Health Care Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "635733fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import IsolationForest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "53f1ed45",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"HealthCare-DataSet.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "dc56ec0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5109 entries, 0 to 5108\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 5109 non-null   int64  \n",
      " 1   gender             5109 non-null   object \n",
      " 2   age                5109 non-null   float64\n",
      " 3   hypertension       5109 non-null   int64  \n",
      " 4   heart_disease      5109 non-null   int64  \n",
      " 5   ever_married       5109 non-null   object \n",
      " 6   work_type          5109 non-null   object \n",
      " 7   Residence_type     5109 non-null   object \n",
      " 8   avg_glucose_level  5109 non-null   float64\n",
      " 9   bmi                4908 non-null   float64\n",
      " 10  smoking_status     5109 non-null   object \n",
      " 11  stroke             5109 non-null   int64  \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 479.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5c48c9da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    <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>NaN</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>32.5</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>34.4</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>24.0</td>\n",
       "      <td>never smoked</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",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cfa6a0c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>5104</th>\n",
       "      <td>18234</td>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>83.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>44873</td>\n",
       "      <td>Female</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>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>19723</td>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>37544</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>44679</td>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "5104  18234  Female  80.0             1              0          Yes   \n",
       "5105  44873  Female  81.0             0              0          Yes   \n",
       "5106  19723  Female  35.0             0              0          Yes   \n",
       "5107  37544    Male  51.0             0              0          Yes   \n",
       "5108  44679  Female  44.0             0              0          Yes   \n",
       "\n",
       "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "5104        Private          Urban              83.75   NaN     never smoked   \n",
       "5105  Self-employed          Urban             125.20  40.0     never smoked   \n",
       "5106  Self-employed          Rural              82.99  30.6     never smoked   \n",
       "5107        Private          Rural             166.29  25.6  formerly smoked   \n",
       "5108       Govt_job          Urban              85.28  26.2          Unknown   \n",
       "\n",
       "      stroke  \n",
       "5104       0  \n",
       "5105       0  \n",
       "5106       0  \n",
       "5107       0  \n",
       "5108       0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2215e6f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Private' 'Self-employed' 'Govt_job' 'children' 'Never_worked']\n"
     ]
    }
   ],
   "source": [
    "# Check for typos and inconsistencies in the 'work_type' column\n",
    "print(data['work_type'].unique())\n",
    "\n",
    "# Replace any inconsistent values in the 'work_type' column\n",
    "data['work_type'] = data['work_type'].replace({'children': 'Never_worked'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9f9dfeff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Private' 'Self-employed' 'Govt_job' 'Never_worked']\n"
     ]
    }
   ],
   "source": [
    "print(data['work_type'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d1c0bbd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert categorical variable 'gender' to numerical values\n",
    "data['gender'] = data['gender'].replace({'Male': 0, 'Female': 1})\n",
    "# Create dummy variables for categorical variable 'smoking_status'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6d6e9062",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>0</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",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>1</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>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>0</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>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>1</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>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>1</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>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56669</td>\n",
       "      <td>0</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>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53882</td>\n",
       "      <td>0</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>27.4</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10434</td>\n",
       "      <td>1</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>22.8</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27419</td>\n",
       "      <td>1</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>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60491</td>\n",
       "      <td>1</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>24.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12109</td>\n",
       "      <td>1</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>29.7</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12095</td>\n",
       "      <td>1</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>36.8</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12175</td>\n",
       "      <td>1</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>27.3</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8213</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5317</td>\n",
       "      <td>1</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>28.2</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>58202</td>\n",
       "      <td>1</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>30.9</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>56112</td>\n",
       "      <td>0</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>37.5</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>34120</td>\n",
       "      <td>0</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>25.8</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27458</td>\n",
       "      <td>1</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>37.8</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>25226</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>Unknown</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       0  67.0             0              1          Yes   \n",
       "1   51676       1  61.0             0              0          Yes   \n",
       "2   31112       0  80.0             0              1          Yes   \n",
       "3   60182       1  49.0             0              0          Yes   \n",
       "4    1665       1  79.0             1              0          Yes   \n",
       "5   56669       0  81.0             0              0          Yes   \n",
       "6   53882       0  74.0             1              1          Yes   \n",
       "7   10434       1  69.0             0              0           No   \n",
       "8   27419       1  59.0             0              0          Yes   \n",
