIs63J5W3WZmv4xKLF1feigzs5WShiU9r8CfSdW9SIE9EzPrM7ODkk5JesLdM38mIQS4xVzr7rpPsrXu/jlJX5T07eif8+i8H0j6bUlrJJ2QdF9HW9MgM7tA0sOS7nD333S6Pa2IuZfgnom7z7r7GkmXSLrazK7K+j1DCPBjki6t+PoSScc71JaWuPvx6OMpSY9orjwUqpNR/bJcxzzV4fY0zd1PRn/4Tkv6oQJ4LlGd9WFJD7r73uhykM8k7l5CfCZl7j4l6d8l3aiMn0kIAf6CpCvM7HIzO1fS1yXt63CbFszMFkeDNDKzxZK+IOnl2j/V1fZJ2hB9vkHSox1sS0vKf8AiX1GXP5dowOxHkg65+/crvhXcM0m6lwCfyaCZDUSfFyT9saRXlfEz6fpZKJIUTSG6X1KfpAfc/W8726KFM7NPa67XLc0dJv3TUO7DzHZLulZzW2OelHSPpFFJD0laIemopK+6e9cPDibcy7Wa+6e6Szoi6VvlumU3MrM/lPQfkiYknY4u36W52nFQz6TGvdyqsJ7J72lukLJPcx3jh9z9b8zst5ThMwkiwAEA84VQQgEAxCDAASBQBDgABIoAB4BAEeAAECgCHAACRYADQKD+HypnPwbFf4uqAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1741,28 +2059,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean absolute error 0.6575946731430905\n",
+      "Mean absolute error 0.6575946731430904\n",
       "R2 Score 0.9814217587854941\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1779,40 +2085,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1.41170600e+01, 1.31656174e+01, 1.22782989e+01, 1.14507826e+01,\n",
-       "       1.06790382e+01, 9.95930678e+00, 9.28808283e+00, 8.66209714e+00,\n",
-       "       8.07830078e+00, 7.53385034e+00, 7.02609405e+00, 6.55255882e+00,\n",
-       "       6.11093829e+00, 5.69908150e+00, 5.31498248e+00, 4.95677045e+00,\n",
-       "       4.62270071e+00, 4.31114616e+00, 4.02058933e+00, 3.74961507e+00,\n",
-       "       3.49690356e+00, 3.26122397e+00, 3.04142839e+00, 2.83644629e+00,\n",
-       "       2.64527931e+00, 2.46699633e+00, 2.30072904e+00, 2.14566760e+00,\n",
-       "       2.00105679e+00, 1.86619226e+00, 1.74041714e+00, 1.62311885e+00,\n",
-       "       1.51372607e+00, 1.41170600e+00, 1.31656174e+00, 1.22782989e+00,\n",
-       "       1.14507826e+00, 1.06790382e+00, 9.95930678e-01, 9.28808283e-01,\n",
-       "       8.66209714e-01, 8.07830078e-01, 7.53385034e-01, 7.02609405e-01,\n",
-       "       6.55255882e-01, 6.11093829e-01, 5.69908150e-01, 5.31498248e-01,\n",
-       "       4.95677045e-01, 4.62270071e-01, 4.31114616e-01, 4.02058933e-01,\n",
-       "       3.74961507e-01, 3.49690356e-01, 3.26122397e-01, 3.04142839e-01,\n",
-       "       2.83644629e-01, 2.64527931e-01, 2.46699633e-01, 2.30072904e-01,\n",
-       "       2.14566760e-01, 2.00105679e-01, 1.86619226e-01, 1.74041714e-01,\n",
-       "       1.62311885e-01, 1.51372607e-01, 1.41170600e-01, 1.31656174e-01,\n",
-       "       1.22782989e-01, 1.14507826e-01, 1.06790382e-01, 9.95930678e-02,\n",
-       "       9.28808283e-02, 8.66209714e-02, 8.07830078e-02, 7.53385034e-02,\n",
-       "       7.02609405e-02, 6.55255882e-02, 6.11093829e-02, 5.69908150e-02,\n",
-       "       5.31498248e-02, 4.95677045e-02, 4.62270071e-02, 4.31114616e-02,\n",
-       "       4.02058933e-02, 3.74961507e-02, 3.49690356e-02, 3.26122397e-02,\n",
-       "       3.04142839e-02, 2.83644629e-02, 2.64527931e-02, 2.46699633e-02,\n",
-       "       2.30072904e-02, 2.14566760e-02, 2.00105679e-02, 1.86619226e-02,\n",
-       "       1.74041714e-02, 1.62311885e-02, 1.51372607e-02, 1.41170600e-02])"
+       "array([14.11706004, 13.16561744, 12.27829889, 11.45078264, 10.67903821,\n",
+       "        9.95930678,  9.28808283,  8.66209714,  8.07830078,  7.53385034,\n",
+       "        7.02609405,  6.55255882,  6.11093829,  5.6990815 ,  5.31498248,\n",
+       "        4.95677045,  4.62270071,  4.31114616,  4.02058933,  3.74961507,\n",
+       "        3.49690356,  3.26122397,  3.04142839,  2.83644629,  2.64527931,\n",
+       "        2.46699633,  2.30072904,  2.1456676 ,  2.00105679,  1.86619226,\n",
+       "        1.74041714,  1.62311885,  1.51372607,  1.411706  ,  1.31656174,\n",
+       "        1.22782989,  1.14507826,  1.06790382,  0.99593068,  0.92880828,\n",
+       "        0.86620971,  0.80783008,  0.75338503,  0.7026094 ,  0.65525588,\n",
+       "        0.61109383,  0.56990815,  0.53149825,  0.49567705,  0.46227007,\n",
+       "        0.43111462,  0.40205893,  0.37496151,  0.34969036,  0.3261224 ,\n",
+       "        0.30414284,  0.28364463,  0.26452793,  0.24669963,  0.2300729 ,\n",
+       "        0.21456676,  0.20010568,  0.18661923,  0.17404171,  0.16231189,\n",
+       "        0.15137261,  0.1411706 ,  0.13165617,  0.12278299,  0.11450783,\n",
+       "        0.10679038,  0.09959307,  0.09288083,  0.08662097,  0.08078301,\n",
+       "        0.0753385 ,  0.07026094,  0.06552559,  0.06110938,  0.05699082,\n",
+       "        0.05314982,  0.0495677 ,  0.04622701,  0.04311146,  0.04020589,\n",
+       "        0.03749615,  0.03496904,  0.03261224,  0.03041428,  0.02836446,\n",
+       "        0.02645279,  0.02466996,  0.02300729,  0.02145668,  0.02001057,\n",
+       "        0.01866192,  0.01740417,  0.01623119,  0.01513726,  0.01411706])"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1820,32 +2121,11 @@
    "source": [
     "elasticcv.alphas_"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -1859,7 +2139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/4-Ridge Lasso And Elasticnet/Ridge Lassso Elastic Regression Practicals/Ridge, Lasso Regression.ipynb b/4-Ridge Lasso And Elasticnet/Ridge Lassso Elastic Regression Practicals/Ridge, Lasso Regression.ipynb
index d6efe934..9e668271 100644
--- a/4-Ridge Lasso And Elasticnet/Ridge Lassso Elastic Regression Practicals/Ridge, Lasso Regression.ipynb	
+++ b/4-Ridge Lasso And Elasticnet/Ridge Lassso Elastic Regression Practicals/Ridge, Lasso Regression.ipynb	
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +208,7 @@
        "4  not fire     "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 42,
    "metadata": {
     "scrolled": true
    },
@@ -354,7 +354,7 @@
        "167  88.9  12.9  14.6 9  12.5  10.4  fire          NaN  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -589,7 +589,7 @@
        "4  not fire          0  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -600,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -624,7 +624,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -635,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -645,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -797,7 +797,7 @@
        "4  not fire          0  "
       ]
      },
-     "execution_count": 33,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -808,7 +808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -832,7 +832,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -843,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -915,7 +915,7 @@
        "122  FWI  Classes         1  "
       ]
      },
-     "execution_count": 35,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -926,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -936,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1008,7 +1008,7 @@
        "122  not fire          1  "
       ]
      },
-     "execution_count": 37,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1030,7 +1030,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1041,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1052,7 +1052,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1065,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1110,7 +1110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1121,7 +1121,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1132,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1141,7 +1141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1186,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1195,7 +1195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1206,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1244,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -1253,7 +1253,7 @@
        "['Rain', 'FFMC', 'DMC', 'DC', 'ISI', 'BUI', 'FWI', 'Classes']"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1264,7 +1264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1477,7 +1477,7 @@
        "max     31.100000    1.000000  "
       ]
      },
-     "execution_count": 53,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1488,7 +1488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1640,7 +1640,7 @@
        "4  0.5  not fire          0  "
       ]
      },
-     "execution_count": 54,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1651,7 +1651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1668,7 +1668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1678,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -1812,7 +1812,7 @@
        "4       0  "
       ]
      },
-     "execution_count": 118,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1823,18 +1823,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1    137\n",
-       "0    106\n",
-       "Name: Classes, dtype: int64"
+       "Classes\n",
+       "fire             131\n",
+       "not fire         101\n",
+       "fire               4\n",
+       "fire               2\n",
+       "not fire           2\n",
+       "not fire           1\n",
+       "not fire           1\n",
+       "not fire           1\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1846,7 +1853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1856,7 +1863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -1983,7 +1990,7 @@
        "4           27  77  16   0.0  64.8  3.0  14.2  1.2  3.9  0.5        0       0"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1994,7 +2001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -2128,7 +2135,7 @@
        "242       1  "
       ]
      },
-     "execution_count": 122,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2139,18 +2146,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "Classes\n",
        "1    137\n",
        "0    106\n",
-       "Name: Classes, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2161,14 +2169,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
-       "<Figure size 1440x1080 with 12 Axes>"
+       "<Figure size 2000x1500 with 12 Axes>"
       ]
      },
      "metadata": {},
@@ -2177,14 +2185,14 @@
    ],
    "source": [
     "## Plot desnity plot for all features\n",
-    "plt.style.use('seaborn')\n",
+    "plt.style.use('seaborn-v0_8')\n",
     "df_copy.hist(bins=50,figsize=(20,15))\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2194,12 +2202,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 864x504 with 1 Axes>"
       ]
@@ -2228,7 +2236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2492,7 +2500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2507,7 +2515,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 576x396 with 2 Axes>"
       ]
@@ -2522,7 +2530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2545,7 +2553,7 @@
     },
     {
      "data": {
-      "image/png": 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22Wdn06ZNueSSS5Icf//YnTt3ejoWuoBgQhdZuHBhBgcHc/nllyc5/p6Bc+fOzcMPPyyY0DDBhC6ycOHC/POf/8zChQvHP3b55ZfnyJEjeec739ngZIB3KwGAAneYAFAgmABQIJgAUCCYAFAgmABQIJgAUCCYAFAgmABQ8H+I97EQ2wQqeQAAAABJRU5ErkJggg==\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 576x396 with 1 Axes>"
       ]
@@ -2561,7 +2569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2724,7 +2732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2733,7 +2741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2748,7 +2756,7 @@
     },
     {
      "data": {
-      "image/png": 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Li/r27av9+/fr+PHj6tmzpyTp/PnzOnLkiKKjoxUfH6/58+erWrVqatGihb3fOp8yBAAAADRv3lwbNmzQkSNHJEm5ubkaO3asypYtK0nau3evVq9erSlTpmjEiBHKz8+X2WxWUlKSgoOD9dVXX6l169aaMWOGUlJSVKFCBc2cOVN9+/bVpEmTVK1aNdWoUUOzZ8/WnDlzFBYWppo1a2rBggV66623lJCQIElatWqV3d87ZwgAALhDTFzxidIyzzg6Ronk41leg1v1dXQM3ME8PT01duxYDR8+XGazWVlZWWrevLmqV6+ubdu26eGHH9Y999yjsLAwlSpVSj4+Pjp16pTq1q2rIUOGaNq0aXJyclJ0dLQefPBBDRw4UF999ZWcnJwUERGhWrVq6cknn1SXLl10+fJlBQQEqGLFigoICFCvXr1UpkwZeXh4qFmzZnZ/7yaz2Wy2+6wAAOCWRS4apZMZaY6OUSJV9PbRuBeGOzoGcEfikiEAAADAwGx6yVD79u3l5eUlSapUqZJef/11RUVFyWQy6ZFHHlFsbKzdP1YJAAAAwP/YrBDk5ORIkubMmWN57vXXX9eAAQMUEhKimJgYJSUlqWXLlraKAAAAAMAKm/3z/N69e5Wdna3evXurR48e+s9//qPU1FTVr19fktSkSRNt3LjRVtMDAAAAuAk2O0NQunRpvfLKK+rUqZMOHz6sV199VWazWSaTSdLVGz1cuHCh0DFSU1N16dIlW0UEAOCO4ebm5ugIJd7u3bstVygAuF5wcHCB22xWCHx9ffXwww/LZDLJ19dXZcqUUWpqqmV7VlaW1buw1alTx1bxAAC48+xb4ugEJZq/v7+jIwB3JJsVgm+++Ub79+/Xu+++q5MnTyozM1MNGzZUSkqKQkJCtH79eoWGhtpqegAAABjY2BlrdCo9s1jGqlDOU1F9niqWsa5ZsGCBwsLC5OrqankuIyNDr776qjw8PBQaGqrQ0FAFBAQU67w3YrNC0LFjR0VHR6tLly4ymUwaPXq0ypYtqxEjRlju1taqVStbTQ8AAAADO5WeqT9OF355uiPFx8erffv21z23f/9+VahQQdOmTbNrFpsVglKlSumDDz742/PXbssMAAAA3C0SExO1bt06Xbp0SUeOHNGrr76qsLAw7dmzR3FxcXJ2dpabm5vi4uKUnJystLQ0DRw4UNOnT5ckXb58WXFxcTp16pQ+/PBDHT9+XG3bttXp06e1aNEi5efnq1+/fjp37pxmzZolJycnBQcHa/DgwUXOzk0AAAAAgGKQmZmp+Ph4ffLJJ/rss88kScOHD1dMTIwSEhLUpUsXjR07Vp06dZKPj48mT55seW2pUqU0dOhQhYaGql+/fteN6+3trfnz56t27dqaNm2aZs2apfnz5+vkyZNKTk4ucm6b3pgMAAAAMIpatWpJkh544AFdvnxZknTq1CnVrl1bkvTEE0/c8Aoaa3x9fSVJR44cUXp6usLDwyVd/ZCe33//vci5KQQAAABAMbj28fp/VqFCBe3du1e1atXS1q1bVbVqVcu++fn5NzWuk9PVi3oqVaqkBx54QDNnzpSrq6sSExMtZaMoKAQAAAC461Qo51kixho1apTi4uJkNpvl7Oys0aNHS5Lq1aun8PBwzZ49+4ZF4kbKlSunnj17qnv37rpy5YoeeughtWnT5razXWMym83mIo8CAABsLnLRKJ3MSHN0jBKporePxr0w3NExgDsSi4oBAAAAA6MQAAAAAAZGIQAAAAAMjEIAAAAAGBiFAAAAADAwCgEAAABgYNyHAAAAAHediSs+UVrmmWIZy8ezvAa36lvoPleuXFF4eLgOHDiggQMHqkOHDsUytz1QCAAAAHDXScs8Y9f7dqSlpens2bNav3693eYsLhQCAAAAoIhGjBihw4cPKyYmRrVr11a1atU0ceJEubq66sUXX9SDDz6oyZMny9nZWZUrV9Z7770nV1dXR8eWxBoCAAAAoMhiY2NVo0YN+fj4WJ7LycnRvHnz9Pzzz2vEiBH66KOPlJCQoIoVK2rx4sUOTHs9zhAAAAAANuDr6ytJSk9P16lTpzRgwABJ0qVLl9SwYUMHJrsehQAAAACwASenqxfjlC1bVvfff7+mT58uLy8vJSUlyd3d3cHp/odCAAAAgLuOj2f5EjOWk5OThg0bpvDwcJnNZnl4eGj8+PHFlK7oTGaz2ezoEAAAwLrIRaPs+qkpd5KK3j4a98JwR8cA7kgsKgYAAAAMjEIAAAAAGBiFAAAAADAwCgEAAABgYBQCAAAAwMAoBAAAAICBUQgAAAAAA6MQAAAAAAZGIQAAAAAMjEIAAAAAGBiFAAAAADAwCgEAAABgYBQCAAAAwMAoBAAAAICBUQgAAAAAA6MQAAAAAAZGIQAAAAAMjEIAAAAAGBiFAAAAADAwCgEAAABgYBQCAAAAwMAoBAAAAICBUQgAAAAAA6MQAAAAAAZGIQAAAAAMjEIAAAAAGBiFAAAAADAwCgEAAABgYBQCAAAAwMAoBAAAAICBUQgAAAAAA6MQAAAAAAZGIQAAAAAMjEIAAAAAGJhNC8GZM2fUtGlTHTp0SL/99pu6dOmirl27KjY2Vvn5+bacGgAAAMBNsFkhyM3NVUxMjEqXLi1JGjNmjAYMGKB58+bJbDYrKSnJVlMDAAAAuEk2KwTjxo3TSy+9pAoVKkiSUlNTVb9+fUlSkyZNtHHjRltNDQAAAOAmudhi0MTERJUrV06NGzfWZ599Jkkym80ymUySJA8PD124cMHqOKmpqbp06ZItIgIAcEdxc3NzdIQSb/fu3crJyXF0DKBECg4OLnCbTQrBokWLZDKZtGnTJv3yyy+KjIxUenq6ZXtWVpa8vb2tjlOnTh1bxAMA4M60b4mjE5Ro/v7+jo4A3JFsUgjmzp1r+bp79+569913NWHCBKWkpCgkJETr169XaGioLaYGAAAAcAvs9rGjkZGRmjZtmjp37qzc3Fy1atXKXlMDAAAAKIBNzhD82Zw5cyxfJyQk2Ho6AAAAALeAG5MBAAAABkYhAAAAAAyMQgAAAAAYGIUAAAAAMDAKAQAAAGBgFAIAAADAwCgEAAAAgIHZ/D4EAOAIE1d8orTMM46OUWL5eJbX4FZ9HR0DAFACUAgA3JXSMs/oZEaao2MAAFDicckQAAAAYGAUAgAAAMDAKAQAAACAgVEIAAAAAANjUTEAAICNjJ2xRqfSMx0do0SqUM5TUX2ecnQMiEIAAABgM6fSM/XH6QuOjgEUikuGAAAAAAOjEAAAAAAGRiEAAAAADIxCAAAAABgYhQAAAAAwMAoBAAAAYGAUAgAAAMDAKAQAAACAgVEIAAAAAAOjEAAAAAAGRiEAAAAADMzF0QHsZeyMNTqVnunoGCVShXKeiurzlKNjAAAAwAEMUwhOpWfqj9MXHB0DAAAAKFG4ZAgAAAAwMAoBAAAAYGAUAgAAAMDAKAQAAACAgVEIAAAAAAOjEAAAAAAGRiEAAAAADIxCAAAAABiY1UJw6NAhTZ8+XZcuXVLHjh3VqFEjbdq0yR7ZAAAAANiY1TsVR0VFqVSpUvLx8dEff/yhSpUqacyYMfr222/tkQ8AAACADVk9Q7B//3717NlTGzZsUOvWrRUeHq7ffvvNHtkAAAAA2JjVQuDl5aVNmzZp06ZNCg4O1nfffacKFSrYIxsAAAAAG7NaCHr27Kl58+apcuXKatasmZKSkvTaa6/ZIxsAAAAAG7O6hqBPnz7q3LmzvLy8JEn/+te/VLlyZZsHAwAAAGB7Vs8QpKenKy4uTk2aNNHOnTs1YcIE/fHHH/bIBgAAAMDGrBaC2NhY7d27V2lpacrMzNSOHTs0ZMgQe2QDAAAAYGNWC8HmzZvVp08fSZKnp6eio6O1e/dumwcDAAAAYHtW1xD4+Pho69atkqR9+/Zp1apVrCEAAAAA7hJWC8HQoUPVv39/mc1mjRgxQm5ubpo+fbo9sgEAAACwMauF4L777tOqVau0c+dO5eXlKTAwUOXLl7dHNgAAAAA2ZrUQdOvWTXFxcWrbtq098gAAAACwI6uFoGnTplq+fLkqVqwoLy8vOTldXYdco0YNm4cDAAAAYFtWC8GyZcskSatXr5Ykmc1mmUwm/fLLL7ZNBgAAAMDmrBaCMWPG2CMHAAAAAAcosBAcPHhQlSpV0mOPPWbPPAAAAADsqMBC0K5dO82cOVO9evWSyWT623ZrlwxduXJFw4cP13//+185OztrzJgxMpvNioqKkslk0iOPPKLY2FjLmgQAAAAA9ldgIRg9erRq1Khx25cMrV27VpL09ddfKyUlxVIIBgwYoJCQEMXExCgpKUktW7a8veQAAAAAiqzAQuDi4iInJyd16NDhtgZu0aKFmjVrJkk6fvy47rvvPv3www+qX7++JKlJkyZKTk6mEAAAAAAOVGAheOeddzRz5kzVq1dPYWFh+uCDD1SzZs1bG9zFRZGRkVq1apU+/PBDrV271nL5kYeHhy5cuFDo61NTU3Xp0qVbmvNG3NzcijzG3W737t3KyclxdAygWPBn/ubw5/7OwnFtXUk7pvmeWVfSvmd3s+Dg4AK3FVgIzGaz5b8HDhy47R/Mx40bp8GDB+vFF1+87huelZUlb2/vQl9bp06d25rzhpb9Wnxj3YX8/f0dHQEoXvuWODpBicef+zsQx3WhSuQxzc8fhSqR3zMDKnRF740WE9+sJUuWKD4+XpJ0zz33yGQyyd/fXykpKZKk9evXq169erc9PgAAAICiK/Q+BAsXLtQPP/wgk8mkOXPmyMfHR9LVojBkyJBCB37mmWcUHR2tbt26KS8vT0OHDlX16tU1YsQITZo0SdWqVVOrVq2K750AAAAAuGWFFoJrdymWpO+++87y9c0UAnd3d02dOvVvzyckJNxqRgAAAAA2UmAh2Lt3rz1zAAAAAHAA7goGAAAAGBiFAAAAADCwAgvB0qVLlZaWpuPHj+vy5cv2zAQAAADATgosBHFxcdq2bZuefvppbd++3Z6ZAAAAANhJgYuKy5cvr0GDBslsNqtXr17XbTOZTNqzZ4/NwwEAAACwrQILweTJk7Vs2TLNmDFDrVq10gMPPGDPXAAAAADsoMBC8Oijj+rRRx9V9erV1bhxY3l6espkMsnNzc2e+QAAAADYkNVPGWratKmGDRumoKAgBQUF6c0331RGRoY9sgEAAACwMauFIC4uTjt37tQbb7yh1157Tdu3b1dcXJw9sgEAAACwsQIvGbomOTlZMTExeu655yRJvr6+FAIAAADgLmH1DIGbm5v2799veXzgwAHWEQAAAAB3CatnCLp27aqpU6fqX//6l0wmk44dO6aIiAh7ZAMAAABgY1YLQd++fXX//fdr5cqVMplMCg8P14svvmiPbAAAAABszGohkKQOHTqoQ4cOts4CAAAAwM6sriEAAAAAcPeiEAAAAAAGZrUQDBo0SLt27bJHFgAAAAB2ZrUQbN++XYcPH7ZDFAAAAAD2ZnVRcZUqVTRixAgtWrRI3t7eMplMMplMmjJlih3iAQAAALAlq4Xg6NGjKleunI4cOWJ5zmQy2TQUAAAAAPuwWgjWrFljjxwAAAAAHMDqGoL09HS98847atKkiXbu3Kl+/frp5MmT9sgGAAAAwMasFoLY2Fjt3btXaWlpunDhgnbs2KHBgwfbIxsAAAAAG7NaCDZv3qw+ffpIkry8vBQdHa3du3fbPBgAAAAA27O6hsDHx0dbt26VJO3bt0+rVq1S5cqVbR4M9sMacQAAAOOyWgiGDh2q/v37y2w2a8SIEXJzc9P06dPtkQ12cl8ZD01c8YnSMs84OkqJ5eNZXoNb9XV0DAAAgGJntRA0atRIq1ev1s8//6y8vDwFBgaqfPny9sgGO0rLPKOTGWmOjgEAAAA7s1oIsrOzNXfuXG3fvl3Ozs46fPiwevTooVKlStkjHwAAAAAbsloIoqKitGLFCvn5+cnJyUkffPCB9u3bpwkTJtgjHwAAAAAbsloIkpOTFR0drZdfflmSNHv2bE2dOtXmwQAAAADYntWPHW3QoIEuX75seXzu3Dk1btzYpqEAAAAA2EeBZwg6duwo6eoaglWrVikxMVF5eXk6evSoGjZsaLeAAAAAAGynwEJwzz33WP5brlw5y/P333+/cnJybJ8MAAAAgM0VWAjmzJljzxwAAAAAHMDqouLNmzfr888/V0ZGhsxmsyTJZDLpn//8p83DAQAAALAtq4XgnXfeUU5OjmrUqCEnJ6trkAEAAADcQawWAicnJ8XGxqpt27b2yAMAAAADMJkcnQDXWC0Eb7/9tj755BOdP3/estBYktq3b2/LXACsGDtjjU6lZzo6RonkV9VHcnZ0CgBAYe4r46GJKz5RWuYZR0cpsXw8y2twq742n8dqIVi1apUOHDigkSNHWp4zmUwUAsDBTqVn6o/TFxwdo0TyKeshlbO+HwDAsdIyz+hkRpqjYxjeTS0q7t27t55//nnWEAAAAAB3GauFoGXLlnJ2dpafn5898gAAAACwI6uFYOfOnTp48KASEhJUunRpSVcvGdq4caPNwwEAAACwLauFoE