1 Stroke Prediction - Data Cleaning and EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "#### Source : Kaggle\n",
    "According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths.\n",
    "\n",
    "This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.\n",
    "\n",
    "Dataset attribute Information:\n",
    "\n",
    "1) id: unique identifier\n",
    "\n",
    "2) gender: \"Male\", \"Female\" or \"Other\"\n",
    "\n",
    "3) age: age of the patient\n",
    "\n",
    "4) hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension\n",
    "\n",
    "5) heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease\n",
    "\n",
    "6) ever_married: \"No\" or \"Yes\"\n",
    "\n",
    "7) work_type: \"children\", \"Govt_jov\", \"Never_worked\", \"Private\" or \"Self-employed\"\n",
    "\n",
    "8) Residence_type: \"Rural\" or \"Urban\"\n",
    "\n",
    "9) avg_glucose_level: average glucose level in blood\n",
    "\n",
    "10) bmi: body mass index\n",
    "\n",
    "11) smoking_status: \"formerly smoked\", \"never smoked\", \"smokes\" or \"Unknown\"\n",
    "\n",
    "12) stroke: 1 if the patient had a stroke or 0 if not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('healthcare_stroke_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5110, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 5110 non-null   int64  \n",
      " 1   gender             5110 non-null   object \n",
      " 2   age                5110 non-null   float64\n",
      " 3   hypertension       5110 non-null   int64  \n",
      " 4   heart_disease      5110 non-null   int64  \n",
      " 5   ever_married       5110 non-null   object \n",
      " 6   work_type          5110 non-null   object \n",
      " 7   Residence_type     5110 non-null   object \n",
      " 8   avg_glucose_level  5110 non-null   float64\n",
      " 9   bmi                4909 non-null   float64\n",
      " 10  smoking_status     5110 non-null   object \n",
      " 11  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 479.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>4909.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36517.829354</td>\n",
       "      <td>43.226614</td>\n",
       "      <td>0.097456</td>\n",
       "      <td>0.054012</td>\n",
       "      <td>106.147677</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0.048728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21161.721625</td>\n",
       "      <td>22.612647</td>\n",
       "      <td>0.296607</td>\n",
       "      <td>0.226063</td>\n",
       "      <td>45.283560</td>\n",
       "      <td>7.854067</td>\n",
       "      <td>0.215320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17741.250000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.245000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36932.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.885000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54682.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.090000</td>\n",
       "      <td>33.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72940.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>97.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          age  hypertension  heart_disease  \\\n",
       "count   5110.000000  5110.000000   5110.000000    5110.000000   \n",
