stroke_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2639e584-9ee1-4bfd-8329-f6887057f01e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('healthcare-dataset-stroke-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "494631df-da58-4c7a-8879-926e1a14e1c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 5110 non-null   int64  \n",
      " 1   gender             5110 non-null   object \n",
      " 2   age                5110 non-null   float64\n",
      " 3   hypertension       5110 non-null   int64  \n",
      " 4   heart_disease      5110 non-null   int64  \n",
      " 5   ever_married       5110 non-null   object \n",
      " 6   work_type          5110 non-null   object \n",
      " 7   Residence_type     5110 non-null   object \n",
      " 8   avg_glucose_level  5110 non-null   float64\n",
      " 9   bmi                4909 non-null   float64\n",
      " 10  smoking_status     5110 non-null   object \n",
      " 11  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 479.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e2c1e94-d9cd-4869-be87-7c4cd37e52e4",
   "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>id</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>4909.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36517.829354</td>\n",
       "      <td>43.226614</td>\n",
       "      <td>0.097456</td>\n",
       "      <td>0.054012</td>\n",
       "      <td>106.147677</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0.048728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21161.721625</td>\n",
       "      <td>22.612647</td>\n",
       "      <td>0.296607</td>\n",
       "      <td>0.226063</td>\n",
       "      <td>45.283560</td>\n",
       "      <td>7.854067</td>\n",
       "      <td>0.215320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17741.250000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.245000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36932.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.885000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54682.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.090000</td>\n",
       "      <td>33.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72940.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>97.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          age  hypertension  heart_disease  \\\n",
       "count   5110.000000  5110.000000   5110.000000    5110.000000   \n",
       "mean   36517.829354    43.226614      0.097456       0.054012   \n",
       "std    21161.721625    22.612647      0.296607       0.226063   \n",
       "min       67.000000     0.080000      0.000000       0.000000   \n",
       "25%    17741.250000    25.000000      0.000000       0.000000   \n",
       "50%    36932.000000    45.000000      0.000000       0.000000   \n",
       "75%    54682.000000    61.000000      0.000000       0.000000   \n",
       "max    72940.000000    82.000000      1.000000       1.000000   \n",
       "\n",
       "       avg_glucose_level          bmi       stroke  \n",
       "count        5110.000000  4909.000000  5110.000000  \n",
       "mean          106.147677    28.893237     0.048728  \n",
       "std            45.283560     7.854067     0.215320  \n",
       "min            55.120000    10.300000     0.000000  \n",
       "25%            77.245000    23.500000     0.000000  \n",
       "50%            91.885000    28.100000     0.000000  \n",
       "75%           114.090000    33.100000     0.000000  \n",
       "max           271.740000    97.600000     1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eb25412b-7c12-44c0-95ff-b8481a0f2cfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'id'}>, <Axes: title={'center': 'age'}>,\n",
       "        <Axes: title={'center': 'hypertension'}>],\n",
       "       [<Axes: title={'center': 'heart_disease'}>,\n",
       "        <Axes: title={'center': 'avg_glucose_level'}>,\n",
       "        <Axes: title={'center': 'bmi'}>],\n",
       "       [<Axes: title={'center': 'stroke'}>, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1900x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=50, figsize=(19,15) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f804f7f9-f17c-40ab-99e3-aaf242007535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.893236911794666"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bmi'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50873422-0fc2-4dcd-a0e9-4aec5e15cd72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 5110 non-null   int64  \n",
      " 1   gender             5110 non-null   object \n",
      " 2   age                5110 non-null   float64\n",
      " 3   hypertension       5110 non-null   int64  \n",
      " 4   heart_disease      5110 non-null   int64  \n",
      " 5   ever_married       5110 non-null   object \n",
      " 6   work_type          5110 non-null   object \n",
      " 7   Residence_type     5110 non-null   object \n",
      " 8   avg_glucose_level  5110 non-null   float64\n",
      " 9   bmi                5110 non-null   float64\n",
      " 10  smoking_status     5110 non-null   object \n",
      " 11  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 479.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df['bmi'] = df['bmi'].fillna(df['bmi'].mean())\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c2e4d02-22d8-4630-96e9-550c10ce248a",
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56669</td>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53882</td>\n",
       "      <td>Male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10434</td>\n",
       "      <td>Female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27419</td>\n",