6dOvL397dHFgAAAAB2ZrUQjB071h45AAAAADiA1ULw5JNP/u05LhkCAAAA7g5WC0HTpk1lMplkNpuVnZ2tTZs2qUWLFvbIBgAAAMDGbvmSoXXr1ik+Pt5mgQAAAADYj9VCsG7dOsvXeXl5SkpK0v79+20aCgAAAIB9WC0Er732muWSoWu6d+9u01AAAAAA7MNqIZg9e7bla5PJpLJly6pGjRo2DQUAAADAPgosBAcPHpQklStX7obbKAUAAADAna/AQvDcc8/JZDLdcJvJZNKePXtsFgoAAACAfRRYCMaMGXPd48OHD2vWrFnKycnR448/Xuigubm5Gjp0qI4dO6bLly+rb9++qlGjhqKiomQymfTII48oNjZWTk5OxfMuAAAAANyWAgtBhw4dJEkXL17Uxx9/rNmzZ+vee+/Vu+++a9lWkG+//VZlypTRhAkTdPbsWXXo0EG1atXSgAEDFBISopiYGCUlJally5bF+24AAAAA3JJCFxV/++23mjhxotLT09WtWze99dZb8vT0tDpo69at1apVK8tjZ2dnpaamqn79+pKkJk2aKDk5mUIAAAAAOFiBhaBr167asWOHnJ2d1alTJ1WpUkVLly61bO/WrVuBg3p4eEiSMjMz1a9fPw0YMEDjxo2zrEnw8PDQhQsXrIZLTU3VpUuXbvrNFMTNza3IYwC7d+9WTk6Oo2NI4phG8ShJxzSs48+9dSXtmOZ7huJQXMd1cHBwgdsKLATbt2+XdPVmZPPnz79um8lkKrQQSNKJEycUERGhrl27ql27dpowYYJlW1ZWlry9va0Gr1OnjtV9btqyX4tvLBiSv7+/oyNcj2MaRVTijmlYt2+JoxOUaCXymObvahSRPY7rAgtBUlLSbQ96+vRp9e7dWzExMXryySclSY8++qhSUlIUEhKi9evXKzQ09LbHBwAAAFA8CiwEDz300G0P+umnnyojI0PTp0/X9OnTJUnDhg3TqFGjNGnSJFWrVu26NQYAAAAAHMPqnYpvx/DhwzV8+PC/PZ+QkGCL6QAAAADcJm4EAAAAABgYhQAAAAAwMAoBAAAAYGAUAgAAAMDAKAQAAACAgVEIAAAAAAOjEAAAAAAGRiEAAAAADIxCAAAAABgYhQAAAAAwMAoBAAAAYGAUAgAAAMDAKAQAAACAgVEIAAAAAAOjEAAAAAAGRiEAAAAADIxCAAAAABgYhQAAAAAwMAoBAAAAYGAUAgAAAMDAKAQAAACAgVEIAAAAAAOjEAAAAAAGRiEAAAAADIxCAAAAABgYhQAAAAAwMAoBAAAAYGAUAgAAAMDAKAQAAACAgVEIAAAAAAOjEAAAAAAGRiEAAAAADIxCAAAAABgYhQAAAAAwMAoBAAAAYGAUAgAAAMDAKAQAAACAgVEIAAAAAAOjEAAAAAAGRiEAAAAADIxCAAAAABgYhQAAAAAwMAoBAAAAYGAUAgAAAMDAKAQAAACAgVEIAAAAAAOjEAAAAAAGRiEAAAAADIxCAAAAABgYhQAAAAAwMBdHBwAAQJLGzlijU+mZjo5RYvlV9ZGcHZ0CwN2IQgAAKBFOpWfqj9MXHB2jxPIp6yGVc3QKAHcjLhkCAAAADMymheDnn39W9+7dJUm//fabunTpoq5duyo2Nlb5+fm2nBoAAADATbBZIfj88881fPhw5eTkSJLGjBmjAQMGaN68eTKbzUpKSrLV1AAAAABuks0KQZUqVTRt2jTL49TUVNWvX1+S1KRJE23cuNFWUwMAAAC4STZbVNyqVSsdPXrU8thsNstkMkmSPDw8dOGC9YVjqampunTpUpGzuLm5FXkMYPfu3ZYzXo7GMY3iwDGNu01JOqYljmsUj+I6roODgwvcZrdPGXJy+t/JiKysLHl7e1t9TZ06dYovwLJfi28sGJK/v7+jI1yPYxpFxDGNu02JO6YljmsUmT2Oa7t9ytCjjz6qlJQUSdL69etVr149e00NAAAAoAB2KwSRkZGaNm2aOnfurNzcXLVq1cpeUwMAAAAogE0vGapUqZIWLlwoSfL19VVCQoItpwMAAABwi7gxGQAAAGBgFAIAAADAwCgEAAAAgIFRCAAAAAADoxAAAAAABkYhAAAAAAyMQgAAAAAYGIUAAAAAMDAKAQAAAGBgFAIAAADAwCgEAAAAgIFRCAAAAAADoxAAAAAABkYhAAAAAAyMQgAAAAAYGIUAAAAAMDAKAQAAAGBgFAIAAADAwCgEAAAAgIFRCAAAAAADoxAAAAAABkYhAAAAAAyMQgAAAAAYGIUAAAAAMDAKAQAAAGBgFAIAAADAwCgEAAAAgIFRCAAAAAADoxAAAAAABkYhAAAAAAyMQgAAAAAYGIUAAAAAMDAKAQAAAGBgFAIAAADAwCgEAAAAgIFRCAAAAAADoxAAAAAABkYhAAAAAAyMQgAAAAAYGIUAAAAAMDAKAQAAAGBgFAIAAADAwCgEAAAAgIFRCAAAAAADoxAAAAAABkYhAAAAAAyMQgAAAAAYGIUAAAAAMDAKAQAAAGBgFAIAAADAwCgEAAAAgIFRCAAAAAADoxAAAAAABuZiz8ny8/P17rvvat++fSpVqpRGjRqlhx9+2J4RAAAAAPyJXc8QrF69WpcvX9aCBQv09ttva+zYsfacHgAAAMBf2PUMwU8//aTGjRtLkurWravdu3fbbe4K5TztNtedpty97nL1LO/oGCWaTwn8/eGYLhjHtHUc03cejuvClcRjWuK4LgzHtHX2Oq5NZrPZbJeZJA0bNkzPPPOMmjZtKklq1qyZVq9eLRcXu/YSAAAAAP+fXS8Z8vT0VFZWluVxfn4+ZQAAAABwILsWgqCgIK1fv16S9J///Ec1a9a05/QAAAAA/sKulwxd+5Sh/fv3y2w2a/To0apevbq9pgcAAADwF3YtBAAAAABKFm5MBgAAABgYhQAAAAAwMD7iB4qPj9eaNWuUm5urLl26qFOnTo6OBNy2xMRELV68WJKUk5OjX375RcnJyfL29nZwMuD25ObmKioqSseOHZOTk5Pi4uJYf4c73uXLlxUdHa3ff/9dnp6eiomJUdWqVR0dy7AoBAaXkpKiHTt2aP78+crOztbMmTMdHQkokrCwMIWFhUmSRo4cqRdeeIEygDvaunXrlJeXp6+//lrJycmaMmWKpk2b5uhYQJEsXLhQ7u7uWrhwoX799VfFxcXpiy++cHQsw+KSIYP78ccfVbNmTUVEROj1119Xs2bNHB0JKBa7du3SwYMH1blzZ0dHAYrE19dXV65cUX5+vjIzM7l/D+4KBw8eVJMmTSRJ1apV06FDhxycyNj4W8Xgzp49q+PHj+vTTz/V0aNH1bdvX/373/+WyWRydDSgSOLj4xUREeHoGECRubu769ixY2rTpo3Onj2rTz/91NGRgCKrXbu21q5dqxYtWujnn3/WyZMndeXKFTk7Ozs6miFxhsDgypQpo0aNGqlUqVKqVq2a3NzclJ6e7uhYQJFkZGTo119/VWhoqKOjAEU2a9YsNWrUSCtWrNDSpUsVFRWlnJwcR8cCiuSFF16Qp6enevToobVr16pOnTqUAQeiEBhccHCwNmzYILPZrJMnTyo7O1tlypRxdCygSLZu3aoGDRo4OgZQLLy9veXl5SVJuvfee5WXl6crV644OBVQNLt27VJwcLDmzJmjFi1aqHLlyo6OZGjcmAwaP368UlJSZDabNXDgQDVu3NjRkYAimTFjhlxcXNSzZ09HRwGKLCsrS0OHDlVaWppyc3PVo0cPtWvXztGxgCJJT0/XoEGDlJ2dLS8vL73//vuqWLGio2MZFoUAAAAAMDAuGQIAAAAMjEIAAAAAGBiFAAAAADAwCgEAAABgYBQCAECx4rMqAODOQiEAAANKSUmRn5+f/Pz8tHbtWknSnj17LM8lJibe1rj79+9Xly5dLI/9/Pw0bty4YskMALANCgEAGJiTk5NSUlIkSVu2bJGTU9H+t7B48WLt2LGjOKIBAOyEQgAABubn52cpBJs3b1atWrUs27Zv366wsDA9/vjjat++vTZt2iRJSkxMlJ+fnz777DOFhISoefPm2rBhg1JSUjRz5kzLuNccP35cXbt21RNPPKH3339fknT+/HmFh4crMDBQTzzxhGJiYpSXl2evtw0A+BMKAQAYWL169bR3716lp6frp59+UnBwsCQpIyNDr7/+ulxcXDRx4kT5+Piob9++Onr0qOW1Gzdu1IgRI3Tx4kVNnTpVtWrVUuvWrSVJX375pWW/5ORkvfjii6pfv75mz56tQ4cOacmSJfrxxx81evRohYeHa+fOndqzZ4993zwAQBKFAAAMLTAwUE5OTvrqq6+UkZGhJ554QtLVhcHnz5/X22+/rZYtWyo2NlbZ2dlav3695bX9+/fXc889p6CgIKWnp+vee+/Vgw8+KElq0KCBZb+2bduqffv26tChgyQpPT1ddevWlZOTk6ZMmaITJ04oMjJSAQEBdnznAIBrKAQAYGDu7u6qXbu25syZo/Lly8vX11eSZDKZrvvvtU8OuvZYkjw8PCRJrq6uhc5xbT9nZ2dJUn5+vh5//HF999136t69u06fPq1evXpp7ty5xfjOAAA3i0IAAAZXr149ZWVlqV69epbn8vPz5eHhoUmTJmn16tUaNWqU3N3d1axZs0LHulYOvv32W+Xn5xe43/Tp0/Xcc89Jklq0aCEXFxedOHGi6G8GAHDLKAQAYHDXisCfC0GZMmX06aef6vLlyxo0aJBOnTql+Ph4PfDAA4WO9fTTT+u+++7TxIkTlZmZWeB+L7/8stq0aaMpU6Zo5MiRatq0qfr06VM8bwgAcEtMZu4gAwAAABgWZwgAAAAAA6MQAAAAAAZGIQAAAAAMjEIAAAAAGBiFAAAAADAwCgEAAABgYBQCAAAAwMAoBAAAAICB/T/em377wfCJqgAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 936x432 with 1 Axes>"
       ]
@@ -2770,7 +2778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2785,7 +2793,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 936x432 with 1 Axes>"
       ]
@@ -2833,7 +2841,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -2847,7 +2855,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/6-Logistic Regression/Logistic Practicals/Logistic Regression Implementation.ipynb b/6-Logistic Regression/Logistic Practicals/Logistic Regression Implementation.ipynb
index fe083025..214c2385 100644
--- a/6-Logistic Regression/Logistic Practicals/Logistic Regression Implementation.ipynb	
+++ b/6-Logistic Regression/Logistic Practicals/Logistic Regression Implementation.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,11 +177,430 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
+       "             estimator=LogisticRegression(), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [100, 10, 1.0, 0.1, 0.01],\n",
+       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
+       "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
+       "                                    &#x27;saga&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
+       "             estimator=LogisticRegression(), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [100, 10, 1.0, 0.1, 0.01],\n",
+       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
+       "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
+       "                                    &#x27;saga&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">estimator: LogisticRegression</label><div class=\"sk-toggleable__content \"><pre>LogisticRegression()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
       "text/plain": [
        "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
        "             estimator=LogisticRegression(), n_jobs=-1,\n",
@@ -192,7 +611,7 @@
        "             scoring='accuracy')"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -203,11 +622,574 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n",
+      "200 fits failed out of a total of 375.\n",
+      "The score on these train-test partitions for these parameters will be set to nan.\n",
+      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+      "\n",
+      "Below are more details about the failures:\n",
+      "--------------------------------------------------------------------------------\n",
+      "25 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver newton-cg supports only 'l2' or None penalties, got l1 penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "25 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "25 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver sag supports only 'l2' or None penalties, got l1 penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "25 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver newton-cg supports only 'l2' or None penalties, got elasticnet penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "25 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got elasticnet penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "25 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 75, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "25 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver sag supports only 'l2' or None penalties, got elasticnet penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "25 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1204, in fit\n",
+      "    raise ValueError(\"l1_ratio must be specified when penalty is elasticnet.\")\n",
+      "ValueError: l1_ratio must be specified when penalty is elasticnet.\n",
+      "\n",
+      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1102: UserWarning: One or more of the test scores are non-finite: [       nan        nan 0.91285714        nan 0.91285714 0.91285714\n",
+      " 0.91285714 0.91285714 0.91285714 0.91285714        nan        nan\n",
+      "        nan        nan        nan        nan        nan 0.91285714\n",
+      "        nan 0.91285714 0.91285714 0.91285714 0.91285714 0.91285714\n",
+      " 0.91285714        nan        nan        nan        nan        nan\n",
+      "        nan        nan 0.91142857        nan 0.91142857 0.91142857\n",
+      " 0.91142857 0.91142857 0.91142857 0.91142857        nan        nan\n",
+      "        nan        nan        nan        nan        nan 0.92285714\n",
+      "        nan 0.92142857 0.91285714 0.91285714 0.91857143 0.91285714\n",
+      " 0.91285714        nan        nan        nan        nan        nan\n",
+      "        nan        nan 0.92142857        nan 0.92428571 0.91285714\n",
+      " 0.91285714 0.92285714 0.91285714 0.91285714        nan        nan\n",
+      "        nan        nan        nan]\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
+       "             estimator=LogisticRegression(), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [100, 10, 1.0, 0.1, 0.01],\n",
+       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
+       "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
+       "                                    &#x27;saga&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
+       "             estimator=LogisticRegression(), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [100, 10, 1.0, 0.1, 0.01],\n",
+       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
+       "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
+       "                                    &#x27;saga&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.01, penalty=&#x27;l1&#x27;, solver=&#x27;saga&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.01, penalty=&#x27;l1&#x27;, solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
       "text/plain": [
        "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
        "             estimator=LogisticRegression(), n_jobs=-1,\n",
@@ -218,7 +1200,7 @@
        "             scoring='accuracy')"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,7 +1211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -238,7 +1220,7 @@
        "{'C': 0.01, 'penalty': 'l1', 'solver': 'saga'}"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -249,7 +1231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -258,7 +1240,7 @@
        "0.9242857142857142"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -269,7 +1251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +1260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -316,7 +1298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,7 +1307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,69 +1317,543 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1312, in fit\n",
-      "    raise ValueError(\"l1_ratio must be between 0 and 1;\"\n",
-      "ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)\n",
-      "\n",
-      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1304, in fit\n",
-      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 442, in _check_solver\n",
-      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
-      "ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
-      "\n",
-      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1304, in fit\n",
-      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 442, in _check_solver\n",
-      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
-      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
-      "\n",
-      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
-      "  warnings.warn(\"The max_iter was reached which means \"\n",
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
-      "  warnings.warn(\"The max_iter was reached which means \"\n",
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
-      "  warnings.warn(\"The max_iter was reached which means \"\n",
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
-      "  warnings.warn(\"The max_iter was reached which means \"\n",
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
-      "  warnings.warn(\"The max_iter was reached which means \"\n",
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1304, in fit\n",
-      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
-      "  File \"C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 442, in _check_solver\n",
-      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
-      "ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.\n",
-      "\n",
-      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n",
+      "30 fits failed out of a total of 50.\n",
+      "The score on these train-test partitions for these parameters will be set to nan.\n",
+      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+      "\n",
+      "Below are more details about the failures:\n",
+      "--------------------------------------------------------------------------------\n",
+      "5 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver sag supports only 'l2' or None penalties, got elasticnet penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "5 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver sag supports only 'l2' or None penalties, got l1 penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "5 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got elasticnet penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "5 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver newton-cg supports only 'l2' or None penalties, got elasticnet penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "5 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1194, in fit\n",
+      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
+      "    raise ValueError(\n",
+      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "5 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1204, in fit\n",
+      "    raise ValueError(\"l1_ratio must be specified when penalty is elasticnet.\")\n",
+      "ValueError: l1_ratio must be specified when penalty is elasticnet.\n",
+      "\n",
+      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1102: UserWarning: One or more of the test scores are non-finite: [0.91142857        nan        nan        nan 0.92142857 0.91142857\n",
+      "        nan        nan        nan 0.91285714]\n",
+      "  warnings.warn(\n"
      ]
     },
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(),\n",
+       "                   param_distributions={&#x27;C&#x27;: [100, 10, 1.0, 0.1, 0.01],\n",
+       "                                        &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
+       "                                        &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;,\n",
+       "                                                   &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
+       "                                                   &#x27;saga&#x27;]},\n",
+       "                   scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(),\n",
+       "                   param_distributions={&#x27;C&#x27;: [100, 10, 1.0, 0.1, 0.01],\n",
+       "                                        &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
+       "                                        &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;,\n",
+       "                                                   &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
+       "                                                   &#x27;saga&#x27;]},\n",
+       "                   scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.01, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.01, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
       "text/plain": [
        "RandomizedSearchCV(cv=5, estimator=LogisticRegression(),\n",
        "                   param_distributions={'C': [100, 10, 1.0, 0.1, 0.01],\n",
@@ -408,7 +1864,7 @@
        "                   scoring='accuracy')"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -419,16 +1875,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9128571428571428"
+       "0.9214285714285714"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -439,16 +1895,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'solver': 'saga', 'penalty': 'l2', 'C': 100}"
+       "{'solver': 'liblinear', 'penalty': 'l1', 'C': 0.01}"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,7 +1915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -468,25 +1924,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9166666666666666\n",
+      "0.9233333333333333\n",
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.93      0.91      0.92       160\n",
-      "           1       0.90      0.92      0.91       140\n",
+      "           0       0.97      0.89      0.93       170\n",
+      "           1       0.87      0.96      0.92       130\n",
       "\n",
       "    accuracy                           0.92       300\n",