       "mean   36517.829354    43.226614      0.097456       0.054012   \n",
       "std    21161.721625    22.612647      0.296607       0.226063   \n",
       "min       67.000000     0.080000      0.000000       0.000000   \n",
       "25%    17741.250000    25.000000      0.000000       0.000000   \n",
       "50%    36932.000000    45.000000      0.000000       0.000000   \n",
       "75%    54682.000000    61.000000      0.000000       0.000000   \n",
       "max    72940.000000    82.000000      1.000000       1.000000   \n",
       "\n",
       "       avg_glucose_level          bmi       stroke  \n",
       "count        5110.000000  4909.000000  5110.000000  \n",
       "mean          106.147677    28.893237     0.048728  \n",
       "std            45.283560     7.854067     0.215320  \n",
       "min            55.120000    10.300000     0.000000  \n",
       "25%            77.245000    23.500000     0.000000  \n",
       "50%            91.885000    28.100000     0.000000  \n",
       "75%           114.090000    33.100000     0.000000  \n",
       "max           271.740000    97.600000     1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5110"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if there are duplicate entries\n",
    "#dataset has 5110 rows\n",
    "unique = df['id'].unique()\n",
    "len(unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#as there are no duplicate values we simply drop 'id' column\n",
    "df.drop(['id'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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",
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       "      <th>1</th>\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>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>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>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": [
       "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0    Male  67.0             0              1          Yes        Private   \n",
       "1  Female  61.0             0              0          Yes  Self-employed   \n",
       "2    Male  80.0             0              1          Yes        Private   \n",
       "3  Female  49.0             0              0          Yes        Private   \n",
       "4  Female  79.0             1              0          Yes  Self-employed   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "1          Rural             202.21   NaN     never smoked       1  \n",
       "2          Rural             105.92  32.5     never smoked       1  \n",
       "3          Urban             171.23  34.4           smokes       1  \n",
       "4          Rural             174.12  24.0     never smoked       1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female', 'Other'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>3116</th>\n",
       "      <td>Other</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>143.33</td>\n",
       "      <td>22.4</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     gender   age  hypertension  heart_disease ever_married work_type  \\\n",
       "3116  Other  26.0             0              0           No   Private   \n",
       "\n",
