Heart Stroke Prediction/Stroke detection.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heart Stroke Prediction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import log_loss\n"
   ]
  },
  {
   "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": [
    "#Loading the dataset\n",
    "df = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('id', axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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>4909.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>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>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>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>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>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>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": [
       "               age  hypertension  heart_disease  avg_glucose_level   \n",
       "count  5110.000000   5110.000000    5110.000000        5110.000000  \\\n",
       "mean     43.226614      0.097456       0.054012         106.147677   \n",
       "std      22.612647      0.296607       0.226063          45.283560   \n",
       "min       0.080000      0.000000       0.000000          55.120000   \n",
       "25%      25.000000      0.000000       0.000000          77.245000   \n",
       "50%      45.000000      0.000000       0.000000          91.885000   \n",
       "75%      61.000000      0.000000       0.000000         114.090000   \n",
       "max      82.000000      1.000000       1.000000         271.740000   \n",
       "\n",
       "               bmi       stroke  \n",
       "count  4909.000000  5110.000000  \n",
       "mean     28.893237     0.048728  \n",
       "std       7.854067     0.215320  \n",
       "min      10.300000     0.000000  \n",
       "25%      23.500000     0.000000  \n",
       "50%      28.100000     0.000000  \n",
       "75%      33.100000     0.000000  \n",
       "max      97.600000     1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             5110 non-null   object \n",
      " 1   age                5110 non-null   float64\n",
      " 2   hypertension       5110 non-null   int64  \n",
      " 3   heart_disease      5110 non-null   int64  \n",
      " 4   ever_married       5110 non-null   object \n",
      " 5   work_type          5110 non-null   object \n",
      " 6   Residence_type     5110 non-null   object \n",
      " 7   avg_glucose_level  5110 non-null   float64\n",
      " 8   bmi                4909 non-null   float64\n",
      " 9   smoking_status     5110 non-null   object \n",
      " 10  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 439.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       67\n",
       "1       61\n",
       "2       80\n",
       "3       49\n",
       "4       79\n",
       "        ..\n",
       "5105    80\n",
       "5106    81\n",
       "5107    35\n",
       "5108    51\n",
       "5109    44\n",
       "Name: age, Length: 5110, dtype: int32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing the missing values with the most frequent value\n",
    "df['bmi'].fillna(df['bmi'].mode()[0], inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check values and their count in the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ever_married\n",
      "Yes    3353\n",
      "No     1757\n",
      "Name: count, dtype: int64\n",
      "work_type\n",
      "Private          2925\n",
      "Self-employed     819\n",
      "children          687\n",
      "Govt_job          657\n",
      "Never_worked       22\n",
      "Name: count, dtype: int64\n",
      "gender\n",
      "Female    2994\n",
      "Male      2115\n",
      "Other        1\n",
      "Name: count, dtype: int64\n",
      "Residence_type\n",
      "Urban    2596\n",
      "Rural    2514\n",
      "Name: count, dtype: int64\n",
      "smoking_status\n",
      "never smoked       1892\n",
      "Unknown            1544\n",
      "formerly smoked     885\n",
      "smokes              789\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['ever_married'].value_counts())\n",
    "print(df['work_type'].value_counts())\n",
    "print(df['gender'].value_counts())\n",
    "print(df['Residence_type'].value_counts())\n",
    "print(df['smoking_status'].value_counts())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replacing the values in columns with numerical values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Residence Type: Urban = 1, Rural = 0\n",
    "- Smoking Status: formerly smoked = 1, never smoked = 2, smokes = 3, Unknown = 0\n",
    "-   Ever_Maried : Yes = 1, No = 0\n",
    "- Gender : Male = 1, Female = 0, Other = 2\n",
    "- Work Type : Private = 0, Self-employed = 1, children = 2, Govt_job = 3, Never_worked = 4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ever_married'].replace({'Yes':1, 'No':0}, inplace=True)\n",
    "df['gender'].replace({'Male':1, 'Female':0,'Other':2}, inplace=True)\n",
    "df['Residence_type'].replace({'Urban':1, 'Rural':0}, inplace=True)\n",
    "df['smoking_status'].replace({'formerly smoked':0, 'never smoked':1, 'smokes':2, 'Unknown':3}, inplace=True)\n",