       "      <td>Female</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.15</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60491</td>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12109</td>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>80.43</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12095</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Rural</td>\n",
       "      <td>120.46</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12175</td>\n",
       "      <td>Female</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>104.51</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8213</td>\n",
       "      <td>Male</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>219.84</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5317</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>214.09</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>58202</td>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>167.41</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>56112</td>\n",
       "      <td>Male</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>191.61</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>34120</td>\n",
       "      <td>Male</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>221.29</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27458</td>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>89.22</td>\n",
       "      <td>37.800000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>25226</td>\n",
       "      <td>Male</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>217.08</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>70630</td>\n",
       "      <td>Female</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Rural</td>\n",
       "      <td>193.94</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13861</td>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>233.29</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>68794</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.70</td>\n",
       "      <td>26.600000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>64778</td>\n",
       "      <td>Male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>208.30</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4219</td>\n",
       "      <td>Male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>102.87</td>\n",
       "      <td>27.200000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>70822</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>104.12</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>38047</td>\n",
       "      <td>Female</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>100.98</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>61843</td>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>189.84</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>54827</td>\n",
       "      <td>Male</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>195.23</td>\n",
       "      <td>28.300000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>69160</td>\n",
       "      <td>Male</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>211.78</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>43717</td>\n",
       "      <td>Male</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>212.08</td>\n",
       "      <td>44.200000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>33879</td>\n",
       "      <td>Male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>83.41</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>39373</td>\n",
       "      <td>Female</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>196.92</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>54401</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>252.72</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>14248</td>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>84.20</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>712</td>\n",
       "      <td>Female</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>84.03</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>47269</td>\n",
       "      <td>Male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>219.72</td>\n",
       "      <td>33.700000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>24977</td>\n",
       "      <td>Female</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>74.63</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>47306</td>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>92.62</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>62602</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>60.91</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4651</td>\n",
       "      <td>Male</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>78.03</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1261</td>\n",
       "      <td>Male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>71.22</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>61960</td>\n",
       "      <td>Male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>144.90</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1845</td>\n",
       "      <td>Female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>90.90</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>7937</td>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>213.03</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>19824</td>\n",
       "      <td>Male</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>243.58</td>\n",
       "      <td>33.600000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>37937</td>\n",
       "      <td>Female</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>109.78</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47472</td>\n",