       "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "3116          Rural             143.33  22.4  formerly smoked       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['gender'] =='Other']  #As there is only one entry for 'Other', we will drop this row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([3116], axis = 0, inplace = True)  #dropping the only entry with gender = Other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hypertension'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ever_married'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Private', 'Self-employed', 'Govt_job', 'children', 'Never_worked'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['work_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Rural'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Residence_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['formerly smoked', 'never smoked', 'smokes', 'Unknown'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['smoking_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "never smoked       1892\n",
       "Unknown            1544\n",
       "formerly smoked     884\n",
       "smokes              789\n",
       "Name: smoking_status, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['smoking_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As the smoking status of 1512 entries is unknown, we will drop this column\n",
    "df.drop(['smoking_status'], axis = 1, inplace = True)  #dropping the only entry with gender = Other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               False\n",
       "age                  False\n",
       "hypertension         False\n",
       "heart_disease        False\n",
       "ever_married         False\n",
       "work_type            False\n",
       "Residence_type       False\n",
       "avg_glucose_level    False\n",
       "bmi                   True\n",
       "stroke               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    4908\n",
       "True      201\n",
       "Name: bmi, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bmi'].isna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357d891640>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot( df['gender'], df['age'])   #No outliers in age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357d9779d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot( df['gender'], df['avg_glucose_level']) #glucose "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357d9eb790>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot( df['gender'], df['bmi'])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357da584f0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['bmi'])  # As BMI as some unrealstic entries, we will remove these outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = np.percentile(df['bmi'], 25)\n",
    "Q3 = np.percentile(df['bmi'], 75)\n",
    "\n",
    "# Interquartile range\n",
    "IQR = Q3 - Q1\n",
    "outlier_step = 1.5 * IQR\n",
    "\n",
    "# Outliers indices\n",
    "outliers_indices = df[(df['bmi'] < Q1 - outlier_step) | (df['bmi'] > Q3 + outlier_step )].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping outliers\n",