       "9   60491       1  78.0             0              0          Yes   \n",
       "10  12109       1  81.0             1              0          Yes   \n",
       "11  12095       1  61.0             0              1          Yes   \n",
       "12  12175       1  54.0             0              0          Yes   \n",
       "13   8213       0  78.0             0              1          Yes   \n",
       "14   5317       1  79.0             0              1          Yes   \n",
       "15  58202       1  50.0             1              0          Yes   \n",
       "16  56112       0  64.0             0              1          Yes   \n",
       "17  34120       0  75.0             1              0          Yes   \n",
       "18  27458       1  60.0             0              0           No   \n",
       "19  25226       0  57.0             0              1           No   \n",
       "\n",
       "        work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0         Private          Urban             228.69  36.6  formerly smoked   \n",
       "1   Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2         Private          Rural             105.92  32.5     never smoked   \n",
       "3         Private          Urban             171.23  34.4           smokes   \n",
       "4   Self-employed          Rural             174.12  24.0     never smoked   \n",
       "5         Private          Urban             186.21  29.0  formerly smoked   \n",
       "6         Private          Rural              70.09  27.4     never smoked   \n",
       "7         Private          Urban              94.39  22.8     never smoked   \n",
       "8         Private          Rural              76.15   NaN          Unknown   \n",
       "9         Private          Urban              58.57  24.2          Unknown   \n",
       "10        Private          Rural              80.43  29.7     never smoked   \n",
       "11       Govt_job          Rural             120.46  36.8           smokes   \n",
       "12        Private          Urban             104.51  27.3           smokes   \n",
       "13        Private          Urban             219.84   NaN          Unknown   \n",
       "14        Private          Urban             214.09  28.2     never smoked   \n",
       "15  Self-employed          Rural             167.41  30.9     never smoked   \n",
       "16        Private          Urban             191.61  37.5           smokes   \n",
       "17        Private          Urban             221.29  25.8           smokes   \n",
       "18        Private          Urban              89.22  37.8     never smoked   \n",
       "19       Govt_job          Urban             217.08   NaN          Unknown   \n",
       "\n",
       "    stroke  \n",
       "0        1  \n",
       "1        1  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  \n",
       "5        1  \n",
       "6        1  \n",
       "7        1  \n",
       "8        1  \n",
       "9        1  \n",
       "10       1  \n",
       "11       1  \n",
       "12       1  \n",
       "13       1  \n",
       "14       1  \n",
       "15       1  \n",
       "16       1  \n",
       "17       1  \n",
       "18       1  \n",
       "19       1  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b7e520ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "ever_married           0\n",
       "work_type              0\n",
       "Residence_type         0\n",
       "avg_glucose_level      0\n",
       "bmi                  201\n",
       "smoking_status         0\n",
       "stroke                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1ff2e1d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = data[\"bmi\"].mean()\n",
    "data.fillna(mean,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "27adc2cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5109 entries, 0 to 5108\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 5109 non-null   int64  \n",
      " 1   gender             5109 non-null   int64  \n",
      " 2   age                5109 non-null   float64\n",
      " 3   hypertension       5109 non-null   int64  \n",
      " 4   heart_disease      5109 non-null   int64  \n",
      " 5   ever_married       5109 non-null   object \n",
      " 6   work_type          5109 non-null   object \n",
      " 7   Residence_type     5109 non-null   object \n",
      " 8   avg_glucose_level  5109 non-null   float64\n",
      " 9   bmi                5109 non-null   float64\n",
      " 10  smoking_status     5109 non-null   object \n",
      " 11  stroke             5109 non-null   int64  \n",
      "dtypes: float64(3), int64(5), object(4)\n",
      "memory usage: 479.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8148712b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "5104    False\n",
       "5105    False\n",
       "5106    False\n",
       "5107    False\n",
       "5108    False\n",
       "Length: 5109, dtype: bool"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f0a99dcc",
   "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>0</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>36.60000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>1</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>28.89456</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>0</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>32.50000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>1</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>34.40000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>1</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>24.00000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5104</th>\n",
       "      <td>18234</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>83.75</td>\n",
       "      <td>28.89456</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>44873</td>\n",
       "      <td>1</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>125.20</td>\n",
       "      <td>40.00000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>19723</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.60000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>37544</td>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.60000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>44679</td>\n",
       "      <td>1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.20000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5109 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0      9046       0  67.0             0              1          Yes   \n",
       "1     51676       1  61.0             0              0          Yes   \n",
       "2     31112       0  80.0             0              1          Yes   \n",
       "3     60182       1  49.0             0              0          Yes   \n",
       "4      1665       1  79.0             1              0          Yes   \n",
       "...     ...     ...   ...           ...            ...          ...   \n",
       "5104  18234       1  80.0             1              0          Yes   \n",
       "5105  44873       1  81.0             0              0          Yes   \n",
       "5106  19723       1  35.0             0              0          Yes   \n",
       "5107  37544       0  51.0             0              0          Yes   \n",
       "5108  44679       1  44.0             0              0          Yes   \n",
       "\n",
       "          work_type Residence_type  avg_glucose_level       bmi  \\\n",
       "0           Private          Urban             228.69  36.60000   \n",
       "1     Self-employed          Rural             202.21  28.89456   \n",
       "2           Private          Rural             105.92  32.50000   \n",
       "3           Private          Urban             171.23  34.40000   \n",
       "4     Self-employed          Rural             174.12  24.00000   \n",