       "      <td>Female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>107.26</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>35626</td>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>99.33</td>\n",
       "      <td>33.700000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>36338</td>\n",
       "      <td>Female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>58.09</td>\n",
       "      <td>39.200000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0    9046    Male  67.0             0              1          Yes   \n",
       "1   51676  Female  61.0             0              0          Yes   \n",
       "2   31112    Male  80.0             0              1          Yes   \n",
       "3   60182  Female  49.0             0              0          Yes   \n",
       "4    1665  Female  79.0             1              0          Yes   \n",
       "5   56669    Male  81.0             0              0          Yes   \n",
       "6   53882    Male  74.0             1              1          Yes   \n",
       "7   10434  Female  69.0             0              0           No   \n",
       "8   27419  Female  59.0             0              0          Yes   \n",
       "9   60491  Female  78.0             0              0          Yes   \n",
       "10  12109  Female  81.0             1              0          Yes   \n",
       "11  12095  Female  61.0             0              1          Yes   \n",
       "12  12175  Female  54.0             0              0          Yes   \n",
       "13   8213    Male  78.0             0              1          Yes   \n",
       "14   5317  Female  79.0             0              1          Yes   \n",
       "15  58202  Female  50.0             1              0          Yes   \n",
       "16  56112    Male  64.0             0              1          Yes   \n",
       "17  34120    Male  75.0             1              0          Yes   \n",
       "18  27458  Female  60.0             0              0           No   \n",
       "19  25226    Male  57.0             0              1           No   \n",
       "20  70630  Female  71.0             0              0          Yes   \n",
       "21  13861  Female  52.0             1              0          Yes   \n",
       "22  68794  Female  79.0             0              0          Yes   \n",
       "23  64778    Male  82.0             0              1          Yes   \n",
       "24   4219    Male  71.0             0              0          Yes   \n",
       "25  70822    Male  80.0             0              0          Yes   \n",
       "26  38047  Female  65.0             0              0          Yes   \n",
       "27  61843    Male  58.0             0              0          Yes   \n",
       "28  54827    Male  69.0             0              1          Yes   \n",
       "29  69160    Male  59.0             0              0          Yes   \n",
       "30  43717    Male  57.0             1              0          Yes   \n",
       "31  33879    Male  42.0             0              0          Yes   \n",
       "32  39373  Female  82.0             1              0          Yes   \n",
       "33  54401    Male  80.0             0              1          Yes   \n",
       "34  14248    Male  48.0             0              0           No   \n",
       "35    712  Female  82.0             1              1           No   \n",
       "36  47269    Male  74.0             0              0          Yes   \n",
       "37  24977  Female  72.0             1              0          Yes   \n",
       "38  47306    Male  58.0             0              0           No   \n",
       "39  62602  Female  49.0             0              0          Yes   \n",
       "40   4651    Male  78.0             0              0          Yes   \n",
       "41   1261    Male  54.0             0              0          Yes   \n",
       "42  61960    Male  82.0             0              1          Yes   \n",
       "43   1845  Female  63.0             0              0          Yes   \n",
       "44   7937    Male  60.0             1              0          Yes   \n",
       "45  19824    Male  76.0             1              0          Yes   \n",
       "46  37937  Female  75.0             0              1           No   \n",
       "47  47472  Female  58.0             0              0          Yes   \n",
       "48  35626    Male  81.0             0              0          Yes   \n",
       "49  36338  Female  39.0             1              0          Yes   \n",
       "\n",
       "        work_type Residence_type  avg_glucose_level        bmi  \\\n",
       "0         Private          Urban             228.69  36.600000   \n",
       "1   Self-employed          Rural             202.21  28.893237   \n",
       "2         Private          Rural             105.92  32.500000   \n",
       "3         Private          Urban             171.23  34.400000   \n",
       "4   Self-employed          Rural             174.12  24.000000   \n",
       "5         Private          Urban             186.21  29.000000   \n",
       "6         Private          Rural              70.09  27.400000   \n",
       "7         Private          Urban              94.39  22.800000   \n",
       "8         Private          Rural              76.15  28.893237   \n",
       "9         Private          Urban              58.57  24.200000   \n",
       "10        Private          Rural              80.43  29.700000   \n",
       "11       Govt_job          Rural             120.46  36.800000   \n",
       "12        Private          Urban             104.51  27.300000   \n",
       "13        Private          Urban             219.84  28.893237   \n",
       "14        Private          Urban             214.09  28.200000   \n",
       "15  Self-employed          Rural             167.41  30.900000   \n",