-      "   macro avg       0.92      0.92      0.92       300\n",
-      "weighted avg       0.92      0.92      0.92       300\n",
+      "   macro avg       0.92      0.93      0.92       300\n",
+      "weighted avg       0.93      0.92      0.92       300\n",
       "\n",
-      "[[146  14]\n",
-      " [ 11 129]]\n"
+      "[[152  18]\n",
+      " [  5 125]]\n"
      ]
     }
    ],
@@ -506,7 +1962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -516,7 +1972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -570,7 +2026,7 @@
        "       2, 0, 2, 0, 1, 2, 1, 0, 1, 2])"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -581,7 +2037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -591,9 +2047,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "logistic=LogisticRegression(multi_class='ovr')\n",
@@ -603,7 +2068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -625,7 +2090,7 @@
        "       0, 0, 1, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 2])"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -636,7 +2101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -676,7 +2141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -687,7 +2152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 32,
    "metadata": {
     "scrolled": true
    },
@@ -700,7 +2165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -715,7 +2180,7 @@
        "       [ 0.79770543, -1.99467372]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -726,7 +2191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -735,7 +2200,7 @@
        "Counter({0: 9846, 1: 154})"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -746,7 +2211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -755,38 +2220,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
-      "  warnings.warn(\n"
+     "ename": "TypeError",
+     "evalue": "scatterplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 1 keyword-only argument) were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[36], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m sns\u001b[38;5;241m.\u001b[39mscatterplot(pd\u001b[38;5;241m.\u001b[39mDataFrame(X)[\u001b[38;5;241m0\u001b[39m],pd\u001b[38;5;241m.\u001b[39mDataFrame(X)[\u001b[38;5;241m1\u001b[39m],hue\u001b[38;5;241m=\u001b[39my)\n",
+      "\u001b[1;31mTypeError\u001b[0m: scatterplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 1 keyword-only argument) were given"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='0', ylabel='1'>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -796,7 +2242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -807,7 +2253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -816,7 +2262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -851,7 +2297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -866,7 +2312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -875,7 +2321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -913,7 +2359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -925,7 +2371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -9591,7 +11037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -9614,7 +11060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9623,7 +11069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9632,7 +11078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -9673,13 +11119,6 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "# roc curve and auc\n",
     "from sklearn.datasets import make_classification\n",
@@ -9692,7 +11131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9702,7 +11141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9713,7 +11152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -9984,7 +11423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -10006,7 +11445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10016,7 +11455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -10285,7 +11724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10295,7 +11734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -10333,7 +11772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10344,7 +11783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -10379,7 +11818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -10414,7 +11853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10423,12 +11862,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -10461,7 +11900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -10474,7 +11913,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1440x3600 with 1 Axes>"
       ]
@@ -10513,7 +11952,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -10527,7 +11966,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/7-SVM/Practicals/Basic SVC Implementation.ipynb b/7-SVM/Practicals/Basic SVC Implementation.ipynb
index 59cad9c9..3d172bf2 100644
--- a/7-SVM/Practicals/Basic SVC Implementation.ipynb	
+++ b/7-SVM/Practicals/Basic SVC Implementation.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,22 +42,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.21257104, -0.1960392 ],\n",
-       "       [ 2.3821402 ,  0.27124703],\n",
-       "       [ 1.03340474, -0.33726287],\n",
+       "array([[-1.16781898,  0.60359396],\n",
+       "       [ 0.27479249,  1.14050187],\n",
+       "       [ 0.64955164, -1.50472302],\n",
        "       ...,\n",
-       "       [-0.45011412,  1.64659531],\n",
-       "       [ 0.63552556, -0.73767318],\n",
-       "       [-2.36576388,  2.23701447]])"
+       "       [ 0.11856648, -2.13585379],\n",
+       "       [ 1.22627126, -0.63082269],\n",
+       "       [-0.73938455, -0.35836791]])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -68,61 +68,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
-       "       0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
-       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
-       "       0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
-       "       1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
-       "       1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
-       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
-       "       0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
-       "       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
-       "       1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1,\n",
-       "       0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
-       "       1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
-       "       1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,\n",
-       "       1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
-       "       0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
-       "       1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
-       "       1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0,\n",
-       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
-       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1,\n",
-       "       0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
-       "       0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
-       "       1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1,\n",
-       "       0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
-       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1,\n",
-       "       1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,\n",
-       "       1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
-       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
-       "       1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
-       "       0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
-       "       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,\n",
-       "       0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
-       "       0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n",
-       "       0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0,\n",
-       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
-       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n",
-       "       0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
-       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
-       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
-       "       1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,\n",
-       "       1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
-       "       0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
-       "       0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
-       "       1, 0, 0, 0, 1, 0, 0, 1, 0, 1])"
+       "array([0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
+       "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
+       "       1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,\n",
+       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
+       "       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1,\n",
+       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,\n",
+       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0,\n",
+       "       0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
+       "       0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1,\n",
+       "       1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
+       "       1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1,\n",
+       "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,\n",
+       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
+       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
+       "       1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
+       "       0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0,\n",
+       "       1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n",
+       "       1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1,\n",
+       "       1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
+       "       1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1,\n",
+       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1,\n",
+       "       1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
+       "       0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
+       "       1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
+       "       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
+       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
+       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
+       "       0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0,\n",
+       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1,\n",
+       "       0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1,\n",
+       "       0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
+       "       1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
+       "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1,\n",
+       "       1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
+       "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0,\n",
+       "       1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
+       "       1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0,\n",
+       "       1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
+       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 0])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -133,27 +133,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0      0.212571\n",
-       "1      2.382140\n",
-       "2      1.033405\n",
-       "3      1.050006\n",
-       "4      1.442964\n",
+       "0     -1.167819\n",
+       "1      0.274792\n",
+       "2      0.649552\n",
+       "3     -1.089182\n",
+       "4      0.686292\n",
        "         ...   \n",
-       "995    1.158510\n",
-       "996    1.068731\n",
-       "997   -0.450114\n",
-       "998    0.635526\n",
-       "999   -2.365764\n",
+       "995    1.143736\n",
+       "996    1.491239\n",
+       "997    0.118566\n",
+       "998    1.226271\n",
+       "999   -0.739385\n",
        "Name: 0, Length: 1000, dtype: float64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -164,47 +164,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:xlabel='0', ylabel='1'>"
+       "<Axes: xlabel='0', ylabel='1'>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.scatterplot(pd.DataFrame(X)[0],pd.DataFrame(X)[1],hue=y)"
+    "sns.scatterplot(x=pd.DataFrame(X)[0],y=pd.DataFrame(X)[1],hue=y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,16 +222,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "SVC(kernel='linear')"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -252,16 +649,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.0521126 , 1.96313735]])"
+       "array([[ 1.65829679, -0.06936985]])"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -300,15 +697,15 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.87      0.84      0.86       132\n",
-      "           1       0.83      0.86      0.85       118\n",
+      "           0       0.85      0.91      0.88       127\n",
+      "           1       0.90      0.84      0.87       123\n",
       "\n",
-      "    accuracy                           0.85       250\n",
-      "   macro avg       0.85      0.85      0.85       250\n",
-      "weighted avg       0.85      0.85      0.85       250\n",
+      "    accuracy                           0.88       250\n",
+      "   macro avg       0.88      0.88      0.88       250\n",
+      "weighted avg       0.88      0.88      0.88       250\n",
       "\n",
-      "[[111  21]\n",
-      " [ 16 102]]\n"
+      "[[116  11]\n",
+      " [ 20 103]]\n"
      ]
     }
    ],
@@ -319,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,16 +725,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "SVC()"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -348,7 +1152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -367,15 +1171,15 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.94      0.83      0.88       132\n",
-      "           1       0.83      0.94      0.88       118\n",
+      "           0       0.88      0.94      0.91       127\n",
+      "           1       0.94      0.87      0.90       123\n",
       "\n",
-      "    accuracy                           0.88       250\n",
-      "   macro avg       0.89      0.89      0.88       250\n",
-      "weighted avg       0.89      0.88      0.88       250\n",
+      "    accuracy                           0.91       250\n",
+      "   macro avg       0.91      0.91      0.91       250\n",
+      "weighted avg       0.91      0.91      0.91       250\n",
       "\n",
-      "[[110  22]\n",
-      " [  7 111]]\n"
+      "[[120   7]\n",
+      " [ 16 107]]\n"
      ]
     }
    ],
@@ -386,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -395,15 +1199,15 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.81      0.87      0.84       132\n",
-      "           1       0.84      0.77      0.81       118\n",
+      "           0       0.84      0.97      0.90       127\n",
+      "           1       0.96      0.81      0.88       123\n",
       "\n",
-      "    accuracy                           0.82       250\n",
-      "   macro avg       0.83      0.82      0.82       250\n",
-      "weighted avg       0.83      0.82      0.82       250\n",
+      "    accuracy                           0.89       250\n",
+      "   macro avg       0.90      0.89      0.89       250\n",
+      "weighted avg       0.90      0.89      0.89       250\n",
       "\n",
-      "[[115  17]\n",
-      " [ 27  91]]\n"
+      "[[123   4]\n",
+      " [ 23 100]]\n"
      ]
     }
    ],
@@ -418,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -427,15 +1231,15 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.84      0.73      0.79       132\n",
-      "           1       0.74      0.85      0.79       118\n",
+      "           0       0.75      0.75      0.75       127\n",
+      "           1       0.74      0.74      0.74       123\n",
       "\n",
-      "    accuracy                           0.79       250\n",
-      "   macro avg       0.79      0.79      0.79       250\n",
-      "weighted avg       0.79      0.79      0.79       250\n",
+      "    accuracy                           0.74       250\n",
+      "   macro avg       0.74      0.74      0.74       250\n",
+      "weighted avg       0.74      0.74      0.74       250\n",
       "\n",
-      "[[ 97  35]\n",
-      " [ 18 100]]\n"
+      "[[95 32]\n",
+      " [32 91]]\n"
      ]
     }
    ],
@@ -450,16 +1254,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1.3649506])"
+       "array([1.01220963])"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -477,7 +1281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -491,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,7 +1304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -508,289 +1312,550 @@
      "output_type": "stream",
      "text": [
       "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.940, total=   0.0s\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.900, total=   0.0s\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.907, total=   0.0s\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.893, total=   0.0s\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.927, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.893, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.893, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.880, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.880, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.867, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
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-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