    "df = df.drop(df[outliers_indices], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357daa47c0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['bmi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357db29670>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(), yticklabels = False, cbar = False, cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    4908\n",
       "True      201\n",
       "Name: bmi, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bmi'].isna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling null values of BMI with its median\n",
    "df['bmi'].fillna(df['bmi'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    5109\n",
       "Name: bmi, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bmi'].isna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357dbb5490>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(), yticklabels = False, cbar = False, cmap = 'viridis') #No nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating inter quartile range for bmi\n",
    "q3, q1 = np.percentile(df['bmi'], [75 ,25])\n",
    "iqr = q3 - q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing outliers for bmi\n",
    "outlier_step = 1.5 * iqr\n",
    "\n",
    "# Outliers indices\n",
    "outliers_indices = df[(df['bmi'] < q1 - outlier_step) | (df['bmi'] > q3 + outlier_step )].index\n",
    "\n",
    "#droping rows\n",
    "df.drop(outliers_indices, axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357df441c0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['bmi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4983, 10)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357dfa03a0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357e039430>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('stroke', data = df, hue = 'gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2895\n",
       "Male      2088\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(138, 10)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['stroke'] == 1) & (df['gender'] == 'Female')].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(108, 10)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['stroke'] == 1) & (df['gender'] == 'Male')].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chances for female:  4.722222222222222 %\n",
      "Chances for male:  5.091258405379443 %\n"
     ]
    }
   ],
   "source": [
    "print('Chances for female: ', 136/2880 * 100, '%')\n",
    "print('Chances for male: ', 106/2082 * 100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# people who had stroke\n",
    "df_stroke = df.loc[df['stroke'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357e09d4c0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_stroke['age'], bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357db985e0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_stroke['bmi'], bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357e174fd0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['avg_glucose_level'], bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357e2d3d90>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_stroke['avg_glucose_level'], bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
       "       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi', 'stroke'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357f35fb50>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('stroke', data = df, hue = 'hypertension')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4506\n",