       "16        Private          Urban             191.61  37.500000   \n",
       "17        Private          Urban             221.29  25.800000   \n",
       "18        Private          Urban              89.22  37.800000   \n",
       "19       Govt_job          Urban             217.08  28.893237   \n",
       "20       Govt_job          Rural             193.94  22.400000   \n",
       "21  Self-employed          Urban             233.29  48.900000   \n",
       "22  Self-employed          Urban             228.70  26.600000   \n",
       "23        Private          Rural             208.30  32.500000   \n",
       "24        Private          Urban             102.87  27.200000   \n",
       "25  Self-employed          Rural             104.12  23.500000   \n",
       "26        Private          Rural             100.98  28.200000   \n",
       "27        Private          Rural             189.84  28.893237   \n",
       "28  Self-employed          Urban             195.23  28.300000   \n",
       "29        Private          Rural             211.78  28.893237   \n",
       "30        Private          Urban             212.08  44.200000   \n",
       "31        Private          Rural              83.41  25.400000   \n",
       "32  Self-employed          Urban             196.92  22.200000   \n",
       "33  Self-employed          Urban             252.72  30.500000   \n",
       "34       Govt_job          Urban              84.20  29.700000   \n",
       "35        Private          Rural              84.03  26.500000   \n",
       "36        Private          Rural             219.72  33.700000   \n",
       "37        Private          Rural              74.63  23.100000   \n",
       "38        Private          Rural              92.62  32.000000   \n",
       "39        Private          Urban              60.91  29.900000   \n",
       "40        Private          Rural              78.03  23.900000   \n",
       "41        Private          Urban              71.22  28.500000   \n",
       "42        Private          Urban             144.90  26.400000   \n",
       "43        Private          Urban              90.90  28.893237   \n",
       "44       Govt_job          Urban             213.03  20.200000   \n",
       "45        Private          Rural             243.58  33.600000   \n",
       "46  Self-employed          Urban             109.78  28.893237   \n",
       "47        Private          Urban             107.26  38.600000   \n",
       "48  Self-employed          Urban              99.33  33.700000   \n",
       "49        Private          Rural              58.09  39.200000   \n",
       "\n",
       "     smoking_status  stroke  \n",
       "0   formerly smoked       1  \n",
       "1      never smoked       1  \n",
       "2      never smoked       1  \n",
       "3            smokes       1  \n",
       "4      never smoked       1  \n",
       "5   formerly smoked       1  \n",
       "6      never smoked       1  \n",
       "7      never smoked       1  \n",
       "8           Unknown       1  \n",
       "9           Unknown       1  \n",
       "10     never smoked       1  \n",
       "11           smokes       1  \n",
       "12           smokes       1  \n",
       "13          Unknown       1  \n",
       "14     never smoked       1  \n",
       "15     never smoked       1  \n",
       "16           smokes       1  \n",
       "17           smokes       1  \n",
       "18     never smoked       1  \n",
       "19          Unknown       1  \n",
       "20           smokes       1  \n",
       "21     never smoked       1  \n",
       "22     never smoked       1  \n",
       "23          Unknown       1  \n",
       "24  formerly smoked       1  \n",
       "25     never smoked       1  \n",
       "26  formerly smoked       1  \n",
       "27          Unknown       1  \n",
       "28           smokes       1  \n",
       "29  formerly smoked       1  \n",
       "30           smokes       1  \n",
       "31          Unknown       1  \n",
       "32     never smoked       1  \n",
       "33  formerly smoked       1  \n",
       "34     never smoked       1  \n",
       "35  formerly smoked       1  \n",
       "36  formerly smoked       1  \n",
       "37  formerly smoked       1  \n",
       "38          Unknown       1  \n",
       "39     never smoked       1  \n",
       "40  formerly smoked       1  \n",
       "41     never smoked       1  \n",
       "42           smokes       1  \n",
       "43  formerly smoked       1  \n",
       "44           smokes       1  \n",
       "45     never smoked       1  \n",
       "46          Unknown       1  \n",
       "47  formerly smoked       1  \n",
       "48     never smoked       1  \n",
       "49           smokes       1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a7e078c-c5f1-427d-94b2-fadbb7719116",
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id gender   age  hypertension  heart_disease ever_married work_type  \\\n",
       "0  9046   Male  67.0             0              1          Yes   Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ae7341b-5003-4af0-bc95-62eac4e0af90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Male\n",
       "1       Female\n",
       "2         Male\n",
       "3       Female\n",
       "4       Female\n",
       "         ...  \n",
       "5105    Female\n",
       "5106    Female\n",
       "5107    Female\n",
       "5108      Male\n",
       "5109    Female\n",
       "Name: gender, Length: 5110, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "774a5071-5012-4ad1-9084-69118274da06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "columns_to_encode = ('gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status')\n",
    "for column in columns_to_encode:\n",