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-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
-      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
-      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.947, total=   0.0s\n",
-      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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-      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.907, total=   0.0s\n",
-      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
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-     ]
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-     "output_type": "stream",
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-      "\n",
-      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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-      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
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-      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
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-      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
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-      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
-      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
-      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.880, total=   0.0s\n",
-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
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-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
-      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.847, total=   0.0s\n",
-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
-      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.847, total=   0.0s\n",
-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
-      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.893, total=   0.0s\n",
-      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
-      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
-      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
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-      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
-      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
-      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
-      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
-      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
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-      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
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-      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
-      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.927, total=   0.0s\n",
-      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
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-      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
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-      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
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-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.907, total=   0.0s\n",
-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.900, total=   0.0s\n",
-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.880, total=   0.0s\n",
-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.940, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.880, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.887, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.893, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.947, total=   0.0s\n",
-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
-      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
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-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
-      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
-      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.880, total=   0.0s\n",
-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
-      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.940, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.880, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.867, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.847, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.847, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.893, total=   0.0s\n",
-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.947, total=   0.0s\n",
-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
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-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.927, total=   0.0s\n",
-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.920, total=   0.0s\n",
-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.953, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.913, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.913, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.893, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.940, total=   0.0s\n",
-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.927, total=   0.0s\n",
-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.893, total=   0.0s\n",
-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
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-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.880, total=   0.0s\n",
-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
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-      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
-      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.927, total=   0.0s\n",
-      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
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-      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
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-      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
-      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.953, total=   0.0s\n",
-      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.900, total=   0.0s\n",
-      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.887, total=   0.0s\n",
-      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.880, total=   0.0s\n",
-      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.940, total=   0.0s\n",
-      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
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-      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
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-      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
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-      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
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-      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
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-      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
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-      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.933, total=   0.0s\n",
-      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
-      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.927, total=   0.0s\n",
-      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
-      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.873, total=   0.0s\n",
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-      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
-      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
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-      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
-      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.927, total=   0.0s\n",
-      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
-      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.880, total=   0.0s\n",
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-      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.887, total=   0.0s\n",
-      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
-      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.887, total=   0.0s\n",
-      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
-      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.953, total=   0.0s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed:    0.7s finished\n"
+      "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.920 total time=   0.0s\n",
+      "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.860 total time=   0.0s\n",
+      "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.827 total time=   0.0s\n",
+      "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.853 total time=   0.0s\n",
+      "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.887 total time=   0.0s\n",
+      "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.820 total time=   0.0s\n",
+      "[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.847 total time=   0.0s\n",
+      "[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.513 total time=   0.0s\n",
+      "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.513 total time=   0.0s\n",
+      "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.927 total time=   0.0s\n",
+      "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.880 total time=   0.0s\n",
+      "[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.920 total time=   0.0s\n",
+      "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.853 total time=   0.0s\n",
+      "[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.880 total time=   0.0s\n",
+      "[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.900 total time=   0.0s\n",
+      "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.827 total time=   0.0s\n",
+      "[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.860 total time=   0.0s\n",
+      "[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.827 total time=   0.0s\n",
+      "[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.847 total time=   0.0s\n",
+      "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.887 total time=   0.0s\n",
+      "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.513 total time=   0.0s\n",
+      "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.507 total time=   0.0s\n",
+      "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.927 total time=   0.0s\n",
+      "[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.900 total time=   0.0s\n",
+      "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.927 total time=   0.0s\n",
+      "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.887 total time=   0.0s\n",
+      "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.887 total time=   0.0s\n",
+      "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.913 total time=   0.0s\n",
+      "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.913 total time=   0.0s\n",
+      "[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.853 total time=   0.0s\n",
+      "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.860 total time=   0.0s\n",
+      "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.913 total time=   0.0s\n",
+      "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.827 total time=   0.0s\n",
+      "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.867 total time=   0.0s\n",
+      "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.867 total time=   0.0s\n",
+      "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.827 total time=   0.0s\n",
+      "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.847 total time=   0.0s\n",
+      "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.867 total time=   0.0s\n",
+      "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.887 total time=   0.0s\n",
+      "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.920 total time=   0.0s\n",
+      "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.853 total time=   0.0s\n",
+      "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.880 total time=   0.0s\n",
+      "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.900 total time=   0.0s\n",
+      "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.933 total time=   0.0s\n",
+      "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.880 total time=   0.0s\n",
+      "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.913 total time=   0.0s\n",
+      "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.920 total time=   0.0s\n",
+      "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.887 total time=   0.0s\n",
+      "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.913 total time=   0.0s\n",
+      "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.840 total time=   0.0s\n",
+      "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.867 total time=   0.0s\n",
+      "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.887 total time=   0.0s\n",
+      "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.913 total time=   0.0s\n",
+      "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.827 total time=   0.0s\n",
+      "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.867 total time=   0.0s\n",
+      "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.867 total time=   0.0s\n",
+      "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.860 total time=   0.0s\n",
+      "[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.860 total time=   0.0s\n",
+      "[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.900 total time=   0.0s\n",
+      "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.913 total time=   0.0s\n",
+      "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.933 total time=   0.0s\n",
+      "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.880 total time=   0.0s\n",
+      "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.880 total time=   0.0s\n",
+      "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.920 total time=   0.0s\n",
+      "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.913 total time=   0.0s\n",
+      "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.873 total time=   0.0s\n",
+      "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.887 total time=   0.0s\n",
+      "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.900 total time=   0.0s\n",
+      "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.913 total time=   0.0s\n",
+      "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.853 total time=   0.0s\n",
+      "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.860 total time=   0.0s\n",
+      "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.893 total time=   0.0s\n",
+      "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.907 total time=   0.0s\n",
+      "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.840 total time=   0.0s\n",
+      "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.853 total time=   0.0s\n",
+      "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.887 total time=   0.0s\n",
+      "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.880 total time=   0.0s\n"
      ]
     },
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n",
+       "                         &#x27;gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
+       "             verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n",
+       "                         &#x27;gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
+       "             verbose=3)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1, gamma=1)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1, gamma=1)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
       "text/plain": [
        "GridSearchCV(cv=5, estimator=SVC(),\n",
        "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
@@ -799,7 +1864,7 @@
        "             verbose=3)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -810,16 +1875,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'C': 1000, 'gamma': 1, 'kernel': 'rbf'}"
+       "{'C': 1, 'gamma': 1, 'kernel': 'rbf'}"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -830,7 +1895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -839,15 +1904,15 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.95      0.91      0.93       132\n",
-      "           1       0.90      0.95      0.93       118\n",
+      "           0       0.88      0.95      0.92       127\n",
+      "           1       0.95      0.87      0.91       123\n",
       "\n",
-      "    accuracy                           0.93       250\n",
-      "   macro avg       0.93      0.93      0.93       250\n",
-      "weighted avg       0.93      0.93      0.93       250\n",
+      "    accuracy                           0.91       250\n",
+      "   macro avg       0.92      0.91      0.91       250\n",
+      "weighted avg       0.91      0.91      0.91       250\n",
       "\n",
-      "[[120  12]\n",
-      " [  6 112]]\n"
+      "[[121   6]\n",
+      " [ 16 107]]\n"
      ]
     }
    ],
@@ -858,6 +1923,633 @@
     "print(confusion_matrix(y_test,y_pred4))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### using param grid and randomizedsearch cv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = [\n",
+    "    # Linear kernel (no gamma, no degree, no coef0)\n",
+    "    {\n",
+    "        'kernel': ['linear'],\n",
+    "        'C': [0.01, 0.1, 1, 10, 100],\n",
+    "        'class_weight': [None, 'balanced']\n",
+    "    },\n",
+    "    # RBF kernel (uses gamma, not degree or coef0)\n",
+    "    {\n",
+    "        'kernel': ['rbf'],\n",
+    "        'C': [0.01, 0.1, 1, 10, 100],\n",
+    "        'gamma': ['scale', 'auto', 0.01, 0.1, 1],\n",