       "...             ...            ...                ...       ...   \n",
       "5104        Private          Urban              83.75  28.89456   \n",
       "5105  Self-employed          Urban             125.20  40.00000   \n",
       "5106  Self-employed          Rural              82.99  30.60000   \n",
       "5107        Private          Rural             166.29  25.60000   \n",
       "5108       Govt_job          Urban              85.28  26.20000   \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",
       "...               ...     ...  \n",
       "5104     never smoked       0  \n",
       "5105     never smoked       0  \n",
       "5106     never smoked       0  \n",
       "5107  formerly smoked       0  \n",
       "5108          Unknown       0  \n",
       "\n",
       "[5109 rows x 12 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bccc9a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x21e86c36ce0>,\n",
       "  <matplotlib.lines.Line2D at 0x21e86c36f80>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x21e86c37220>,\n",
       "  <matplotlib.lines.Line2D at 0x21e86c374c0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x21e86c36a40>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x21e86c37760>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x21e86c37a00>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data[\"avg_glucose_level\"],vert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ed7a74b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x21e8704fb80>,\n",
       "  <matplotlib.lines.Line2D at 0x21e8704fe20>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x21e86e40100>,\n",
       "  <matplotlib.lines.Line2D at 0x21e86e403a0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x21e8704f8e0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x21e86e40640>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x21e86e408e0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identify outliers in the \"avg_glucose_level\" column using the IQR method\n",
    "q1 = data[\"avg_glucose_level\"].quantile(0.25)\n",
    "q3 = data[\"avg_glucose_level\"].quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "upper = q3 + 1.5 * iqr\n",
    "lower = q1 - 1.5 * iqr\n",
    "outliers = data[(data[\"avg_glucose_level\"] < lower) | (data[\"avg_glucose_level\"] > upper)]\n",
    "# Remove outliers from the DataFrame\n",
    "data = data.drop(outliers.index)\n",
    "plt.boxplot(data[\"avg_glucose_level\"],vert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "246cdf70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x21e86e951b0>,\n",
       "  <matplotlib.lines.Line2D at 0x21e86e95450>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x21e86e956f0>,\n",
       "  <matplotlib.lines.Line2D at 0x21e86e95870>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x21e86e94f10>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x21e86e95b10>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x21e86e95db0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data[\"bmi\"],vert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "285f0c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x21e880eb940>,\n",
       "  <matplotlib.lines.Line2D at 0x21e880ebbe0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x21e880ebe80>,\n",
       "  <matplotlib.lines.Line2D at 0x21e8811c160>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x21e880eb6a0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x21e8811c400>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x21e8811c6a0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identify outliers in the 'bmi' column using the IQR method\n",
    "q1 = data['bmi'].quantile(0.25)\n",
    "q3 = data['bmi'].quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "upper = q3 + 1.5 * iqr\n",
    "lower = q1 - 1.5 * iqr\n",
    "outliers = data[(data['bmi'] < lower) | (data['bmi'] > upper)]\n",
    "# Remove outliers from the DataFrame\n",
    "data = data.drop(outliers.index)\n",
    "plt.boxplot(data[\"bmi\"],vert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "256336d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x21e858e3ee0>,\n",
       "  <matplotlib.lines.Line2D at 0x21e858e2890>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x21e86b9ec20>,\n",
       "  <matplotlib.lines.Line2D at 0x21e86b9f670>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x21e858e2a40>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x21e86b9e4d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x21e85985480>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data[\"age\"],vert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ed24eede",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "15809f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "38ba62a7",
   "metadata": {
    "scrolled": false
   },
   "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>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>0</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>32.50000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53882</td>\n",
       "      <td>0</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>27.40000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10434</td>\n",
       "      <td>1</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>22.80000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27419</td>\n",
       "      <td>1</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>28.89456</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60491</td>\n",
       "      <td>1</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>24.20000</td>\n",
       "      <td>Unknown</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",
       "2  31112       0  80.0             0              1          Yes   Private   \n",
       "6  53882       0  74.0             1              1          Yes   Private   \n",
       "7  10434       1  69.0             0              0           No   Private   \n",
       "8  27419       1  59.0             0              0          Yes   Private   \n",
       "9  60491       1  78.0             0              0          Yes   Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level       bmi smoking_status  stroke  \n",
       "2          Rural             105.92  32.50000   never smoked       1  \n",
       "6          Rural              70.09  27.40000   never smoked       1  \n",
       "7          Urban              94.39  22.80000   never smoked       1  \n",
       "8          Rural              76.15  28.89456        Unknown       1  \n",
       "9          Urban              58.57  24.20000        Unknown       1  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0be7f815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4199 entries, 2 to 5108\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 4199 non-null   int64  \n",
      " 1   gender             4199 non-null   int64  \n",
      " 2   age                4199 non-null   float64\n",
      " 3   hypertension       4199 non-null   int64  \n",
      " 4   heart_disease      4199 non-null   int64  \n",
      " 5   ever_married       4199 non-null   object \n",
      " 6   work_type          4199 non-null   object \n",
      " 7   Residence_type     4199 non-null   object \n",
      " 8   avg_glucose_level  4199 non-null   float64\n",
      " 9   bmi                4199 non-null   float64\n",
      " 10  smoking_status     4199 non-null   object \n",
      " 11  stroke             4199 non-null   int64  \n",
      "dtypes: float64(3), int64(5), object(4)\n",
      "memory usage: 426.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5e97233d",
   "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>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>4199.000000</td>\n",
       "      <td>4199.000000</td>\n",
       "      <td>4199.000000</td>\n",
