mlProjectExploratoryAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d51b3ffb-c42e-4be4-ba08-d03927a27186",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import norm, poisson\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fa5da61-792f-42c1-9840-28c7d088628e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"train_strokes.csv\")\n",
    "df = df.drop(['id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0bedb758-e430-4f4e-a48a-350303712365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43400"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fbbd98c2-4b1f-4511-9f5e-1cc4b42a930b",
   "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>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>95.12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>87.96</td>\n",
       "      <td>39.2</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>110.89</td>\n",
       "      <td>17.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Never_worked</td>\n",
       "      <td>Rural</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>NaN</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",
       "0    Male   3.0             0              0           No      children   \n",
       "1    Male  58.0             1              0          Yes       Private   \n",
       "2  Female   8.0             0              0           No       Private   \n",
       "3  Female  70.0             0              0          Yes       Private   \n",
       "4    Male  14.0             0              0           No  Never_worked   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Rural              95.12  18.0              NaN       0  \n",
       "1          Urban              87.96  39.2     never smoked       0  \n",
       "2          Urban             110.89  17.6              NaN       0  \n",
       "3          Rural              69.04  35.9  formerly smoked       0  \n",
       "4          Rural             161.28  19.1              NaN       0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "788cce6f-e481-4283-b27e-094def0161df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 43400 entries, 0 to 43399\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             43400 non-null  object \n",
      " 1   age                43400 non-null  float64\n",
      " 2   hypertension       43400 non-null  int64  \n",
      " 3   heart_disease      43400 non-null  int64  \n",
      " 4   ever_married       43400 non-null  object \n",
      " 5   work_type          43400 non-null  object \n",
      " 6   Residence_type     43400 non-null  object \n",
      " 7   avg_glucose_level  43400 non-null  float64\n",
      " 8   bmi                41938 non-null  float64\n",
      " 9   smoking_status     30108 non-null  object \n",
      " 10  stroke             43400 non-null  int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 3.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e149d61f-86f9-4b02-b47a-d11eeb0fa932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.981959\n",