+    "        'class_weight': [None, 'balanced']\n",
+    "    },\n",
+    "    # Polynomial kernel (uses degree, gamma, and coef0)\n",
+    "    {\n",
+    "        'kernel': ['poly'],\n",
+    "        'C': [0.01, 0.1, 1, 10],\n",
+    "        'degree': [2, 3, 4],\n",
+    "        'gamma': ['scale', 'auto', 0.01, 0.1],\n",
+    "        'coef0': [0.0, 0.5, 1.0],\n",
+    "        'class_weight': [None, 'balanced']\n",
+    "    },\n",
+    "    # Sigmoid kernel (uses gamma and coef0, not degree)\n",
+    "    {\n",
+    "        'kernel': ['sigmoid'],\n",
+    "        'C': [0.01, 0.1, 1, 10],\n",
+    "        'gamma': ['scale', 'auto', 0.01, 0.1],\n",
+    "        'coef0': [0.0, 0.5, 1.0],\n",
+    "        'class_weight': [None, 'balanced']\n",
+    "    }\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "\n",
+    "random=RandomizedSearchCV(estimator=SVC(),param_distributions=param_grid,refit=True,scoring='accuracy',cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=SVC(),\n",
+       "                   param_distributions=[{&#x27;C&#x27;: [0.01, 0.1, 1, 10, 100],\n",
+       "                                         &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                                         &#x27;kernel&#x27;: [&#x27;linear&#x27;]},\n",
+       "                                        {&#x27;C&#x27;: [0.01, 0.1, 1, 10, 100],\n",
+       "                                         &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                                         &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.01, 0.1,\n",
+       "                                                   1],\n",
+       "                                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
+       "                                        {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                                         &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                                         &#x27;coef0&#x27;: [0.0, 0.5, 1.0],\n",
+       "                                         &#x27;degree&#x27;: [2, 3, 4],\n",
+       "                                         &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.01, 0.1],\n",
+       "                                         &#x27;kernel&#x27;: [&#x27;poly&#x27;]},\n",
+       "                                        {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                                         &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                                         &#x27;coef0&#x27;: [0.0, 0.5, 1.0],\n",
+       "                                         &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.01, 0.1],\n",
+       "                                         &#x27;kernel&#x27;: [&#x27;sigmoid&#x27;]}],\n",
+       "                   scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=SVC(),\n",
+       "                   param_distributions=[{&#x27;C&#x27;: [0.01, 0.1, 1, 10, 100],\n",
+       "                                         &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                                         &#x27;kernel&#x27;: [&#x27;linear&#x27;]},\n",
+       "                                        {&#x27;C&#x27;: [0.01, 0.1, 1, 10, 100],\n",
+       "                                         &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                                         &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.01, 0.1,\n",
+       "                                                   1],\n",
+       "                                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
+       "                                        {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                                         &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                                         &#x27;coef0&#x27;: [0.0, 0.5, 1.0],\n",
+       "                                         &#x27;degree&#x27;: [2, 3, 4],\n",
+       "                                         &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.01, 0.1],\n",
+       "                                         &#x27;kernel&#x27;: [&#x27;poly&#x27;]},\n",
+       "                                        {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                                         &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                                         &#x27;coef0&#x27;: [0.0, 0.5, 1.0],\n",
+       "                                         &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.01, 0.1],\n",
+       "                                         &#x27;kernel&#x27;: [&#x27;sigmoid&#x27;]}],\n",
+       "                   scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1, coef0=0.5, degree=4, kernel=&#x27;poly&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1, coef0=0.5, degree=4, kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomizedSearchCV(cv=5, estimator=SVC(),\n",
+       "                   param_distributions=[{'C': [0.01, 0.1, 1, 10, 100],\n",
+       "                                         'class_weight': [None, 'balanced'],\n",
+       "                                         'kernel': ['linear']},\n",
+       "                                        {'C': [0.01, 0.1, 1, 10, 100],\n",
+       "                                         'class_weight': [None, 'balanced'],\n",
+       "                                         'gamma': ['scale', 'auto', 0.01, 0.1,\n",
+       "                                                   1],\n",
+       "                                         'kernel': ['rbf']},\n",
+       "                                        {'C': [0.01, 0.1, 1, 10],\n",
+       "                                         'class_weight': [None, 'balanced'],\n",
+       "                                         'coef0': [0.0, 0.5, 1.0],\n",
+       "                                         'degree': [2, 3, 4],\n",
+       "                                         'gamma': ['scale', 'auto', 0.01, 0.1],\n",
+       "                                         'kernel': ['poly']},\n",
+       "                                        {'C': [0.01, 0.1, 1, 10],\n",
+       "                                         'class_weight': [None, 'balanced'],\n",
+       "                                         'coef0': [0.0, 0.5, 1.0],\n",
+       "                                         'gamma': ['scale', 'auto', 0.01, 0.1],\n",
+       "                                         'kernel': ['sigmoid']}],\n",
+       "                   scoring='accuracy')"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.fit(X_train,y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred=random.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.88      0.95      0.92       127\n",
+      "           1       0.95      0.87      0.91       123\n",
+      "\n",
+      "    accuracy                           0.91       250\n",
+      "   macro avg       0.92      0.91      0.91       250\n",
+      "weighted avg       0.91      0.91      0.91       250\n",
+      "\n",
+      "[[121   6]\n",
+      " [ 16 107]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred4))\n",
+    "print(confusion_matrix(y_test,y_pred4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'kernel': 'poly',\n",
+       " 'gamma': 'scale',\n",
+       " 'degree': 4,\n",
+       " 'coef0': 0.5,\n",
+       " 'class_weight': None,\n",
+       " 'C': 1}"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.best_score_"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -868,7 +2560,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -882,7 +2574,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/7-SVM/Practicals/SVM Kernels Implementation.ipynb b/7-SVM/Practicals/SVM Kernels Implementation.ipynb
index 9f09eb71..d0cf10fa 100644
--- a/7-SVM/Practicals/SVM Kernels Implementation.ipynb	
+++ b/7-SVM/Practicals/SVM Kernels Implementation.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -72,7 +72,7 @@
        "       -8.88128118, -8.82827705, -8.77378994, -8.71779204, -8.66025404])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,29 +95,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1e5b2b8f640>"
+       "<matplotlib.collections.PathCollection at 0x2ec46fe2db0>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -128,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -336,7 +334,7 @@
        "       [-8.66025404, -5.        ]])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -347,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -377,50 +375,17 @@
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8.660254</td>\n",
-       "      <td>-5.00000</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>8.717792</td>\n",
-       "      <td>-4.89899</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>8.773790</td>\n",
-       "      <td>-4.79798</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>8.828277</td>\n",
-       "      <td>-4.69697</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>8.881281</td>\n",
-       "      <td>-4.59596</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         X1       X2  Y\n",
-       "0  8.660254 -5.00000  0\n",
-       "1  8.717792 -4.89899  0\n",
-       "2  8.773790 -4.79798  0\n",
-       "3  8.828277 -4.69697  0\n",
-       "4  8.881281 -4.59596  0"
+       "Empty DataFrame\n",
+       "Columns: [X1, X2, Y]\n",
+       "Index: []"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -431,13 +396,13 @@
     "df1['Y']=0\n",
     "df2 =pd.DataFrame(np.vstack([y1,x1]).T,columns=['X1','X2'])\n",
     "df2['Y']=1\n",
-    "df = df1.append(df2)\n",
+    "df = df1.merge(df2)\n",
     "df.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -467,50 +432,17 @@
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>195</th>\n",
-       "      <td>-1.969049</td>\n",
-       "      <td>-4.59596</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>196</th>\n",
-       "      <td>-1.714198</td>\n",
-       "      <td>-4.69697</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>197</th>\n",
-       "      <td>-1.406908</td>\n",
-       "      <td>-4.79798</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>198</th>\n",
-       "      <td>-0.999949</td>\n",
-       "      <td>-4.89899</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>199</th>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-5.00000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "           X1       X2  Y\n",
-       "195 -1.969049 -4.59596  1\n",
-       "196 -1.714198 -4.69697  1\n",
-       "197 -1.406908 -4.79798  1\n",
-       "198 -0.999949 -4.89899  1\n",
-       "199 -0.000000 -5.00000  1"
+       "Empty DataFrame\n",
+       "Columns: [X1, X2, Y]\n",
+       "Index: []"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -532,27 +464,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0      0\n",
-       "1      0\n",
-       "2      0\n",
-       "3      0\n",
-       "4      0\n",
-       "      ..\n",
-       "195    1\n",
-       "196    1\n",
-       "197    1\n",
-       "198    1\n",
-       "199    1\n",
-       "Name: Y, Length: 400, dtype: int64"
+       "Series([], Name: Y, dtype: int64)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -563,9 +484,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "With n_samples=0, test_size=0.25 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[22], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m## Split the dataset into train and test\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[1;32m----> 3\u001b[0m X_train,X_test,y_train,y_test\u001b[38;5;241m=\u001b[39mtrain_test_split(X,y,test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.25\u001b[39m,random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    211\u001b[0m         )\n\u001b[0;32m    212\u001b[0m     ):\n\u001b[1;32m--> 213\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    221\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    222\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    223\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2780\u001b[0m, in \u001b[0;36mtrain_test_split\u001b[1;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[0;32m   2777\u001b[0m arrays \u001b[38;5;241m=\u001b[39m indexable(\u001b[38;5;241m*\u001b[39marrays)\n\u001b[0;32m   2779\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(arrays[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m-> 2780\u001b[0m n_train, n_test \u001b[38;5;241m=\u001b[39m _validate_shuffle_split(\n\u001b[0;32m   2781\u001b[0m     n_samples, test_size, train_size, default_test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.25\u001b[39m\n\u001b[0;32m   2782\u001b[0m )\n\u001b[0;32m   2784\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m shuffle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m   2785\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m stratify \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\mudas\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2410\u001b[0m, in \u001b[0;36m_validate_shuffle_split\u001b[1;34m(n_samples, test_size, train_size, default_test_size)\u001b[0m\n\u001b[0;32m   2407\u001b[0m n_train, n_test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(n_train), \u001b[38;5;28mint\u001b[39m(n_test)\n\u001b[0;32m   2409\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_train \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m-> 2410\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   2411\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWith n_samples=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, test_size=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m and train_size=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2412\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresulting train set will be empty. Adjust any of the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2413\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maforementioned parameters.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(n_samples, test_size, train_size)\n\u001b[0;32m   2414\u001b[0m     )\n\u001b[0;32m   2416\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m n_train, n_test\n",
+      "\u001b[1;31mValueError\u001b[0m: With n_samples=0, test_size=0.25 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters."
+     ]
+    }
+   ],
    "source": [
     "## Split the dataset into train and test\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -574,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -618,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -725,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -774,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -786,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -939,7 +875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3586,7 +3522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6150,7 +6086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6175,7 +6111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6200,7 +6136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6225,7 +6161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6258,7 +6194,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -6272,7 +6208,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/7-SVM/Practicals/Support Vector Regression Implementation.ipynb b/7-SVM/Practicals/Support Vector Regression Implementation.ipynb
index edb724d8..c2a1fb1c 100644
--- a/7-SVM/Practicals/Support Vector Regression Implementation.ipynb	
+++ b/7-SVM/Practicals/Support Vector Regression Implementation.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -119,7 +119,7 @@
        "4       24.59  3.61  Female     No  Sun  Dinner     4"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -150,7 +150,7 @@
       " 5   time        244 non-null    category\n",
       " 6   size        244 non-null    int64   \n",
       "dtypes: category(4), float64(2), int64(1)\n",
-      "memory usage: 7.3 KB\n"
+      "memory usage: 7.4 KB\n"
      ]
     }
    ],
@@ -160,18 +160,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "sex\n",
        "Male      157\n",
        "Female     87\n",
-       "Name: sex, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -182,18 +183,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "smoker\n",
        "No     151\n",
        "Yes     93\n",
-       "Name: smoker, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -204,20 +206,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "day\n",
        "Sat     87\n",
        "Sun     76\n",
        "Thur    62\n",
        "Fri     19\n",
-       "Name: day, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -228,18 +231,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "time\n",
        "Dinner    176\n",
        "Lunch      68\n",
-       "Name: time, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -250,14 +254,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +270,7 @@
        "Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -277,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -390,7 +394,7 @@
        "184  3.00  Male    Yes  Sun  Dinner     2"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -419,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -534,7 +538,7 @@
        "184  3.00    1       1  Sun     0     2"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -545,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -556,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -647,7 +651,7 @@
        "69   2.09    1       1  Sat     0     2"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -658,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -670,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -680,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -692,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -701,7 +705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -770,7 +774,7 @@