       "      <td>4199.000000</td>\n",
       "      <td>4199.000000</td>\n",
       "      <td>4199.000000</td>\n",
       "      <td>4199.000000</td>\n",
       "      <td>4199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36053.071207</td>\n",
       "      <td>0.591808</td>\n",
       "      <td>40.815166</td>\n",
       "      <td>0.073351</td>\n",
       "      <td>0.039295</td>\n",
       "      <td>89.124420</td>\n",
       "      <td>27.660840</td>\n",
       "      <td>0.037152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21176.917002</td>\n",
       "      <td>0.491558</td>\n",
       "      <td>22.584918</td>\n",
       "      <td>0.260742</td>\n",
       "      <td>0.194319</td>\n",
       "      <td>19.401583</td>\n",
       "      <td>6.405892</td>\n",
       "      <td>0.189156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.000000</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>17243.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>74.565000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35759.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.060000</td>\n",
       "      <td>27.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54300.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.985000</td>\n",
       "      <td>31.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72940.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>142.820000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id       gender          age  hypertension  heart_disease  \\\n",
       "count   4199.000000  4199.000000  4199.000000   4199.000000    4199.000000   \n",
       "mean   36053.071207     0.591808    40.815166      0.073351       0.039295   \n",
       "std    21176.917002     0.491558    22.584918      0.260742       0.194319   \n",
       "min       67.000000     0.000000     0.080000      0.000000       0.000000   \n",
       "25%    17243.500000     0.000000    22.000000      0.000000       0.000000   \n",
       "50%    35759.000000     1.000000    42.000000      0.000000       0.000000   \n",
       "75%    54300.500000     1.000000    58.000000      0.000000       0.000000   \n",
       "max    72940.000000     1.000000    82.000000      1.000000       1.000000   \n",
       "\n",
       "       avg_glucose_level          bmi       stroke  \n",
       "count        4199.000000  4199.000000  4199.000000  \n",
       "mean           89.124420    27.660840     0.037152  \n",
       "std            19.401583     6.405892     0.189156  \n",
       "min            55.120000    10.300000     0.000000  \n",
       "25%            74.565000    23.100000     0.000000  \n",
       "50%            87.060000    27.600000     0.000000  \n",
       "75%           101.985000    31.700000     0.000000  \n",
       "max           142.820000    44.500000     1.000000  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "cbbd9805",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var of age  510.0785000142846\n",
      "var of hypertension  0.06798664942230284\n",
      "var of heart_disease  0.03775996031633022\n",
      "var of avg_glucose_level  376.4214327210442\n",
      "var of bmi  41.03545309441743\n"
     ]
    }
   ],
   "source": [
    "import statistics\n",
    "va_age=statistics.variance(df['age'])\n",
    "va_hypertension=statistics.variance(df['hypertension'])\n",
    "va_heart_disease=statistics.variance(df['heart_disease'])\n",
    "va_avg_glucose_level=statistics.variance(df['avg_glucose_level'])\n",
    "va_bmi=statistics.variance(df['bmi'])\n",
    "print(\"var of age \" , va_age)\n",
    "print(\"var of hypertension \",va_hypertension)\n",
    "print(\"var of heart_disease \",va_heart_disease)\n",
    "print(\"var of avg_glucose_level \",va_avg_glucose_level)\n",
    "print(\"var of bmi \",va_bmi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc723cef",
   "metadata": {},
   "source": [
    "# visualization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54cb77e0",
   "metadata": {},
   "source": [
    "## Histogram\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "fef5d9ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 11., 168., 425., 628., 831., 922., 558., 331., 197., 128.]),\n",
       " array([10.3 , 13.72, 17.14, 20.56, 23.98, 27.4 , 30.82, 34.24, 37.66,\n",
       "        41.08, 44.5 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "## Histogram\n",
    "plt.hist(df['bmi'],color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4a2a29",
   "metadata": {},
   "source": [
    "## Density Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4c24e0cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_26276\\2700717116.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['bmi'],\n",
      "c:\\Users\\moham\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2511: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  kdeplot(**{axis: a}, ax=ax, color=kde_color, **kde_kws)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='bmi', ylabel='Density'>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(df['bmi'], \n",
    "             kde_kws = {'shade': True},\n",
    "             color='red',\n",
    "             hist=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a4d91d",
   "metadata": {},
   "source": [
    "## Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8bf598ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x21e881b5450>,\n",
       "  <matplotlib.lines.Line2D at 0x21e881b56f0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x21e881b5990>,\n",
       "  <matplotlib.lines.Line2D at 0x21e881b5c30>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x21e8836fbe0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x21e881b5ed0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x21e881b6170>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df['avg_glucose_level'],vert=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54500a9e",
   "metadata": {},
   "source": [
    "## count plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "44c3c857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='work_type', ylabel='count'>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dOnXS3XffrZiYGA0dOlQ//PBDVdcIAABwVVUqEI0ZM0ZpaWn66KOPdPz4cR0/flx/+9vflJaWprFjx1Z1jQAAAFdVpW6ZLV26VH/961+VlJTktN13330KDAzUgAEDNHv27KqqDwAA4Kqr1BWiH374QZGRkeXaIyIiuGUGAACuO5UKRImJiZo0aZJOnz7ttBUWFuqFF15QYmJilRUHAABwLVTqltnrr7+uXr166eabb9Ztt90ml8ulzMxM+fv7a+XKlVVdIwAAwFVVqUDUokUL7d27VwsXLtSuXbtkjNHDDz+sQYMGKTAwsKprBAAAuKoqFYimTp2qyMhIDRs2zKN97ty5Onr0qCZMmFAlxQEAAFwLlRpDNGfOHN16663l2ps3b64//elPV1wUAADAtVSpQJSbm6vo6Ohy7fXq1VNOTs4VFwUAAHAtVSoQxcbG6rPPPivX/tlnnykmJuaKiwIAALiWKjWG6PHHH1dKSopKSkrUtWtXSdLq1as1fvx4vqkaAABcdyoViMaPH6/vv/9eI0aMUHFxsSQpICBAEyZM0MSJE6u0QAAAgKutUoHI5XJp2rRpeu6555SVlaXAwEA1atRI/v7+VV0fAADAVVepQFSmdu3auuOOO6qqFgAAAK+o1KBqAACAGwmBCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYz6uB6D//+Y/uv/9+xcTEyOVyafny5R7TjTFKTU1VTEyMAgMDlZSUpB07dnj0KSoq0siRIxUeHq6goCD16dNHhw4d8uiTl5en5ORkud1uud1uJScn6/jx41d56wAAwPXCq4Ho1KlTuu222zRr1qzzTn/llVc0ffp0zZo1S5s2bVJUVJS6d++uEydOOH1SUlK0bNkyLV68WOvWrdPJkyfVu3dvnT171ukzcOBAZWZmasWKFVqxYoUyMzOVnJx81bcPAABcH1zGGOPtIiTJ5XJp2bJl6tevn6Qfrw7FxMQoJSVFEyZMkPTj1aDIyEhNmzZNw4cPV35+vurVq6cFCxbooYcekiQdPnxYsbGx+uSTT9SzZ09lZWWpWbNmSk9PV7t27SRJ6enpSkxM1K5du9SkSZMK1VdQUCC32638/HyFhIRUaJ42z7x7mXsBV0vGq496uwQAgBdU9PO72o4hys7OVm5urnr06OG0+fv7q3Pnzlq/fr0kKSMjQyUlJR59YmJilJCQ4PTZsGGD3G63E4YkqX379nK73U4fAABgt5reLuBCcnNzJUmRkZEe7ZGRkfrmm2+cPn5+fqpbt265PmXz5+bmKiIiotzyIyIinD7nU1RUpKKiIud9QUFB5TYEAABUe9X2ClEZl8vl8d4YU67tXOf2OV//Sy1n6tSpziBst9ut2NjYy6wcAABcL6ptIIqKipKkcldxjhw54lw1ioqKUnFxsfLy8i7a59tvvy23/KNHj5a7+vRTEydOVH5+vvM6ePDgFW0PAACovqptIKpfv76ioqK0atUqp624uFhpaWnq0KGDJKlNmzby9fX16JOTk6Pt27c7fRITE5Wfn6+NGzc6fT7//HPl5+c7fc7H399fISEhHi8AAHBj8uoYopMnT+qrr75y3mdnZyszM1OhoaG65ZZblJKSoilTpqhRo0Zq1KiRpkyZolq1amngwIGSJLfbraFDh2rs2LEKCwtTaGioxo0bpxYtWqhbt26SpKZNm+ree+/VsGHDNGfOHEnSE088od69e1f4CTMAAHBj82og2rx5s7p06eK8HzNmjCRp8ODBmj9/vsaPH6/CwkKNGDFCeXl5ateunVauXKng4GBnnhkzZqhmzZoaMGCACgsLdc8992j+/Pny8fFx+ixatEijRo1ynkbr06fPBb/7CAAA2KfafA9Rdcf3EF3f+B4iALDTdf89RAAAANcKgQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsV9PbBQAAUN2lders7RLwfzr/J+2qLJcrRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9B1QBQCR1ndvR2Cfg/n438zNsl4AbAFSIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9ap1IEpNTZXL5fJ4RUVFOdONMUpNTVVMTIwCAwOVlJSkHTt2eCyjqKhII0eOVHh4uIKCgtSnTx8dOnToWm8KAACoxmp6u4BLad68uf71r3857318fJx/v/LKK5o+fbrmz5+vxo0b68UXX1T37t21e/duBQcHS5JSUlL00UcfafHixQoLC9PYsWPVu3dvZWRkeCwLuFIHJrfwdgn4P7c8v83bJQC4zlT7QFSzZk2Pq0JljDF6/fXX9dvf/lb9+/eXJL3zzjuKjIzUe++9p+HDhys/P19vv/22FixYoG7dukmSFi5cqNjYWP3rX/9Sz549r+m2AACA6qla3zKTpL179yomJkb169fXww8/rK+//lqSlJ2drdzcXPXo0cPp6+/vr86dO2v9+vWSpIyMDJWUlHj0iYmJUUJCgtPnQoqKilRQUODxAgAAN6ZqHYjatWund999V//85z/11ltvKTc3Vx06dNCxY8eUm5srSYqMjPSYJzIy0pmWm5srPz8/1a1b94J9LmTq1Klyu93OKzY2tgq3DAAAVCfVOhD16tVLDzzwgFq0aKFu3brp448/lvTjrbEyLpfLYx5jTLm2c1Wkz8SJE5Wfn++8Dh48WMmtAAAA1V21DkTnCgoKUosWLbR3715nXNG5V3qOHDniXDWKiopScXGx8vLyLtjnQvz9/RUSEuLxAgAAN6brKhAVFRUpKytL0dHRql+/vqKiorRq1SpnenFxsdLS0tShQwdJUps2beTr6+vRJycnR9u3b3f6AAAAVOunzMaNG6f7779ft9xyi44cOaIXX3xRBQUFGjx4sFwul1JSUjRlyhQ1atRIjRo10pQpU1SrVi0NHDhQkuR2uzV06FCNHTtWYWFhCg0N1bhx45xbcAAAAFI1D0SHDh3SI488ou+++0716tVT+/btlZ6erri4OEnS+PHjVVhYqBEjRigvL0/t2rXTypUrne8gkqQZM2aoZs2aGjBggAoLC3XPPfdo/vz5fAcRAABwVOtAtHjx4otOd7lcSk1NVWpq6gX7BAQEaObMmZo5c2YVVwcAAG4U19UYIgAAgKuBQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPasC0RtvvKH69esrICBAbdq00dq1a71dEgAAqAasCURLlixRSkqKfvvb3+qLL77Q3XffrV69eunAgQPeLg0AAHiZNYFo+vTpGjp0qB5//HE1bdpUr7/+umJjYzV79mxvlwYAALysprcLuBaKi4uVkZGhX//61x7tPXr00Pr16887T1FRkYqKipz3+fn5kqSCgoIKr/dsUWElqsXVcDnHrbJOnD571deBirkWx/tM4Zmrvg5UzLU43qfOcLyri8s93mX9jTEX7WdFIPruu+909uxZRUZGerRHRkYqNzf3vPNMnTpVL7zwQrn22NjYq1Ijri73zF96uwRcS1Pd3q4A15B7AsfbKu7KHe8TJ07IfZF5rQhEZVwul8d7Y0y5tjITJ07UmDFjnPelpaX6/vvvFRYWdsF5bkQFBQWKjY3VwYMHFRIS4u1ycJVxvO3C8baLrcfbGKMTJ04oJibmov2sCETh4eHy8fEpdzXoyJEj5a4alfH395e/v79HW506da5WidVeSEiIVb9AtuN424XjbRcbj/fFrgyVsWJQtZ+fn9q0aaNVq1Z5tK9atUodOnTwUlUAAKC6sOIKkSSNGTNGycnJatu2rRITE/Xmm2/qwIED+uUvGVsCAIDtrAlEDz30kI4dO6bJkycrJydHCQkJ+uSTTxQXF+ft0qo1f39/TZo0qdztQ9yYON524XjbheN9cS5zqefQAAAAbnBWjCECAAC4GAIRAACwHoEIAABYj0BksaSkJKWkpHi7DFynhgwZon79+lW4/5o1a+RyuXT8+PGrVtP1KDU1Va1atSrXFhkZKZfLpeXLl3ulrgu53ON+tZxvv6F6uBqfLdfi545AdIMYMmSIXC6XXC6XfH191aBBA40bN06nTp264DwffPCBfve731VpHdXxBH69y83N1ejRo9WwYUMFBAQoMjJSd911l/70pz/phx9+qJJ1VCas/OEPf9D8+fOrZP3XsyNHjmj48OG65ZZb5O/vr6ioKPXs2VMbNmyo1PKysrL0wgsvaM6cOcrJyVGvXr2quGJcibJz7csvv+zRvnz5cqv+isGNyJrH7m1w7733at68eSopKdHatWv1+OOP69SpU5o9e7ZHv5KSEvn6+io0NNRLlaKivv76a3Xs2FF16tTRlClT1KJFC505c0Z79uzR3LlzFRMToz59+niltop886sNHnjgAZWUlOidd95RgwYN9O2332r16tX6/vvvK7W8ffv2SZL69u3LB2w1FRAQoGnTpmn48OGqW7eut8txlJ3bbV3/leIK0Q2k7H+nsbGxGjhwoAYNGqTly5c7l5bnzp2rBg0ayN/fX8YYj8uaEydOVPv27csts2XLlpo0aZIkadOmTerevbvCw8PldrvVuXNnbdmyxekbHx8vSfrFL34hl8vlvJekjz76SG3atFFAQIAaNGigF154QWf469GXNGLECNWsWVObN2/WgAED1LRpU7Vo0UIPPPCAPv74Y91///2SpAMHDqhv376qXbu2QkJCNGDAAH377beSpN27d8vlcmnXrl0ey54+fbri4+OVnZ2tLl26SJLq1q0rl8ulIUOGXLK2cy9hFxUVadSoUYqIiFBAQIDuuusubdq0qdx8n332mW677TYFBASoXbt22rZtWyX3jvcdP35c69at07Rp09SlSxfFxcXpzjvv1MSJE/Xzn/9ckpSfn68nnnhCERERCgkJUdeuXfXll1+ed3mpqanOMa1Ro8ZFA9GlfqdcLpfmzJmj3r17q1atWmratKk2bNigr776SklJSQoKClJiYqITwMrW36pVK82ZM0exsbGqVauWHnzwwYteObzYcTfGqGHDhvr973/vMc/27dtVo0YNZ90V2Ucvv/yyIiMjFRwcrKFDh+r06dMXrOlq69atm6KiojR16tQL9lm/fr06deqkwMBAxcbGatSoUc4V+4qcbyVp3rx5atq0qQICAnTrrbfqjTfecKbt379fLpdLf/7zn5WUlKSAgAAtXLjwgvUYY1SvXj0tXbrUaWvVqpUiIiKc9xs2bJCvr69Onjwp6eLnFUkX/Gw514oVK+R2u/Xuu+9Kkv773//qoYceUt26dRUWFqa+fftq//79Tv+zZ89qzJgxqlOnjsLCwjR+/PhL/qX6KmFwQxg8eLDp27evR9vIkSNNWFiYmTRpkgkKCjI9e/Y0W7ZsMV9++aUpLS01nTt3NqNHjzbGGLNt2zYjyXz11VfO/Nu3bzeSzO7du40xxqxevdosWLDA7Ny50+zcudMMHTrUREZGmoKCAmOMMUeOHDGSzLx580xOTo45cuSIMcaYFStWmJCQEDN//nyzb98+s3LlShMfH29SU1Ov/o65jn333XfG5XKZqVOnXrRfaWmpuf32281dd91lNm/ebNLT003r1q1N586dnT5t2rQxzz77rMd8bdq0MRMnTjRnzpwxS5cudY51Tk6OOX78+CXrO/dnbtSoUSYmJsZ88sknZseOHWbw4MGmbt265tixY8YYYz799FMjyTRt2tSsXLnSbN261fTu3dvEx8eb4uLiiu+YaqSkpMTUrl3bpKSkmNOnT5ebXlpaajp27Gjuv/9+s2nTJrNnzx4zduxYExYW5uyXSZMmmdtuu80YY8yJEyfMvHnzjCSTk5NjcnJyzrveivxOSTI33XSTWbJkidm9e7fp16+fiY+PN127djUrVqwwO3fuNO3btzf33nuvM0/ZuaJr167miy++MGlpaaZhw4Zm4MCBTp/LPe4vvfSSadasmUf9v/rVr0ynTp0qvI+WLFli/Pz8zFtvvWV27dplfvvb35rg4GBnv11LZdv/wQcfmICAAHPw4EFjjDHLli0zZR+pW7duNbVr1zYzZswwe/bsMZ999pm5/fbbzZAhQ4wxFTvfvvnmmyY6OtosXbrUfP3112bp0qUmNDTUzJ8/3xhjTHZ2tpFk4uPjnT7//e9/L1p7//79zdNPP22MMeb77783vr6+pk6dOmbHjh3GGGOmTJli2rVrZ4yp2HmlIp8t77//vgkODjbLly83xhhz6tQp06hRI/PYY4+ZrVu3mp07d5qBAweaJk2amKKiImOMMdOmTTNut9v89a9/dT5rgoODy33GVTUC0Q3i3JPU559/bsLCwsyAAQPMpEmTjK+vrxNQyvz0h9YYY1q2bGkmT57svJ84caK54447LrjOM2fOmODgYPPRRx85bZLMsmXLPPrdfffdZsqUKR5tCxYsMNHR0ZexhfZJT083kswHH3zg0R4WFmaCgoJMUFCQGT9+vFm5cqXx8fExBw4ccPrs2LHDSDIbN240xhgzffp006BBA2f67t27jSTnRFgWVvLy8ipc309/5k6ePGl8fX3NokWLnOnFxcUmJibGvPLKKx7rWLx4sdPn2LFjJjAw0CxZsqTC661u/vrXv5q6deuagIAA06FDBzNx4kTz5ZdfGmN+/E9ESEhIubD0s5/9zMyZM8cY4xmIjPH8YL2QivxOSfIIwRs2bDCSzNtvv+20vf/++yYgIMB5P2nSJOPj4+N8yBtjzD/+8Q9To0YNJ5xd7nE/fPiw8fHxMZ9//rkzvV69es4He0X2UWJiovnlL3/pMb1du3ZeDUTGGNO+fXvz2GOPGWM8j1tycrJ54oknPOZbu3atqVGjhiksLDTGXPp8Gxsba9577z2PZfzud78ziYmJxpj/H4hef/31Ctf+v//7vyYhIcEYY8zy5ctN27ZtTf/+/c0f//hHY4wxPXr0MBMmTDDGmAqdVy712fLHP/7RuN1u8+9//9uZ9vbbb5smTZqY0tJSp62oqMgEBgaaf/7zn8YYY6Kjo83LL7/sTC8pKTE333zzVQ9E3DK7gfz9739X7dq1FRAQoMTERHXq1EkzZ86UJMXFxalevXoXnX/QoEFatGiRpB8vr77//vsaNGiQM/3IkSP65S9/qcaNG8vtdsvtduvkyZM6cODARZebkZGhyZMnq3bt2s5r2LBhysnJqbJBwTeyc2+bbNy4UZmZmWrevLmKioqUlZWl2NhYxcbGOn2aNWumOnXqKCsrS5L08MMP65tvvlF6erokadGiRWrVqpWaNWtWJTXu27dPJSUl6tixo9Pm6+urO++806mhTGJiovPv0NBQNWnSpFyf68kDDzygw4cP68MPP1TPnj21Zs0atW7dWvPnz1dGRoZOnjypsLAwj5//7Oxsj1tVF/PT+cr+9mJFf6datmzp/DsyMlKS1KJFC4+206dPq6CgwGm75ZZbdPPNNzvvExMTVVpaqt27d5errSLHPTo6Wj//+c81d+5cST+ep06fPq0HH3zQ2ZZL7aOsrCyPn5uyurxt2rRpeuedd7Rz506P9oyMDM2fP99je3r27KnS0lJlZ2dLuvj59ujRozp48KCGDh3qsYwXX3yx3M9N27ZtK1xvUlKSduzYoe+++05paWlKSkpSUlKS0tLSdObMGa1fv16dO3eWpAqdV6QLf7YsXbpUKSkpWrlypXNLvmzffPXVVwoODna2KzQ0VKdPn9a+ffuUn5+vnJwcj+Nbs2bNy9rOymJQ9Q2kS5cumj17tnx9fRUTE+MxuC0oKOiS8w8cOFC//vWvtWXLFhUWFurgwYN6+OGHnelDhgzR0aNH9frrrysuLk7+/v5KTExUcXHxRZdbWlqqF154Qf379y83LSAg4DK20C4NGzY879ifBg0aSJICAwMl/XgyPd9Yk5+2R0dHq0uXLnrvvffUvn17vf/++xo+fHiV1Wr+7/7+uXVcqLZzXe+DhwMCAtS9e3d1795dzz//vB5//HFNmjRJI0aMUHR0tNasWVNunjp16lRo2ZmZmc6/Q0JCJFX8d+qn54CyfXy+ttLS0guuv6zPhX7Gzjft3OP++OOPKzk5WTNmzNC8efP00EMPqVatWs66r3QfeUunTp3Us2dP/eY3v/EYd1daWqrhw4dr1KhR5ea55ZZbJF38fFt2PN566y21a9fOY34fHx+P9xU5t5dJSEhQWFiY0tLSlJaWpsmTJys2NlYvvfSSNm3apMLCQt11112SKnZeudj6W7VqpS1btmjevHm64447PH7W2rRp44TBn7rUf9qvNgLRDSQoKEgNGzas9Pw333yzOnXqpEWLFqmwsFDdunVz/lcpSWvXrtUbb7yh++67T5J08OBBfffddx7L8PX11dmzZz3aWrdurd27d19RbTYKCwtT9+7dNWvWLI0cOfKCJ55mzZrpwIEDOnjwoPO/uZ07dyo/P19NmzZ1+g0aNEgTJkzQI488on379nmEXT8/P0kqd+wqqmHDhvLz89O6des0cOBAST8+cbJ58+Zy30eSnp7ufCjk5eVpz549uvXWWyu13uqqWbNmWr58uVq3bq3c3FzVrFnT4yGDy3G+35ur+Tt14MABHT58WDExMZJ+HGhbo0YNNW7c+Ly1VeS433fffQoKCtLs2bP1j3/8Q//5z388tuVS+6hp06ZKT0/Xo48+6rSVXe30tpdfflmtWrXy2D+tW7fWjh07Lnp8Lna+jYyM1E033aSvv/7a4yr9lXK5XOrUqZP+9re/afv27br77rsVHByskpIS/elPf1Lr1q0VHBwsqeLnlQv52c9+ptdee01JSUny8fHRrFmzJP24b5YsWeIMoD+f6Ohopaenq1OnTpKkM2fOKCMjQ61bt66K3XBB3DKDh0GDBmnx4sX6y1/+ov/5n//xmNawYUMtWLBAWVlZ+vzzzzVo0CDnKkWZ+Ph4rV69Wrm5ucrLy5MkPf/883r33XeVmpqqHTt2KCsrS0uWLNGzzz57zbbrevXGG2/ozJkzatu2rZYsWaKsrCzt3r1bCxcu1K5du+Tj46Nu3bqpZcuWGjRokLZs2aKNGzfq0UcfVefOnT0uM/fv318FBQV68skn1aVLF910003OtLi4OLlcLv3973/X0aNHnadMKiooKEhPPvmknnnmGa1YsUI7d+7UsGHD9MMPP2jo0KEefSdPnqzVq1dr+/btGjJkiMLDw6vFF/1VxrFjx9S1a1ctXLhQW7duVXZ2tv7yl7/olVdeUd++fdWtWzclJiaqX79++uc//6n9+/dr/fr1evbZZ7V58+ZKr/dq/k4FBARo8ODB+vLLL7V27VqNGjVKAwYMUFRUVLm+FT3uPj4+GjJkiCZOnKiGDRt63A6pyD4aPXq05s6dq7lz52rPnj2aNGmSduzYccXbWhVatGihQYMGOcMTJGnChAnasGGDnnrqKWVmZmrv3r368MMPNXLkSI95L3a+TU1N1dSpU/WHP/xBe/bs0bZt2zRv3jxNnz79iupNSkrSe++9p5YtWyokJMQJSYsWLVJSUpLTr6LnlYtp3LixPv30U+f2Wdk2h4eHq2/fvlq7dq2ys7OVlpam0aNH69ChQ5J+PN4vv/yyli1bpl27dmnEiBHX5gtdr+oIJVwz53vKrMy5gzbLnDuo2hhj8vLyjL+/v6lVq5Y5ceKEx7QtW7aYtm3bGn9/f9OoUSPzl7/8xcTFxZkZM2Y4fT788EPTsGFDU7NmTRMXF+e0r1ixwnTo0MEEBgaakJAQc+edd5o333yzkltrl8OHD5unn37a1K9f3/j6+pratWubO++807z66qvm1KlTxhhjvvnmG9OnTx8TFBRkgoODzYMPPmhyc3PLLevBBx80kszcuXPLTZs8ebKJiooyLpfLDB48+JJ1nfszV1hYaEaOHGnCw8ONv7+/6dixozP40pj/P6j6o48+Ms2bNzd+fn7mjjvuMJmZmZe/U6qJ06dPm1//+temdevWxu12m1q1apkmTZqYZ5991vzwww/GGGMKCgrMyJEjTUxMjPH19TWxsbFm0KBBzmDVygyqNubSv1M65wGHskG4X3zxhdN27mD6slreeOMNExMTYwICAkz//v3N999/78xzuce9zL59+4wkZ7D1T11qHxnz49Nq4eHhpnbt2mbw4MFm/PjxXh9UXWb//v3G39/f47ht3LjRdO/e3dSuXdsEBQWZli1bmpdeesljvoudb40xZtGiRaZVq1bGz8/P1K1b13Tq1Ml5yOJ8x7Miyp5wGzdunNM2Y8YMI8n8/e9/9+h7qfNKRT9bdu7caSIiIsyYMWOMMcbk5OSYRx991PmZadCggRk2bJjJz883xvw4iHr06NEmJCTE1KlTx4wZM8Y8+uijV31QtcuYa/FwP4AbzSOPPCIfH5+LfvcJri+pqalavny5x7ilqvLZZ58pKSlJhw4d8rgVD1QX3DIDcFnOnDmjnTt3asOGDWrevLm3y0E1V1RUpK+++krPPfecBgwYQBhCtUUgAlDOgQMHPB73/emrTp06SkhIUPPmzZ3HwIELef/999WkSRPl5+frlVde8XY5N7RevXpd8Pd2ypQp3i6v2uOWGYByzpw54/FV+ueKj49XzZo8pApUJ//9739VWFh43mmhoaH8/cpLIBABAADrccsMAABYj0AEAACsRyACAADWIxABAADrEYgA3LD2798vl8t1Vb5oEMCNhUAEABfgcrm0fPlyb5cB4BogEAG4IRUXF3u7BADXEQIRAK/46KOPVKdOHZWWlkqSMjMz5XK59Mwzzzh9hg8frkceeUSStHTpUjVv3lz+/v6Kj4/Xa6+95rG8+Ph4vfjiixoyZIjcbreGDRtWbp2lpaUaNmyYGjdurG+++eai9cXHx0uSfvGLX8jlcik+Pl779+9XjRo1yv2l+pkzZyouLk7GGK1Zs0Yul0sff/yxbrvtNgUEBKhdu3batm2bxzzr169Xp06dFBgYqNjYWI0aNUqnTp2q2M4DUOUIRAC8olOnTjpx4oS++OILSVJaWprCw8OVlpbm9FmzZo06d+6sjIwMDRgwQA8//LC2bdum1NRUPffcc5o/f77HMl999VUlJCQoIyNDzz33nMe04uJiDRgwQJs3b9a6desUFxd30fo2bdokSZo3b55ycnK0adMmxcfHq1u3bpo3b55H33nz5mnIkCFyuVxO2zPPPKPf//732rRpkyIiItSnTx+VlJRIkrZt26aePXuqf//+2rp1q5YsWaJ169bp6aefvrydCKDqGADwktatW5vf//73xhhj+vXrZ1566SXj5+dnCgoKTE5OjpFksrKyzMCBA0337t095n3mmWdMs2bNnPdxcXGmX79+Hn2ys7ONJLN27VrTrVs307FjR3P8+PEK1yfJLFu2zKNtyZIlpm7duub06dPGGGMyMzONy+Uy2dnZxhhjPv30UyPJLF682Jnn2LFjJjAw0CxZssQYY0xycrJ54oknPJa7du1aU6NGDVNYWFjh+gBUHa4QAfCapKQkrVmzRsYYrV27Vn379lVCQoLWrVunTz/9VJGRkbr11luVlZWljh07eszbsWNH7d27V2fPnnXa2rZte971PPLIIzp58qRWrlwpt9t9RTX369dPNWvW1LJlyyRJc+fOVZcuXZxbbGUSExOdf4eGhqpJkybKysqSJGVkZGj+/Pkef3yzZ8+eKi0tVXZ29hXVB6ByCEQAvCYpKUlr167Vl19+qRo1aqhZs2bq3Lmz0tLSnNtlkmSM8bgdVdZ2rqCgoPOu57777tPWrVuVnp5+xTX7+fkpOTlZ8+bNU3Fxsd577z099thjFZq3bBtKS0s1fPhwZWZmOq8vv/xSe/fu1c9+9rMrrhHA5ePPVQPwmrJxRK+//ro6d+4sl8ulzp07a+rUqcrLy9Po0aMlSc2aNdO6des85l2/fr0aN24sHx+fS67nySefVEJCgvr06aOPP/7YCVqX4uvr63EFqszjjz+uhIQEvfHGGyopKVH//v3L9UlPT9ctt9wiScrLy9OePXt06623SpJat26tHTt2qGHDhhWqA8DVxxUiAF7jdrvVqlUrLVy4UElJSZJ+DElbtmzRnj17nLaxY8dq9erV+t3vfqc9e/bonXfe0axZszRu3LgKr2vkyJF68cUX1bt373Lh6kLi4+O1evVq5ebmKi8vz2lv2rSp2rdvrwkTJuiRRx5RYGBguXknT56s1atXa/v27RoyZIjCw8PVr18/SdKECRO0YcMGPfXUU8rMzNTevXv14YcfauTIkRXeHgBVi0AEwKu6dOmis2fPOuGnbt26atasmerVq6emTZtK+vGKyp///GctXrxYCQkJev755zV58mQNGTLkstaVkpKiF154Qffdd5/Wr19/yf6vvfaaVq1apdjYWN1+++0e04YOHari4uIL3i57+eWXNXr0aLVp00Y5OTn68MMP5efnJ0lq2bKl0tLStHfvXt199926/fbb9dxzzyk6OvqytgdA1XGZ892IBwBc1EsvvaTFixeX+36hNWvWqEuXLsrLy1OdOnW8UxyAy8YVIgC4DCdPntSmTZs0c+ZMjRo1ytvlAKgiBCIAVlq0aJHHY+8/fTVv3vyC8z399NO666671Llz5wo/XQag+uOWGQArnThxQt9+++15p/n6+l7ym6wB3FgIRAAAwHrcMgMAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArPf/AHmx40fj6gEUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'work_type',data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "82e41de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ever_married', ylabel='count'>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df[\"ever_married\"],data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "bdf14ff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hypertension', ylabel='count'>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df[\"hypertension\"],data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a66a1b",
   "metadata": {},
   "source": [
    "## Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ece6cf91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='Count'>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['gender'].value_counts().plot(kind='bar', xlabel='gender', ylabel='Count', rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2788a369",
   "metadata": {},
   "source": [
    "## Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bdd7a954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#ff9999','#66b3ff']\n",
    "df.groupby('heart_disease').size().plot(kind='pie', autopct='%.2f',colors=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b33b5549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#99ff99','#ffcc99']\n",
    "df.groupby('gender').size().plot(kind='pie', autopct='%.2f',colors=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eb1308e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('smoking_status').size().plot(kind='pie', autopct='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "034d5a0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df.groupby('Residence_type').size().plot(kind='pie', autopct='%.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3eb3242",
   "metadata": {},
   "source": [
    "## Stacked column chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "cecb1c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_df = df.groupby(['smoking_status', 'gender'])[['stroke']].count().reset_index()\n",
    "pivot_df = grouped_df.pivot(index='smoking_status', columns='gender', values='stroke')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "149e8f11",
   "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>gender</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>621</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>formerly smoked</th>\n",
       "      <td>284</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>never smoked</th>\n",
       "      <td>535</td>\n",
       "      <td>993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smokes</th>\n",
       "      <td>274</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender             0    1\n",
       "smoking_status           \n",
       "Unknown          621  742\n",
       "formerly smoked  284  388\n",
       "never smoked     535  993\n",
       "smokes           274  362"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouped_df.head(5)\n",
    "pivot_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "42ed4852",
   "metadata": {},
   "outputs": [],
   "source": [
    "Dframe = pd.DataFrame({\n",
    "   'smoking_status': ['Unknown','formerly smoked','never smoked','smokes'],\n",
    "   'Male':[708,407,663,337],\n",
    "   'Female':[836,477,1229,452]\n",
    "   })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "d05672c3",
   "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>smoking_status</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>708</td>\n",
       "      <td>836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>407</td>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>never smoked</td>\n",
       "      <td>663</td>\n",
       "      <td>1229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>smokes</td>\n",
       "      <td>337</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    smoking_status  Male  Female\n",
       "0          Unknown   708     836\n",
       "1  formerly smoked   407     477\n",
       "2     never smoked   663    1229\n",