       "1    0.018041\n",
       "Name: stroke, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stroke'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d2910e0-7dfe-4408-8e16-8fcf6758de22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    42617\n",
       "1      783\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stroke'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "65301920-7991-4533-8f36-268f07a1f23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'stroke'}>]], dtype=object)"
      ]
     },
     "execution_count": 18,
     "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": [
    "df.hist(column = 'stroke')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5cd0d49-9dc4-48df-b965-f94de7f54040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'bmi'}>]], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pw2CQJHUYDJKkDoNBktRhMEiSOgwGSVKHwSBJ6jAYJEkdBoMkqcNgkCR1GAySpA6DQZLUYTBIkjoMBklSh8EgSeoYKhiSvCnJA0n+JMnTSX44yQVJHk7ypfZ4ft/6tyQ5nOSZJFf11S9PcrAtu73d4lOSNALDHjF8BPhUVX0P8P307vm8G3ikqjYDj7R5klwCbAcuBa4G7kiypu3nTmAnvftAb27LJUkjsOhgSHIe8OPAXQBV9bdV9VfANmBfW20fcE2b3gbcX1UvV9WzwGHgiiQXA+dV1aNVVcC9fdtIkpbZMPd8fgvwFeC3knw/8DngfcBYVR0FqKqjSS5q668DHuvbfqrVXmnTM+uzJNlJ78iCsbExJicnhxj+aOzaMt2ZH1s7u3a2WejrdPz48TPytT3d7Mts9mSwYfsyTDCcA/wg8AtV9XiSj9BOG53EoOsGNUd9drFqL7AXYHx8vCYmJhY04JXght2f7Mzv2jLNbQeHeRlWviPXTSxo/cnJSc7E1/Z0sy+z2ZPBhu3LMNcYpoCpqnq8zT9ALyhebKeHaI/H+tbf0Lf9euCFVl8/oC5JGoFFB0NV/QXwfJK3t9KVwFPAAWBHq+0AHmzTB4DtSc5NsoneReYn2mmnl5Jsbe9Gur5vG0nSMhv2HMYvAB9L8hrgy8DP0Aub/UluBJ4DrgWoqkNJ9tMLj2ng5qp6te3nJuAeYC3wUPuRJI3AUMFQVX8EjA9YdOVJ1t8D7BlQfxK4bJixSJKWhp98liR1GAySpA6DQZLUYTBIkjoMBklSh8EgSeowGCRJHQaDJKnDYJAkdZzdX+upFWHjjG+Unc+uLdOzvoV2MY7c+o6h9yGtRh4xSJI6DAZJUofBIEnqMBgkSR0GgySpY+hgSLImyReS/H6bvyDJw0m+1B7P71v3liSHkzyT5Kq++uVJDrZlt7c7uUmSRmApjhjeBzzdN78beKSqNgOPtHmSXAJsBy4FrgbuSLKmbXMnsJPe7T43t+WSpBEYKhiSrAfeAXy0r7wN2Nem9wHX9NXvr6qXq+pZ4DBwRZKLgfOq6tGqKuDevm0kScts2COG3wTeD/xdX22sqo4CtMeLWn0d8HzfelOttq5Nz6xLkkZg0Z98TvJO4FhVfS7JxKlsMqBWc9QH/c6d9E45MTY2xuTk5CmNdSXZtWW6Mz+2dnZttVuqnpyJfz7mcvz48bPuOQ3Lngw2bF+G+UqMHwXeleSngNcC5yX5HeDFJBdX1dF2muhYW38K2NC3/XrghVZfP6A+S1XtBfYCjI+P18TExBDDH42ZX/Wwa8s0tx30m0n6LVVPjlw3MfxgVpDJyUnOxD/zp5M9GWzYviz6VFJV3VJV66tqI72Lyp+pqvcCB4AdbbUdwINt+gCwPcm5STbRu8j8RDvd9FKSre3dSNf3bSNJWman47+qtwL7k9wIPAdcC1BVh5LsB54CpoGbq+rVts1NwD3AWuCh9iNJGoElCYaqmgQm2/RfAleeZL09wJ4B9SeBy5ZiLKdiod/2KUmriZ98liR1GAySpA6DQZLUYTBIkjoMBklSh8EgSeowGCRJHQaDJKnDYJAkdRgMkqQOg0GS1GEwSJI6DAZJUofBIEnqMBgkSR0GgySpY9HBkGRDkj9I8nSSQ0ne1+oXJHk4yZfa4/l929yS5HCSZ5Jc1Ve/PMnBtuz2dotPSdIIDHPEMA3sqqrvBbYCNye5BNgNPFJVm4FH2jxt2XbgUuBq4I4ka9q+7gR20rsP9Oa2XJI0AosOhqo6WlWfb9MvAU8D64BtwL622j7gmja9Dbi/ql6uqmeBw8AVSS4GzquqR6uqgHv7tpEkLbMluedzko3ADwCPA2NVdRR64ZHkorbaOuCxvs2mWu2VNj2zPuj37KR3ZMHY2BiTk5OLGu+uLdOL2u50GFu7ssazEixVTxb752OlOn78+Fn3nIZlTwYbti9DB0OSNwC/C/xSVf31HJcHBi2oOeqzi1V7gb0A4+PjNTExseDxAtyw+5OL2u502LVlmtsOLkk+nzWWqidHrpsYfjAryOTkJIv9M3+2sieDDduXod6VlOTb6IXCx6rqE638Yjs9RHs81upTwIa+zdcDL7T6+gF1SdIIDPOupAB3AU9X1W/0LToA7GjTO4AH++rbk5ybZBO9i8xPtNNOLyXZ2vZ5fd82kqRlNszx+o8CPw0cTPJHrfYB4FZgf5