    "df['work_type'].replace({'Private':0, 'Self-employed':1, 'children':2, 'Govt_job':3, 'Never_worked':4}, inplace=True)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find correlation between the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "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.corr()['stroke'][:-1].sort_values().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>202.21</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</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       1   4             0              1             1          0  \\\n",
       "1       0   4             0              0             1          1   \n",
       "2       1   4             0              1             1          0   \n",
       "3       0   3             0              0             1          0   \n",
       "4       0   4             1              0             1          1   \n",
       "\n",
       "   Residence_type  avg_glucose_level   bmi  smoking_status  stroke  \n",
       "0               1             228.69  36.6               0       1  \n",
       "1               0             202.21  28.7               1       1  \n",
       "2               0             105.92  32.5               1       1  \n",
       "3               1             171.23  34.4               2       1  \n",
       "4               0             174.12  24.0               1       1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace age with number wrt to age group\n",
    "# 0 = 0-12 , 1 = 13-19 , 2 = 20-30 , 3 = 31-60 , 4 = 61-100 \n",
    "df['age'] = pd.cut(x=df['age'], bins=[0, 12, 19, 30, 60, 100], labels=[0, 1, 2, 3,4])\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visulaizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'gender', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Residence_type', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4,4,figsize=(20, 20))\n",
    "sns.countplot(x = 'gender', data = df,hue = 'stroke', ax=ax[0,0])\n",
    "sns.countplot(x = 'age', data = df,hue = 'hypertension', ax=ax[0,1])\n",
    "sns.countplot(x = 'age', data = df,hue = 'heart_disease', ax=ax[0,2])\n",
    "sns.countplot(x = 'age', data = df,hue = 'stroke', ax=ax[0,3])\n",
    "sns.countplot(x = 'hypertension', data = df,hue = 'stroke', ax=ax[1,0])\n",
    "sns.countplot(x = 'heart_disease', data = df,hue = 'stroke', ax=ax[1,1])\n",
    "sns.countplot(x = 'ever_married', data = df,hue = 'stroke', ax=ax[1,2])\n",
    "sns.countplot(x = 'age', data = df,hue = 'ever_married', ax=ax[1,3])\n",
    "sns.countplot(x = 'work_type', data = df,hue = 'stroke', ax=ax[2,0])\n",
    "sns.countplot(x = 'Residence_type', data = df,hue = 'stroke', ax=ax[2,1])\n",
    "sns.countplot(x = 'smoking_status', data = df,hue = 'stroke', ax=ax[2,2])\n",
    "sns.lineplot(x = 'bmi', y = 'avg_glucose_level', data = df,hue = 'stroke', ax=ax[2,3])\n",
    "sns.countplot(x = 'age', data = df,hue = 'smoking_status', ax=ax[3,0])\n",
    "sns.countplot( x = 'work_type', data = df,hue = 'Residence_type', ax=ax[3,1])\n",
    "sns.countplot(x = 'work_type', data = df,hue = 'smoking_status', ax=ax[3,2])\n",
    "sns.countplot(x = 'Residence_type', data = df,hue = 'smoking_status', ax=ax[3,3])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('stroke', axis=1), df['stroke'], test_size=0.2, random_state=42)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9393346379647749"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the model\n",
    "lr.fit(X_train, y_train)\n",
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9393346379647749"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testing the model\n",
    "lr_pred = lr.predict(X_test)\n",
    "accuracy_score(y_test, lr_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC()\n",
    "svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9393346379647749"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the model\n",
    "svm.fit(X_train, y_train)\n",
    "svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9393346379647749"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testing the model\n",
    "sv_pred = svm.predict(X_test)\n",
    "accuracy_score(y_test, sv_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9099804305283757"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the model\n",
    "dt.fit(X_train, y_train)\n",
    "dt.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9099804305283757"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testing the model\n",
    "dt_pred = dt.predict(X_test)\n",
    "accuracy_score(y_test, dt_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9373776908023483"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the model\n",
    "knn.fit(X_train, y_train)\n",
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9373776908023483"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testing the model\n",
    "knn_pred = knn.predict(X_test)\n",
    "accuracy_score(y_test, knn_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(metrics.confusion_matrix(y_test, lr_pred), annot=True, fmt='d')\n",
    "plt.title('Accuracy Score: {}'.format(accuracy_score(y_test, lr_pred)))\n",
    "plt.ylabel('Predicted')\n",
    "plt.xlabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model Accuracy Score: 0.9393346379647749\n",
      "Logistic Regression Model F1 score:  0.0\n",
