KNN_Algorithm_Example(Simple)_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest Neighbors with Python\n",
    "You've been given a classified data set from a company! They've hidden the feature column names but have given you the data and the target classes.\n",
    "\n",
    "We'll try to use KNN to create a model that directly predicts a class for a new data point based off of the features.\n",
    "\n",
    "Let's grab it and use it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv ('E:\\AI_ML_Self_CodesAndProjects\\Classified_Data.csv', index_col = 0) # Setting the first column as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th>TARGET CLASS</th>\n",
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       "      <td>0.957877</td>\n",
       "      <td>1.285597</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.522692</td>\n",
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       "      <td>0.668976</td>\n",
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       "      <td>1.115596</td>\n",
       "      <td>0.646691</td>\n",
       "      <td>1.463812</td>\n",
       "      <td>1.419167</td>\n",
       "      <td>1</td>\n",
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       "        WTT       PTI       EQW       SBI       LQE       QWG       FDJ  \\\n",
       "0  0.913917  1.162073  0.567946  0.755464  0.780862  0.352608  0.759697   \n",
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       "\n",
       "        PJF       HQE       NXJ  TARGET CLASS  \n",
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize the Variables\n",
    "Because the KNN classifier predicts the class of a given test observation by identifying the observations that are nearest to it, the scale of the variables matters. Any variables that are on a large scale will have a much larger effect on the distance between the observations, and hence on the KNN classifier, than variables that are on a small scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the MinMaxScaler\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit and transform the DataFrame using MinMaxScaler\n",
    "normalized_data = scaler.fit_transform(df.drop('TARGET CLASS',axis=1)) # Not using the target class to fit the data to scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the normalized data back to a DataFrame\n",
    "normalized_df = pd.DataFrame(normalized_data, columns=df.columns[:-1]) # Converting everything back to dataframe except last one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "      <td>0.490986</td>\n",
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       "      <td>0.785889</td>\n",
       "      <td>0.727165</td>\n",
       "      <td>0.761269</td>\n",
       "      <td>0.409326</td>\n",
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       "      <td>0.365101</td>\n",
       "      <td>0.294082</td>\n",
       "      <td>0.392456</td>\n",
       "      <td>0.687196</td>\n",
       "      <td>0.314229</td>\n",
       "      <td>0.578492</td>\n",
       "      <td>0.233666</td>\n",
       "      <td>0.722546</td>\n",
       "      <td>0.621463</td>\n",
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       "      <td>1.285597</td>\n",
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       "      <td>0.653046</td>\n",
       "      <td>0.825624</td>\n",
       "      <td>1.142504</td>\n",
       "      <td>0.875128</td>\n",
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       "      <td>1.380003</td>\n",
       "      <td>1.522692</td>\n",
       "      <td>1.153093</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.627280</td>\n",
       "      <td>0.668976</td>\n",
       "      <td>1.232537</td>\n",
       "      <td>0.703727</td>\n",
       "      <td>1.115596</td>\n",
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       "      <th>995</th>\n",
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       "      <td>1.034006</td>\n",
       "      <td>0.853116</td>\n",
       "      <td>0.622460</td>\n",
       "      <td>1.036610</td>\n",
       "      <td>0.586240</td>\n",
       "      <td>0.746811</td>\n",
       "      <td>0.319752</td>\n",
       "      <td>1.117340</td>\n",
       "      <td>1.348517</td>\n",
       "      <td>1</td>\n",
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       "      <th>996</th>\n",
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       "      <td>0.955786</td>\n",
       "      <td>0.941835</td>\n",
       "      <td>0.792882</td>\n",
       "      <td>1.414277</td>\n",
       "      <td>1.269540</td>\n",
       "      <td>1.055928</td>\n",
       "      <td>0.713193</td>\n",
       "      <td>0.958684</td>\n",
       "      <td>1.663489</td>\n",
       "      <td>0</td>\n",
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       "      <th>997</th>\n",
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       "      <td>0.982462</td>\n",
       "      <td>0.781905</td>\n",
       "      <td>0.916738</td>\n",
       "      <td>0.901031</td>\n",
       "      <td>0.884738</td>\n",
       "      <td>0.386802</td>\n",
       "      <td>0.389584</td>\n",
       "      <td>0.919191</td>\n",
       "      <td>1.385504</td>\n",
       "      <td>1</td>\n",
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       "      <th>998</th>\n",
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       "      <td>0.665696</td>\n",
       "      <td>1.608612</td>\n",
       "      <td>0.943859</td>\n",
       "      <td>0.855806</td>\n",
       "      <td>1.061338</td>\n",
       "      <td>1.277456</td>\n",
       "      <td>1.188063</td>\n",
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       "      <td>0.961184</td>\n",
       "      <td>0.417006</td>\n",
       "      <td>0.799784</td>\n",
       "      <td>0.934399</td>\n",
       "      <td>0.424762</td>\n",
       "      <td>0.778234</td>\n",
       "      <td>0.907962</td>\n",
       "      <td>1.257190</td>\n",
       "      <td>1.364837</td>\n",
       "      <td>1</td>\n",
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       "0    0.913917  1.162073  0.567946  0.755464  0.780862  0.352608  0.759697   \n",
       "1    0.635632  1.003722  0.535342  0.825645  0.924109  0.648450  0.675334   \n",
       "2    0.721360  1.201493  0.921990  0.855595  1.526629  0.720781  1.626351   \n",