IbgeeAawGq6lCS/cBT9N7RdHNVvdq2uwm4B1gLPNR+JEkjsOhgqKr/xeDrAwBXnmSbPcCeAfUngcsWOxZJ0tLxk8+SpA6DQZLUYTBIkjoMBklSh8EgSeowGCRJHX4Xg85aG0f01SdHbn3HSH6vtFQ8YpAkdRgMkqQOg0GS1GEwSJI6DAZJUofBIEnqMBgkSR0GgySpw2CQJHWsmE8+J7ka+AiwBvhoVd064iFJi3K6PnG9a8s0N8yzbz91raWwIo4YkqwB/jPwz4BLgPckuWS0o5Kk1WmlHDFcARyuqi8DJLkf2Ebv/tCSTpHfD6WlkKoa9RhI8m7g6qr6uTb/08A/rKqfn7HeTmBnm3078MyyDvT0uBD46qgHscLYk8Hsy2z2ZLC5+vL3q+rb59p4pRwxZEBtVmJV1V5g7+kfzvJJ8mRVjY96HCuJPRnMvsxmTwYbti8r4hoDMAVs6JtfD7wworFI0qq2UoLhD4HNSTYleQ2wHTgw4jFJ0qq0Ik4lVdV0kp8H/ge9t6veXVWHRjys5XJWnRpbIvZkMPsymz0ZbKi+rIiLz5KklWOlnEqSJK0QBoMkqcNgWCZJNiT5gyRPJzmU5H2tfkGSh5N8qT2eP+qxLrcka5J8Icnvt3l7krwpyQNJ/qT9mflh+wJJfrn9/flikvuSvHY19iXJ3UmOJfliX+2kfUhyS5LDSZ5JctV8+zcYls80sKuqvhfYCtzcvvZjN/BIVW0GHmnzq837gKf75u1J73vDPlVV3wN8P73+rOq+JFkH/CIwXlWX0XujynZWZ1/uAa6eURvYh/bvzHbg0rbNHe1riE7KYFgmVXW0qj7fpl+i9xd9Hb2v/tjXVtsHXDOSAY5IkvXAO4CP9pVXe0/OA34cuAugqv62qv6KVd6X5hxgbZJzgNfR+7zTqutLVX0W+NqM8sn6sA24v6perqpngcP0vobopAyGEUiyEfgB4HFgrKqOQi88gItGOLRR+E3g/cDf9dVWe0/eAnwF+K12iu2jSV7PKu9LVf058OvAc8BR4BtV9WlWeV/6nKwP64Dn+9abarWTMhiWWZI3AL8L/FJV/fWoxzNKSd4JHKuqz416LCvMOcAPAndW1Q8A32R1nB6ZUztnvg3YBHwn8Pok7x3tqM4Ip/SVQ/0MhmWU5NvohcLHquoTrfxikovb8ouBY6Ma3wj8KPCuJEeA+4GfSPI7rO6eQO9/dFNV9Xibf4BeUKz2vvwk8GxVfaWqXgE+AfwI9uWEk/VhwV85ZDAskyShd8746ar6jb5FB4AdbXoH8OByj21UquqWqlpfVRvpXRz7TFW9l1XcE4Cq+gvg+SRvb6Ur6X0F/aruC71TSFuTvK79fbqS3rW61d6XE07WhwPA9iTnJtkEbAaemGtHfvJ5mST5R8D/BA7y/8+nf4DedYb9wHfR+4N/bVXNvKh01ksyAfxKVb0zyZtZ5T1J8g/oXZB/DfBl4Gfo/Udutffl3wL/nN67/L4A/BzwBlZZX5LcB0zQ+3rtF4EPAr/HSfqQ5FeBn6XXt1+qqofm3L/BIEnq56kkSVKHwSBJ6jAYJEkdBoMkqcNgkCR1GAySpA6DQZLU8f8AjEREHkIuGZ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column = \"bmi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78d5d83d-6bc1-412a-af91-76c0715c6ef5",
   "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>2914</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",
       "    <tr>\n",
       "      <th>10768</th>\n",
       "      <td>Other</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>83.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15620</th>\n",
       "      <td>Other</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>62.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>smokes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15838</th>\n",
       "      <td>Other</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>89.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19748</th>\n",
       "      <td>Other</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>95.59</td>\n",
       "      <td>32.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21041</th>\n",
       "      <td>Other</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>75.76</td>\n",
       "      <td>32.2</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21192</th>\n",
       "      <td>Other</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>94.88</td>\n",
       "      <td>37.2</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24252</th>\n",
       "      <td>Other</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>91.75</td>\n",