       "1     477\n",
       "Name: hypertension, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hypertension'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181, 10)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stroke.loc[df_stroke['hypertension'] == 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 10)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stroke.loc[df_stroke['hypertension'] == 1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chances for people without hypertension:  3.988413547237077 %\n",
      "Chances for people with hypertension:  13.291139240506327 %\n"
     ]
    }
   ],
   "source": [
    "print('Chances for people without hypertension: ', 179/4488 * 100, '%')\n",
    "print('Chances for people with hypertension: ', 63/474 * 100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357f3a6160>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('stroke', data = df, hue = 'heart_disease')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4708\n",
       "1     275\n",
       "Name: heart_disease, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['heart_disease'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199, 10)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stroke.loc[df_stroke['heart_disease'] == 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47, 10)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stroke.loc[df_stroke['heart_disease'] == 1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chances for people without heart_disease:  4.159556313993174 %\n",
      "Chances for people with heart_disease:  17.153284671532848 %\n"
     ]
    }
   ],
   "source": [
    "print('Chances for people without heart_disease: ', 195/4688 * 100, '%')\n",
    "print('Chances for people with heart_disease: ', 47/274 * 100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357f400700>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAY9klEQVR4nO3dfZBU9Z3v8fcHHMHwcOMDGMIQBi1UEFyCEx6iVhSSQLxRxFwDCQrRVOEajFKxNlf0BrhJqGipsRZFDSuIXlGYqAgx6i5LyPqwiBmQ8HiJXEEYoWDA1QAJKvC9f/QBG2jmNDjdPcN8XlVdfc73/M45356i/Hge+rQiAjMzs7o0K3UDZmbW8DkszMwslcPCzMxSOSzMzCyVw8LMzFKdVOoGCuWMM86IioqKUrdhZtaoLFmyZHtEtDu8fsKGRUVFBdXV1aVuw8ysUZH0bq66T0OZmVkqh4WZmaVyWJiZWaoT9pqFmTVNn3zyCTU1NezZs6fUrTRoLVu2pLy8nLKysrzGOyzM7IRSU1NDmzZtqKioQFKp22mQIoIdO3ZQU1NDly5d8lrHp6HM7ISyZ88eTj/9dAdFHSRx+umnH9PRl8PCzE44Dop0x/o3cliYmVmqgoWFpJaS3pT0Z0mrJP3vpH6apPmS3k7eT81aZ5ykdZLWShqUVb9Q0opk2WT5fxvMzIqqkBe4PwIGRMQuSWXAa5JeAq4GFkTEXZJuB24H/qek7sBw4Hzgi8C/SzonIvYBDwOjgTeAF4HBwEsF7J0L/+mJQm6+UVlyz8hSt2Bmicsvv5ynnnqKz3/+83mNnzFjBtXV1Tz44IOfab8FO7KIjF3JbFnyCmAI8HhSfxy4KpkeAsyKiI8iYj2wDugjqQPQNiIWReZn/Z7IWsfMrCT27dtX0O1HBPv37z9i/sUXX8w7KOpTQa9ZSGouaRmwDZgfEYuBMyNiC0Dy3j4Z3hHYlLV6TVLrmEwfXs+1v9GSqiVV19bW1u+HMbNG7cknn6RPnz706tWLG2+8kSlTpvDTn/704PIZM2bw4x//OOfYA8HQunVrxo8fT9++fVm0aFHO/VRUVHDHHXfQv39/KisrWbp0KYMGDeLss8/mkUceAWDXrl0MHDiQ3r1707NnT+bOnQvAhg0b6NatGz/60Y/o3bs3r7766iHzmzZtoqKigu3bt9fZ52OPPcY555zD1772NV5//fV6+fsVNCwiYl9E9ALKyRwl9KhjeK7rEFFHPdf+pkZEZURUtmt3xEMTzayJWrNmDbNnz+b1119n2bJlNG/enNatW/Pcc88dHDN79myGDRuWc+zMmTMB2L17Nz169GDx4sVcfPHFR91fp06dWLRoEZdccgk/+MEPeOaZZ3jjjTcYP348kPlC3Jw5c1i6dCkLFy7ktttuI3PiBNauXcvIkSN566236