        "       [0.  , 1.  , 0.  , 3.  , 1.  , 0.  , 0.  , 2.  ]])"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -781,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -792,16 +796,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
+      ],
       "text/plain": [
        "SVR()"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -812,7 +1223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -821,14 +1232,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.46028114561159283\n",
+      "0.4602811456115927\n",
       "4.1486423210190235\n"
      ]
     }
@@ -841,7 +1252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -850,7 +1261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -864,7 +1275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -872,282 +1283,550 @@
      "output_type": "stream",
      "text": [
       "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] ......... C=0.1, gamma=1, kernel=rbf, score=-0.067, total=   0.0s\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] ......... C=0.1, gamma=1, kernel=rbf, score=-0.058, total=   0.0s\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] ......... C=0.1, gamma=1, kernel=rbf, score=-0.145, total=   0.0s\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.025, total=   0.0s\n",
-      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
-      "[CV] ......... C=0.1, gamma=1, kernel=rbf, score=-0.089, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.013, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.021, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
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-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
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-      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.050, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ...... C=0.1, gamma=0.01, kernel=rbf, score=-0.053, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
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-      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ...... C=0.1, gamma=0.01, kernel=rbf, score=-0.108, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
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-      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
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-      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
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-      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
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-      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
-      "[CV] ..... C=0.1, gamma=0.001, kernel=rbf, score=-0.167, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
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-      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
-      "[CV] ..... C=0.1, gamma=0.001, kernel=rbf, score=-0.105, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.083, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.073, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.173, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.013, total=   0.0s\n",
-      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
-      "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.110, total=   0.0s\n",
-      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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-      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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-      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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-      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
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-      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
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-      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
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-      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
-      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.121, total=   0.0s\n",
-      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
-      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.221, total=   0.0s\n",
-      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
-      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.269, total=   0.0s\n",
-      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
-      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.305, total=   0.0s\n",
-      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
-      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.252, total=   0.0s\n",
-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
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-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
-      "[CV] ....... C=1, gamma=0.001, kernel=rbf, score=-0.014, total=   0.0s\n",
-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
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-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
-      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.050, total=   0.0s\n",
-      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
-      "[CV] ....... C=1, gamma=0.001, kernel=rbf, score=-0.051, total=   0.0s\n",
-      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
-      "[CV] ...... C=1, gamma=0.0001, kernel=rbf, score=-0.080, total=   0.0s\n",
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-      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
-      "[CV] ...... C=1, gamma=0.0001, kernel=rbf, score=-0.167, total=   0.0s\n",
-      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
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-      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
-      "[CV] ...... C=1, gamma=0.0001, kernel=rbf, score=-0.104, total=   0.0s\n",
-      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
-      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.166, total=   0.0s\n",
-      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
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-      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
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-      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
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-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.156, total=   0.0s\n",
-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.433, total=   0.0s\n",
-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.706, total=   0.0s\n",
-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.395, total=   0.0s\n",
-      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
-      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.541, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.204, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.474, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.701, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.432, total=   0.0s\n",
-      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
-      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.592, total=   0.0s\n",
-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
-      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.144, total=   0.0s\n",
-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
-      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.277, total=   0.0s\n",
-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
-      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.338, total=   0.0s\n",
-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
-      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.342, total=   0.0s\n",
-      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
-      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.283, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ..... C=10, gamma=0.0001, kernel=rbf, score=-0.049, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ..... C=10, gamma=0.0001, kernel=rbf, score=-0.013, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ..... C=10, gamma=0.0001, kernel=rbf, score=-0.094, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.051, total=   0.0s\n",
-      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
-      "[CV] ..... C=10, gamma=0.0001, kernel=rbf, score=-0.050, total=   0.0s\n",
-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
-      "[CV] ......... C=100, gamma=1, kernel=rbf, score=-0.040, total=   0.0s\n",
-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
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-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
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-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
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-      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
-      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.242, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.147, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.537, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.710, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.499, total=   0.0s\n",
-      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
-      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.354, total=   0.0s\n",
-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.154, total=   0.0s\n",
-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.515, total=   0.0s\n",
-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.727, total=   0.0s\n",
-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.368, total=   0.0s\n",
-      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
-      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.581, total=   0.0s\n",
-      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
-      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.215, total=   0.0s\n",
-      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
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-      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.717, total=   0.0s\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
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-     "text": [
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-      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.601, total=   0.0s\n",
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-      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
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-      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.517, total=   0.0s\n",
-      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
-      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.217, total=   0.0s\n",
-      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
-      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.518, total=   0.0s\n",
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-      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.716, total=   0.0s\n",
-      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
-      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.347, total=   0.0s\n",
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-      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.216, total=   0.0s\n",
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-      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.505, total=   0.0s\n",
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-      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.718, total=   0.0s\n",
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-      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.420, total=   0.0s\n",
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed:    0.2s finished\n"
+      "[CV 1/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.067 total time=   0.0s\n",
+      "[CV 2/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.058 total time=   0.0s\n",
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+      "[CV 5/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.089 total time=   0.0s\n",
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+      "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.021 total time=   0.0s\n",
+      "[CV 3/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.010 total time=   0.0s\n",
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+      "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.050 total time=   0.0s\n",
+      "[CV 1/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.053 total time=   0.0s\n",
+      "[CV 2/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.028 total time=   0.0s\n",
+      "[CV 3/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.108 total time=   0.0s\n",
+      "[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.040 total time=   0.0s\n",
+      "[CV 5/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.058 total time=   0.0s\n",
+      "[CV 1/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.080 total time=   0.0s\n",
+      "[CV 2/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.068 total time=   0.0s\n",
+      "[CV 3/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.167 total time=   0.0s\n",
+      "[CV 4/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.006 total time=   0.0s\n",
+      "[CV 5/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.105 total time=   0.0s\n",
+      "[CV 1/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.083 total time=   0.0s\n",
+      "[CV 2/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.073 total time=   0.0s\n",
+      "[CV 3/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.173 total time=   0.0s\n",
+      "[CV 4/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.013 total time=   0.0s\n",
+      "[CV 5/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.110 total time=   0.0s\n",
+      "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.018 total time=   0.0s\n",
+      "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.051 total time=   0.0s\n",
+      "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.080 total time=   0.0s\n",
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+      "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.081 total time=   0.0s\n",
+      "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.189 total time=   0.0s\n",
+      "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.254 total time=   0.0s\n",
+      "[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.533 total time=   0.0s\n",
+      "[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.347 total time=   0.0s\n",
+      "[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.503 total time=   0.0s\n",
+      "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.121 total time=   0.0s\n",
+      "[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.221 total time=   0.0s\n",
+      "[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.269 total time=   0.0s\n",
+      "[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.305 total time=   0.0s\n",
+      "[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.252 total time=   0.0s\n",
+      "[CV 1/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.049 total time=   0.0s\n",
+      "[CV 2/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.014 total time=   0.0s\n",
+      "[CV 3/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.096 total time=   0.0s\n",
+      "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.050 total time=   0.0s\n",
+      "[CV 5/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.051 total time=   0.0s\n",
+      "[CV 1/5] END ....C=1, gamma=0.0001, kernel=rbf;, score=-0.080 total time=   0.0s\n",
+      "[CV 2/5] END ....C=1, gamma=0.0001, kernel=rbf;, score=-0.068 total time=   0.0s\n",
+      "[CV 3/5] END ....C=1, gamma=0.0001, kernel=rbf;, score=-0.167 total time=   0.0s\n",
+      "[CV 4/5] END ....C=1, gamma=0.0001, kernel=rbf;, score=-0.006 total time=   0.0s\n",
+      "[CV 5/5] END ....C=1, gamma=0.0001, kernel=rbf;, score=-0.104 total time=   0.0s\n",
+      "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.166 total time=   0.0s\n",
+      "[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.231 total time=   0.0s\n",
+      "[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.468 total time=   0.0s\n",
+      "[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.212 total time=   0.0s\n",
+      "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.377 total time=   0.0s\n",
+      "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.156 total time=   0.0s\n",
+      "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.433 total time=   0.0s\n",
+      "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.706 total time=   0.0s\n",
+      "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.395 total time=   0.0s\n",
+      "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.541 total time=   0.0s\n",
+      "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.204 total time=   0.0s\n",
+      "[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.474 total time=   0.0s\n",
+      "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.701 total time=   0.0s\n",
+      "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.432 total time=   0.0s\n",
+      "[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.592 total time=   0.0s\n",