       "      <td>32.2</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26199</th>\n",
       "      <td>Other</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>72.05</td>\n",
       "      <td>21.2</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26831</th>\n",
       "      <td>Other</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>65.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40768</th>\n",
       "      <td>Other</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Urban</td>\n",
       "      <td>101.30</td>\n",
       "      <td>18.1</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",
       "2914   Other  26.0             0              0           No   Private   \n",
       "10768  Other  46.0             0              0           No   Private   \n",
       "15620  Other  27.0             0              0           No   Private   \n",
       "15838  Other  53.0             0              0          Yes   Private   \n",
       "19748  Other  39.0             0              0          Yes  Govt_job   \n",
       "21041  Other  19.0             0              0           No   Private   \n",
       "21192  Other  49.0             0              0          Yes   Private   \n",
       "24252  Other  51.0             0              0          Yes   Private   \n",
       "26199  Other  10.0             0              0           No  children   \n",
       "26831  Other  53.0             0              0          Yes  Govt_job   \n",
       "40768  Other  11.0             0              0           No  children   \n",
       "\n",
       "      Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "2914           Rural             143.33  22.4  formerly smoked       0  \n",
       "10768          Rural              83.28   NaN     never smoked       0  \n",
       "15620          Urban              62.45   NaN           smokes       0  \n",
       "15838          Rural              89.65   NaN              NaN       0  \n",
       "19748          Urban              95.59  32.0  formerly smoked       0  \n",
       "21041          Rural              75.76  32.2  formerly smoked       0  \n",
       "21192          Urban              94.88  37.2     never smoked       0  \n",
       "24252          Rural              91.75  32.2  formerly smoked       0  \n",
       "26199          Rural              72.05  21.2  formerly smoked       0  \n",
       "26831          Urban              65.29   NaN              NaN       0  \n",
       "40768          Urban             101.30  18.1  formerly smoked       0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['gender'] == 'Other']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2fd37a68-97ac-4ec8-a456-52798461050a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(df[df['gender'] == 'Other'].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9c8d45ff-2be2-48d7-bf37-bad04348931e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hypertension', ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4,figsize=(25,7))\n",