Ny58xHzdX2mmTNnsmXLFiZMmMDrr7/O/PnzWb16db38DYvypbyI+EDSH8lca9gqqUNEbElOMW1LhtUAnbJWKwc2J/XyHHUzs7wsWLCAJUuW8JWvfAWAv//977Rv356zzjqLN954g65du7J27VouuugipkyZknMsQPPmzfnOd76Tur8rr7wSgJ49e7Jr1y7atGlDmzZtaNmyJR988AGtWrXijjvu4JVXXqFZs2a89957bN26FYDOnTvTr1+/g9s6fD7tMy1evJhLL72UA//DPGzYMP7yl78c75/uoIKFhaR2wCdJUJwCfB24G5gHjALuSt7nJqvMA56S9GsyF7i7Am9GxD5JOyX1AxYDI4EHCtW3mZ14IoJRo0bxq1/96pD6tGnTqKqq4rzzzmPo0KFIOupYyBwRNG/ePHV/LVq0AKBZs2YHpw/M7927l5kzZ1JbW8uSJUsoKyujoqLi4BfkWrVqdci2Dp9P+0zPP/98Qb5nUsjTUB2AhZKWA38ic83iBTIh8Q1JbwPfSOaJiFVAFbAaeBkYk9wJBXAT8CiZi97/jwLfCWVmJ5aBAwfyzDPPsG1b5kTG+++/z7vvvsvVV1/N888/z9NPP82wYcPqHFufPvzwQ9q3b09ZWRkLFy48ru0frc++ffvyxz/+kR07dvDJJ5/w29/+tl56LtiRRUQsB76co74DGHiUdSYBk3LUq4G6rneYmR1V9+7d+eUvf8k3v/lN9u/fT1lZGVOmTKFz5850796d1atX06dPn9Sx9WXEiBFcccUVVFZW0qtXL84777x6+0z9+vVj4sSJ9O/fnw4dOtC7d+96uXNLBy6qnGgqKyvjs/xSnr9n8Sl/z8IakzVr1tCtW7dSt9Eo5PpbSVoSEZWHj/XjPszMLJUfUW5mdhyGDh3K+vXrD6ndfffdDBo06ChrNG4OCzOz4zBnzpxSt1BUPg1lZmapHBZmZpbKYWFmZql8zcLMLFHft8yn3XYeEVxyySXceeedfOtb3wKgqqqK6dOn8/LLL9drL5+Vw8LMrEQk8cgjj3DNNddw2WWXsW/fPu68884GFxTgsDAzK6kePXpwxRVXcPfdd7N7926uvfZaJk2axIoVK9i7dy8TJ05kyJAhrFq1iuuvv56PP/6Y/fv38+yzz9K1a9ei9emwMDMrsQkTJtC7d29OPvlkvv3tbzNgwACmT5/OBx98QJ8+ffj617/OI488wq233sqIESP4+OOPC/7jS4dzWJiZlVirVq0YNmwYrVu3pqqqit/97nfce++9AOzZs4eNGzfSv39/Jk2aRE1NDVdffXVRjyrAYWFm1iA0a9aMZs2aERE8++yznHvuuYcs79atG3379uX3v/89gwYN4tFHH2XAgAHF669oezIzs1SDBg3igQceOPjLeW+99RYA77zzDmeddRa33HILV155JcuXLy9qXz6yMDNLNIQnLP/sZz9j7NixXHDBBUQEFRUVvPDCC8yePZsnn3ySsrIyvvCFLxz8idZicViYmTUAEydOPDj9m9/85ojl48aNY9y4cUXs6FA+DWVmZqkcFmZmlsphYWZmqRwWZmaWymFhZmapHBZmZpbKt86amSU2/rxnvW7vS+NXpI6RxE9+8hPuu+8+AO6991527dp1yK20DYGPLMzMSqhFixY899xzbN++vdSt1MlhYWZWQieddBKjR4/m/vvvP2LZu+++y8CBA7ngggsYOHAgGzduLEGHGQULC0mdJC2UtEbSKkm3JvWJkt6TtCx5XZ61zjhJ6yStlTQoq36hpBXJssmSVKi+zcyKbcyYMcycOZMPP/zwkPrNN9/MyJEjWb58OSNGjOCWW24pUYeFPbLYC9wWEd2AfsAYSd2TZfdHRK/k9SJAsmw4cD4wGHhIUvNk/MPAaKBr8hpcwL7NzIqqbdu2jBw5ksmTJx9SX7RoEd///vcBuO6663jttddK0R5QwLCIiC0RsTSZ3gmsATrWscoQYFZEfBQR64F1QB9JHYC2EbEoMo9hfAK4qlB9m5mVwtixY5k2bRq7d+8+6phSnlQpyjULSRXAl4HFSelmScslTZd0alLrCGzKWq0mqXVMpg+v59rPaEnVkqpra2vr8ROYmRXWaaedxne/+12mTZt2sPbVr36VWbNmATBz5kwuvvjiUrVX+FtnJbUGngXGRsRfJT0M/AKI5P0+4AYgV2RGHfUjixFTgakAlZWVOceYmR1NPre6FtJtt93Ggw8+eHB+8uTJ3HDDDdxzzz20a9eOxx57rGS9FTQsJJWRCYqZEfEcQERszVr+L8ALyWwN0Clr9XJgc1Ivz1E3M2v0du3adXD6zDPP5G9/+9vB+YqKCv7whz+Uoq0jFPJuKAHTgDUR8euseoesYUOBlcn0PGC4pBaSupC5kP1mRGwBdkrql2xzJDC3UH2bmdmRCnlkcRFwHbBC0rKkdgfwPUm9yJxK2gDcCBARqyRVAavJ3Ek1JiL2JevdBMwATgFeSl5mZlYkBQuLiHiN3NcbXqxjnUnApBz1aqBH/XVnZieyiCjpnUONwYHf+M6Xv8FtZieUli1bsmPHjmP+j2FTEhHs2LGDli1b5r2OHyRoZieU8vJyampq8O3zdWvZsiXl5eXpAxMOCzM7oZSVldGlS5dSt3HC8WkoMzNL5bAwM7NUDgszM0vlsDAzs1QOCzMzS+WwMDOzVA4LMzNL5bAwM7NUDgszM0vlsDAzs1QOCzMzS+WwMDOzVA4LMzNL5bAwM7NUDgszM0vlsDAzs1QOCzMzS+WwMDOzVA4LMzNL5bAwM7NUDgszM0vlsDAzs1QFCwtJnSQtlLRG0ipJtyb10yTNl/R28n5q1jrjJK2TtFbSoKz6hZJWJMsmS1Kh+jYzsyMV8shiL3BbRHQD+gFjJHUHbgcWRERXYEEyT7JsOHA+MBh4SFLzZFsPA6OBrslrcAH7NjOzwxQsLCJiS0QsTaZ3AmuAjsAQ4PFk2OPAVcn0EGBWRHwUEeuBdUAfSR2AthGxKCICeCJrHTMzK4KiXLOQVAF8GVgMnBkRWyATKED7ZFhHYFPWajVJrWMyfXg9135GS6qWVF1bW1ufH8HMrEkreFhIag08C4yNiL/WNTRHLeqoH1mMmBoRlRFR2a5du2Nv1szMcipoWEgqIxMUMyPiuaS8NTm1RPK+LanXAJ2yVi8HNif18hx1MzMrkkLeDSVgGrAmIn6dtWgeMCqZHgXMzaoPl9RCUhcyF7LfTE5V7ZTUL9nmyKx1zMysCE4q4LYvAq4DVkhaltTuAO4CqiT9ENgIXAMQEaskVQGrydxJNSYi9iXr3