+      "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.144 total time=   0.0s\n",
+      "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.277 total time=   0.0s\n",
+      "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.338 total time=   0.0s\n",
+      "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.342 total time=   0.0s\n",
+      "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.283 total time=   0.0s\n",
+      "[CV 1/5] END ...C=10, gamma=0.0001, kernel=rbf;, score=-0.049 total time=   0.0s\n",
+      "[CV 2/5] END ...C=10, gamma=0.0001, kernel=rbf;, score=-0.013 total time=   0.0s\n",
+      "[CV 3/5] END ...C=10, gamma=0.0001, kernel=rbf;, score=-0.094 total time=   0.0s\n",
+      "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.051 total time=   0.0s\n",
+      "[CV 5/5] END ...C=10, gamma=0.0001, kernel=rbf;, score=-0.050 total time=   0.0s\n",
+      "[CV 1/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.040 total time=   0.0s\n",
+      "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.190 total time=   0.0s\n",
+      "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.429 total time=   0.0s\n",
+      "[CV 4/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.095 total time=   0.0s\n",
+      "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.242 total time=   0.0s\n",
+      "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.147 total time=   0.0s\n",
+      "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.537 total time=   0.0s\n",
+      "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.710 total time=   0.0s\n",
+      "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.499 total time=   0.0s\n",
+      "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.354 total time=   0.0s\n",
+      "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.154 total time=   0.0s\n",
+      "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.515 total time=   0.0s\n",
+      "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.727 total time=   0.0s\n",
+      "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.368 total time=   0.0s\n",
+      "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.581 total time=   0.0s\n",
+      "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.215 total time=   0.0s\n",
+      "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.502 total time=   0.0s\n",
+      "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.717 total time=   0.0s\n",
+      "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.420 total time=   0.0s\n",
+      "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.601 total time=   0.0s\n",
+      "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.146 total time=   0.0s\n",
+      "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.284 total time=   0.0s\n",
+      "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.346 total time=   0.0s\n",
+      "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.346 total time=   0.0s\n",
+      "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.288 total time=   0.0s\n",
+      "[CV 1/5] END ......C=1000, gamma=1, kernel=rbf;, score=-0.908 total time=   0.0s\n",
+      "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.139 total time=   0.0s\n",
+      "[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.040 total time=   0.0s\n",
+      "[CV 4/5] END ......C=1000, gamma=1, kernel=rbf;, score=-1.755 total time=   0.0s\n",
+      "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.056 total time=   0.0s\n",
+      "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.042 total time=   0.0s\n",
+      "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.493 total time=   0.0s\n",
+      "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.114 total time=   0.0s\n",
+      "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.048 total time=   0.0s\n",
+      "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.284 total time=   0.0s\n",
+      "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.120 total time=   0.0s\n",
+      "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.572 total time=   0.0s\n",
+      "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.752 total time=   0.0s\n",
+      "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.425 total time=   0.0s\n",
+      "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.517 total time=   0.0s\n",
+      "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.217 total time=   0.0s\n",
+      "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.518 total time=   0.0s\n",
+      "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.716 total time=   0.0s\n",
+      "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.347 total time=   0.0s\n",
+      "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.635 total time=   0.0s\n",
+      "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.216 total time=   0.0s\n",
+      "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.505 total time=   0.0s\n",
+      "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.718 total time=   0.0s\n",
+      "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.420 total time=   0.0s\n",
+      "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.602 total time=   0.0s\n"
      ]
     },
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVR(),\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n",
+       "                         &#x27;gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
+       "             verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=SVR(),\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n",
+       "                         &#x27;gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
+       "             verbose=3)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: SVR</label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=1000, gamma=0.0001)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=1000, gamma=0.0001)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
       "text/plain": [
        "GridSearchCV(estimator=SVR(),\n",
        "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
@@ -1156,7 +1835,7 @@
        "             verbose=3)"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1170,7 +1849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1179,7 +1858,7 @@
        "{'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1190,7 +1869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1199,7 +1878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1248,7 +1927,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -1262,7 +1941,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/8-NAive Baye's/Handwritten Notes/3.0-Naive Bayes Implementation.ipynb b/8-NAive Baye's/Handwritten Notes/3.0-Naive Bayes Implementation.ipynb
index 1c3e284a..7dd95a44 100644
--- a/8-NAive Baye's/Handwritten Notes/3.0-Naive Bayes Implementation.ipynb	
+++ b/8-NAive Baye's/Handwritten Notes/3.0-Naive Bayes Implementation.ipynb	
@@ -10,7 +10,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 222,
+   "id": "b00a9f5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
    "id": "f942f499-8e3b-4a99-a9ea-f432fedf51ca",
    "metadata": {},
    "outputs": [],
@@ -20,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 224,
    "id": "db67dcc9-a0bc-4443-a16e-1a3e4a585aef",
    "metadata": {},
    "outputs": [],
@@ -30,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 225,
    "id": "241d0b77-319c-45a4-8259-542d77bae16b",
    "metadata": {},
    "outputs": [],
@@ -40,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 226,
    "id": "4d2710f0-4a01-49d4-a7ba-03e2bcf9e5dd",
    "metadata": {},
    "outputs": [
@@ -56,7 +69,7 @@
        "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 226,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -67,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 227,
    "id": "fbb22228-5deb-4c9e-8602-c09859159d2e",
    "metadata": {},
    "outputs": [],
@@ -77,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 228,
    "id": "6ec2f6ee-c147-49e2-a5f5-3fe17b2c8de2",
    "metadata": {},
    "outputs": [
@@ -191,7 +204,7 @@
        "       [4.6, 3.2, 1.4, 0.2]])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 228,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 229,
    "id": "ef1fde3c-2dd3-42d1-b2d3-40211cea5aaa",
    "metadata": {},
    "outputs": [],
@@ -212,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 230,
    "id": "60a2da00-7c50-420a-a978-e4b0cb618d5d",
    "metadata": {},
    "outputs": [],
@@ -222,20 +235,424 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 231,
    "id": "ce278a97-43e8-4bf8-a541-d70f09e56af3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-11 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-11 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-11 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-11 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-11 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-11 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-11 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-11 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-11 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-11 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-11 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "GaussianNB()"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 231,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -246,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 232,
    "id": "fd6d2d1f-a596-4cb4-a051-3b9bb54e5898",
    "metadata": {},
    "outputs": [],
@@ -256,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 233,
    "id": "f715a301-ec5c-4264-b217-e96f2b285707",
    "metadata": {},
    "outputs": [],
@@ -266,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 234,
    "id": "e747a2a1-0e79-4f68-a1e6-55392ed80b6e",
    "metadata": {},
    "outputs": [
@@ -292,14 +709,14 @@
     }
    ],
    "source": [
-    "print(confusion_matrix(y_pred,y_test))\n",
-    "print(accuracy_score(y_pred,y_test))\n",
-    "print(classification_report(y_pred,y_test))"
+    "print(confusion_matrix(y_test,y_pred))\n",
+    "print(accuracy_score(y_test,y_pred))\n",
+    "print(classification_report(y_test,y_pred))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 235,
    "id": "905c3702-3283-4823-a78e-39bfc54d1725",
    "metadata": {},
    "outputs": [],
@@ -309,7 +726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 236,
    "id": "6b08f934-d66a-436e-ae7f-2a094ec36a17",
    "metadata": {},
    "outputs": [
@@ -476,27 +893,2331 @@
        "[244 rows x 7 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 236,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sns.load_dataset('tips')"
+    "df1=sns.load_dataset('tips')\n",
+    "df1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 237,
    "id": "e4422049-e066-4160-85d1-dac26bba81fe",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "enc=OneHotEncoder()\n",
+    "encoded=enc.fit_transform(df1[['day','sex']]).toarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "id": "c9fd24b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoder_df=pd.DataFrame(encoded,columns=enc.get_feature_names_out())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "id": "510d72af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>day_Fri</th>\n",
+       "      <th>day_Sat</th>\n",
+       "      <th>day_Sun</th>\n",
+       "      <th>day_Thur</th>\n",
+       "      <th>sex_Female</th>\n",
+       "      <th>sex_Male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>244 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     day_Fri  day_Sat  day_Sun  day_Thur  sex_Female  sex_Male\n",
+       "0        0.0      0.0      1.0       0.0         1.0       0.0\n",
+       "1        0.0      0.0      1.0       0.0         0.0       1.0\n",
+       "2        0.0      0.0      1.0       0.0         0.0       1.0\n",
+       "3        0.0      0.0      1.0       0.0         0.0       1.0\n",
+       "4        0.0      0.0      1.0       0.0         1.0       0.0\n",
+       "..       ...      ...      ...       ...         ...       ...\n",
+       "239      0.0      1.0      0.0       0.0         0.0       1.0\n",
+       "240      0.0      1.0      0.0       0.0         1.0       0.0\n",
+       "241      0.0      1.0      0.0       0.0         0.0       1.0\n",
+       "242      0.0      1.0      0.0       0.0         0.0       1.0\n",
+       "243      0.0      0.0      0.0       1.0         1.0       0.0\n",
+       "\n",
+       "[244 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoder_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "id": "f654123f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1=pd.concat([df1,encoder_df],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "id": "55b24a59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "      <th>day_Fri</th>\n",
+       "      <th>day_Sat</th>\n",
+       "      <th>day_Sun</th>\n",
+       "      <th>day_Thur</th>\n",
+       "      <th>sex_Female</th>\n",
+       "      <th>sex_Male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>29.03</td>\n",
+       "      <td>5.92</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>27.18</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>22.67</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>17.82</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>18.78</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>244 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     total_bill   tip     sex smoker   day    time  size  day_Fri  day_Sat  \\\n",
+       "0         16.99  1.01  Female     No   Sun  Dinner     2      0.0      0.0   \n",
+       "1         10.34  1.66    Male     No   Sun  Dinner     3      0.0      0.0   \n",
+       "2         21.01  3.50    Male     No   Sun  Dinner     3      0.0      0.0   \n",
+       "3         23.68  3.31    Male     No   Sun  Dinner     2      0.0      0.0   \n",
+       "4         24.59  3.61  Female     No   Sun  Dinner     4      0.0      0.0   \n",
+       "..          ...   ...     ...    ...   ...     ...   ...      ...      ...   \n",
+       "239       29.03  5.92    Male     No   Sat  Dinner     3      0.0      1.0   \n",
+       "240       27.18  2.00  Female    Yes   Sat  Dinner     2      0.0      1.0   \n",
+       "241       22.67  2.00    Male    Yes   Sat  Dinner     2      0.0      1.0   \n",
+       "242       17.82  1.75    Male     No   Sat  Dinner     2      0.0      1.0   \n",
+       "243       18.78  3.00  Female     No  Thur  Dinner     2      0.0      0.0   \n",
+       "\n",
+       "     day_Sun  day_Thur  sex_Female  sex_Male  \n",
+       "0        1.0       0.0         1.0       0.0  \n",
+       "1        1.0       0.0         0.0       1.0  \n",
+       "2        1.0       0.0         0.0       1.0  \n",
+       "3        1.0       0.0         0.0       1.0  \n",
+       "4        1.0       0.0         1.0       0.0  \n",
+       "..       ...       ...         ...       ...  \n",
+       "239      0.0       0.0         0.0       1.0  \n",
+       "240      0.0       0.0         1.0       0.0  \n",
+       "241      0.0       0.0         0.0       1.0  \n",
+       "242      0.0       0.0         0.0       1.0  \n",
+       "243      0.0       1.0         1.0       0.0  \n",
+       "\n",
+       "[244 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "id": "0534ee73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1.drop(columns=['day', 'sex'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "id": "6f1c13b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "      <th>day_Fri</th>\n",
+       "      <th>day_Sat</th>\n",
+       "      <th>day_Sun</th>\n",
+       "      <th>day_Thur</th>\n",
+       "      <th>sex_Female</th>\n",
+       "      <th>sex_Male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>29.03</td>\n",
+       "      <td>5.92</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>27.18</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>22.67</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>17.82</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>18.78</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>244 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     total_bill   tip smoker    time  size  day_Fri  day_Sat  day_Sun  \\\n",
+       "0         16.99  1.01     No  Dinner     2      0.0      0.0      1.0   \n",
+       "1         10.34  1.66     No  Dinner     3      0.0      0.0      1.0   \n",
+       "2         21.01  3.50     No  Dinner     3      0.0      0.0      1.0   \n",
+       "3         23.68  3.31     No  Dinner     2      0.0      0.0      1.0   \n",
+       "4         24.59  3.61     No  Dinner     4      0.0      0.0      1.0   \n",
+       "..          ...   ...    ...     ...   ...      ...      ...      ...   \n",
+       "239       29.03  5.92     No  Dinner     3      0.0      1.0      0.0   \n",
+       "240       27.18  2.00    Yes  Dinner     2      0.0      1.0      0.0   \n",
+       "241       22.67  2.00    Yes  Dinner     2      0.0      1.0      0.0   \n",
+       "242       17.82  1.75     No  Dinner     2      0.0      1.0      0.0   \n",
+       "243       18.78  3.00     No  Dinner     2      0.0      0.0      0.0   \n",
+       "\n",
+       "     day_Thur  sex_Female  sex_Male  \n",
+       "0         0.0         1.0       0.0  \n",
+       "1         0.0         0.0       1.0  \n",
+       "2         0.0         0.0       1.0  \n",
+       "3         0.0         0.0       1.0  \n",
+       "4         0.0         1.0       0.0  \n",
+       "..        ...         ...       ...  \n",
+       "239       0.0         0.0       1.0  \n",
