{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "425b7bcd-8fa1-44fa-b747-a2002d765fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Load your dataset into a DataFrame\n",
    "df = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
    "# Preview the first 5 rows\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3539a03a-21bc-448b-9609-5d29c50aa609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     int64\n",
       "gender                object\n",
       "age                  float64\n",
       "hypertension           int64\n",
       "heart_disease          int64\n",
       "ever_married          object\n",
       "work_type             object\n",
       "Residence_type        object\n",
       "avg_glucose_level    float64\n",
       "bmi                  float64\n",
       "smoking_status        object\n",
       "stroke                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1a6597fb-ad7c-409f-ba07-331a2f952a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>4909.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36517.829354</td>\n",
       "      <td>43.226614</td>\n",
       "      <td>0.097456</td>\n",
       "      <td>0.054012</td>\n",
       "      <td>106.147677</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0.048728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21161.721625</td>\n",
       "      <td>22.612647</td>\n",
       "      <td>0.296607</td>\n",
       "      <td>0.226063</td>\n",
       "      <td>45.283560</td>\n",
       "      <td>7.854067</td>\n",
       "      <td>0.215320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17741.250000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.245000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36932.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.885000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54682.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.090000</td>\n",
       "      <td>33.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72940.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>97.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          age  hypertension  heart_disease  \\\n",
       "count   5110.000000  5110.000000   5110.000000    5110.000000   \n",
       "mean   36517.829354    43.226614      0.097456       0.054012   \n",
       "std    21161.721625    22.612647      0.296607       0.226063   \n",
       "min       67.000000     0.080000      0.000000       0.000000   \n",
       "25%    17741.250000    25.000000      0.000000       0.000000   \n",
       "50%    36932.000000    45.000000      0.000000       0.000000   \n",
       "75%    54682.000000    61.000000      0.000000       0.000000   \n",
       "max    72940.000000    82.000000      1.000000       1.000000   \n",
       "\n",
       "       avg_glucose_level          bmi       stroke  \n",
       "count        5110.000000  4909.000000  5110.000000  \n",
       "mean          106.147677    28.893237     0.048728  \n",
       "std            45.283560     7.854067     0.215320  \n",
       "min            55.120000    10.300000     0.000000  \n",
       "25%            77.245000    23.500000     0.000000  \n",
       "50%            91.885000    28.100000     0.000000  \n",
       "75%           114.090000    33.100000     0.000000  \n",
       "max           271.740000    97.600000     1.000000  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5ad66686-4d10-4540-bac0-e6644bae4507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "ever_married           0\n",
       "work_type              0\n",
       "Residence_type         0\n",
       "avg_glucose_level      0\n",
       "bmi                  201\n",
       "smoking_status         0\n",
       "stroke                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "891a3d3b-8001-4600-821a-0fd61c919090",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns dropped: []\n",
      "Remaining columns: Index(['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
      "       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
      "       'smoking_status', 'stroke'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "missing_percentage = df.isnull().mean() * 100\n",
    "threshold = 30\n",
    "# Filter columns with more than the threshold percentage of missing values\n",
    "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n",
    "# Drop the columns\n",
    "df = df.drop(columns=columns_to_drop)\n",
    "# Display the resulting dataframe after removing columns with too many missing valu\n",
    "print(f\"Columns dropped: {list(columns_to_drop)}\")\n",
    "print(f\"Remaining columns: {df.columns}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2991e23e-f990-4e0a-9494-d7531263d2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "0   9046       1  67.0             0              1             1          2   \n",
       "1  51676       0  61.0             0              0             1          3   \n",
       "2  31112       1  80.0             0              1             1          2   \n",
       "3  60182       0  49.0             0              0             1          2   \n",
       "4   1665       0  79.0             1              0             1          3   \n",
       "\n",
       "   Residence_type  avg_glucose_level   bmi  smoking_status  stroke  \n",
       "0               1             228.69  36.6               1       1  \n",
       "1               0             202.21   NaN               2       1  \n",
       "2               0             105.92  32.5               2       1  \n",
       "3               1             171.23  34.4               3       1  \n",
       "4               0             174.12  24.0               2       1  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lable_encode =LabelEncoder()\n",
    "\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    "    df[column] = lable_encode.fit_transform(df[column])\n",
    "\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "833aa91f-0507-493e-9052-d1e6d821bb32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in columns with mode\n",
    "for column in df.select_dtypes(include=['float64']).columns:\n",
    "    mean_value = df[column].mean() # Get the mode (most frequent value)\n",
    "    df[column] = df[column].fillna(mean_value)\n",
    "\n",
    "# Fill missing values in categorical columns with mode\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    "    mode_value = df[column].mode()[0] # Get the mode (most frequent value)\n",
    "    df[column] = df[column].fillna(mode_value)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "78f873c7-e54b-434a-8f0f-9ffb7b3d36da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "gender               0\n",
       "age                  0\n",
       "hypertension         0\n",
       "heart_disease        0\n",
       "ever_married         0\n",
       "work_type            0\n",
       "Residence_type       0\n",
       "avg_glucose_level    0\n",
       "bmi                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "00a4d281-e7e5-4555-a802-16f42a8091b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_outliers_with_nan(column):\n",
    "    Q1 = column.quantile(0.25)\n",
    "    Q3 = column.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    # Replace outliers with NaN\n",
    "    return column.where((column >= lower_bound) & (column <= upper_bound), np.nan)\n",
    "    \n",
    "# Replace outliers with NaN for each feature\n",
    "for col in df.columns:\n",
    "    df[col] = replace_outliers_with_nan(df[col])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "df4c4c5c-26f7-4824-80aa-4964a97abb0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of null values in each column:\n",
      "id                     0\n",
      "gender                 0\n",
      "age                    0\n",
      "hypertension         498\n",
      "heart_disease        276\n",
      "ever_married           0\n",
      "work_type            657\n",
      "Residence_type         0\n",
      "avg_glucose_level    627\n",
      "bmi                  126\n",
      "smoking_status         0\n",
      "stroke               249\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "null_counts = df.isnull().sum()\n",
    "print(\"Sum of null values in each column:\")\n",
    "print(null_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0f633343-4b68-4257-9a38-23a78a2231d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     int64\n",
       "gender                 int32\n",
       "age                  float64\n",
       "hypertension         float64\n",
       "heart_disease        float64\n",
       "ever_married           int32\n",
       "work_type            float64\n",
       "Residence_type         int32\n",
       "avg_glucose_level    float64\n",
       "bmi                  float64\n",
       "smoking_status         int32\n",
       "stroke               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "54c8b5ec-6b07-4e14-8239-da69a9719fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in columns with mean\n",
    "for column in df.select_dtypes(include=['float64']).columns:\n",
    "    mean_value = df[column].mean() # Get the mode (most frequent value)\n",
    "    df[column] = df[column].fillna(mean_value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c443405a-b4ed-47a4-bcef-a3a3fcc7f7d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of null values in each column:\n",
      "id                   0\n",
      "gender               0\n",
      "age                  0\n",
      "hypertension         0\n",
      "heart_disease        0\n",
      "ever_married         0\n",
      "work_type            0\n",
      "Residence_type       0\n",
      "avg_glucose_level    0\n",
      "bmi                  0\n",
      "smoking_status       0\n",
      "stroke               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "null_counts = df.isnull().sum()\n",
    "print(\"Sum of null values in each column:\")\n",
    "print(null_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "87064a19-1634-4d6c-88be-8279dd5c0413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(df['age'], bins=15, kde=True, color='blue')\n",
    "plt.title('Age Distribution')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "89ef68ce-3ddc-499a-ab79-54570c05b3f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hm\\AppData\\Local\\Temp\\ipykernel_20916\\4220900263.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(x='work_type', data=df, palette='Set2')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Countplot for stroke\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='work_type', data=df, palette='Set2')\n",
    "plt.title('Work type Count')\n",
    "plt.xlabel('Work type')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7d401297-27ff-4250-bb1b-eaf1148c0dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x='age', y='smoking_status', data=df, hue='work_type', palette='Set1')\n",
    "plt.title('Age vs smoking')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Smoking')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "76e9164b-7592-4ee8-afd7-feadba27a246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for Age vs Blood Pressure\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x='age', y='gender', data=df, hue='stroke', palette='Set2')\n",
    "plt.title('Age vs gender')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "29b95abc-d1b1-4a6a-b1cf-6b29c6597aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hm\\AppData\\Local\\Temp\\ipykernel_20916\\133182456.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x='stroke', y='heart_disease', data=df, palette='Set1')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot for Hemoglobin by Classification\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(x='stroke', y='heart_disease', data=df, palette='Set1')\n",
    "plt.title('heart_disease Levels by stroke')\n",
    "plt.xlabel('stroke')\n",
    "plt.ylabel('heart_disease')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "386e97fc-9548-4e4f-bf38-7d53e6a3f32a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
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