       "3    1.234204  1.386726  0.653046  0.825624  1.142504  0.875128  1.409708   \n",
       "4    1.279491  0.949750  0.627280  0.668976  1.232537  0.703727  1.115596   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "995  1.010953  1.034006  0.853116  0.622460  1.036610  0.586240  0.746811   \n",
       "996  0.575529  0.955786  0.941835  0.792882  1.414277  1.269540  1.055928   \n",
       "997  1.135470  0.982462  0.781905  0.916738  0.901031  0.884738  0.386802   \n",
       "998  1.084894  0.861769  0.407158  0.665696  1.608612  0.943859  0.855806   \n",
       "999  0.837460  0.961184  0.417006  0.799784  0.934399  0.424762  0.778234   \n",
       "\n",
       "          PJF       HQE       NXJ  TARGET CLASS  \n",
       "0    0.643798  0.879422  1.231409             1  \n",
       "1    1.013546  0.621552  1.492702             0  \n",
       "2    1.154483  0.957877  1.285597             0  \n",
       "3    1.380003  1.522692  1.153093             1  \n",
       "4    0.646691  1.463812  1.419167             1  \n",
       "..        ...       ...       ...           ...  \n",
       "995  0.319752  1.117340  1.348517             1  \n",
       "996  0.713193  0.958684  1.663489             0  \n",
       "997  0.389584  0.919191  1.385504             1  \n",
       "998  1.061338  1.277456  1.188063             1  \n",
       "999  0.907962  1.257190  1.364837             1  \n",
       "\n",
       "[1000 rows x 11 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>WTT</th>\n",
       "      <th>PTI</th>\n",
       "      <th>EQW</th>\n",
       "      <th>SBI</th>\n",
       "      <th>LQE</th>\n",
       "      <th>QWG</th>\n",
       "      <th>FDJ</th>\n",
       "      <th>PJF</th>\n",
       "      <th>HQE</th>\n",
       "      <th>NXJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.477912</td>\n",
       "      <td>0.517593</td>\n",
       "      <td>0.255846</td>\n",
       "      <td>0.446856</td>\n",
       "      <td>0.348797</td>\n",
       "      <td>0.064235</td>\n",
       "      <td>0.327526</td>\n",
       "      <td>0.231719</td>\n",
       "      <td>0.338214</td>\n",
       "      <td>0.471767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.298068</td>\n",
       "      <td>0.403864</td>\n",
       "      <td>0.234836</td>\n",
       "      <td>0.490999</td>\n",
       "      <td>0.456119</td>\n",
       "      <td>0.274872</td>\n",
       "      <td>0.268036</td>\n",
       "      <td>0.480550</td>\n",
       "      <td>0.168622</td>\n",
       "      <td>0.680091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.353470</td>\n",
       "      <td>0.545904</td>\n",
       "      <td>0.483996</td>\n",
       "      <td>0.509837</td>\n",
       "      <td>0.907533</td>\n",
       "      <td>0.326371</td>\n",
       "      <td>0.938657</td>\n",
       "      <td>0.575397</td>\n",
       "      <td>0.389811</td>\n",
       "      <td>0.514970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.684900</td>\n",
       "      <td>0.678940</td>\n",
       "      <td>0.310686</td>\n",
       "      <td>0.490986</td>\n",
       "      <td>0.619742</td>\n",
       "      <td>0.436264</td>\n",
       "      <td>0.785889</td>\n",
       "      <td>0.727165</td>\n",
       "      <td>0.761269</td>\n",
       "      <td>0.409326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.714167</td>\n",
       "      <td>0.365101</td>\n",
       "      <td>0.294082</td>\n",
       "      <td>0.392456</td>\n",
       "      <td>0.687196</td>\n",
       "      <td>0.314229</td>\n",
       "      <td>0.578492</td>\n",
       "      <td>0.233666</td>\n",
       "      <td>0.722546</td>\n",
       "      <td>0.621463</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>995</th>\n",
       "      <td>0.540622</td>\n",
       "      <td>0.425615</td>\n",
       "      <td>0.439613</td>\n",
       "      <td>0.363198</td>\n",
       "      <td>0.540406</td>\n",
       "      <td>0.230579</td>\n",
       "      <td>0.318439</td>\n",
       "      <td>0.013646</td>\n",
       "      <td>0.494684</td>\n",
       "      <td>0.565135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.259226</td>\n",
       "      <td>0.369436</td>\n",
       "      <td>0.496785</td>\n",
       "      <td>0.470391</td>\n",
       "      <td>0.823357</td>\n",
       "      <td>0.717082</td>\n",
       "      <td>0.536417</td>\n",
       "      <td>0.278421</td>\n",
       "      <td>0.390341</td>\n",
       "      <td>0.816257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.621092</td>\n",
       "      <td>0.388595</td>\n",
       "      <td>0.393724</td>\n",
       "      <td>0.548295</td>\n",
       "      <td>0.438829</td>\n",
       "      <td>0.443107</td>\n",
       "      <td>0.064574</td>\n",
       "      <td>0.060641</td>\n",
       "      <td>0.364369</td>\n",
       "      <td>0.594624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.588408</td>\n",
       "      <td>0.301913</td>\n",
       "      <td>0.152232</td>\n",
       "      <td>0.390393</td>\n",
       "      <td>0.968955</td>\n",
       "      <td>0.485200</td>\n",
       "      <td>0.395298</td>\n",
       "      <td>0.512713</td>\n",
       "      <td>0.599986</td>\n",
       "      <td>0.437207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.428501</td>\n",
       "      <td>0.373313</td>\n",
       "      <td>0.158579</td>\n",
       "      <td>0.474733</td>\n",
       "      <td>0.463829</td>\n",
       "      <td>0.115608</td>\n",
       "      <td>0.340598</td>\n",
       "      <td>0.409495</td>\n",
       "      <td>0.586658</td>\n",
       "      <td>0.578147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          WTT       PTI       EQW       SBI       LQE       QWG       FDJ  \\\n",
       "0    0.477912  0.517593  0.255846  0.446856  0.348797  0.064235  0.327526   \n",
       "1    0.298068  0.403864  0.234836  0.490999  0.456119  0.274872  0.268036   \n",
       "2    0.353470  0.545904  0.483996  0.509837  0.907533  0.326371  0.938657   \n",
       "3    0.684900  0.678940  0.310686  0.490986  0.619742  0.436264  0.785889   \n",
       "4    0.714167  0.365101  0.294082  0.392456  0.687196  0.314229  0.578492   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "995  0.540622  0.425615  0.439613  0.363198  0.540406  0.230579  0.318439   \n",
       "996  0.259226  0.369436  0.496785  0.470391  0.823357  0.717082  0.536417   \n",
