correct assignment 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "84ddba29",
   "metadata": {},
   "source": [
    "# Find the accuracy mse,mae using Decision Tree,KNN & Logistic Regression algorithm.Create a dataframe & make a barplot using these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f46781e7",
   "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>size(cm)</th>\n",
       "      <th>class(y)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.500</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.125</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.410</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.810</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.050</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size(cm) class(y)\n",
       "0     9.500   Female\n",
       "1    10.125     Male\n",
       "2    10.410     Male\n",
       "3     9.810   Female\n",
       "4    11.050     Male"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "df=pd.read_excel('shoe (1).xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87029d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[df.isna().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9fafcfa1",
   "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>size(cm)</th>\n",
       "      <th>class(y)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.125</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.410</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.810</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size(cm)  class(y)\n",
       "0     9.500         0\n",
       "1    10.125         1\n",
       "2    10.410         1\n",
       "3     9.810         0\n",
       "4    11.050         1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "df['class(y)']=le.fit_transform(df['class(y)'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f038fa99",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop('class(y)',axis=1)\n",
    "y=df[['class(y)']]\n",
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3,random_state=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1896cde",
   "metadata": {},
   "source": [
    "# Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "93578648",
   "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>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 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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "dt=tree.DecisionTreeClassifier()\n",
    "dt.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "54a49650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred=dt.predict(xtest)\n",
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d6d5478",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0       1          1\n",
       "1       0          0\n",
       "2       0          0\n",
       "3       0          0\n",
       "4       1          1\n",
       "5       0          0\n",
       "6       1          1\n",
       "7       1          0\n",
       "8       1          1\n",
       "9       0          0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "y_test=np.ravel(ytest)\n",
    "table=pd.DataFrame({'Actual': y_test,'Predicted':ypred})\n",
    "table.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ba0f483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9166666666666666"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_accuracy=dt.score(xtest,ytest)\n",
    "dt_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6ed76ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08333333333333333"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error,mean_absolute_error\n",
    "dt_mse=mean_squared_error(ytest,ypred)\n",
    "dt_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d5a4a680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08333333333333333"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_mae=mean_absolute_error(ytest,ypred)\n",
    "dt_mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0761ca27",
   "metadata": {},
   "source": [
    "# KNN Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4ebed567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=10)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=10)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "kmn=KNeighborsClassifier(n_neighbors=10)\n",
    "kmn.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2a4e28dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypredk=kmn.predict(xtest)\n",
    "ypredk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf415a64",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0       1          1\n",
       "1       0          0\n",
       "2       0          0\n",
       "3       0          0\n",
       "4       1          1\n",
       "5       0          0\n",
       "6       1          1\n",
       "7       1          0\n",
       "8       1          1\n",
       "9       0          0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytestk=np.ravel(ytest)\n",
    "tablek=pd.DataFrame({'Actual': ytestk, 'Predicted': ypredk})\n",
    "tablek.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2fb46ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9166666666666666"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmn_accuracy=kmn.score(xtest,ytest)\n",
    "kmn_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0830e63c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08333333333333333"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmn_mse=mean_squared_error(ytest,ypredk)\n",
    "kmn_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b4425613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08333333333333333"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmn_mae=mean_absolute_error(ytest,ypredk)\n",
    "kmn_mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb2a964",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d315bc68",
   "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>LogisticRegression()</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lo=LogisticRegression()\n",
    "lo.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a594d146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred1=lo.predict(xtest)\n",
    "ypred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "63ed6417",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0       1          1\n",
       "1       0          0\n",
       "2       0          0\n",
       "3       0          0\n",
       "4       1          1\n",
       "5       0          0\n",
       "6       1          1\n",
       "7       1          0\n",
       "8       1          1\n",
       "9       0          0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest1=np.ravel(ytest)\n",
    "tabel1=pd.DataFrame({'Actual':ytest1,'Predicted': ypred1})\n",
    "tabel1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "596cd3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9166666666666666"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lo_accuracy=lo.score(xtest,ytest)\n",
    "lo_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c018b1c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08333333333333333"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lo_mse=mean_squared_error(ytest,ypred1)\n",
    "lo_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "41c4e717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08333333333333333"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lo_mae=mean_absolute_error(ytest,ypred)\n",
    "lo_mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567ffbf7",
   "metadata": {},
   "source": [
    "# Dataframe & Barplot using these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3e576b0b",
   "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>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model       MAE       MSE  Accuracy\n",
       "0        Decision Tree  0.083333  0.083333  0.916667\n",
       "1                  KNN  0.083333  0.083333  0.916667\n",
       "2  Logistic Regression  0.083333  0.083333  0.916667"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = ['Decision Tree','KNN','Logistic Regression']\n",
    "mae_scores=[dt_mae,kmn_mae,lo_mae]\n",
    "mse_scores=[dt_mse,kmn_mse,lo_mse]\n",
    "accuracy_scores=[dt_accuracy,kmn_accuracy,lo_accuracy]\n",
    "data={'Model': models,'MAE': mae_scores,'MSE':mse_scores,'Accuracy':accuracy_scores}\n",
    "new_df=pd.DataFrame(data)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c9816d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df_plot=new_df.melt('Model',var_name='Metric',value_name='Value')\n",
    "sns.barplot(x='Value',y='Model',hue='Metric',data=df_plot)\n",
    "plt.title('Model Performance Metrics')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb259ba3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}