QTMAE4BXkpeZmZWJAULi4h4jdzXGwAGHmWdScCkHPVqoEf9dWdmZsfC3+A2M7NUDgszM0vlsDAzs1QOCzMzS+WwMDOzVA4LMzNL5bAwM7NUDgszM0vlsDAzs1QOCzMzS+WwMDOzVHmFhaQF+dTMzOzEVOeDBCW1BD4HnCHpVD59MGBb4IsF7s3MzBqItKfO3giMJRMMS/g0LP4KTClgX2Zm1oDUGRYR8c/AP0v6cUQ8UKSezMysgcnr9ywi4gFJXwUqsteJiCcK1Jc1IBt/3rPULTQYXxq/otQtmJVEXmEh6f8AZwPLgAO/XheAw8LMrAnI95fyKoHuERGFbMbMzBqmfL9nsRL4QiEbMTOzhivfI4szgNWS3gQ+OlCMiCsL0pWZmTUo+YbFxEI2YWZmDVu+d0P9R6EbMTOzhivfu6F2krn7CeBkoAzYHRFtC9WYmZk1HPkeWbTJnpd0FdCnIB2ZmVmDc1xPnY2I54EB9dyLmZk1UPk+dfbqrNf/kHQXn56WOto60yVtk7QyqzZR0nuSliWvy7OWjZO0TtJaSYOy6hdKWpEsmyxJh+/LzMwKK9+7oa7Imt4LbACGpKwzA3iQI7/lfX9E3JtdkNQdGA6cT+ahhf8u6ZyI2Ac8DIwG3gBeBAYDL+XZt5mZ1YN8r1lcf6wbjohXJFXkOXwIMCsiPgLWS1oH9JG0AWgbEYsAJD0BXIXDwsysqPI9DVUuaU5yWmmrpGcllR/nPm+WtDw5TXVqUusIbMoaU5PUOibTh9fNzKyI8r3A/Rgwj8wpoo7A75LasXqYzAMJewFbgPuSeq7rEFFHPSdJoyVVS6qura09jvbMzCyXfMOiXUQ8FhF7k9cMoN2x7iwitkbEvojYD/wLn95+WwN0yhpaDmxO6uU56kfb/tSIqIyIynbtjrk9MzM7inzDYrukayU1T17XAjuOdWeSOmTNDiXzgELIHLUMl9RCUhegK/BmRGwBdkrql9wFNRKYe6z7NTOzzybfu6FuIHNn0/1kTgP9J1DnRW9JTwOXkvn97hpgAnCppF7JNjaQ+dlWImKVpCpgNZm7rcYkd0IB3ETmzqpTyFzY9sVtM7MiyzcsfgGMioj/ApB0GnAvmRDJKSK+l6M8rY7xk4BJOerVQI88+zQzswLI9zTUBQeCAiAi3ge+XJiWzMysock3LJpl3eZ64Mgi36MSMzNr5PL9D/59wH9KeobM9YbvkuOUkZmZnZjy/Qb3E5KqyTw8UMDVEbG6oJ2ZmVmDkfeppCQcHBBmZk3QcT2i3MzMmhaHhZmZpXJYmJlZKoeFmZmlcliYmVkqh4WZmaVyWJiZWSqHhZmZpXJYmJlZKoeFmZmlcliYmVkqh4WZmaVyWJiZWSqHhZmZpXJYmJlZKoeFmZmlcliYmVkqh4WZmaVyWJiZWSqHhZmZpSpYWEiaLmmbpJVZtdMkzZf0dvJ+ataycZLWSVoraVBW/UJJK5JlkyWpUD2bmVluhTyymAEMPqx2O7AgIroCC5J5JHUHhgPnJ+s8JKl5ss7DwGiga/I6fJtmZlZgBQuLiHgFeP+w8hDg8WT6ceCqrPqsiPgoItYD64A+kjoAbSNiUUQE8ETWOmZmViTFvmZxZkRsAUje2yf1jsCmrHE1Sa1jMn143czMiqihXODOdR0i6qjn3og0WlK1pOra2tp6a87MrKkrdlhsTU4tkbxvS+o1QKesceXA5qRenqOeU0RMjYjKiKhs165dvTZuZtaUFTss5gGjkulRwNys+nBJLSR1IXMh+83kVNVOSf2Su6BGZq1jZmZFclKhNizpaeBS4AxJNcAE4C6gStIPgY3ANQARsUpSFbAa2AuMiYh9yaZuInNn1SnAS8nLzMyKqGBhERHfO8qigUcZPwmYlKNeDfSox9bMzOwYNZQL3GZm1oA5LMzMLJXDwszMUjkszMwslcPCzMxSOSzMzCyVw8LMzFI5LMzMLJXDwszMUjkszMwslcPCzMxSOSzMzCyVw8LMzFI5LMzMLJXDwszMUjkszMwslcPCzMxSOSzMzCyVw8LMzFI5LMzMLJXDwszMUjkszMwslcPCzMxSOSzMzCyVw8LMzFI5LMzMLFVJwkLSBkkrJC2TVJ3UTpM0X9LbyfupWePHSVonaa2kQaXo2cysKSvlkcVlEdErIiqT+duBBRHRFViQzCOpOzAcOB8YDDwkqXkpGjYza6oa0mmoIcDjyfTjwFVZ9VkR8VFErAfWAX1K0J+ZWZNVqrAI4N8kLZE0OqmdGRFbAJL39km9I7Apa92apHYESaMlVUuqrq2tLVDrZmZNz0kl2u9FEbFZUntgvqT/W8dY5ahFroERMRWYClBZWZlzjJmZHbuSHFlExObkfRswh8xppa2SOgAk79uS4TVAp6zVy4HNxevWzMyKHhaSWklqc2Aa+CawEpgHjEqGjQLmJtPzgOGSWkjqAnQF3ixu12ZmTVspTkOdCcyRdGD/T0XEy5L+BFRJ+iGwEbgGICJWSaoCVgN7gTERsa8EfZuZNVlFD4uIeAf4hxz1HcDAo6wzCZhU4NbMzOwoGtKts2Zm1kA5LMzMLJXDwszMUjkszMwslcPCzMxSOSzMzCyVw8LMzFI5LMzMLJXDwszMUjkszMwslcPCzMxSOSzMzCyVw8LMzFKV6pfyzOwzuPCfnih1Cw3GkntGlrqFJsFHFmZmlsphYWZmqRwWZmaWymFhZmapHBZmZpbKYWFmZqkcFmZmlsphYWZmqRwWZmaWymFhZmapHBZmZpaq0YSFpMGS1kpaJ+n2UvdjZtaUNIoHCUpqDkwBvgHUAH+SNC8iVpe2MzMrtY0/71nqFhqML41fUbBtN5Yjiz7Auoh4JyI+BmYBQ0rck5lZk9EojiyAjsCmrPkaoO/hgySNBkYns7skrS1Cbye8znAGsL3UfTQIE1TqDuww/veZpX7+fXbOVWwsYZHrLxBHFCKmAlML307TIqk6IipL3YdZLv73WRyN5TRUDdApa74c2FyiXszMmpzGEhZ/ArpK6iLpZGA4MK/EPZmZNRmN4jRUROyVdDPwr0BzYHpErCpxW02JT+1ZQ+Z/n0WgiCNO/ZuZmR2isZyGMjOzEnJYmJlZKoeF1cmPWbGGStJ0SdskrSx1L02Bw8KOKusxK98CugPfk9S9tF2ZHTQDGFzqJpoKh4XVxY9ZsQYrIl4B3i91H02Fw8LqkusxKx1L1IuZlZDDwuqS12NWzOzE57CwuvgxK2YGOCysbn7MipkBDgurQ0TsBQ48ZmUNUOXHrFhDIelpYBFwrqQaST8sdU8nMj/uw8zMUvnIwszMUjkszMwslcPCzMxSOSzMzCyVw8LMzFI5LMzqiaSxkj53jOtU+Kmp1hg4LMzqz1ggZ1gkT/A1a7QcFmbHQVIrSb+X9GdJKyVNAL4ILJS0MBmzS9LPJS0G+kv6STJ2paSxObZ5lqS3JH1F0tmSXpa0RNKrks4r8kc0O8RJpW7ArJEaDGyOiP8OIOm/AdcDl0XE9mRMK2BlRIyXdGGyvC+ZBzQulvQfwH8l659L5hHw10fEMkkLgH+MiLcl9QUeAgYU8fOZHcLf4DY7DpLOIfMYlCrghYh4VdIGoPJAWEjaC7SIiH2SbgVOj4jxybJfALVknrW1mExofCciVklqnSxbm7XLFhHRrUgfz+wIPrIwOw4R8ZfkaOFy4FeS/i3HsD0RsS+ZzvW49wM+JPO7IRcBq8icHv4gInrVZ89mn4WvWZgdB0lfBP4WEU8C9wK9gZ1Am6Os8gpwlaTPSWoFDAVeTZZ9DFwFjJT0/Yj4K7Be0jXJviTpHwr4ccxS+cjC7Pj0BO6RtB/4BLgJ6A+8JGlLRFyWPTgilkqaAbyZlB6NiLckVSTLd0v6NjBf0m5gBPCwpP8FlJG5nvHnwn8ss9x8zcLMzFL5NJSZmaVyWJiZWSqHhZmZpXJYmJlZKoeFmZmlcliYmVkqh4WZmaX6/7tIebo2ZEq7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('stroke', data = df, hue = 'ever_married')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    3255\n",
       "No     1728\n",
       "Name: ever_married, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ever_married'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(217, 10)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stroke.loc[df_stroke['ever_married'] == 'Yes'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29, 10)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stroke.loc[df_stroke['ever_married'] == 'No'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chances for married:  6.606977462179684 %\n",
      "Chances for single:  1.625072547881602 %\n"
     ]
    }
   ],
   "source": [
    "print('Chances for married: ', 214/3239 * 100, '%')\n",
    "print('Chances for single: ', 28/1723 * 100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2357f4687c0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('stroke', data = df, hue = 'Residence_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Urban    2538\n",
       "Rural    2445\n",
       "Name: Residence_type, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Residence_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133, 10)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stroke.loc[df_stroke['Residence_type'] == 'Urban'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113, 10)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stroke.loc[df_stroke['Residence_type'] == 'Rural'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chances for urban:  5.1424050632911396 %\n",
      "Chances for rural:  4.6014790468364835 %\n"
     ]
    }
   ],
   "source": [
    "print('Chances for urban: ', 130/2528 * 100, '%')\n",
    "print('Chances for rural: ', 112/2434 * 100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Key insights\n",
    "\n",
    "More men had strokes than women\n",
    "\n",
    "More old people had strokes\n",
    "\n",
    "Most of the people who had strokes 1) were diabatic /\n",
    "                                         2) had hypertension /\n",
    "                                         3) had heart disease /\n",
    "                                         4) were married (can be related to age as most of the old people are married) /\n",
    "                                         5) high glucose level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickling for later use\n",
    "import pickle\n",
    "df.to_pickle('stroke_cleaned_data.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}