       "997  0.621092  0.388595  0.393724  0.548295  0.438829  0.443107  0.064574   \n",
       "998  0.588408  0.301913  0.152232  0.390393  0.968955  0.485200  0.395298   \n",
       "999  0.428501  0.373313  0.158579  0.474733  0.463829  0.115608  0.340598   \n",
       "\n",
       "          PJF       HQE       NXJ  \n",
       "0    0.231719  0.338214  0.471767  \n",
       "1    0.480550  0.168622  0.680091  \n",
       "2    0.575397  0.389811  0.514970  \n",
       "3    0.727165  0.761269  0.409326  \n",
       "4    0.233666  0.722546  0.621463  \n",
       "..        ...       ...       ...  \n",
       "995  0.013646  0.494684  0.565135  \n",
       "996  0.278421  0.390341  0.816257  \n",
       "997  0.060641  0.364369  0.594624  \n",
       "998  0.512713  0.599986  0.437207  \n",
       "999  0.409495  0.586658  0.578147  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(normalized_data,df['TARGET CLASS'],test_size=0.30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using KNN\n",
    "Remember that we are trying to come up with a model to predict TARGET CLASS or not. We'll start with k=1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluations\n",
    "Let's evaluate our KNN model!\n",
    "Accuracy evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  1.0\n",
      "Test set Accuracy:  0.9233333333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, knn.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ofcourse it gave too high a train accuracy as k was used as 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing a K Value\n",
    "Let's go ahead and use the elbow method to pick a good K Value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "# Will take some time\n",
    "for i in range(1,40):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.07666666666666666, 0.06333333333333334, 0.08333333333333333, 0.056666666666666664, 0.06333333333333334, 0.06333333333333334, 0.06333333333333334, 0.05333333333333334, 0.07333333333333333, 0.06333333333333334, 0.07666666666666666, 0.06666666666666667, 0.07333333333333333, 0.06, 0.06333333333333334, 0.06333333333333334, 0.07, 0.06, 0.06666666666666667, 0.06, 0.07, 0.06, 0.06, 0.05333333333333334, 0.06, 0.05333333333333334, 0.056666666666666664, 0.04666666666666667, 0.04666666666666667, 0.043333333333333335, 0.05, 0.04666666666666667, 0.05333333333333334, 0.05, 0.05333333333333334, 0.05, 0.056666666666666664, 0.05333333333333334, 0.06]\n"
     ]
    }
   ],
   "source": [
    "print(error_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MaRksaMtwVVUuYMvN3Xu7f3ybBW3GGGNMy2BBW4Zbv37frlFwKT8AtmxJbX2MMcYYkx42ezTDTZwI33yz73Z/0GYtbcYYY0zLYEFbhjv44ODbDzgAfvlLOOyw1NbHGGOMMelh3aMZTBXuvRcWLNh3X4cOcOedcNxxqa+XMcYYY1LPgrYEVFbC2NF1dG1fS25OA13b1zJ2dB2Vld6cv7oabroJXn89+P5vvrHu0UyU7O+FMcaYlsmCtjjNmgUDympoM3kCc7b3pU5bMWd7X9pMnsCAshpmzUr8GqFytPkddRSMHZv4dYx3UvG9MMYY0zLZmLY4VFbC5RfWMGPHmQxk3v+2l7KcP9XfzHfr/x9DL3yVeUuKKC2N/zqRgraOHa2lLZOk6nthjDGmZbKWtjhMvKeOq+sf3OsXc6CBzGNU/UNMuq8uoetECtqKiy1oyySp+l4YY4xpmSxoi8O0pxu4qv7hsGVG1T/EtKf2JHQdf9DWrVvw/Ra0ZZZUfS+MMca0TBa0xWFTdQE9WRm2TA9Wsam6dULXufJKWLHCukezRaq+F8YYY1omC9ri0LltHSvpGbbMKnrQue3OhK5TUAA9e7q1R4P54Q9h/PiELmE8lKrvhTHGmJbJgrY4DB+Rw5T8a8OWmZx/HcNH5oYtE8ljj8GUKaH3n302XH99QpcwHkrV98IYY0zLJKqa7jokXf/+/XVBsAy1caqsdGkdms4S9JvLAIYWJj5LcNAgaN0aXnst+P6aGli9Gnr3dq1yJr1S9b0wxhjTvInIQlXt33S7tbTFobQUpk4vYmjhq4zPv4tKSqgnj0pKGJ9/F0MLX2Xq9MR/MVdVhR7PBvDSS3DEEVjS1gwR+L34Bcn7XhhjjGmZLGiL05AhMG9JEXXXjGFQ+6W0kToGtl1K3TVjmLekiCFDEr9GpKDNFo3PPEOGwNzFRTyYO4bjWi2lTU4dg9p7+70wxhjTMlly3QSUlsK9Ewu4d6J/S6Fn596xwy1jFS5o69jRPVvQllmKiqB2TwF/uRcmToRLLy3kttvSXStjjDHZzlraPLJ6Nbzxhnfn27jRPe+/f+gy1tKWmfzd1X36QF4eLF6c3voYY4xpHqylzSMTJ8J990FtLeR6MDmwZ0/YtQsaGkKXsaAtMy1f7p5LS6GsDN5/P731McYY0zwktaVNRM4Rkc9EpEJExgXZXyAiz/v2vycivXzbLxORRQGPBhH5lm/fm75z+veFaYtKndJSqK+Hr77y7pz5+eFnhe63Hzz4IJx+unfXNIk76SR4+GEXeJeVwZdfwjffpLtWxhhjsl3SgjYRyQUmAUOAI4FLReTIJsWuAraq6iHAfcBfAVT1GVX9lqp+CxgJrFDVRQHHXebfr6obknUPsfDPCPRqJmd5ucvBVl0dukxuLlx3HfTt6801jTcOOQR+8hMXcJeVuW0ffZTeOhljjMl+yWxpOwGoUNXlqroLeA4Y1qTMMOBJ38/TgTNERJqUuRR4Non19ERJiXv2d40lau5c14rWqlX4cp984h4mc7z9dmPwfuyxcM010L59eutkjDEm+yUzaDsIWB3weo1vW9Ayqrob2AZ0alLmYvYN2h73dY3eGiTIA0BErhGRBSKyYKN/VH8SHXywG3TuVUtbVZUbsxYpaLvsMvjlL725pvHGD38If/mL+/nAA+Fvf7PWUGOMMYlLZtAWLJhquvxC2DIiciKwQ1UDO5cuU9WjgVN8j5HBLq6qj6hqf1Xt36VLl9hqHoe8PPj3v12rihci5Wjzs0XjM0t1tfvs/C2vAHv2uNnFxhhjTCKSGbStAQ4OeN0dWBuqjIjkAR2ALQH7L6FJK5uqfuV73g5Mw3XDZoSzzoJevbw514YN0QVtxcWwZUvkciY1vvzSPQeuevDzn8NRR4WfCWyMMcZEksygbT7QR0R6i0grXAA2o0mZGcAVvp8vBF5X32KoIpIDXIQbC4dvW56IdPb9nA+cD2TMEO9PPgm/wHss9uxxXWuRFBdbS1sm8XePB7a09e0L27fDypXpqZMxxpjmIWlBm2+M2g3AbOAT4AVVXSYifxCRob5iU4BOIlIB/BwITAvybWCNqgYO7S8AZovIEmAR8BXwaLLuIVb//jeMGuVNEPXuu/DMM5HL+YM2bdrxbNIiMEebn38G6ZIlqa+PMcaY5iOpyXVVdSYws8m23wb8vBPXmhbs2DeBAU221QDHeV5Rj/h/US9fDsd5UMvgUyz2NmKEywumGl15k1yXXAJHHNGY+Bhc16iIC9qGNZ0/bYwxxkTJlrHykL9LLNEZpOvWwfe+B++8E7ns0Ue7QCDHPsmMcOCB7LMofNu2LqC3ljZjjDGJsF/1HvIqV9vq1fCvf0XXzbp5M8ycaePaMsWTT8IHH+y7/fbbXcJdY4wxJl4WtHmoXTu3wHuiLW1VVe45mtmjixbBeefB0qWJXbO5q6yEsaPr6Nq+ltycBrq2r2Xs6DrP8uqBmzwyahT8/e/77rv4YjjzTO+uBam5J2OMMZnDgjaPvfUW3H13YueIJWjzj52ytB+hzZoFA8pqaDN5AnO296VOWzFne1/aTJ7AgLIaZs3y5jqrV8Pu3XvPHPWrrYU333Rd315I1T0ZY4zJHBa0eezww6FDh8TOEU/QZt2jwVVWwuUX1jBjx5n8qf5mSllOHnsoZTl/qr+ZGTvO5PILazxpnQo2c9Rv3To47TQ3wzhRqbwnY4wxmcOCNo998AH85jewa1f852jVys1AbN06ctmOHd2zBW3BTbynjqvrH2Qg84LuH8g8RtU/xKT76hK+VrAcbX69erkJCV5MRkjlPRljjMkcFrR5bOlSuOOOxBKp/vKX8PHH0ZVt187NHLXu0eCmPd3AVfUPhy0zqv4hpj21J+FrLV8O+fluHdqmcnLcTF8vgrZU3pMxxpjMYUGbx7xK+xGtnBx45RU3AN7sa1N1AT0JH0H3YBWbqqNo1ozgN79xQVlubvD9ZWVuf6KJkFN5T8YYYzKHBW0eC0ywG6+LLoI//zn68mec4d2ap81N57Z1rKRn2DKr6EHntjsTvlZRkRvTGEpZmevGXrMmseuk8p6MMcZkDgvaPNatmxuLlkhL22uvwVdfRV/+zTdh9uz4r9ecDR+Rw5T8a8OWmZx/HcNHhmgei8Gtt4ZPiPz978O8edFNMAknlfdkjDEmc1jQ5rGcHNdFGm9ryq5drjUmll/sf/4z/O538V2vubvhpgIezR/N3L1XRPufuQxgcv51XD+2IKHrbN3qEui+917oMgccACee6CaaJCJV92SMMSazWNCWBHPnwnPPxXfshg3uOZagrbjYJiKEUloKU6cXMbTwVW7OvYtKSqgnj0pKGJ9/F0MLX2Xq9KKgaTpi4W9ZjXSel1+GZ59N7FqB9/QLkndPxhhjMosFbUnQvn38i7fHkqPNr2NHS/kRzpAhMG9JEQsHjuFoltJG6jip3VLqrhnDvCVF+6wVGo9og7ZHH3WzixM1ZAi8OKuICYzh+NZLaZNTx6D23t6TMcaYzGJBWxK8/z5ccQVs2hTf8SeeCD16RF++uNgFbYnOSmzOSkvhxFMK2JVbyKWX5dBu/0LunVjgWWuUf+JJ797hy5WVwaefQp0HKdQ+/xzqKeCdhYXMmZvDDb8s5J4HvLsnY4wxmcWCtiTYtAmmToUvvoj92OOOc4PVjzkm+mOKi926l9u3x369lqSqyq0Ne/jhrmWsutq7c69d61pH27YNX66szH1Wn3yS+DXLy+HAA10i5jlz3ESIzZsTP68xxpjMZEFbEqQ6V9uIES7/V2Fhaq6XraqrXWBVVuZeL13q3bkfeAAqKiKXO/po97x4ceLXfPhh+Ne/XFe8v3XNlq4yxpjmy4K2JOjd2/0ijSdX2223wcknx3ZMt24uGMjLi/16Lcnzz8P8+Y1BmxerEwSK1MoGcMghLiWMFy1txcXQv7/72f+HQiL5AY0xxmQ2C9qSoKAAunePr9Xjs89g/frYjtmwASZOhC+/jP16LU1enhsv2L69d0Hbrl0wfDi8/np011+xIrbkycE8/zzceWfjOMZUt+4aY4xJPQvakuSoo9zYpVitXx978tV162DMGLdYvQlO1QVW/u7EO++ECy7w5twrVrg0HtHm5uvaNf7ZxX5/+xs880zjedq0cePbVqxI7LzGGGMyl3WoJcmsWfEdV1UFhx0W2zEdO7pnS/sR2rZtLrDydyf+5CfenTvadB9+CxfC/ffD3Xe7iRGxqqmBd991gXqgJUtcl6kxxpjmyVraMkxVVewtbf5f1Ba0hebPf+cPkmprXRLkr79O/Nz+cWT+LspIvvkGnnoq/skIb7/tumQHD957e6dObkUOY4wxzZP9F58k778Pp5/uxqhFS9Udc8IJsV2rqMiNlbJVEUJrutLEokVw0knw3/8mfu7KStc92a1bdOX9M0jjHVNXXu6WwjrllL23v/MOXHUV7LR14o0xplmyoC2J3njDJVKNlgi88AL8+MexXUfEVkWIpOlKE337umcvJiPs2eNmpEY7Tq1zZzf+LN6Wtu3b4Ywz9k3xsnIlPPaYTUgxxpjmysa0JYl/fFOqUjC8956NZwqnvh66dGkM2tq1c92ZXuRLu//+2I8pK4s/YHz00eCrXwR+5444Ir5zG2OMyVzW0pYkHTu6tBKxpGB45RXXCvPhh7Ffr1cv6NAh9uNaiksvdV2kgeMFEwmcEnXccW78WaxLj/nLB2vVs7QfxhjTvFnQliT+LPWxtLStW+eWIWrfPvbr/b//Bw8+GPtxLVm/fm6psR074j9HVZVLhlxeHttxt9/uUrTEmvpj1Cj43veC7+vSxSX4tQS7xhjTPFnQlkQnn+zGLkWr6birWLz4ItxzT+zHtRTjx8NNN+29beRIeOstyM+P/7wVFS79Rn19YvWLhqpLJdOmTfD9Im5d1USC0ExQWQljR9fRtX0tuTkNdG1fy9jRdVndgtgc78kYk3oWtCXRhAkweXL05auq3ODyaJZDasomIoT3xhv7rjVaWuoC60SCNn+rVrQ52vxU4dxzY1sZYdky1xrbNNVHoPffh0ceia0umWTWLBhQVkObyROYs70vddqKOdv70mbyBAaU1cSd/zCdmuM9GWPSI6lBm4icIyKfiUiFiIwLsr9ARJ737X9PRHr5tl8mIosCHg0i8i3fvuNEZKnvmAkiieaWzxzx5GjzKy52OccaGrytU3MR6r196SWYOTP+81ZWuhauXr1iO04E1q6NLeWIvws2XNCWzf8aKivh8gtrmLHjTP5UfzOlLCePPZSynD/V38yMHWdy+YU1WdU61RzvyRiTPkkL2kQkF5gEDAGOBC4VkSObFLsK2KqqhwD3AX8FUNVnVPVbqvotYCSwQlUX+Y55CLgG6ON7nJOse0jUBx9Anz7R/2IeNAguuyy+axUXu9abbdviO745Uw0dtN1xB9x1V/znXr7crTNbUBD7sWVlsc1efeUVt1rGwQeHLvP223DWWa5FLttMvKeOq+sfZCDzgu4fyDxG1T/EpPvqUlyz+DXHezLGpE8yW9pOACpUdbmq7gKeA4Y1KTMMeNL383TgjCAtZ5cCzwKIyAFAe1Wdq6oKTAVCDMtOv/32c2OePv88uvLXXQd//GN817KlrEKrrnYrIAQL2vwzSGOdxenXrZtLiByPsjLX2rZpU3Tlhw2DsWPDl6mtdS1yX3wRX53SadrTDVxV/3DYMqPqH2LaU3Es6psmzfGejDHpk8yg7SBgdcDrNb5tQcuo6m5gG9CpSZmL8QVtvvKBy3IHOycAInKNiCwQkQUbN26M6wYS1aOHW6kg2tl8dQn8sX3xxS7pau/e8Z+juaquhmOOCb7MVFmZW0li7dr4zn3nnfDEE/EdW1bmnpuOtQvl2msjr5ma6vyAXtpUXUBPVoYt04NVbKpunaIaJa453pMxJn2SGbQFG13TtD0jbBkRORHYoaofxXBOt1H1EVXtr6r9u3TpEk19PZeXBz17Rpc3a88eNwnh9tvju1br1m4CQzaPaUqWAw5wXdUXXLDvPn/glI58bf36ufFpeVGkuF66FKL526NHD5f/LRvHSHVuW8dKeoYts4oedG6bPet0Ncd7MsakTzKDtjVA4Oib7kDT9oz/lRGRPIoroUgAACAASURBVKADELiC5iU0trL5y3ePcM6MEm2utk2b3CSCeFc12LTJpbSYPz++41uqWFu7Ai1b5oLyN96I79pdu7pxak3XEA3myivh+9+PXK5VKxe4ZWNL2/AROUzJvzZsmcn51zF8ZG6KapS45nhPxpj0SWbQNh/oIyK9RaQVLgCb0aTMDOAK388XAq/7xqohIjnARbixcACo6jpgu4gM8I19uxz4VxLvIWFDhri0EpEkkqMNYNcuuPde16Jk9vbMM3DCCW52bVP77efW6vzFL2I/b0UFrFrllsRKRKQcb5s3w8KFboJBNAYNys4lzW64qYBH80czlwFB989lAJPzr+P6sXHM+kiT5nhPxpj0SVrQ5hujdgMwG/gEeEFVl4nIH0RkqK/YFKCTiFQAPwcC04J8G1ijqk3bDK4DJgMVQCWQ0VmObrzRBVORrF/vnhNJ+QE2ESGYL76ABQtC57/r1ct1KcbK3wUZbKxctP7yFzeJZE+YceivveYmSoRL9RHo6adh4sT465QupaUwdXoRQwtfZVz+XVRSQj15VFLC+Py7GFr4KlOnF8WcEy+dAu/p5pzmcU/GmPRJ6oLxqjoTmNlk228Dft6Ja00LduybsO+fp6q6AOjraUWTrKHBPcKNXUq0pa1NG5d2YsuWyGVbmqoq6NQp9Ps/dy5MmQKTJsWWumP5ctdS55+5G48DDnATJSoqXDqPYMrL3bqyxx8f/3WyxZAh8IMRRTw8dQxPtBnNpu2t6Vi0kxFX5DJvbEFWBjdDhsC8JUUMO2cMkypHUyet6dx2J8NHZu89GWPSw1ZESLJPP4WiIvjHP8KXO/xwNyYtlmWvmrJVEYKLlLR41SoXtH36aWznraxMrJUNIk+EUHVB22mnRTdhAeCdd+CII+Ibp5cJ5s+HficUsGpjITl5OVx5fSH3Tszu4Ka0FLRVAWd+t5Dde3JYUVXIPQ9k9z0ZY1LPgrYkO+gg2Lkz8sDw44+Hu++Obwkrv44ds3/dyWSIFLTFO4N04ED4wQ/irxe44Co3N/S1RVz3aCyzigsLXQCajbnaNm6EDz90XcGtWrn3Jx0ze722cyd89pn7rj39tPt3Hm+aGWNMy5XU7lHjBql36RI5BcPGja6LM5GgbfFiFwCYvR19NOy/f+j9ffq4btFYg4Pf/jZymUhat3bdouGuHWtrTDbnanvtNffsH79XVhb/7NxM8sknbtxiWZlLyLxnj/vMDwqaZdIYY4KzoC0FSksjB20jRrjZje+9F/91LGAL7uHwCenJy4OjjootaNu927WCefGe33CDC96CufdeN1Eilha9Dh1cq2s25morL3fjBPv3d6/9LVObN7txidnq8MPh3XddgO7/zixe7Ma7GWNMtKx7NAVKSiK3eiSyWLzf1KkwZkxi52ipvvWt2LqW337btYy++27i177uOvjxj/fdvns33HYb/Oc/sZ8zmj8UMtGVV8L99zcGNonk0cskbdrASSe5wHO//VwuvebQ7WuMSS0L2lLgggvcL6NwvAjaPvgAnnwycrmW5MsvXRfUvyJk85s8Gf773+jPu3y5y6/mRfeWKqxc6VqTAr3/PnzzTfSpPgKdd55buivbDBoEl1/e+Prkk934vGgSEGeyxx9v7PqFxjVvjTEmFtY9mgI/+EH47q2GBjemLdGgrbjYrT+6e3f0Mw2bu/Xr3YDv/Pzw5WJd/quy0r3H3btHLhvJmjWuC3TSJBg9unF7ebmrVzwL0v/ud4nXK9XmznXr7556auPnUVQUOhVKNhk3Ds4/H844w72+4gr46qv01skYk32spS0FVF1Qtm1b8P2bN7uByV4EbRA8839L5c9/161b+HI7d7rxRVOmRHfe5ctdoOVFcNy9u+sya9ryUl7uxnbFO5ZL1f1BkC3+/Ge46qp9A+iXXnL7slVVFWzY0NjVC3DhhfCzn6WvTsaY7GRBWwqsXetmLz7zTPD9BQVw332uhSER/iSvlqutUbRJi1u3hkWLou8i9SJHm5/Ivt1lu3e7pLvRLl3V1Pz5buZyYJdcJquvhzffDN4V/MYb8Mc/hl81IpP5P9fAoE3V5QdcsyY9dTLGZCfrREuBAw5wQUGoyQjt27vlrhLVubN7WK62Rv6gLVzKD79YxhmNHJnYSgjBrv3EE65lLCfHteAtWhR/oNKtG9TUZE/aj/fec137wYK2sjKorXWB8qGHpr5uifJ/p44+unHbnj2u2/f6611+RmOMiYa1tKVATg707h16Nt+GDW6w9e7diV3nnHNcN2y/fomdpzk59FCXTiXSmDZw79uyZdF9Dj/7mQvcvNKvn2tZW7HCvVZ1z/GmFDnwQJecNltmkJaXu38nwcbvxZv8OFN88on7PDp3btwWT5oZY4yxoC1FSktDt3o8/bTL/L59e2rr1BJccgk89VR0ZcvKYNcu+Pzz8OWqq12Xtz+w8sLgwfDss43j1wYOjG0VhKZyc90fCtnS0vbuu25VEP+4zEBHHukCumwNcB591K3y0JTNIDXGxMqCthQpKXGtHsF+0VdVuVaR/fZL7Brbt7tZqv/8Z2LnaU5iGYh/7LHwne+4SQnhzJzpUn14mTusZ08XYHboAKtXu+7CoqLEzun/zmWDWbPg738Pvq91a5ecdt261NbJKyLBu+fLyty/fX8XvjHGRGJj2lLk4ovdf9J79uw747Cqyv2nHmvaiaYKCtzC9MceC9/7XmLnai6OOsrl+nr00chljzwyuiWT/K1XXk1E8Fu0yHWV+wenx5OfLdDFF2dPWon8fDj44ND7P/zQ/WGTbb74ws18veWWfVOXBCYOTnTmuDGmZbCWthQ56SSXziBYiggvEuuC+6VWVGSzRwOtXesWUI9FpNa5ykoXZCeyTmywc156QR3fP6eWUVc1UCi1TH6wLqGWsiuugF/9yrs6Jstf/hJ5HddkBGyVlTB2dB1d29eSm9NA1/a1jB2d2Hve1Pz5LrFuff2++447DqZN23tWqTHGhGNBW4rs2eNWLFi1at99XgVt4MYEWdDm1Na6FQVieW/Hj3cLyIdTWRn7Iu7hzJoFA8pqGLpiAku0L7toxRLtS+HkCQwoq2HWrPjPvXWrm0WayaZMcf82wvn8cxg2DBYu9Oaa/ve8zeQJzNnelzptxZztfWnjwXseaMkS14oYLEFwhw5w6aXRzWw2xhiwoC1l6utdotQnnth33x//CD//uTfXKS6GLVu8OVe2izZHW6COHV33Z7j3cPly74K2ykq4/MIaZuw4k7823Ewpy8ljD6Us50/1NzNjx5lcfmFNXK0/H3/s7mfGDG/qmgwrVkBFReSu4DZt3H28/37i1wx8z/9U7+173tSSJa7bPdTs5Y8/Dj2WzxhjmrKgLUVat3aD14PN5jvvvMblbRJ1xBHe5g/LZvEEbdEsUH7HHZHXko3WxHvquLr+QQYyL+j+gcxjVP1DTLqvLuZz9+rlnjN5Bml5uXuOFLSFWjUiHsl8z5tavDh89+cTT7iUNMG6T40xpikL2lIo2Gy+2lp49VWXX80Lzz8Pjz3mzbmyXceOcMMNbuZhtKLJCXbZZXDaaYnVzW/a0w1cVf9w2DKj6h9i2lOxZ9ktLHSJnTN5Bml5ucthdsQR4csFWzUiXsl8zwPt2OFWpTjmmNBlok0zY4wxYEFbSgXL1fbll66VIVuWG8omffrAAw/AIYdEf0y3bi4JaqjgYO1al46jLvFGGAA2VRfQk5Vhy/RgFZuqW8d1/pKSzG5p69TJzXKNZua0P2hLdD3VZL/nfoWFLmn22LGhy2R74mBjTGpFFbSJSBsRCTKU1sSipMT90q+tbdwWTxdeOJMnJ76GaXNRXR17cCXilhT79reD7//nP2HAAO/GDXZuW8dKeoYts4oedG4bIXlcCKWlmd3S9tBDcO+90ZU98US3csTXXyd2zWS/57E4/HA33s2CNmNMNCIGbSLyXWAR8B/f62+JSAYPbc5cF18Ms2fvvTSR10FbVRW8/bZ3LUHZbPx413IWq1//OvQSVZWVblB8POcNZviIHKbkXxu2zOT86xg+Mr71rEaOjJxOI11indU6YgS8807iYzaT/Z773Xyzq3M4rVq5rmEL2owx0Yimpe33wAnA1wCqugjolbwqNV99+sBZZ+2dc8rroM2/DJCl/WhMWhwrVZd9v7p6332Vla7FNNFEyH433FTAo/mjmcuAoPvnMoDJ+ddx/diCuM5/5plw9dWJ1DB5zj/fpfFItWS/535vvBHdagcvvOCWsjPGmEiiCdp2q+q2pNekBWhogH/9a++cVFVVruUt2JqL8bCgrVG8+e8WLXKD4//zn333+YM2r5SWwtTpRQwtfJXx+XdRSQn15FFJCePz72Jo4atMnV4Ud4qR+no3EzbTlkqqqXHrjQbLXxbOhReGbgWNVuB7/kvx/j0Hl5fxo4+iS5x72GHe/fs3xjRv0QRtH4nIcCBXRPqIyAPAnCTXq1kScb9wHn+8cduVV7r8UzkeTQnx/+dvudpcoBJPN+YRRwRfoFzV2xxtfkOGwLwlRdRdM4ZB7ZfSJqeOQe2XUnfNGOYtKWLIkPjPvWWLCxxeeMG7+nrh7bddQBnPUl3vvZf49YcMgTmLini0YAzH5je+55suTfw9B7d81c6d0QVtGzbA73/v7Vq2xpjmKZpQYQxwFFAHTAO2AT9LZqWaK5F9B4Yfcgice6531zjwQDjhhODLZbU08ba0tW7tWj+CBW3//ndyuhtLS+HeiQWs31bI7j05rN9WyL0TCxIOEPff3y1tlmmTEcrL3Vq5J58c23FlZS4ZrxerPOzYAdt2FnD/I+49Hzy0kNlvFHjSkur/7kQTtO3ZA7fdFt26t8aYli2aoO08Vf21qh7ve/wGGJrsijVXTdN+vPwyLFjg3fnLylxLxIknenfObKTqJiIMjfObGiwnWE4OfOc7LsN9tgj2h0ImeOUVOOUUN6kjFmVl7rNdtizxOjRN7DtoEKxe7U3OtKIiOP30yPnnIHKaGWOM8YsmaBsf5TYThZISl5ttjy9v53XXwcSJ6a1TcyTiZu/F0/0GLjj48ku3dqnfsmUwfXr2zczNtFxtqm6GbjxLt3mZ16y83AXgBx3kXvu/K/5gLhHnnedyL7aOItWbl4mDjTHNW8igTUSG+MavHSQiEwIeTwC7ozm5iJwjIp+JSIWIjAuyv0BEnvftf09EegXsKxORuSKyTESWikhr3/Y3fedc5Htk1XLLpaUuA/rate6X14YN3s0cBTfZ4Zhj4P77vTtnNqqudkFXvMsDDRsGU6funZ5l+nT44Q+9qV8q+Vt3E01K6xURt1B6POPGevVyK1IcfHDi9fjJT+DWWxtfl5ZC797eBG2xfu/69XMTF/YktgiDMaaZC9fSthZYAOwEFgY8ZgBnRzqxiOQCk4AhwJHApSLStGPpKmCrqh4C3Af81XdsHvA0cK2qHgV8Bwj8b/AyVf2W77EhUl0yyQ9+4BaJPuAA2LbNBXBeBm05Oe4XdCa1rKTDW2+5FqbAmbqxOOooN2mkqKhxW2WlWwOzILFMECl3xRXw4ovuj4RMMHs2fPZZfMfm5Lj0GGdH/B8osh/8AC65ZO9tgwe7sWWJrAW6bRu0bQuPPhr9MWVlLpj96qv4r2uMaf5CDldX1cXAYhGZpqrx/Bd2AlChqssBROQ5YBjwcUCZYbg8cADTgYkiIsBZwBJfHVDVzXFcPyN16eIe4H2ONr+OHS3lhxfv7aJFbrD6SSe518uXe5vuI1WOPto9MoEq/OhHbmzgs8/Gf55Nm9wSWPHmy/vvf92/k6OO2nv76NEumEskD99HH7k/xvzdrtEYPhwuv9y7WeTGmOYpmv8ieonIdBH5WESW+x9RHHcQsDrg9RrftqBlVHU3bmZqJ+BQQEVktoh8ICI3NznucV/X6K2+IG8fInKNiCwQkQUbvVqN3SN/+xvMnJm8oK242II2L97bn/7UjYvzq6z0Pt1HKtTXu+/bJ5+kuyYuoFm/Pv6xhgBTprg/fBJplfrZz1yA1lS/fq4VL5HZ14sXu+doZo76tWplAZsxJrJo/pt4HHgIN47tNGAq8FQUxwULppp20IQqkwecDFzme/6+iJzh23+Zqh4NnOJ7BE21qaqPqGp/Ve3fxd+0lSHuugueegqOOw7mz3cpOrxUXGx52qqqoF272GcnBvIPDld1LW7r12dn0KYK3/1uYi1bXnnlFfecSNDmT8gb78D9jRvhww9D1+HDD90fVvFassT9G4ylpQ3gD3+AW26J/7rGmOYvmqCtjaq+BoiqrlTV3wOnR3HcGiBwuHB33Di5oGV849g6AFt8299S1U2qugOYCRwLoKpf+Z634/LGeRzyJF9JiWu1KSqC/v2hfXtvz3/KKS4gbMnWr0+8BbOsDLZvh5Ur3SzAL75wyZCzTatWbuB+JqT9KC93i6QnMpHA39Xrb9GK1WuvuedQQduLL8L117uxafFYsqRxjFosPv4485IgG2MySzRB204RyQG+EJEbROT7QDQzNucDfUSkt4i0Ai7BTWIINAO4wvfzhcDrqqrAbKBMRAp9wdypwMcikicinQFEJB84H/goirpkFP9svrfe2nt1BK/8/vcwYYL3580mo0a5lotEBKaXyMlxiZC9Wig+1ZrmB0yH+nqYMyexVjaADh2gZ8/4W9rKy2G//dwfTMEMHuxmcb75ZnznHz48vuC+rAxWrIg/WDTGNH/RjNy4ESgEfgr8EdfKdkXYI3Bj1ETkBlwAlgs8pqrLROQPwAJVnQFMAZ4SkQpcC9slvmO3isi9uMBPgZmq+m8RKQJm+wK2XOBVIIY5WpmhpAQ2b4YHH3SB249/nO4aNT9nnpn4Ofr2dc9Llriu1kWL3Di3wDQg2aKkxK17m075+bBqlVveKVGJ5DV7800444zQn+PAga4V/JVX4lvQ/oYb4qtXv37u+aOPXKJfY4xpKmLQpqrzfT9WAz8GEJGe0ZxcVWfiujYDt/024OedwEUhjn0al/YjcFsNkPUdf/5xUe+95/0kBIBHHoHf/Mb91V5Y6P35s8HcuS7nViItY23bulmGRx4Jv/udG4d4443e1TGVSkvdWK7t210Ami777efNea691s0gjccHH8DXX4fe36oVnHpqfPnaNmyA3btdSp9Yu0cDW3YtaDPGBBO2e1REBorIhf4Etr6Et9OAd1JSu2ZqyBCXab9bt+QEbaruF3RLnUFaX+/SdCQymNzv5JNdagj/zNFEUkGk08iRLhhIZGJGoq67DqZN8+Zc557rUmTEw9+9Gs7gwS4BdqyB4cMPu1x+O3bEXq/u3V1i7Gz9jhljki/cigh3AY8BFwD/FpHfAeXAe0Cf1FSveWrTxrV2xLugeSQdO7rnlhq0bfClW/bivf30U7j9dli6NDtztPkddJAbwJ9IKotEbNrkgmivJkM0NLggNNZxerfe6lqiI7n6ajcDu3Pn2M6/eLEb+xiYlDlaIq4V8NprYz/WGNMyhGtpOw84RlUvxSW7HQecrKr3+7o1TRwqK2Hs6DqKW9eyckUDL71Qy9jRdZ7O7Csuds/xpP3w169r+1pycxro2j66+sVzXLzXisSr/HeVlXDLjXXccWstX61poHyG959VqjQ0uKDprbfiOz7Rz7fr/g0UaC0fL/Lm/VOFE0+ESZOiP6a+3i3vFs0qGUVFrps0Vv6Zo8YYkwzhgrZaf3CmqluBz1T1i9RUq3maNQsGlNXQZvIEFtT1ZRetWLirL20mT2BAWQ2zZnlzHX/QFmtLW2D95mzvS522Ys72yPWL57h4rxUNL4I2f/2OfG0CH5G8zypVcnLgV7+C55+P/VivPt+P6Euvl7x5/3Jz3USRWCYjvPeeG9MX7ezV5593Y9uiXQ+0utoFqokEbbNmuXQoX34Z/zmMMc2YqgZ9AF/jUnL4H3u9DnVcJj6OO+44TbeKCtXOhdU6hwGqrqFgr8ccBmjnwmqtqEj8WmvXql5yieqcOcmvXzzHJfu9ePxxd6p4j0/lZ5VK/furnnVWbMdk4ufrd+WVql26qDY0RFf+t79VzclR3bIluvJPP+2qPH9+dOXnzXPl//nP6MoHM3du4ucwxmQ/XJaNfeKZcC1tw4B7Ah5NX5sYTLynjqvrH2Qg84LuH8g8RtU/xKT76hK+1gEHuOz3Awd6W7+r6h/i/rvq2LmT/z2iva8Jdzce93931jEqie/F6afDc8/FnpHeL5WfVSrFk6stlvdi167UfL5+ZWVuwo2/ZTWS8nKXm83fEh2JP21MtLNIS0vdYvax/Ltrqm9fN7Yt3nQmxphmLlgk19wemdDStn+7HVpBSdCWB/+jghLt2r7Gs2tG2wIRS/3aUPO/TW3aRH9cu7zG41qT+vciFun4rFJh/HjVvDzV+vroj4nlvbjkktR+vm+84U43e3bksrt3q559tuof/xjbNfr1Uz3ttLiqF7dDDlG98MLUXtMYk1kI0dImbl/z1r9/f12wYEFa65Cb00CdtiKP0ANk6smjTU4du/ckvnJ0r15uvckHHvC2fq2ljjv+5OqXlwe33BzlfUkdt/uO+/X4BupI3nsxd65rrRgwIOZDgdR/VqkyZYpbKeLLL933IxqxvBcv/r8cPvkk+Z+v3/btboWFAQNcGo9k+OUv3eSFrVsjzwh9+WXX2nbEEYld84ILXILdzz5L7DzGmOwlIgtVdZ91W7LnN06W69y2jpWETw61ih50buvNxNy8vNhmj0Zbvy7tdjJuHIwbB7/4RQz3FXBc53bJfS9uvRV+/vO4DgVS/1mlysUXu+9EtAEbxPZeDBuWms/Xr107OPvs6AK2Xbviu8b558P3vhd5Uo+qy4XnxfJxQ4fCOee4cxpjTKBIyXVzffnaTIKGj8hhSn74BEyT869j+Ehv1kgqLo5t9mi89YvnuGS/F4nmv0v1Z5UqbdtGP57LLxM/30ALFrgWxEjKyuCmm2I//6mnukXcu3cPX271arfKghfpPq64wrXuWZJdY8w+gvWZBj6A18F1o2brIxPGtKV6RuLgwaonnpj8+mXi7MIuXVR/8pP4jk1F/dLptttUp02Lvnwmfr6BbrlFNT9fddeu0GW+/NJd+v7747/OV1+F3//SS+4a77wT/zUC7dql+vXX3pzLGJN9CDGmLZqg7R5cmo+RwA/8j0jHZdIjE4I2VdWZM90vs3H5d2kFJbqLPK2gRMfl36WdC6t15kzvrnXxxaqHHhpf/W7Oi61+8dxXst6L+npVEdVbb43v+GTXL90OPTT2Qe4jR6q2IbbvRarev2eecf+LLVkSuswjj7gyH38c3zUefNAdv3Zt6DJ33OHKbNsW3zUCNTSoduqkOmZM4ucyxmSnRIK2x4M8Hot0XCY9MiVoU3WtEGOv36ld29dobs4e7dq+Rsdev9PzVpsnn3R5qVJVv3iO2+sY2aOdixJ/L9atc9/qiRPjP0fQ+iXxs0qlIUNUjzkm+vIbNqi2a+dabhP6fJP0/i1d6j7vp58OXeaii1QPOii22dSBFi5015g6NXSZiy9W7d07vvMHM3Cg6qmnenc+Y0x2CRW02exRs5fNm92jT5/sHFNTV+fGOfXqFX+etubshhvgqafc+KtoPt8bb3QzkJctg8MPT379YlVf78bq3Xgj/PWv++7fswe6dIFhw+Dxx+O7RkODGyM5ZAhMnRq8zObNboH5o4+O7xpNXXutG0u3eXN2/js0xiQm7tmjItJdRP4hIhtEpEpEXhSRCMNyTbqpQk2N+4UTi+eeg8MOgxUrklKtkGpr3bU/+iix8xQUwKBBFrCFUloK33wT3czir7+GyZPhqqsyM2ADyM+HI4+EpUuD76+vh9tugx/9KP5r5OS4RLuvvhp6RmenTt4FbOAmNGzdCl995d05jTHZL5qUH4/jxrQdCBwEvOTbZjLYY4+5Fog1a2I77pVXoKQEevdOTr1CUYXLLoPp0xM7z7JlriVpxw5v6tXclJS4fGNr10Yuu99+sGgR/OEPya9XIl5+Gf75z+D7WreGMWPcLNBEDB4M69a571dTFRVw++3RvafR8s9CtZURjDGBognauqjq46q62/d4AuiS5HqZBMWzaHx9PbzxRvQLanupsNB1ySb6S2rmTLj8cti925t6NTfnn++S0kZqFaqtdc+HHALduiW/Xok46CBo1Sr4vtmzYf36xK9x3nnuj4GDD9533zvvuNyA1dWJX8evrAzuuMP9mzDGGL9ogrZNIjLCl7MtV0RGAJuTXTGTGH/QFkuC3fffd7/Q0xG0gftFlWjQVlXlWlfatfOmTs1Nbm50Y6SGDUusSzGV1q51Y9o+/HDv7TU1blWQezxYKblrVxgxIngi3yVLoE0b1/Xslfbt4Ve/sqDNGLO3aIK2K4EfAuuBdcCFvm0mg8XT0lZe7sbvnH56cuoUSVkZVFa6wDFeGza4X7A2eDu0sWODD9r3Ky93j379UlenROTmumS0b7+99/a333atx2ed5c11vvrKTcrY2WQhhyVL3ELvuR7nWt60ybXiGWOMX8QVEYALVHWoqnZR1f1V9XuqujJF9TNx6tjRPccStI0ZA7NmxZ413yv+cTyJTEZIdDWEluD9993nHExDA9xyC/TsCaNHp7Ze8eraFfbff99W2ldecRNTTj7Zm+t88AH89KduvVM/VVi8ODkB7v33w3e+s2+QaIxpucIGbaq6BxiWoroYD3Xp4rpXYllWp1Mn71ol4nHGGbByZfwLvYMFbdEoLYXly4Pve/551814++0u4MkWwbrWy8vhlFNc16UXvvMdt6ZveXnjti1b3Fg2L5avaqqszKUs+eQT789tjMlO0XSPvisiE0XkFBE51v9Ies1MQtq0cQOZjz8+uvJz5sDdd3s7mDpWRUXQo0diXZsvveRaKExopaVuVnFd3d7bVeHee12r0fDh6albvMrKXAvtnj3udVWVm+np5fjMdu3cHxSBQVunTu7fUpRNJgAAIABJREFUzNVXe3cdP5tBaoxpKi+KMif5ngMn/iuQppFPJlpbtjQmF43kmWfgySdd9086vfACfPEF/PrX8R0fbHaf2VtJiQvQVqxwOfn8RFyX4rp1bmxjNikrc0MC1q1zi7t37eruz6tWNr/Bg+H3v3dJbzt1cttyc70fzwZu5m7r1ha0GWMaRRrTlgM8pKqnNXlYwJYFBg50GfCjUV7uclmFSp2QKm+9BXfeGTqJaTjffAN//jN8/LH39WpODjvMJcsNnPBRV+fGsxUXu2S12ebyy91Ege4Bab979nRj3bw0eLALbhcudK9vv92l+0iG3Fw3wWHx4uSc3xiTfSKNaWsAovy1bzJNcXF0ExFWrHCtW+kcz+ZXVuaCr1WrYj925Uo3ji/RVRWauxNOcOOk+gcskHLHHXDssdmblDiwS13VreLw+uveX+eEE9ysTv+/lb//vTGAS4b/+z+4777knd8Yk12i6QQpF5FfiMjBItLR/0h6zUzCog3a/GN00pWfLZB/HE88rQtVVe7ZJiLEZv16l8vs8MNdkuNs9YtfwPXXuyWtHnvMBfFey81tnF29a5cLfpMxCcFv0CBvl8cyxmS3aPO0XQ+8DSz0PWz19SzQsWN0Qdvq1W6B9SOOSHqVIurb1z3HM47HgrboVFbCMUfW0aFVLbk5DRzWo5Y9tXVJGUyfKpWVMOtfdTzxUC3H9GugNbXMeaOOykrvr/XSS1B6cB0H7FfL7voGHplQy9jR3l+rshKuH1VHxzbuc+raPrrrVFbC2NF1dG0f23Hx1C8V1zEm3TLpux4xaFPV3kEeJamonElMcXF0KyL84Q/w+eeZkZC2XTs49FDXBRUrC9oimzULBpTVcOanE/igvi912ooP6vvyUyZwydCakPnbMpn/ns5fPoEl2pc6WvERfeny3AQGlHl7T7NmwY8vruH7aybwfm1fdtGK+bV9aTPZ22v576n91AnM3+k+pznbI1/Hf1ybyROYsz364+KtX7KvY0y6Zdx3XVWDPoCbA36+qMm+P4U6rkm5c4DPgApgXJD9BcDzvv3vAb0C9pUBc4FlwFKgtW/7cb7XFcAEQCLV47jjjtOW6I03VCdNSnctYrd7d3zH3XyzaqtWqg0N3tanuaioUO1cWK1zGKDqhn7t9ZjDAO1cWK0VFemuafRSeU+pula818n0+hmTbdL5XQcWaLC4KthGV54Pgv0c7HWI43OBSqAEaAUsBo5sUmY08LDv50uA530/5wFLgH6+152AXN/P7wMDAQFmAUMi1aWlBm3RuPtu1W9/W3XnznTXJHG7dqmuXZvuWmSuG6/bqePz7wz6n4//MS7/Lh17ffZ8GVJ5T6m6VrzXyfT6GZNt0vldjydo+zDYz8Fehzh+IDA74PV4YHyTMrOBgdoYqG3yBWPnAk8HOecBwKcBry8F/hapLi01aKuuVl26VHXHjtBlTjtNtV+/1NUpGkuXqg4erLp4cbpr0rzs326HVlAS9j+gCkq0a/uadFc1aqm8p1RdK9rrdGpToy++qP97ZFr9sul7ZEww6fyuhwrawo1p0xA/B3sdzEHA6oDXa3zbgpZR1d3ANlyr2qGAishsEflARG4OKL8mwjkBEJFrRGSBiCzYuHFjFNVtfl57zc08C5W3bMcOePfdzJg1Gqh1azejNdZUCn/5Czz3XHLq1Bxsqi6gJ+GnVPZgFZuqW6eoRolL5T2l6lrRXmdLbWsuuID/PTKtftn0PTImmEz8rocL2vqJyDcish0o8/3sfx3NJPRgw9qbBnuhyuQBJwOX+Z6/LyJnRHlOt1H1EVXtr6r9u0SzJEAz5E9NEGoG6dtvu7QFmRa0lZS41BOxpv144AGX0d8E17ltHSvpGbbMKnrQuW32rFCeyntK1bWivk7RThYv5n+PjKtfFn2PjAkmE7/rIYM2Vc1V1faq2k5V83w/+1/nR3HuNUDgokLdgbWhyohIHtAB2OLb/paqblLVHcBM4Fjf9oCc50HPaXz8QVuoGaSvvOIWBT/llNTVKRo5Oa6FMJa0Hw0NsGEDdOuWvHplu+EjcpiSf23YMpPzr2P4yCSsyZQkqbynVF0r2uuM+FEuZWX875Fp9cum75ExwWTkdz1Yn6kXD1xr2XKgN40TEY5qUuZ69p6I8ILv52LgA6DQd55XgfN8++YDA2iciHBupLq01DFta9a4bveHHw6+/6mnVMeNS22donX11aqdOkU/E3TTJnev//d/ya1XNmuOs/5s9mj21M+YbJNVs0e9eOAmFHyOm0X6a9+2PwBDfT+3Bv6OS9/xPlAScOwIXLqPj4A7A7b3922rBCZiKT9Cqqlxn/Cf/5zumsTuiSdUzzxTdfv26MovW+bu9dlnk1uvbDdzpvtPaFz+XVpBie4iTyso0XH5d2nnwmqdOTPdNYxdKu8pVdeK9zqprt8vc/a+zi1Z/D0yJhj/d/3m3NT+n5mWoC1THi01aGtoUJ08WXXJkn33rVypunlz6uuULP/9r2pOjurrr6e7JpmvokJ17PU7tWv7Gs3N2aNd29fo2Ot3ZnXLSCrvKVXXivc6TY/rXFSjN45OTv369NypRTnN53tkTDAVFaoHdNqpbXNT910PFbSJ29e89e/fXxcssJW3Ao0c6WZorluXGSshhKIaff327HHPuTaUxhgApkyBUaNg2TI48khvz71rl1sq74orYNIk2L4d5syBs8/29jrGpFtVlRsv/ac/wfjxqbmmiCxU1f5Nt0ez9qjJYh99tO8sTFV49VU47bTMDtjOPht+/OPoy+fmWsBmTKAzznDP5eXen3vuXKipaZx9/sQTcM45sHy599cyJp1efdU9Z0KmBQvamrlrr4Wf/3zvbR99BOvXZ8YXMJz8/OhztT3zDNxwQ3LrY0y26dUL+vRJTiqc8nL3R9Jpp7nX/v9PkhEgGpNO5eWuVfmYY9JdEwvamr1gi8b7/1PN9KCtrAw+/RTq6iKXff11+Mc/kl8nY7LN4MHw1luuO9NL558P99wDHTq414cdBgcfbEGbaX4++ADOPDMzenIsaGvmiov3Ta5bXt74H2wmKyuD3btd4BZJVRV07Zr8OhmTbQYPdt2Yc+d6e94BA+BnP2t8LeKu9dprjeNLjWkOPvwQHnoo3bVwLGhr5oIFbRMmwCOPpKc+sejXzz1Hk2R3/XoL2owJ5owzXEv0gAHenXPZMreiStPgbPBg+Prr2JegMyaT5ea67tFMYEFbM1dcDN9841qs/Pr0gW9/O311ilafPvCjH0GPHpHLWkubMcG1a+fGnRUUeHfOBx5w3aMNDXtvP/dcN/Hp+OO9u5Yx6TR6tJs1miksaGvmLrkEXn658fXf/w7TpqWvPrHIy4PHH4dTTw1fThVatYLu3cOXM6alqqyEceNCL2kXq/JyFwjmN1nQsH17N6whk2elGxOtXbtg6lT46qt016SRBW3N3OGHw3nnuQAI4M47M6dvPhqqkf/BiLhfSrffnpo6GZNt1q+Hv/7VdZMmqrLSpfUINZFp8WK4+mqork78WsakU9O0NpnAgrZmbssWmDEDNm6EzZvdWJNM+gJGMnGia0HbsCHdNTEme51wgusm9WJmZ6TZ55s2weTJbsaqMdmsaVqbTGBBWzP36acwbJgL1l57zbVcZVPQ5s/iHm4ywgcfuNbEjz9OTZ2MyTb5+e4XjxdB21tvuXGmhx4afP+gQdC6taX+MNmvvNz9weNPa5MJLGhr5vwzXrZudV/ADh2ya5BwWZl7Dhe0VVTAzJmWZsCYcM46C7780nVvJuLJJ90fgKHGrbVu7SY6JSOhrzGp0tDgGg0uvjjdNdmbBW3NXHGxe966FVaudH9t+8e3ZYMuXeCAA8IHbVVV7tlmjxoT2uDB/7+9e4+Oqrz3P/7+kgvkIlII4gUBQdqfEqMVqkEqtSpWKvWytOqheuwp4AWhFbUVbKuntpWqtVi8LgWt6FGx1gu2YBu1ohZBsSLgBU04gihXUUwgDIE8vz+eyUkIk2FmMpOZ2fN5rTUrM3s/e+9vdvbMfLOfm/+n7cMP27efwkI49NC9H+u992DNmvYdSyRdOnXyHeFajkWYCbLo61sS0ZS0bd7s//Pdvj298SSiomLvSVteHvTo0XExiWSbgQN9e7P2/NM2cyasWOE7NUTrITpiBPTtC6tXq1e3ZKeNG6GsLPN6QutOW4DV1MA1V4Qoop7rf9lIr671TLky1O7qkY72k5/Atde2vX79en9HLhOmGBHJVGbtv8u+t6rRJhUVvir2uOPad7wmNTUwaXyIXl3ryevkP8smjc++zzJJrlReF5WVMGZM+/eTbEraAmrePKis2ErRjOkso5wQhSyoLadoxnQqK7Yyb166I4zdyJFwzjltr+/WDY4+uuPiEclWb70Fhx0GixbFv21trR8CIZaOTGb+0djoOz+1R8vPsgW15YRc9n6WSfKk8rpoGtYmI79XnHOBfwwePNjlkupq58qK69wCKp3zn5m7PRZQ6cqK61x1dbojjc3Onc4tWuSyJl6RTLVxo3Nmzv361/Fv++yz/iPk+edjKz9/vnP77efckiXxH6tJ0D7LJDlSfV3cfbff1YoVyY07HsBiFyGf0Z22ALrj1hDjGu5iKAsjrh/KQsY23M2d00IdHFlidu3ywwjMnJnuSESyW1kZfP3riQ3HUVXle4YOGxZb+QED/PiK7elFGrTPMkmOVF8XVVV+WJuBA9sTZWooaQugRx5uZEzDPVHLjG24m0ceyo4xMgoLfZVOpM4IzsGxx8J993V8XCLZaMQIWLDAV3fGo3NnOPNMn7jF4qCD/JAJ7RmvLWifZZIcqbwudu70M4eMGJF5nRBASVsgbarrTF9WRS3Th9Vsqovx0zcDtNWDtLYWXn8dtmzp+JhEstGIEf6LKd4ZC26+GR59NP5jvfJK4r3Wg/hZJu2XyuvCOT+jxyWXJBpdailpC6Cy0hCr6Bu1zGr6UFaaPeN/VFTAxx/78eZa0hhtIvEZNgwuvND3uI5VKMHaxxEjfML26quJbR/EzzJpv1ReFwUFcPbZmTsIvZK2ABp9QSdmFlwatcyMgssYfWH2jJHRNDPCsmW7L1fSJhKfLl1g1izfrCBWP/qRn+UgXt/6Fkyc6AfITkQQP8uk/VJ5XTz8sB8YOlMpaQugCVd15r6C8bxGZcT1r1HJjILLuHxS5w6OLHFDh8JLL+3ZBXvdOv9TSZtI7JzzMyN88cXeyzY2NjfMjldpKUyfDoMGxb8tBPOzTNovVddFbS3813/58QgzlZK2ABowAGY9UcLpxc8zpeAWauhPA/nU0J8pBbdwevHzzHqihAED0h1p7Pbd1//XXlq6+/LSUl/dk+h/8iK56N13/YTvTz+997JLl/rR4WMZny2SnTth4cLYEsTWBgyAP9xTwsk8z08tGJ9l0n5N33GjujzPzzrtfl1Mbsd1MX++v14TvdY7gpK2gBo5EhYuLSF08USGdV1GUacQw7ouI3TxRBYuLWHkyHRHGL9XX4V779192amn+uX77ZeemESy0eGH+7vTsQzH0dT7M9Evsn//298pT3Sw05dfhoaCEj6/IDifZdJ+I0fCKWeWcFen5uviuH2WcX/xRK78RWLXRVUVFBXFPqxNOphr73DVWWDIkCFu8eLF6Q5D2umKK/zQHl9+qSmrRNrrggt80rZunZ8cuy2nnAKffgrLlyd2nF27fKeHM8+E+++Pb9t334UjjoAf/ximTfPLduyA99/3Y2gVFSUWkwTDgAG+6n3OHP/aOT912scfwwcfQHFxfPs77DA/Z+5zzyU/1niZ2ZvOuSGtl+tOm2SNigrYts1PL9Lkhz+Es85KW0giWeuUU3y1Z6ShdFoaOzb63L97k5cHJ57o72LEe4/g5pt9E4if/7x52T/+AUce6e/gSe5audI/Wt4BNoObboJPPoHbb49vf5s2+emrMrlqFJS0SRY58kj/s+WXzIoV8Q8SKiJw8sn+594Gvz33XBg9un3HOuUUWLPGv1/jcccd8Le/+ZkcmkT6HJDc03TdnnLK7suHD4dRo2DqVPjss9j3V1YGmzfDuHHJizEVUpq0mdmpZrbCzKrNbHKE9Z3NbHZ4/SIz6xde3s/M6s1sSfhxT4ttXgrvs2mdWjPliMMP99U4LT+s169Xz1GRRBx4IDz7rO8t15Z//Sv+RCuSprsXsc6O4JxvEF5aCt/85u7reveGbt2UtOW6mhpflfnVr+65bupU34wm3qkPS0uha9fkxJcqKUvazCwPuBMYCRwO/IeZHd6q2Bjgc+fcocA04KYW62qcc0eFH60HZPlBi3UbUvU7SGYpKvJv0HffbV6mpE0kcaNG7X4Xq7UJE5IzMvwhh/ieebHexZgzx7dVatkUoolZ2zOkSO64+WbftjHSVFPl5b6D2tVXx7avxkb/XnjqqeTGmAqpvNN2DFDtnFvpnNsBPAac0arMGUDTiChPACeZZeJsX5IpXnoJHnvMP6+r823c9t8/rSGJZK0vv4Rbb4U339xz3YYNsGRJ8tr4DB8e27ylO3fClCn+y7itseEqKvxA242NyYlNslO06+m443zNTCxTqC1d6qvhs6GpTSqTtoOAj1u8XhNeFrGMc24nsAXoEV53iJm9ZWbzzez4Vts9EK4a/WVbSZ6ZXWxmi81s8caNG9v9y0hm6NWruefojh1w3nnNbVxEJD55eT5Bmj17z3UvvOB/Jitp27jRd2h4663o5R580I9IP3Uq5OdHLjN2LPz5z/F3bJBg+MMf/JAfDQ3Ry82f76vT93ZXtqnavqmdZyZLZdIWKZlq/RZrq8xaoI9z7uvAlcAjZtZU0/wD59wRwPHhx4WRDu6cu9c5N8Q5N6RnPJPsSUb76CNfXbN0KXTv7u+6fec76Y5KJDuVlPg7EpHamlVVwVe+AoMHJ+dYBQW+Z1+0Kqht2+C66/y4bmee2Xa5I4/073sN/ZOb5szxTWMKCqKXO+IIP+TMlCnRy1VV+er4Aw9MXoypksqkbQ1wcIvXvYFP2ypjZvnAvsBm51zIOfcZgHPuTaAG+Gr49Sfhn7XAI/hqWMkRZn6A3QUL9F+2SDKMGOGrQTe0ah38wgtw0knJS4y6dfOTcEfrjPDQQ35MuJtuitxWqaV//MN3lJDcUlfnP/9juQPcvbu/uzt3rm9aE8n27fDKK5k/1EeTVCZtbwADzewQMysEzgfmtCozB7go/Pwc4EXnnDOznuGODJhZf2AgsNLM8s2sLLy8ABgFJDjko2SjPn38lFZLl/rkrVu3Pb9sRCR2TUMmNFWHNnnrLd/YO5lGjIDXX297SquxY31Sd3zrBjERTJjQPOCu5I758321aKxJ1oQJvor0mmsi/6O/cSOccAJ897tJDTNlUpa0hduoTQD+DrwHPO6ce8fMbjCz08PFZgI9zKwaXw3aNCzIcGCpmb2N76BwqXNuM9AZ+LuZLQWWAJ8A96Xqd5DM07Ln2Lp1sGWLr8IRkcQcfbSfBu5//3f35d27+16fyTRihO888M9/7rmuocHf1Yu1XZF6kOamqirfAaH1UDBtKSqCG27w/yy8+uqe6w8+2E+xli132tpo5pkczrm5wNxWy65r8Xw78P0I2/0F+EuE5VuBJLWwkGxVUQGzZvn2Cj167L1dg4i0LS8PVq+Gzp2bl11/PfTrF30Mt0RUVvp2Q+vX77581Srftu6BB/YcLLUtFRXw5JOwdatvmye5YdAguPzy2HoiN/nP//RTVFVW7rnuiy98jU220IwIknWOPNLfGXj3XY3RJpIMLRO2hgbfO+/115N/nMJCPy/kpa1G3rzuOj8a/WGHxb6vigpf3fXOO8mNUTLbuHHw+9/Ht01eXnPCVl/fvHz9ev+P/31ZVF+npE2yztixUF3tewUpaRNpv61bfbuee+6BhQt9Y+9Y73jFq2ly+qb2RW+/7Tsg/PjHvqoqVhUVzdtLbvjkE39tJurOO+FrX2vexwsv+Or6o45KTnwdQUmbZJ2VK2HS+BDvLK5n/j8b6dW1nknjQ9TUpDsykey0bh28+1aIn06s54ThjXShnqq/puY9tXgxHNA9RI/ievI6NfLNwfWUFoQ477z49tOvn0/YLrpor0VzXk2N/8zs1dWf81R+ZqbyWNdc4+/GJjpywODB/k7vyBN9fBf+oJEi6nn4/uz5/lDSJlll3jyorNhKwT3TWRwqJ0QhC2rLKZoxncqKrcybl+4IRbJL03vqh7XTWbLTv6eWU063h5L/npo3D0Z+aysXfD6dN7aXE3KFLNlVzmU7p/Od4+M7VqdO/m5bYWHy4guipr9v0YzpLKj15zxVn5mpPFZjo++EMHz43oeDacvnn8M+eVs59o1wfBSyjHJKZmbR94dzLvCPwYMHO8l+1dXOlRXXuQVUOuf/2drtsYBKV1Zc56qr0x2pSHboyPdUKo716qvOXX21c42N7Y8viLL979vSkiV+Vw88kJnxJRuw2EXIZ3SnTbLGHbeGGNdwF0NZGHH9UBYytuFu7pwW6uDIRLJTR76nUnGspUt9o/Q1a9odXiBl+9+3paZBmRMdmiMo3x/mcmBY+SFDhrjFixenOwxpp15d61lQW84AVrZZpob+DOu6jHVbijswMpHs1JHvqVQc61//8uN1/fWvcNpp7QovkLL979vSd77jk/NEewtn2/eHmb3pnBuyx3IlbZIt8jo1EnKF5LOrzTIN5FPUKcTOXbqJLLI3HfmeSsWxtmzxY2zdeOPe55fMRdn+921p6VLYtAlOPDEz40u2tpK29EcmEqOy0hCr6Bu1zGr6UFa6vYMiEsluHfmeSsWx9t3X9yLVzAiRZfvft6WKisQTNgjO94eSNskaoy/oxMyCS6OWmVFwGaMvTNIM1yIB15HvqVQdq6IC1q5tT2TBFYS/L/iZL55+OtHIvMB8f0TqnRC0h3qPBkO29f4RyXRB6F0YCrU/tqAKwt/XOee+/nXnhg/P3PhSgTZ6j6Y9oeqIh5K24Jg717/xJhfc4qrp73aQ76rp7yYX3OLKiuvc3LnpjlAku3Tke0rv347XdM6vIvXn/JJLnCumzl2Tv/uxfpZ3i+veJbFjbdjgM5Vf/7r98WXT9aekTQKjutq5SZdvd726bnV5nXa5Xl23ukmXb8+Y/5BEsk1HvqeSfazPP3funHOce/rp5MYZJG+84Vwh2123zs3n/OxR211pqXPvvJOcY2zZ4lxZmXOVlc5dMX73v+9++253hx7qXEND/Pt99FGfqSxalJw4s+X7o62kTb1HRUQka+3aBfvsA5ddBrfemu5oMtPjj8N558FrrzVPnL5pE/Tv7xv3t7e9GMB118Gvfw1vvAFDWvV5fPJJOPtsmDEDxoyJb79jxsBTT8HGjX7i91yh3qMiIhI4eXlQXq4epNFUVfmeti2TqbIyP5fnM8/48e7aY+1anzCfd96eCRvAWWf5ZPH662Hbtvj2vWKFTyxzKWGLRkmbiIhktYoKJW1tcc4nbSeeCPn5u6+74go44ACfvLWn0u2pp2DHDvjNbyKvN4ObboJPPoHp0+Pb9yuvwJ/+lHhsQaOkTUREstoRR8CGDbBuXbojyTzOwR//CJMm7bmupAT++7/9nbY330z8GOPHwwcfwKGHtl1m+HC46CLo0SO+fZtBaWnisQVN/t6LiIiIZK7Bg+Eb34DPPoP99093NJmlUyc444y21//oR3DMMXDUUYntf/166NULDjlk72XjvWM2bpxP8n73u4RCCyTdaRMRkaz2zW/C66/DoEHpjiTzPP44vP122+vz85sTtrq6+Pa9aBEcfDD87W+xb7NrFzzwAKxaFb1cQwPMnu2nKpNmStpEREQCqKEBxo6Fu+7ae9mbboLDD4+9o4Bz8LOfwVe+4qs+Y7V2re/pe/310cstWgS1tTBiROz7zgVK2kREJOtdfTUcf3y6o8gsr78ee+IzdCh8/DHcfnts+547F15+2Sdf++wTe0y9e8OPfwyzZkXvPFJV5at22zPfaBApaRMRkayXn+/vzjQ0pDuSzFFV5Rvyx5L4DB8Oo0bB1Km+bWA0u3bB5Mm+48G4cfHHNXmyH4JkypS2y1RV+XaK3brFv/8gU9ImIiJZr6LCJ2wrVqQ7ksxRVeXHTevePbbyU6f6O3NTp0Yvt3ixP8833ggFBfHH1b27T9jmzoWXXtpzvXNw7LFw4YXx7zvolLSJiEjWq6jwP6M1us8l9fX+XMTTJqy83A/Lcf/90TslHHssfPghnHNO4vFNnAgnnxx5nRlMmwaXX574/oNKSZuIiGS9r33N3/XRILteUZEfu+7qq+Pb7sYbYdmytsdGW7vW/+zb1ydX7YmvqgpOOGHPdatX+ypY2ZOSNhERyXoFBX6Q1/LydEeSOYqLfe/OeOy/Pxx0kK+ibH237fPP/bAqN9yQvBhra/0UWC3bIp50EowenbxjBImSNhERCYTbblM7qCbnnQcPP5z49mefDeeeu/uy3/0Ovvgi+mC98Zo/398NnDnTv/7oI6iuhmHDkneMIFHSJiIigfHll34ezGxQUwOTxofo1bWevE6N9Opaz6TxIWpq2rff1av9oLqbNiW+j6FDYd48OPeM5vhuv7meQQNDSZ1W6rTTfGeJyVf64ww4pJEu1PPv19p/HoIopUmbmZ1qZivMrNrMJkdY39nMZofXLzKzfuHl/cys3syWhB/3tNhmsJktC28z3aw9teoiIhIU8+f7oST+9a90R7J38+ZBZcVWimZMZ0FtOSFXyILacopmTKeyYivz5iW+76oq/7M9A9N+9atQYlvpO6c5vmWUM2pl++Nr6bnnYOXyrYytDx+HQpZTzgF/Se5xAsM5l5IHkAfUAP2BQuBt4PBWZcYD94Sfnw/MDj/vByxvY7+vA0MBA+YBI/cWy+DBg52IiATbunXOgXO33ZbuSKKrrnaurLjOLaDSB9zqsYBKV1Zc56qrE9v/uec6d+CBzjU2ZmYxVT0/AAASOklEQVR8HX2cbAQsdhHymVTeaTsGqHbOrXTO7QAeA1rXhJ8BPBh+/gRwUrQ7Z2Z2ANDVOfda+JeaBZyZ/NBFRCTb9OoF++2X+cN+3HFriHENdzGUhRHXD2UhYxvu5s5pobj33dgIL7zgh9NItB4qlfGl4zhBksqk7SDg4xav14SXRSzjnNsJbAF6hNcdYmZvmdl8Mzu+Rfk1e9mniIjkqIqKzB/245GHGxnTcE/UMmMb7uaRh+If9+Lzz317tO99L9HoUhtfOo4TJPkp3HekHN/FWGYt0Mc595mZDQaeNrNBMe7T79jsYuBigD59+sQctIiIZK+KCj9B+s6dfmqrTLSprjN9WRW1TB9Ws6muS9z77tEDnn020ci8VMaXjuMESSrvtK0BDm7xujfwaVtlzCwf2BfY7JwLOec+A3DOvYlvG/fVcPnee9kn4e3udc4Ncc4N6dmzZxJ+HRERyXTf/74fTX/nznRH0ray0hCr6Bu1zGr6UFa6Pe59f/FFolE1S2V86ThOkKQyaXsDGGhmh5hZIb6jwZxWZeYAF4WfnwO86JxzZtbTzPIAzKw/MBBY6ZxbC9SaWWW47dt/As+k8HcQEZEsUlkJl14KXTL45szoCzoxs+DSqGVmFFzG6Avz4trvtm1+cNybb25PdKmLL13HCZKUJW3hNmoTgL8D7wGPO+feMbMbzOz0cLGZQA8zqwauBJqGBRkOLDWzt/EdFC51zm0Or7sMmAFU4+/AqUOwiIj8nw8+gOXL0x1F2yZc1Zn7CsbzGpUR179GJTMKLuPySZ3j2u/LL0MoBEcemZnxpes4gRKpS2nQHhryQ0Qkdwwa5Nz3vpfuKKKbO9e5rvl17kpucdX0dzvId9X0dz/tdIvrUVTn5s6Nf59XXulcYaFzW7cmJ76y4jo3uWD3+CYX3OLKihOLL53HyTa0MeSH+XXBNmTIELd48eJ0hyEiIh1g9GhYsMBPiZTJ+veHvF0har/Yxaa6LnQv3k7ttjxOP6czs2fHv7+KCujZ0w/5kQw1NXDntBCPPOTjKyvdzugL87h8UmcGDEjOMTryONnEzN50zg3ZY7mSNhERCZLf/Q6mTPGN8vfdN93RRPbxx9CnD/zhDzBpUvPySZNg+nRfvXvYYbHvb+1aOPBAmDoVJu8x/5Bkm7aSNs09KiIigVJR4X8uW5beOKJpa6qpn/8cSkrg2mvj219xMdx7r5/oXYJLSZuIiARKU0P8TB5kt6oKDjgABg3afXlZGVxzDTz9dHxzqO67L4wbBwMHJjdOySwZOvSgiIhIYg48EObMgWOOSXckbbvuOli1KvJUU1dc4dvkxTo4sHMwa5a/a3fggcmNUzKL2rSJiIhkseXL4YgjYMYMGDMm3dFIMqhNm4iI5IwVK3yD/sbGdEeyp+eeg9mz/R2yaD77DG68ce+zO7TVPk6CR0mbiIgEziuvwE9+kpnDfvz+9/Db30auGm1p/nzfMeHBB6OXq6qCr33N90aVYFPSJiIigdPUg/Ttt9MbR2v19fDqq7HdFTvrLD8t1/XX+ymqIgmFfHKnu2y5QUmbiIgEzqBB/k5WpvUgfeUVn2jFkmSZwU03wSefwO23Ry6zZIlP6JS05QYlbSIiEjglJXDooZmXtFVVQWEhDB8eW/nhw2HUKD9o7ubNe64/9lg/sK6SttygpE1ERALpyCMzb4Dd996DYcP8YLixmjoVTjqp7SrS/feHoqLkxCeZTUN+iIhIIK1bB127xpcgdYS6Oigtbf9+PvsMfvhD+MUv/B03CQ4N+SEiIjll//0zL2GDxBO2FSvgzjubX7/4Ivz1r7BrV3LiksynpE1ERAKnpgYmXhyiW+d68jo10qtrPZPGh6ip2ft2k8aH6NU1vu1icdVVMGFC4tvPnOm3v/A8H9/55zZSRD2zZyUnPsl8StpERCRQ5s2DyoqtlP5pOm/uKCfkCllQW07RjOlUVmxl3rzo2xXNmM6C2ti3i4Vz8NhjsHFj4vsYMgSK2cr+fw7HRyHLKKfk/vbHJ9lBbdpERCQwamp84jVn28kMZeEe61+jktOLn2fh0hIGDGj/drF65x0oL4f77oOxY+PfPtXxSWZRmzYREQm8O24NMa7hroiJDcBQFjK24W7unBZKynaxau9UU6mOT7KD7rSJiEhg9Opaz4Lacgawss0yNfRnSOdlXPWL5l4Kt98c23bDui5j3Zb4ezecdhp8+CF88EHcmwKx/16JxieZpa07bfnpCEZERCQVNtV1pi+ropbpw2q2hLrwy182L+tksW23qa5LQnEddRQcf3xCmwKx/16JxifZQdWjIiISGGWlIVbRN2qZ1fRhv67baWjg/x6xbldWuj2huH77W5g8OaFNgdTHJ9lBSZuIiATG6As6MbPg0qhlZhRcxugL88jP5/8e8WwXrzVrYOfOuDfbTSrjk+yhNm0iIhIYmdh79Nhj/cwMTZ0REqHeo7lFvUdFRCTwBgyAWU+UcHrx80wpuIUa+tNAPjX0Z0rBLZxe/DyzntgzsYm23eQo2+3N5s3wxht+vtF0/F4SLEraREQkUEaOhIVLSwhdPJFhXZdR1CnEsK7LCF08kYVLSxg5Mr7tHu42ke9f1PZ20bz4oh9YN9GhPmKJb2+/lwSHqkdFRESi+MEP4Kmn/JAdBx0U37aXXAKPPuondy8oSE18EjyqHhUREUnAb37jOxL86lfxb1tVBSeeqIRNkkNJm4iISBSHHALjx/sJ299/P/btnPPbXHNN6mKT3JLSpM3MTjWzFWZWbWZ7jFBjZp3NbHZ4/SIz69dqfR8zqzOzq1ss+8jMlpnZEjNTnaeIiKTcL34BJSXwxz/Gvo0ZfPvbMHRo6uKS3JKyGRHMLA+4ExgBrAHeMLM5zrl3WxQbA3zunDvUzM4HbgLOa7F+GjAvwu6/7ZzblKLQRUREdlNW5jsVVFTEvs1DD/len8cdl7q4JLek8k7bMUC1c26lc24H8BhwRqsyZwAPhp8/AZxkZgZgZmcCK4F3UhijiIhITIYMgcJCqK/3VZ/R7NwJEyfC/fd3TGySG1KZtB0EfNzi9ZrwsohlnHM7gS1ADzMrAa4BIjX7dMA/zOxNM7s46VGLiIi04f33/d2zZ5+NXm7xYtiyJTlDfYg0SWXSZhGWtf7fpK0yvwKmOefqIqwf5pw7GhgJXG5mwyMe3OxiM1tsZos3btwYT9wiIiIRHXoo7LMPTJkSfWqqqirfpu2kkzouNgm+VCZta4CDW7zuDXzaVhkzywf2BTYDxwI3m9lHwBXAtWY2AcA592n45wbgKXw17B6cc/c654Y454b07NkzWb+TiIjksPx8mDoV3n0XHnyw7XJVVXD00b4tnEiypDJpewMYaGaHmFkhcD4wp1WZOcBF4efnAC8673jnXD/nXD/gNuBG59wdZlZiZvsAhKtQTwGWp/B3EBER2c1ZZ/n5RK+/HrZt23P9jh3w3nuqGpXkS1nvUefczvDdsb8DecD9zrl3zOwGYLFzbg4wE3jIzKrxd9jO38tuewFPhfsq5AOPOOeeS9XvICIi0poZ3HwzfOtb8Oc/w0UX7b6+sBDWrvUdFkSSSdNYiYiIJGDhQn/HzSK1zhZpB01jJSIikkSVlT5ha11FeuaZcO+96YlJgk1Jm4iISIKefBJ694ZVq/zrNWvgmWegtja9cUkwKWkTERFJ0De+AVu3wve+E6JX13r6HtxIF+pZ8nqImpp0RydBo6RNREQkQcuXQ5fGrZyyYjoLassJUchyyun91HQqK7YyL9JEjCIJUkcEERGRBNTUQGXFVuZsO5mhLNxj/WtUcnrx8yxcWsKAAWkIULKWOiKIiIgk0R23hhjXcFfEhA1gKAsZ23A3d04LdXBkElRK2kRERBLwyMONjGm4J2qZsQ1388hDuzooIgk6JW0iIiIJ2FTXmb6silqmD6vZVNelgyKSoFPSJiIikoCy0hCr6Bu1zGr6UFa6vYMikqBT0iYiIpKA0Rd0YmbBpVHLzCi4jNEX5nVQRBJ0StpEREQSMOGqztxXMJ7XqIy4/jUqmVFwGZdP6tzBkUlQKWkTERFJwIABMOuJEk4vfp4pBbdQQ38ayKeG/kwpuIXTi59n1hMa7kOSR0mbiIhIgkaOhIVLSwhdPJFhXZdR1CnEsK7LCF08kYVLSxg5Mt0RSpBocF0RERGRDKLBdUVERESymJI2ERERkSygpE1EREQkCyhpExEREckCStpEREREsoCSNhEREZEsoKRNREREJAsoaRMRERHJAjkxuK6ZbQRWxbFJGbApReFkE52HZjoXzXQumulceDoPzXQumulcNIv3XPR1zvVsvTAnkrZ4mdniSCMR5xqdh2Y6F810LprpXHg6D810LprpXDRL1rlQ9aiIiIhIFlDSJiIiIpIFlLRFdm+6A8gQOg/NdC6a6Vw007nwdB6a6Vw007lolpRzoTZtIiIiIllAd9pEREREsoCSthbM7FQzW2Fm1WY2Od3xpJOZfWRmy8xsiZktTnc8HcnM7jezDWa2vMWy7mZWZWYfhn9+JZ0xdpQ2zsV/m9kn4WtjiZl9N50xdgQzO9jM/mlm75nZO2b2k/DynLsuopyLXLwuupjZ62b2dvhc/Cq8/BAzWxS+LmabWWG6Y02lKOfhT2b2vy2uiaPSHWtHMbM8M3vLzP4afp2Ua0JJW5iZ5QF3AiOBw4H/MLPD0xtV2n3bOXdUDnbZ/hNwaqtlk4EXnHMDgRfCr3PBn9jzXABMC18bRznn5nZwTOmwE7jKOXcYUAlcHv58yMXroq1zAbl3XYSAE51zRwJHAaeaWSVwE/5cDAQ+B8akMcaO0NZ5APhpi2tiSfpC7HA/Ad5r8Top14SStmbHANXOuZXOuR3AY8AZaY5J0sA59zKwudXiM4AHw88fBM7s0KDSpI1zkXOcc2udc/8OP6/FfxgfRA5eF1HORc5xXl34ZUH44YATgSfCywN/XUQ5DznJzHoDpwEzwq+NJF0TStqaHQR83OL1GnL0gyjMAf8wszfN7OJ0B5MBejnn1oL/0gL2S3M86TbBzJaGq08DXyXYkpn1A74OLCLHr4tW5wJy8LoIV4MtATYAVUAN8IVzbme4SE58l7Q+D865pmvit+FrYpqZdU5jiB3pNuBnQGP4dQ+SdE0oaWtmEZbl7H8KwDDn3NH46uLLzWx4ugOSjHE3MABfDbIWuDW94XQcMysF/gJc4Zz7Mt3xpFOEc5GT14Vzbpdz7iigN77G5rBIxTo2qo7X+jyYWTkwBfh/wDeA7sA1aQyxQ5jZKGCDc+7NlosjFE3omlDS1mwNcHCL172BT9MUS9o55z4N/9wAPIX/MMpl683sAIDwzw1pjidtnHPrwx/QjcB95Mi1YWYF+CTlf5xzT4YX5+R1Eelc5Op10cQ59wXwEr6dXzczyw+vyqnvkhbn4dRwVbpzzoWAB8iNa2IYcLqZfYRvZnUi/s5bUq4JJW3N3gAGhnt4FALnA3PSHFNamFmJme3T9Bw4BVgefavAmwNcFH5+EfBMGmNJq6YkJewscuDaCLdJmQm855z7Q4tVOXddtHUucvS66Glm3cLPi4CT8W38/gmcEy4W+OuijfPwfot/aAzfhivw14Rzbopzrrdzrh8+j3jROfcDknRNaHDdFsJd1G8D8oD7nXO/TXNIaWFm/fF31wDygUdy6VyY2aPACUAZsB64HngaeBzoA6wGvu+cC3wD/TbOxQn4KjAHfARc0tSuK6jM7JvAK8AymtupXItvy5VT10WUc/Ef5N51UYFvVJ6HvwnyuHPuhvBn6GP4KsG3gAvCd5sCKcp5eBHoia8eXAJc2qLDQuCZ2QnA1c65Ucm6JpS0iYiIiGQBVY+KiIiIZAElbSIiIiJZQEmbiIiISBZQ0iYiIiKSBZS0iYiIiGQBJW0iInEws7oWz79rZh+aWZ90xiQiuSF/70VERKQ1MzsJuB04xTm3Ot3xiEjwKWkTEYmTmR2Pn6rpu865mnTHIyK5QYPriojEwcwagFrgBOfc0nTHIyK5Q23aRETi0wAsAMakOxARyS1K2kRE4tMInAt8w8yuTXcwIpI71KZNRCROzrltZjYKeMXM1jvnZqY7JhEJPiVtIiIJcM5tNrNTgZfNbJNz7pl0xyQiwaaOCCIiIiJZQG3aRERERLKAkjYRERGRLKCkTURERCQLKGkTERERyQJK2kRERESygJI2ERERkSygpE1EREQkCyhpExEREckC/x8sTM3J0RSc6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.043333333333333335\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "# Lowest error rate value and index(essentially value of k)\n",
    "print(min(error_rate))\n",
    "print(error_rate.index(min(error_rate)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, now let's use the value of k as 29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=29)\n",
    "knn.fit(X_train,y_train)\n",
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.9371428571428572\n",
      "Test set Accuracy:  0.9533333333333334\n"
     ]
    }
   ],
   "source": [
    "# Accuracy\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, knn.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[149,  10],\n",
       "       [  4, 137]], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.94      0.96       159\n",
      "           1       0.93      0.97      0.95       141\n",
      "\n",
      "    accuracy                           0.95       300\n",
      "   macro avg       0.95      0.95      0.95       300\n",
      "weighted avg       0.95      0.95      0.95       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report card using Confusion matrix\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19019020948>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion matrix for visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "matrix = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels=df['TARGET CLASS'])\n",
    "\n",
    "sns.heatmap(cm, annot=True,fmt=\"d\",cmap='Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}