    "    df_column_encoded = ordinal_encoder.fit_transform(df[[column]])\n",
    "    df[[column]] = df_column_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b219edbb-4dae-4444-a457-bedf702a2a21",
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.21</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "0   9046     1.0  67.0             0              1           1.0        2.0   \n",
       "1  51676     0.0  61.0             0              0           1.0        3.0   \n",
       "2  31112     1.0  80.0             0              1           1.0        2.0   \n",
       "3  60182     0.0  49.0             0              0           1.0        2.0   \n",
       "4   1665     0.0  79.0             1              0           1.0        3.0   \n",
       "\n",
       "   Residence_type  avg_glucose_level        bmi  smoking_status  stroke  \n",
       "0             1.0             228.69  36.600000             1.0       1  \n",
       "1             0.0             202.21  28.893237             2.0       1  \n",
       "2             0.0             105.92  32.500000             2.0       1  \n",
       "3             1.0             171.23  34.400000             3.0       1  \n",
       "4             0.0             174.12  24.000000             2.0       1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "29828429-f1e9-4057-9c50-0da578db0de4",
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002511</td>\n",
       "      <td>0.003538</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>-0.001296</td>\n",
       "      <td>0.013690</td>\n",
       "      <td>-0.015757</td>\n",
       "      <td>-0.001403</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.014074</td>\n",
       "      <td>0.006388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.002511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028202</td>\n",
       "      <td>0.020994</td>\n",
       "      <td>0.085447</td>\n",
       "      <td>-0.031005</td>\n",
       "      <td>0.056422</td>\n",
       "      <td>-0.006738</td>\n",
       "      <td>0.055180</td>\n",
       "      <td>-0.026109</td>\n",
       "      <td>-0.062581</td>\n",
       "      <td>0.008929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.003538</td>\n",
       "      <td>-0.028202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.276398</td>\n",
       "      <td>0.263796</td>\n",
       "      <td>0.679125</td>\n",
       "      <td>-0.361642</td>\n",
       "      <td>0.014180</td>\n",
       "      <td>0.238171</td>\n",
       "      <td>0.325942</td>\n",
       "      <td>0.265199</td>\n",
       "      <td>0.245257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.020994</td>\n",
       "      <td>0.276398</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108306</td>\n",
       "      <td>0.164243</td>\n",
       "      <td>-0.051761</td>\n",
       "      <td>-0.007913</td>\n",
       "      <td>0.174474</td>\n",
       "      <td>0.160189</td>\n",
       "      <td>0.111038</td>\n",
       "      <td>0.127904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_disease</th>\n",
       "      <td>-0.001296</td>\n",
       "      <td>0.085447</td>\n",
       "      <td>0.263796</td>\n",
       "      <td>0.108306</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114644</td>\n",
       "      <td>-0.028023</td>\n",
       "      <td>0.003092</td>\n",
       "      <td>0.161857</td>\n",
       "      <td>0.038899</td>\n",
       "      <td>0.048460</td>\n",
       "      <td>0.134914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ever_married</th>\n",
       "      <td>0.013690</td>\n",
       "      <td>-0.031005</td>\n",
       "      <td>0.679125</td>\n",
       "      <td>0.164243</td>\n",
       "      <td>0.114644</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.352722</td>\n",
       "      <td>0.006261</td>\n",
       "      <td>0.155068</td>\n",
       "      <td>0.335705</td>\n",
       "      <td>0.259647</td>\n",
       "      <td>0.108340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type</th>\n",
       "      <td>-0.015757</td>\n",
       "      <td>0.056422</td>\n",
       "      <td>-0.361642</td>\n",
       "      <td>-0.051761</td>\n",
       "      <td>-0.028023</td>\n",
       "      <td>-0.352722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007316</td>\n",
       "      <td>-0.050513</td>\n",
       "      <td>-0.299448</td>\n",
       "      <td>-0.305927</td>\n",
       "      <td>-0.032316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residence_type</th>\n",
       "      <td>-0.001403</td>\n",
       "      <td>-0.006738</td>\n",
       "      <td>0.014180</td>\n",
       "      <td>-0.007913</td>\n",
       "      <td>0.003092</td>\n",
       "      <td>0.006261</td>\n",
       "      <td>-0.007316</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004946</td>\n",
       "      <td>-0.000120</td>\n",
       "      <td>0.008237</td>\n",
       "      <td>0.015458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <td>0.001092</td>\n",
       "      <td>0.055180</td>\n",
       "      <td>0.238171</td>\n",
       "      <td>0.174474</td>\n",
       "      <td>0.161857</td>\n",
       "      <td>0.155068</td>\n",
       "      <td>-0.050513</td>\n",
       "      <td>-0.004946</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168751</td>\n",
       "      <td>0.063437</td>\n",
       "      <td>0.131945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.002999</td>\n",
       "      <td>-0.026109</td>\n",
       "      <td>0.325942</td>\n",
       "      <td>0.160189</td>\n",
       "      <td>0.038899</td>\n",
       "      <td>0.335705</td>\n",
       "      <td>-0.299448</td>\n",
       "      <td>-0.000120</td>\n",