+       "240       0.0         1.0       0.0  \n",
+       "241       0.0         0.0       1.0  \n",
+       "242       0.0         0.0       1.0  \n",
+       "243       1.0         1.0       0.0  \n",
+       "\n",
+       "[244 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "id": "79d7a166",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "smoker\n",
+       "No     151\n",
+       "Yes     93\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 244,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1['smoker'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "id": "37d77268",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "time\n",
+       "Dinner    176\n",
+       "Lunch      68\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 245,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1['time'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "id": "f2c3dae6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transforming time\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "enc=LabelEncoder()\n",
+    "df1['time']=enc.fit_transform(df1['time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "id": "43a01abc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "      <th>day_Fri</th>\n",
+       "      <th>day_Sat</th>\n",
+       "      <th>day_Sun</th>\n",
+       "      <th>day_Thur</th>\n",
+       "      <th>sex_Female</th>\n",
+       "      <th>sex_Male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_bill   tip smoker  time  size  day_Fri  day_Sat  day_Sun  day_Thur  \\\n",
+       "0       16.99  1.01     No     0     2      0.0      0.0      1.0       0.0   \n",
+       "1       10.34  1.66     No     0     3      0.0      0.0      1.0       0.0   \n",
+       "2       21.01  3.50     No     0     3      0.0      0.0      1.0       0.0   \n",
+       "3       23.68  3.31     No     0     2      0.0      0.0      1.0       0.0   \n",
+       "4       24.59  3.61     No     0     4      0.0      0.0      1.0       0.0   \n",
+       "\n",
+       "   sex_Female  sex_Male  \n",
+       "0         1.0       0.0  \n",
+       "1         0.0       1.0  \n",
+       "2         0.0       1.0  \n",
+       "3         0.0       1.0  \n",
+       "4         1.0       0.0  "
+      ]
+     },
+     "execution_count": 247,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "id": "c36f1627",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1['smoker']=df1['smoker'].map({'Yes': 1, 'No': 0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "id": "ee188047",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X=df1.drop('smoker',axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "id": "5305c75f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "      <th>day_Fri</th>\n",
+       "      <th>day_Sat</th>\n",
+       "      <th>day_Sun</th>\n",
+       "      <th>day_Thur</th>\n",
+       "      <th>sex_Female</th>\n",
+       "      <th>sex_Male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>29.03</td>\n",
+       "      <td>5.92</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>27.18</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>22.67</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>17.82</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>18.78</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>244 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     total_bill   tip  time  size  day_Fri  day_Sat  day_Sun  day_Thur  \\\n",
+       "0         16.99  1.01     0     2      0.0      0.0      1.0       0.0   \n",
+       "1         10.34  1.66     0     3      0.0      0.0      1.0       0.0   \n",
+       "2         21.01  3.50     0     3      0.0      0.0      1.0       0.0   \n",
+       "3         23.68  3.31     0     2      0.0      0.0      1.0       0.0   \n",
+       "4         24.59  3.61     0     4      0.0      0.0      1.0       0.0   \n",
+       "..          ...   ...   ...   ...      ...      ...      ...       ...   \n",
+       "239       29.03  5.92     0     3      0.0      1.0      0.0       0.0   \n",
+       "240       27.18  2.00     0     2      0.0      1.0      0.0       0.0   \n",
+       "241       22.67  2.00     0     2      0.0      1.0      0.0       0.0   \n",
+       "242       17.82  1.75     0     2      0.0      1.0      0.0       0.0   \n",
+       "243       18.78  3.00     0     2      0.0      0.0      0.0       1.0   \n",
+       "\n",
+       "     sex_Female  sex_Male  \n",
+       "0           1.0       0.0  \n",
+       "1           0.0       1.0  \n",
+       "2           0.0       1.0  \n",
+       "3           0.0       1.0  \n",
+       "4           1.0       0.0  \n",
+       "..          ...       ...  \n",
+       "239         0.0       1.0  \n",
+       "240         1.0       0.0  \n",
+       "241         0.0       1.0  \n",
+       "242         0.0       1.0  \n",
+       "243         1.0       0.0  \n",
+       "\n",
+       "[244 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "id": "9b6b9d15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y=df1['smoker']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "id": "67a294e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      0\n",
+       "1      0\n",
+       "2      0\n",
+       "3      0\n",
+       "4      0\n",
+       "      ..\n",
+       "239    0\n",
+       "240    1\n",
+       "241    1\n",
+       "242    0\n",
+       "243    0\n",
+       "Name: smoker, Length: 244, dtype: category\n",
+       "Categories (2, int64): [1, 0]"
+      ]
+     },
+     "execution_count": 252,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "id": "c396e2e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(244, 10)"
+      ]
+     },
+     "execution_count": 253,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "id": "83d1660f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.7,random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "id": "022831cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-12 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-12 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-12 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-12 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-12 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-12 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "GaussianNB()"
+      ]
+     },
+     "execution_count": 255,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "gau=GaussianNB()\n",
+    "gau.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "id": "b36fc721",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred=gau.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "id": "c6b997db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[36 11]\n",
+      " [13 14]]\n",
+      "0.6756756756756757\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.73      0.77      0.75        47\n",
+      "           1       0.56      0.52      0.54        27\n",
+      "\n",
+      "    accuracy                           0.68        74\n",
+      "   macro avg       0.65      0.64      0.64        74\n",
+      "weighted avg       0.67      0.68      0.67        74\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(confusion_matrix(y_test,y_pred))\n",
+    "print(accuracy_score(y_test,y_pred))\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "id": "2507cec0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df1.corr(),annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "id": "4843eb13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = [\n",
+    "    {\n",
+    "        'solver': ['liblinear'],\n",
+    "        'penalty': ['l1', 'l2'],\n",
+    "        'C': [0.01, 0.1, 1, 10],\n",
+    "        'class_weight': [None, 'balanced']\n",
+    "    },\n",
+    "    {\n",
+    "        'solver': ['lbfgs', 'sag', 'newton-cg'],\n",
+    "        'penalty': ['l2'],\n",
+    "        'C': [0.01, 0.1, 1, 10],\n",
+    "        'class_weight': [None, 'balanced']\n",
+    "    },\n",
+    "    {\n",
+    "        'solver': ['saga'],\n",
+    "        'penalty': ['l1', 'l2'],\n",
+    "        'C': [0.01, 0.1, 1, 10],\n",
+    "        'class_weight': [None, 'balanced']\n",
+    "        # ❌ No l1_ratio here\n",
+    "    },\n",
+    "    {\n",
+    "        'solver': ['saga'],\n",
+    "        'penalty': ['elasticnet'],\n",
+    "        'C': [0.01, 0.1, 1, 10],\n",
+    "        'l1_ratio': [0.1, 0.5, 0.9],  # ✅ Only used for elasticnet\n",
+    "        'class_weight': [None, 'balanced']\n",
+    "    }\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "id": "630c7d66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import GridSearchCV,StratifiedKFold\n",
+    "\n",
+    "reg=LogisticRegression(max_iter=7000)\n",
+    "grid=GridSearchCV(estimator=reg,param_grid=param_grid,scoring='accuracy',refit=True,cv=StratifiedKFold())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "id": "463c851a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-16 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-16 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-16 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-16 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-16 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-16 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-16 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-16 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
+       "             estimator=LogisticRegression(max_iter=7000),\n",
+       "             param_grid=[{&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                          &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                          &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;], &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                          &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n",
+       "                          &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;sag&#x27;, &#x27;newton-cg&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                          &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                          &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;], &#x27;solver&#x27;: [&#x27;saga&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                          &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                          &#x27;l1_ratio&#x27;: [0.1, 0.5, 0.9],\n",
+       "                          &#x27;penalty&#x27;: [&#x27;elasticnet&#x27;], &#x27;solver&#x27;: [&#x27;saga&#x27;]}],\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
+       "             estimator=LogisticRegression(max_iter=7000),\n",
+       "             param_grid=[{&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                          &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                          &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;], &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                          &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n",
+       "                          &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;sag&#x27;, &#x27;newton-cg&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                          &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                          &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;], &#x27;solver&#x27;: [&#x27;saga&#x27;]},\n",
+       "                         {&#x27;C&#x27;: [0.01, 0.1, 1, 10],\n",
+       "                          &#x27;class_weight&#x27;: [None, &#x27;balanced&#x27;],\n",
+       "                          &#x27;l1_ratio&#x27;: [0.1, 0.5, 0.9],\n",
+       "                          &#x27;penalty&#x27;: [&#x27;elasticnet&#x27;], &#x27;solver&#x27;: [&#x27;saga&#x27;]}],\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1, max_iter=7000, penalty=&#x27;l1&#x27;, solver=&#x27;saga&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1, max_iter=7000, penalty=&#x27;l1&#x27;, solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
+       "             estimator=LogisticRegression(max_iter=7000),\n",
+       "             param_grid=[{'C': [0.01, 0.1, 1, 10],\n",
+       "                          'class_weight': [None, 'balanced'],\n",
+       "                          'penalty': ['l1', 'l2'], 'solver': ['liblinear']},\n",
+       "                         {'C': [0.01, 0.1, 1, 10],\n",
+       "                          'class_weight': [None, 'balanced'], 'penalty': ['l2'],\n",
+       "                          'solver': ['lbfgs', 'sag', 'newton-cg']},\n",
+       "                         {'C': [0.01, 0.1, 1, 10],\n",
+       "                          'class_weight': [None, 'balanced'],\n",
+       "                          'penalty': ['l1', 'l2'], 'solver': ['saga']},\n",
+       "                         {'C': [0.01, 0.1, 1, 10],\n",
+       "                          'class_weight': [None, 'balanced'],\n",
+       "                          'l1_ratio': [0.1, 0.5, 0.9],\n",
+       "                          'penalty': ['elasticnet'], 'solver': ['saga']}],\n",
+       "             scoring='accuracy')"
+      ]
+     },
+     "execution_count": 270,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "id": "5aebb3c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred=grid.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "id": "fe953031",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'C': 1, 'class_weight': None, 'penalty': 'l1', 'solver': 'saga'}"
+      ]
+     },
+     "execution_count": 274,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "id": "646cda9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7058823529411765"
+      ]
+     },
+     "execution_count": 275,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9fa47d8",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -510,7 +3231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/9-K Nearest Neighbor/3.0-KNNClassifier.ipynb b/9-K Nearest Neighbor/3.0-KNNClassifier.ipynb
index a6b8c8f6..adea783e 100644
--- a/9-K Nearest Neighbor/3.0-KNNClassifier.ipynb	
+++ b/9-K Nearest Neighbor/3.0-KNNClassifier.ipynb	
@@ -100,20 +100,424 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "id": "2447f6f0-3e57-4e0c-bec1-4cf48dc0bce3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "KNeighborsClassifier()"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -125,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "id": "99dcc07e-f8f1-496b-8797-906741317be5",
    "metadata": {},
    "outputs": [],
@@ -135,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "id": "3ddc3012-dc0e-4448-93d5-1bf3ea89c760",
    "metadata": {},
    "outputs": [],
@@ -145,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 10,
    "id": "7ed6873d-3295-41b1-8fd8-489231b50db6",
    "metadata": {},
    "outputs": [
@@ -176,10 +580,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "badf3ed3-24c0-4994-82ec-3333c4571eed",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (1670823703.py, line 3)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[11], line 3\u001b[1;36m\u001b[0m\n\u001b[1;33m    for i k=1,2,3,4,5,6,7,8,9,10\u001b[0m\n\u001b[1;37m          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
     "## Task \n",
     "GridsearchCV\n",
@@ -187,35 +600,11 @@
     "\n",
     "## K best "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e3c7c6d9-33d7-4215-b68e-c726fbf7c3f6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7e500a8a-3530-45f0-8ddc-3729ffd26a74",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4134f91f-db58-431f-bd85-43d0932d8d45",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -229,7 +618,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/9-K Nearest Neighbor/3.0-KNNRegressor.ipynb b/9-K Nearest Neighbor/3.0-KNNRegressor.ipynb
index 9f4d3e7c..ae822ae8 100644
--- a/9-K Nearest Neighbor/3.0-KNNRegressor.ipynb	
+++ b/9-K Nearest Neighbor/3.0-KNNRegressor.ipynb	
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "c267463c-174a-4429-9ccd-d436bef976e6",
    "metadata": {},
    "outputs": [],
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "4491fcf7-8b97-44b6-b5ee-48274bb26817",
    "metadata": {},
    "outputs": [],
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "id": "b142e965-cb36-491c-8852-dc85bce80cdc",
    "metadata": {},
    "outputs": [],
@@ -43,20 +43,424 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "id": "05c22146-1f73-4146-bd14-11c7d5579b3c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=6)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(n_neighbors=6)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "KNeighborsRegressor(n_neighbors=6)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -68,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "id": "6fb7a4cc-28f5-4865-b4a9-3f5e6305606b",
    "metadata": {},
    "outputs": [],
@@ -78,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "7dfad2a3-704c-4630-bf54-26506962816e",
    "metadata": {},
    "outputs": [],
@@ -88,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "9a8064b4-ce5e-491d-8441-c7bc252ba4c3",
    "metadata": {},
    "outputs": [
@@ -107,51 +511,11 @@
     "print(mean_absolute_error(y_test,y_pred))\n",
     "print(mean_squared_error(y_test,y_pred))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "354d30c5-882b-416b-81eb-49f294826948",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba987ca3-079f-4ecd-80bf-ced51e654e7f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "16118cfb-f4a8-43bc-a82d-56faa6a362c4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e4faff29-e655-49b2-9e84-f0c36a0e17e4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8d97d09a-229c-4854-b77a-530ef0615b63",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -165,7 +529,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,