       "3           smokes   337     452"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "36d7eb4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'The number of Smoking Status'}, xlabel='smoking_status'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S4ddffy3SvmjRIqhUKgkqIl2ys7PD5s2bi7Rv2bIFdnZ2ElREuubp6Ynp06dj9erV2LNnD9q3bw8ASEpKgqOjo8TVlT2cE6MH/P39cfz4cVSrVk3qUkjH5s2bh65du2Lnzp1o1qwZAODw4cO4cuUK/vjjD4mro9I2depUDBo0CLGxsZo5MYcPH0ZERAR+++03iasjXZg/fz769OmDLVu24IsvvkCNGjUAABs3boS3t7fE1ZU9nBOjB5YuXYpp06ZhwIAB8PDwgJGRkdbyoKAgiSojXbh+/ToWLlyI8+fPQwiBevXqYfjw4RyJ0VNHjhzBjz/+iMTERM3xDg0NRdOmTaUujd6gR48ewcDAoMjP9/KOIUYPVKhQ8llBhULBy26JiGQkIyMDGzduxJUrV/DZZ5/B1tYWcXFxcHR0xFtvvSV1eWUK58ToAbVaXeKLAUb/7Nu3D3379oW3tzf++ecfAMDq1auxf/9+iSsjXbhy5Qq+/PJL9O7dG+np6QCAiIgInD17VuLKSBdOnz6NmjVrYtasWfj++++RkZEBANi8eTMmTpwobXFlEEOMHnj48KHUJdAb8scff8Df3x9mZmaIi4tDXl4eAODBgweYMWOGxNVRaduzZw88PDxw5MgR/PHHH8jOzgbw5BfdlClTJK6OdGHMmDEYMGAALl26BFNTU017QEAA9u7dK2FlZRNDjB6wtraGt7c3Jk2ahJ07dyInJ0fqkkhHpk+fjl9//RVLlizROjfu7e2NuLg4CSsjXZgwYQKmT5+OyMhIrTu1+vr64tChQxJWRrpy7NgxDBs2rEj7W2+9hbS0NAkqKtsYYvTAnj17EBQUhLi4OHTr1g02NjZo1qwZJkyYgL///lvq8qgUXbhwAS1atCjSbmVlpRl2Jv1x5swZdO7cuUh7pUqVcPfuXQkqIl0zNTUt9nlpFy5cQKVKlSSoqGxjiNEDXl5emDBhAiIiInD//n3s3bsXderUwZw5cxAYGCh1eVSKnJ2dcfny5SLt+/fv5yX2esja2hqpqalF2k+ePMkJnnqqY8eOmDZtGgoKCgA8uTgjJSUFEyZMQNeuXSWuruzhfWL0xPnz5xEbG4s9e/YgNjYWBQUF6NChA1q2bCl1aVSKhg0bhk8//RTLli2DQqHAzZs3cejQIYwbNw5fffWV1OVRKevduzfGjx+P33//HQqFAmq1GgcOHMC4ceP4TDQ99f3336Ndu3ZwcHBAbm4uWrZsibS0NHh5eeGbb76Rurwyh5dY6wEnJycUFBSgVatW8PHxQYsWLTRPQCX988UXX2DevHl49OgRAMDExATjxo3D119/LXFlVNoKCgoQEhKC8PBwCCFgaGiIwsJC9O7dGytWrICBgYHUJZKOREdHIy4uDmq1Go0aNUKbNm0ghHjpJ5yXFwwxeuCdd95BYmIi3nnnHfj4+MDHxwfvv/8+KlasKHVpVMry8/NhbGyMhw8f4ty5c1Cr1ahXrx4qVqyIO3fuwN7eXuoSSQeuXr2q+YXWsGFD1KxZE7m5uTAzM5O6NCplM2fOLPZS6sLCQvTt2xfr16+XoKqyiyFGT2RkZGDv3r3Ys2cP9uzZg7Nnz+Ltt9+Gr68vvv32W6nLo1LSqVMnbNq0qcgNDm/duoXWrVsjISFBospIFz755BP8/PPPRdpzcnLQvn17PrVcDzk6OuLrr7/G0KFDNW2FhYXo2bMnEhISkJiYKGF1ZQ9DjJ65d+8eYmNjsXXrVqxbt443vNMzTZs2Rb169bB8+XJNW2pqKlq1aoX69etj48aNElZHpa1mzZro0aMHpk+frmnLycnRPNl63759UpVGOnLixAm0adMGixYtQvfu3VFQUIAePXrg/PnziI6OhpOTk9QllikMMXpg8+bNiI2NRWxsLM6ePQs7Ozu8//778PHxga+vL+rXry91iVRK7t69ixYtWsDPzw/z5s3DP//8g1atWqFBgwYIDw9/7iMoSH6SkpLw3nvvYdy4cRg9ejQePHgAf39/GBoa4u+//4aFhYXUJZIOxMbGomPHjli1ahWWLl2KK1euIDo6mk+xLgZDjB5wcHBAixYtNPNh3N3dpS6JdOjGjRt477330LlzZ2zfvh2NGjXC2rVrOclTTyUkJMDHxweTJ09GeHg4TExMsH37dgYYPffnn3+ia9euqFu3LqKjoznfrQQMMUQydOnSJbz33nv44IMPsHr1al6xoOcOHz6MNm3aoGnTpvjrr784oVfPdOnSpdj2w4cPo0aNGloBZtOmTW+qLFlgiNEThYWF2LJlCxITE6FQKFC3bl107NiR/zvXAzY2NsWGlIcPH8LExETrGN+7d+9NlkY60LBhw2KP97Vr1+Dg4KAVYPioCf0wYMCAl+777/lwxJvd6YXLly+jXbt2+Oeff1C7dm0IIXDx4kWoVCps374d1atXl7pE+g/mz58vdQn0BnXq1EnqEugNYzB5fRyJ0QPt2rWDEAJr166Fra0tgCcTQPv27YsKFSpg+/btEldIRESv4vbt27hw4QIUCgVq1arF5yaVgCFGD1hYWODw4cNF7tJ76tQpNG/eHNnZ2RJVRrrw7KnDevXqISgoiKcO9diJEye0jnfDhg2lLol0JCcnByNHjsSqVaugVqsBAAYGBujXrx8WLFgAc3NziSssW3g6SQ+YmJjgwYMHRdqzs7NhbGwsQUWkKzx1WL6kp6ejZ8+eiI2NhbW1NYQQyMzMhK+vL8LDw/m/cz00ZswY7NmzB9u2bUPz5s0BPHnAa2hoKMaOHYuFCxdKXGHZwptK6IHAwEAMHToUR44cgRACQggcPnwYw4cPR1BQkNTlUSkKDQ1F9erVcf36dcTFxeHkyZNISUmBm5sbQkNDpS6PStnIkSORlZWFs2fP4t69e7h//z4SEhKQlZXF462n/vjjDyxduhQBAQGwsrKClZUV2rVrhyVLlvBmlsXg6SQ9kJGRgf79+2Pbtm0wMjICADx+/BhBQUFYsWIFlEqlxBVSaeGpw/JFqVQiKioK7777rlb70aNH4efnh4yMDGkKI50xNzfHiRMnULduXa32s2fPokmTJsjJyZGosrKJp5P0gLW1NbZu3YpLly7h/PnzEEKgXr16qFGjhtSlUSnjqcPyRa1Wa/5j8m9GRkaa+RKkX7y8vDBlyhSsWrUKpqamAIDc3FxMnToVXl5eEldX9nAkhkhG+vXrh7i4OCxduhRNmjQBABw5cgRDhgxB48aNsWLFCmkLpFLVsWNHZGRkYP369XBxcQEA/PPPP+jTpw9sbGywefNmiSuk0paQkIC2bdvi0aNHaNCgARQKBeLj42FqaoqdO3fyMTLPYIjRA4WFhVixYgV2796N9PT0Iv9Di46OlqgyKm08dVi+XL9+HR07dkRCQgJUKhUUCgVSUlLg4eGBrVu3onLlylKXSDqQm5uLNWvWaI2s9+nTh3dqLgZDjB4YMWIEVqxYgfbt28PZ2bnI3T7nzZsnUWWkKzx1WL5ERkZqHe82bdpIXRJRmcAQowfs7e2xatUqtGvXTupSiIjoP/rnn39w4MCBYkfWeVWaNk7s1QPGxsb8n3g5IYTAxo0bERMTU+wPOD4cTv8cPXoUsbGxxR7vuXPnSlQV6cry5csxfPhwGBsbw87OTmtkXaFQMMQ8gyMxemDOnDm4evUqfvrpJz7NWM+FhoZi8eLF8PX1haOjY5HjzWew6JcZM2bgyy+/RO3atYscb4VCwfluekilUmH48OGYOHEiKlTgrdxehCFGD3Tu3BkxMTGwtbVF/fr1i1ySyf+d6w9bW1usWbOGpw7LCUdHR8yaNQshISFSl0JviJ2dHY4ePcq7b78knk7SA9bW1ujcubPUZdAboFQqUa1aNanLoDekQoUKmlvPU/kwaNAg/P7775gwYYLUpcgCR2KIZGTlypWIiIjAsmXLeLllOTB79mzcvHkT8+fPl7oUekMKCwsRGBiI3NxceHh4FBlZ5zwobQwxemDNmjXo27dvscs+++wzfPfdd2+4ItKVhw8fokuXLjhw4ACqVq1a5AdcXFycRJWRLqjVarRv3x4XL15EvXr1eKq4HPj6668xZcoUzoN6STydpAdGjBgBa2trBAYGarWPHj0a4eHhDDF6JCQkBCdOnEDfvn2LndhL+mXkyJGIiYmBr69vkStVSD/NnTsXy5Yt4zyol8SRGD0QERGBnj174s8//0SLFi0APPnht2nTJuzevRt16tSRuEIqLRYWFti5cyfee+89qUuhN8DS0hLh4eFo37691KXQG+Lk5IR9+/ahZs2aUpciC7x+Sw+0bdsWv/76Kzp16oTjx4/j448/xqZNmxATE8MAo2dUKhWsrKykLoPeEFtbW16lUs58+umnWLBggdRlyAZHYvTIwoULMXr0aFSqVAkxMTG8AZ4e2r59OxYsWIBff/0VVatWlboc0rHly5cjIiICy5cvh7m5udTl0BvQuXNnREdHw87OjrfMeAkMMTI1ZsyYYts3btyIhg0bav3vjbPZ9YeNjQ0ePnyIx48fw9zcvMgPuHv37klUGelCw4YNceXKFQghOJG7nBgwYMBzl/OGlto4sVemTp48WWx79erVkZWVpVnOiYD6hZfali+dOnWSugR6w3755Reo1WpYWFgAAJKTk7FlyxbUrVsX/v7+EldX9nAkhoiIqIzw8/NDly5dMHz4cGRkZKBOnTowMjLCnTt3MHfuXHz00UdSl1imcGIvkYzExcXhzJkzmvdbt25Fp06dMGnSJOTn50tYGenC9evXcePGDc37o0ePYtSoUVi8eLGEVZEuxcXF4f333wfwZHqAo6Mjrl27hlWrVuHHH3+UuLqyhyFGD+Tk5GDy5Mnw9vZGjRo1UK1aNa0X6Y9hw4bh4sWLAICrV6+iR48eMDc3x++//47PP/9c4uqotPXu3RsxMTEAgLS0NLRp0wZHjx7FpEmTMG3aNImrI114+PAhLC0tAQC7du1Cly5dUKFCBTRr1gzXrl2TuLqyh3Ni9MDgwYOxZ88eBAcHw9nZmfNg9NjFixfxzjvvAAB+//13tGzZEuvWrcOBAwfQs2dPzpnRMwkJCWjSpAkA4H//+x88PDxw4MAB7Nq1C8OHD8dXX30lcYVU2mrUqIEtW7agc+fO2LlzJ0aPHg0ASE9P5+0VisEQowf+/vtvbN++nQ+KKweEEFCr1QCAqKgozV2aVSoV7ty5I2VppAMFBQUwMTEB8OR4BwUFAQDq1KmD1NRUKUsjHfnqq6/Qu3dvjB49Gq1bt4aXlxeAJ6MyDRs2lLi6soenk/SAjY0NbG1tpS6D3gBPT09Mnz4dq1evxp49ezR3ck1KSoKjo6PE1VFpq1+/Pn799Vfs27cPkZGRaNu2LQDg5s2bsLOzk7g60oUPP/wQKSkpOH78OCIiIjTtrVu3xrx58ySsrGzi1Ul6YM2aNdi6dStWrlzJG2LpudOnT6NPnz5ISUnBmDFjMGXKFABPHjNx9+5drFu3TuIKqTTFxsaic+fOyMrKQv/+/bFs2TIAwKRJk3D+/Hne+IzKPYYYPcAbYtGjR49gYGBQ5NiT/BUWFiIrKws2NjaatuTkZJibm8PBwUHCyoikxzkxeqBjx46czFvOmZqaSl0C6YiBgYFWgAHAR04Q/X8ciSEiIiJZ4sReGatQoQIMDAyKvGxsbNCsWTOeLyciIr3G00kytnnz5mLbMzIycPToUfTt2xcrV65Et27d3nBlREREusfTSXrs559/xqpVq3DkyBGpS6FSEhsbCx8fH6nLoDegoKAAfn5+WLRoEWrVqiV1OURlEk8n6TE/Pz/NLepJP7Rt2xbVq1fH9OnTcf36danLIR0yMjJCQkICJ+0TPQdDjB7Lzc3lVSt65ubNm/j000+xadMmuLm5wd/fH//73//48Ec91a9fPyxdulTqMojKLJ5O0mMjR47ElStXsGPHDqlLIR2Ij4/HsmXLsH79eqjVavTp0weDBg1CgwYNpC6NSsnIkSOxatUq1KhRA56enrCwsNBaPnfuXIkqIyobGGJkbMyYMcW2Z2Zm4vjx47hy5Qr27dvH523osZs3b2Lx4sX49ttvYWhoiEePHsHLywu//vor6tevL3V59B/5+vqWuEyhUCA6OvoNVkNU9jDEyFhJP+CsrKxQp04dfPzxx3B1dX3DVZGuFRQUYOvWrVi2bBkiIyPh6emJQYMGoVevXrh37x7Gjx+P+Ph4nDt3TupSiYh0iiGGSEZGjhyJ9evXAwD69u2LwYMHw93dXatPSkoKqlatqnnaNcnf5cuXceXKFbRo0QJmZmYQQnDCLxF4nxgiWTl37hwWLFiArl27wtjYuNg+Li4uiImJecOVkS7cvXsX3bt3R0xMDBQKBS5duoRq1aph8ODBsLa2xpw5c6QukUhSvDqJSEZ2796NXr16lRhgAMDQ0BAtW7Z8g1WRrowePRpGRkZISUnRekJ9jx49EBERIWFlRGUDR2KIyrg///zzpfsGBQXpsBJ603bt2oWdO3eicuXKWu01a9bEtWvXJKqKqOxgiCEq4zp16vRS/RQKBQoLC3VbDL1ROTk5WiMwT925cwcmJiYSVERUtvB0ElEZp1arX+rFAKN/WrRogVWrVmneKxQKqNVqfPfdd8+9/JqovODVSUQywWfplD/nzp2Dj48PGjdujOjoaAQFBeHs2bO4d+8eDhw4gOrVq0tdIpGkOBJDJBN8lk75U69ePZw+fRpNmjTBBx98gJycHHTp0gUnT55kgCECR2KIZGXs2LEwMjLCt99+K3UpRESS48ReIhnJz8/Hb7/9prlTL5+lo9/c3NzQt29f9O3bF7Vr15a6HKIyhyMxRDLCZ+mUL3PnzsX69etx4sQJNGzYEMHBwejRowecnZ2lLo2oTGCIISIq4y5evIi1a9ciPDwcV69eha+vL/r27Yt+/fpJXRqRpBhiiGSIz9Ipvw4fPoyPPvoIp0+f5mX1VO5xTgyRjPBZOuXX0aNHsW7dOmzYsAGZmZn48MMPpS6JSHK8xJpIRvgsnfLl4sWLmDJlCmrWrInmzZvj3Llz+Pbbb3Hr1i1s2LBB6vKIJMeRGCIZ4bN0ypc6derA09MTn3zyCXr27AknJyepSyIqUxhiiGSEz9IpX86fP8+7MxM9B08nEckIn6VTvtSqVQsZGRn47bffMHHiRNy7dw8AEBcXh3/++Ufi6oikx6uTiGSEz9IpX06fPo3WrVvD2toaycnJuHDhAqpVq4bJkyfj2rVrWoGWqDziSAyRjPBZOuXL6NGjMWDAAFy6dAmmpqaa9oCAAOzdu1fCyojKBo7EEBGVUUqlEnFxcahevTosLS1x6tQpVKtWDdeuXUPt2rXx6NEjqUskkhQn9hLJzKNHj3D69Gmkp6dDrVZrLQsKCpKoKtIFU1NTZGVlFWm/cOECKlWqJEFFRGULQwyRjERERKBfv364c+dOkWUKhYJ3cNUzHTt2xLRp0/C///0PwJNjnJKSggkTJqBr164SV0ckPZ5OIpKRGjVqwN/fH1999RUcHR2lLod0LCsrC+3atcPZs2fx4MEDuLi4IC0tDV5eXtixY0eRp5gTlTcMMUQyYmVlxUm85VB0dDTi4uKgVqvRqFEjtGnTRuqSiMoEhhgiGRk4cCCaN2+OQYMGSV0KEZHkGGKIZOThw4fo1q0bKlWqBA8PDxgZGWktDw0Nlagy0pXdu3dj9+7dxU7kXrZsmURVEZUNnNhLJCPr1q3Dzp07YWZmhtjYWCgUCs0yhULBEKNnpk6dimnTpsHT0xPOzs5ax5uIOBJDJCtOTk4IDQ3FhAkTUKEC71Wp75ydnTF79mwEBwdLXQpRmcSfgkQykp+fjx49ejDAlBP5+fnw9vaWugyiMos/CYlkpH///tiwYYPUZdAbMnjwYKxbt07qMojKLM6JIZKRwsJCzJ49Gzt37sTbb79dZGLv3LlzJaqMdOHRo0dYvHgxoqKieLyJisE5MUQy4uvrW+IyhUKB6OjoN1gN6RqPN9HzMcQQyURhYSH2798PDw8P2NraSl0OEZHkGGKIZMTU1BSJiYlwc3OTuhQiIslxYi+RjHh4eODq1atSl0FEVCZwJIZIRnbt2oXx48fj66+/RuPGjYs8ANDKykqiyoiI3jyGGCIZ+ff9Yf5991YhBBQKBQoLC6Uoi4hIErzEmkhGYmJipC6BiKjM4EgMERERyRJHYohkJiMjA0uXLkViYiIUCgXq1auHgQMHQqlUSl0aEdEbxZEYIhk5fvw4/P39YWZmhiZNmkAIgePHjyM3Nxe7du1Co0aNpC6RiOiNYYghkpH3338fNWrUwJIlS2Bo+GQg9fHjxxg8eDCuXr2KvXv3SlwhEdGbwxBDJCNmZmY4efIk6tSpo9V+7tw5eHp64uHDhxJVRkT05vFmd0QyYmVlhZSUlCLt169fh6WlpQQVERFJhyGGSEZ69OiBQYMGYcOGDbh+/Tpu3LiB8PBwDB48GL169ZK6PCKiN4pXJxHJyPfffw+FQoF+/frh8ePHAAAjIyN89NFH+PbbbyWujojozeKcGKIy7vTp03B3d9e6W+/Dhw9x5coVCCFQo0YNmJubS1ghEZE0GGKIyjgDAwOkpqbCwcEB1apVw7Fjx2BnZyd1WUREkuOcGKIyztraGklJSQCA5ORkqNVqiSsiIiobOCeGqIzr2rUrWrZsCWdnZygUCnh6esLAwKDYvlevXn3D1RERSYchhqiMW7x4Mbp06YLLly8jNDQUQ4YM4eXURETgnBgiWRkwYAB+/PFHhhgiIjDEEBERkUxxYi8RERHJEkMMERERyRJDDBEREckSQwwRERHJEkMMEelESEgIOnXqVOLysLAwvPPOO2+sHiLSPwwxRCSJcePGYffu3VKXoVG1alXMnz//lT/n4+ODUaNGlXo9RPRivNkdEUmiYsWKqFixotRlEJGMcSSGqJzYuHEjPDw8YGZmBjs7O7Rp0wY5OTma0z4zZsyAo6MjrK2tMXXqVDx+/BifffYZbG1tUblyZSxbtkxrfWfOnEGrVq006xs6dCiys7NL3P6JEyfg4OCAb775BkDR00lP6/j+++/h7OwMOzs7fPLJJygoKND0SU1NRfv27WFmZgY3NzesW7fulUZQwsLCUKVKFZiYmMDFxQWhoaEAnoymXLt2DaNHj4ZCoYBCoQAA3L17F7169ULlypVhbm4ODw8PrF+/XqvmPXv24IcfftB8Ljk5GStWrIC1tbXWtrds2aJZLwCcOnUKvr6+sLS0hJWVFRo3bozjx4+/1H4Q0RMciSEqB1JTU9GrVy/Mnj0bnTt3xoMHD7Bv3z48vddldHQ0KleujL179+LAgQMYNGgQDh06hBYtWuDIkSPYsGEDhg8fjg8++AAqlQoPHz5E27Zt0axZMxw7dgzp6ekYPHgwRowYgRUrVhTZfmxsLDp16oSZM2fio48+KrHOmJgYODs7IyYmBpcvX0aPHj3wzjvvYMiQIQCAfv364c6dO4iNjYWRkRHGjBmD9PT0l/oONm7ciHnz5iE8PBz169dHWloaTp06BQDYtGkTGjRogKFDh2q2BQCPHj1C48aNMX78eFhZWWH79u0IDg5GtWrV0LRpU/zwww+4ePEi3N3dMW3aNABApUqVXqqePn36oGHDhli4cCEMDAwQHx8PIyOjl/osEf1/goj03okTJwQAkZycXGRZ//79haurqygsLNS01a5dW7z//vua948fPxYWFhZi/fr1QgghFi9eLGxsbER2dramz/bt20WFChVEWlqaZr0dO3YUW7ZsEZaWlmLdunVa250yZYpo0KBBkToeP36saevWrZvo0aOHEEKIxMREAUAcO3ZMs/zSpUsCgJg3b94Lv4M5c+aIWrVqifz8/GKXu7q6vtR62rVrJ8aOHat537JlS/Hpp59q9Vm+fLlQKpVabZs3bxb//pFraWkpVqxY8cLtEVHJeDqJqBxo0KABWrduDQ8PD3Tr1g1LlizB/fv3Ncvr16+PChX+78eBo6MjPDw8NO8NDAxgZ2enGfVITExEgwYNYGFhoenTvHlzqNVqXLhwQdN25MgRdO3aFStXrkSvXr1eWGf9+vW1ntDt7Oys2eaFCxdgaGiIRo0aaZbXqFEDNjY2L/UddOvWDbm5uahWrRqGDBmCzZs34/Hjx8/9TGFhIb755hu8/fbbsLOzQ8WKFbFr1y6kpKS81DafZ8yYMRg8eDDatGmDb7/9FleuXPnP6yQqbxhiiMoBAwMDREZG4u+//0a9evWwYMEC1K5dG0lJSQBQ5DSGQqEotk2tVgMAhBBa8zue7fdU9erVUadOHSxbtgz5+fkvrPNF2yxOSe3PUqlUuHDhAn7++WeYmZnh448/RosWLbTm3Dxrzpw5mDdvHj7//HNER0cjPj4e/v7+L9yXChUqFKnr2e2EhYXh7NmzaN++PaKjo1GvXj1s3rz5pfaFiJ5giCEqJxQKBZo3b46pU6fi5MmTMDY2fu1fmvXq1UN8fDxycnI0bQcOHECFChVQq1YtTZu9vT2io6Nx5coV9OjR47mB4UXq1KmDx48f4+TJk5q2y5cvIyMj46XXYWZmhqCgIPz444+IjY3FoUOHcObMGQCAsbExCgsLtfrv27cPHTt2RN++fdGgQQNUq1YNly5d0upT3OcqVaqEBw8eaH0/8fHxReqpVasWRo8ejV27dqFLly5Yvnz5S+8LETHEEJULR44cwYwZM3D8+HGkpKRg06ZNuH37NurWrfta6+vTpw9MTU3Rv39/JCQkICYmBiNHjkRwcDAcHR21+jo4OCA6Ohrnz59Hr169XngKpyR16tRBmzZtMHToUBw9ehQnT57E0KFDYWZmVuKo0L+tWLECS5cuRUJCAq5evYrVq1fDzMwMrq6uAJ7cJ2bv3r34559/cOfOHQBPTldFRkbi4MGDSExMxLBhw5CWlqa13qpVq+LIkSNITk7GnTt3oFar0bRpU5ibm2PSpEm4fPky1q1bpzXhOTc3FyNGjEBsbCyuXbuGAwcO4NixY699PIjKK4YYonLAysoKe/fuRbt27VCrVi18+eWXmDNnDgICAl5rfebm5ti5cyfu3buHd999Fx9++CFat26Nn376qdj+Tk5OiI6OxpkzZ9CnT58iIxcva9WqVXB0dESLFi3QuXNnDBkyBJaWljA1NX3hZ62trbFkyRI0b94cb7/9Nnbv3o1t27bBzs4OADBt2jQkJyejevXqmiuMJk+ejEaNGsHf3x8+Pj5wcnIqchficePGwcDAAPXq1UOlSpWQkpICW1tbrFmzBjt27NBclh0WFqb5jIGBAe7evYt+/fqhVq1a6N69OwICAjB16tTX+l6IyiuFeNkTykREZcyNGzegUqkQFRWF1q1bS10OEb1hDDFEJBvR0dHIzs6Gh4cHUlNT8fnnn+Off/7BxYsXeY8VonKIp5OISDYKCgowadIk1K9fH507d0alSpU0N75bu3at5lEGz77q168vdelEpAMciSEivfDgwQPcunWr2GVGRkaaCbxEpD8YYoiIiEiWeDqJiIiIZIkhhoiIiGSJIYaIiIhkiSGGiIiIZIkhhoiIiGSJIYaIiIhkiSGGiIiIZOn/AXOujbX+i3cDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dframe.plot.bar(x='smoking_status', \n",
    "              stacked=True, \n",
    "              title='The number of Smoking Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "484bb4b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n"
     ]
    }
   ],
   "source": [
    "unique_values = df['work_type'].unique()\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "ff4ea497",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['work_type'] = data['work_type'].replace({'Private': 0, 'Govt_job': 1, 'Self-employed': 2, 'Never_worked': 3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f2de9a0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Rural' 'Urban']\n"
     ]
    }
   ],
   "source": [
    "unique_values = df['Residence_type'].unique()\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5eadd3f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Residence_type'] = data['Residence_type'].replace({'Rural': 0, 'Urban': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "355fab4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "unique_values = df['Residence_type'].unique()\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "abdd3b6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['never smoked' 'Unknown' 'smokes' 'formerly smoked']\n"
     ]
    }
   ],
   "source": [
    "unique_values = df['smoking_status'].unique()\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0de67211",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['smoking_status'] = data['smoking_status'].replace({'never smoked': 0, 'smokes': 1, 'formerly smoked': 2, 'Unknown': 3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "df99d52a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 3 1 2]\n"
     ]
    }
   ],
   "source": [
    "unique_values = df['smoking_status'].unique()\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "73751fde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Yes' 'No']\n"
     ]
    }
   ],
   "source": [
    "unique_values = df['ever_married'].unique()\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3e4f6520",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ever_married'] = df['ever_married'].replace({'Yes': 0, 'No': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "a5c621c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "unique_values = df['gender'].unique()\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "84d87576",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender'] = df['gender'].replace({'Other': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "d5f6053b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "y=df['stroke']\n",
    "x=df.drop('stroke',axis=1)\n",
    "x\n",
    "X_train, X_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "e0798d9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9523809523809523\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "nb = GaussianNB()\n",
    "nb.fit(X_train, y_train)\n",
    "\n",
    "y_pred = nb.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "8d80a1aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9523809523809523\n",
      "Confusion matrix:\n",
      " [[800   0]\n",
      " [ 40   0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "confusion = confusion_matrix(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Confusion matrix:\\n\", confusion)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "7d586c04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.042177587268017916\n",
      "R-squared: 0.06998420074020484\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "# Create an instance of the linear regression model\n",
    "lr = LinearRegression()\n",
    "\n",
    "# Fit the model to the training data\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "# Print the mean squared error and R-squared value of the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"Mean squared error:\", mse)\n",
    "print(\"R-squared:\", r2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "8c3d9cf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9488095238095238\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "# Create an instance of the k-NN classifier\n",
    "k = 3 # choose the number of neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors=k)\n",
    "\n",
    "# Fit the classifier to the training data\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "# Print the accuracy score of the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  }
 ],
 "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",
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