       "      <td>0.168751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.219148</td>\n",
       "      <td>0.038947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status</th>\n",
       "      <td>0.014074</td>\n",
       "      <td>-0.062581</td>\n",
       "      <td>0.265199</td>\n",
       "      <td>0.111038</td>\n",
       "      <td>0.048460</td>\n",
       "      <td>0.259647</td>\n",
       "      <td>-0.305927</td>\n",
       "      <td>0.008237</td>\n",
       "      <td>0.063437</td>\n",
       "      <td>0.219148</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>0.006388</td>\n",
       "      <td>0.008929</td>\n",
       "      <td>0.245257</td>\n",
       "      <td>0.127904</td>\n",
       "      <td>0.134914</td>\n",
       "      <td>0.108340</td>\n",
       "      <td>-0.032316</td>\n",
       "      <td>0.015458</td>\n",
       "      <td>0.131945</td>\n",
       "      <td>0.038947</td>\n",
       "      <td>0.028123</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id    gender       age  hypertension  heart_disease  \\\n",
       "id                 1.000000  0.002511  0.003538      0.003550      -0.001296   \n",
       "gender             0.002511  1.000000 -0.028202      0.020994       0.085447   \n",
       "age                0.003538 -0.028202  1.000000      0.276398       0.263796   \n",
       "hypertension       0.003550  0.020994  0.276398      1.000000       0.108306   \n",
       "heart_disease     -0.001296  0.085447  0.263796      0.108306       1.000000   \n",
       "ever_married       0.013690 -0.031005  0.679125      0.164243       0.114644   \n",
       "work_type         -0.015757  0.056422 -0.361642     -0.051761      -0.028023   \n",
       "Residence_type    -0.001403 -0.006738  0.014180     -0.007913       0.003092   \n",
       "avg_glucose_level  0.001092  0.055180  0.238171      0.174474       0.161857   \n",
       "bmi                0.002999 -0.026109  0.325942      0.160189       0.038899   \n",
       "smoking_status     0.014074 -0.062581  0.265199      0.111038       0.048460   \n",
       "stroke             0.006388  0.008929  0.245257      0.127904       0.134914   \n",
       "\n",
       "                   ever_married  work_type  Residence_type  avg_glucose_level  \\\n",
       "id                     0.013690  -0.015757       -0.001403           0.001092   \n",
       "gender                -0.031005   0.056422       -0.006738           0.055180   \n",
       "age                    0.679125  -0.361642        0.014180           0.238171   \n",
       "hypertension           0.164243  -0.051761       -0.007913           0.174474   \n",
       "heart_disease          0.114644  -0.028023        0.003092           0.161857   \n",
       "ever_married           1.000000  -0.352722        0.006261           0.155068   \n",
       "work_type             -0.352722   1.000000       -0.007316          -0.050513   \n",
       "Residence_type         0.006261  -0.007316        1.000000          -0.004946   \n",
       "avg_glucose_level      0.155068  -0.050513       -0.004946           1.000000   \n",
       "bmi                    0.335705  -0.299448       -0.000120           0.168751   \n",
       "smoking_status         0.259647  -0.305927        0.008237           0.063437   \n",
       "stroke                 0.108340  -0.032316        0.015458           0.131945   \n",
       "\n",
       "                        bmi  smoking_status    stroke  \n",
       "id                 0.002999        0.014074  0.006388  \n",
       "gender            -0.026109       -0.062581  0.008929  \n",
       "age                0.325942        0.265199  0.245257  \n",
       "hypertension       0.160189        0.111038  0.127904  \n",
       "heart_disease      0.038899        0.048460  0.134914  \n",
       "ever_married       0.335705        0.259647  0.108340  \n",
       "work_type         -0.299448       -0.305927 -0.032316  \n",
       "Residence_type    -0.000120        0.008237  0.015458  \n",
       "avg_glucose_level  0.168751        0.063437  0.131945  \n",
       "bmi                1.000000        0.219148  0.038947  \n",
       "smoking_status     0.219148        1.000000  0.028123  \n",
       "stroke             0.038947        0.028123  1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52a042ed-8fb2-4035-b5e1-6961bb32da6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.drop(columns=['stroke'])\n",
    "Y = df['stroke']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4c085bb4-32f5-469d-b1ba-cddb464facff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'gender',\n",
       " 'age',\n",
       " 'hypertension',\n",
       " 'heart_disease',\n",
       " 'ever_married',\n",
       " 'work_type',\n",
       " 'Residence_type',\n",
       " 'avg_glucose_level',\n",
       " 'bmi',\n",
       " 'smoking_status']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "12642c7b-75bc-495c-a6f3-c9c2880f5f11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "0.9561815336463224\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "0.9561815336463224\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "0.9553990610328639\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "def test_model(model, x_train, x_test, y_train, y_test):\n",
    "    model.fit(x_train,y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    print(accuracy_score(y_test, y_pred))\n",
    "\n",
    "\n",
    "\n",
    "svc = SVC()\n",
    "random_forest = RandomForestClassifier()\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "models = (svc, random_forest, knn)\n",
    "\n",
    "for m in models:\n",
    "    test_model(m, x_train, x_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9958c30-696d-4077-aee2-2ce62ee3c4c0",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}