      "Logistic Regression Model Mean Absolute Error:  0.060665362035225046\n",
      "Logistic Regression Model Mean Squared Error:  0.060665362035225046\n",
      "Logistic Regression Model log loss:  2.1866012819229583\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression Model Accuracy Score:',accuracy_score(y_test, lr_pred))\n",
    "print('Logistic Regression Model F1 score: ',metrics.f1_score(y_test, lr_pred))\n",
    "print('Logistic Regression Model Mean Absolute Error: ',metrics.mean_absolute_error(y_test, lr_pred))\n",
    "print('Logistic Regression Model Mean Squared Error: ',metrics.mean_squared_error(y_test, lr_pred))\n",
    "print('Logistic Regression Model log loss: ',log_loss(y_test, lr_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(metrics.confusion_matrix(y_test, sv_pred), annot=True, fmt='d')\n",
    "plt.title('Accuracy Score: {}'.format(accuracy_score(y_test, sv_pred)))\n",
    "plt.ylabel('Predicted')\n",
    "plt.xlabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Model Accuracy Score: 0.9393346379647749\n",
      "SVM Model F1 score:  0.0\n",
      "SVM Model Mean Absolute Error:  0.060665362035225046\n",
      "SVM Model Mean Squared Error:  0.060665362035225046\n",
      "SVM Model log loss:  2.1866012819229583\n"
     ]
    }
   ],
   "source": [
    "print('SVM Model Accuracy Score:',accuracy_score(y_test, sv_pred))\n",
    "print('SVM Model F1 score: ',metrics.f1_score(y_test, sv_pred))\n",
    "print('SVM Model Mean Absolute Error: ',metrics.mean_absolute_error(y_test, sv_pred))\n",
    "print('SVM Model Mean Squared Error: ',metrics.mean_squared_error(y_test, sv_pred))\n",
    "print('SVM Model log loss: ',log_loss(y_test, sv_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(metrics.confusion_matrix(y_test, dt_pred), annot=True, fmt='d')\n",
    "plt.title('Accuracy Score: {}'.format(accuracy_score(y_test, dt_pred)))\n",
    "plt.ylabel('Predicted')\n",
    "plt.xlabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Model Accuracy Score: 0.9099804305283757\n",
      "Decision Tree Model F1 score:  0.2459016393442623\n",
      "Decision Tree Model Mean Absolute Error:  0.09001956947162426\n",
      "Decision Tree Model Mean Squared Error:  0.09001956947162426\n",
      "Decision Tree Model log loss:  3.2446341602727773\n"
     ]
    }
   ],
   "source": [
    "print('Decision Tree Model Accuracy Score:',accuracy_score(y_test, dt_pred))\n",
    "print('Decision Tree Model F1 score: ',metrics.f1_score(y_test, dt_pred))\n",
    "print('Decision Tree Model Mean Absolute Error: ',metrics.mean_absolute_error(y_test, dt_pred))\n",
    "print('Decision Tree Model Mean Squared Error: ',metrics.mean_squared_error(y_test, dt_pred))\n",
    "print('Decision Tree Model log loss: ',log_loss(y_test, dt_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(metrics.confusion_matrix(y_test, knn_pred), annot=True, fmt='d')\n",
    "plt.title('Accuracy Score: {}'.format(accuracy_score(y_test, knn_pred)))\n",
    "plt.ylabel('Predicted')\n",
    "plt.xlabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Model Accuracy Score: 0.9373776908023483\n",
      "KNN Model F1 score:  0.0\n",
      "KNN Model Mean Absolute Error:  0.06262230919765166\n",
      "KNN Model Mean Squared Error:  0.06262230919765166\n",
      "KNN Model log loss:  2.2571368071462796\n"
     ]
    }
   ],
   "source": [
    "print('KNN Model Accuracy Score:',accuracy_score(y_test, knn_pred))\n",
    "print('KNN Model F1 score: ',metrics.f1_score(y_test, knn_pred))\n",
    "print('KNN Model Mean Absolute Error: ',metrics.mean_absolute_error(y_test, knn_pred))\n",
    "print('KNN Model Mean Squared Error: ',metrics.mean_squared_error(y_test, knn_pred))\n",
    "print('KNN Model log loss: ',log_loss(y_test, knn_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = ['Logistic Regression', 'SVM', 'Decision Tree', 'KNN']\n",
    "accuracy = [accuracy_score(y_test, lr_pred), accuracy_score(y_test, sv_pred), accuracy_score(y_test, dt_pred), accuracy_score(y_test, knn_pred)]\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(models, accuracy, color = 'Maroon', width = 0.4)\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Model Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model accuracies of Logistic Regression, SVM and KNN  are quite similar i.e. 93.8 %. The accuracy of Decision Tree Classifier is 91.8 %. So, we can use any of these models to predict the heart stroke.\n",
    "\n",
    "According to the graphs age v/s hypertension, heart disease showing chances of stroke, the number of person having a stroke shows dependece upon heart disease and hypertension. But when we plot the graph of heart disease and hypertension against the stroke, the persons with lower chances of hypertension and heart disease has increased chances of stroke. This is a peculiar thing and needs to be investigated further. In addition to that non somkers have higher chances of stroke than smokers. This is also a peculiar thing and needs to be investigated further. However person having BMI between20 to 50 have higher chances of stroke.\n",
    "\n",
    "Last but not least other features such as martial status, residence type as well as work type are showing effect on the chances of stroke."
   ]
  }
 ],
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