    "\n",
    "fig.suptitle(\"Counting cols\", fontsize=35)\n",
    "\n",
    "sns.countplot(x=\"gender\", data=df,ax=ax1)\n",
    "sns.countplot(x=\"stroke\", data=df,ax=ax2)\n",
    "sns.countplot(x=\"ever_married\", data=df,ax=ax3)\n",
    "sns.countplot(x=\"hypertension\", data=df,ax=ax4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78750370-de6d-42dc-bd6b-4c50fc9984e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Residence_type', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JByijfl+Sma/JzBsGafvjwKsoo1AHciclsPvKOm3Euvp7IeWGhTcNUOR3wFsz85h1tTUaMnMBZa7qOZTR6vey9mjtMSkzbwVeALyLEpi/izJq+/fAxZTR9vtm5m+B5s0dR2xaksx8ODMPA97Mum/geCODBL3riPgXAh+njPYeyM+AF2XmZ4bZ3XbeSwmm/3Ed5ZYB/wbsnZl3jMB2JUmStJ6cikSSJKlHRcQEysjcXShTjmwJrKAELH8F3JiZa9aj3adTpjJ5KmX073LgOuCqXI8vj/XGfC8A/oIyfcTdlGD3z9anf1p/EfEd4HX16Xsy84uDld+A7ewMvIgy+n9zyhQfvwN+mZm/G0Y7E4C/BHanzAG/ElgKXJaZSzrQ74mUaVCeSRl1/uS6zbspfwPXrO8NKiVJkjSyDGxLkiRJG4GImEqZZ3vTmvXczLyui12SJEmS1ptTkUiSJEnjXEQ8gTItTSuofbVBbUmSJPUyA9uSJElSj4qIiRGxMCLeGxE7tVm/SUS8kjIf9asaq/551DopSZIkdYBTkUiSJEk9qs4J/Wgj607gVspNELcAng1s1a/aiZn5vtHpoSRJktQZBrYlSZKkHlVvrvgoEEMo/hAwJzPndLZXkiRJUucZ2JYkSZJ6WETsDLwReCnwZ8BkYNu6+h7gJuDHwFcy886udFKSJEkaYQa2JUmSJEmSJEk9xZtHSpIkSZIkSZJ6ioFtSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZIkSZIkST3FwLYkSZIkSZIkqacY2JYkSZIkSZIk9RQD25IkSZIkSZKknmJgW5IkSZIkSZLUUwxsS5IkSZIkSZJ6ioFtSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZIkSZIkST3FwLYkSZIkSZIkqacY2JYkSZIkSZIk9RQD25IkSZIkSZKknjKx2x0Ybdttt11Omzat292QpGH5xS9+cXdmTu52P0aT52tJvcjztST1Bs/XktQbBjtfb3SB7WnTprFgwYJud0OShiUiftftPow2z9eSepHna0nqDZ6vJak3DHa+dioSSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZIkSZIkST3FwLYkSZIkSZIkqacY2JYkSZIkSZIk9RQD25IkSZIkSZKknmJgW5IkSZIkSZLUUwxsS5IkSZIkSZJ6ioFtSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZKkHhARO0XEJRFxY0QsjIgP1PxjI+L3EXFNXQ5s1DkmIhZFxM0RcUAjf++IuK6uOyEiouZPioiza/4VETFt1HdUkiRJGgID25IkSVJvWAV8ODOfA+wLHBURu9d1x2fmXnX5HkBdNxPYA5gBnBQRE2r5k4HZwG51mVHzjwDuy8xdgeOB40ZhvyRJkqRhM7AtSZIk9YDMXJqZV9f0CuBGYMogVQ4GzsrMlZl5K7AI2CcidgS2zMzLMzOBM4BDGnXm1vQ5wP6t0dySJEnSWGJgW5IkSeoxdYqQ5wNX1Kz3RsS1EXFaRGxd86YAdzSqLa55U2q6f/5adTJzFfAAsG0n9kGSJEnaEAa2JUmSpB4SEZsD5wIfzMw/UKYVeQawF7AU+EyraJvqOUj+YHX692F2RCyIiAXLly8f3g5IkiRJI8DAtiRJktQjImJTSlD7a5l5HkBm3pWZqzNzDfAlYJ9afDGwU6P6VGBJzZ/aJn+tOhExEdgKuLd/PzLzlMycnpnTJ0+ePFK7J0mSJA2ZgW1JkiSpB9S5rk8FbszMzzbyd2wUeyNwfU3PB2ZGxKSI2IVyk8grM3MpsCIi9q1tHg5c0Kgzq6YPBX5c5+GWJEmSxpSJ3e5AL9n7H8/odhc0Cn7x6cO73QVJkkbEZ8+/87H0h9741C72RCPkxcDbgesi4pqa9zHgrRGxF2XKkNuAdwNk5sKImAfcAKwCjsrM1bXekcDpwGbAhXWBEjg/MyIWUUZqz+zoHo0zt//Ln3e7Cxtk509c1+0uSJI2Qr3++al169R3DAPbkiRJUg/IzJ/Rfg7s7w1SZw4wp03+AmDPNvkPA4dtQDclSeOQA/3GPwf5qRc5FYkkSZIkSZIkqacY2JYkSZIkSZIk9RQD25IkSZIkSZKknmJgW5IkSZIkSZLUUwxsS5IkSZIkSZJ6SscD2xExISJ+GRHfqc+3iYiLIuKW+rh1o+wxEbEoIm6OiAMa+XtHxHV13QkRETV/UkScXfOviIhpnd4fSZKkXnTyeXdx8nl3dbsbkiRJkjQiRmPE9geAGxvPjwYuzszdgIvrcyJid2AmsAcwAzgpIibUOicDs4Hd6jKj5h8B3JeZuwLHA8d1dlckSZIkSZIkSd3W0cB2REwFXgd8uZF9MDC3pucChzTyz8rMlZl5K7AI2CcidgS2zMzLMzOBM/rVabV1DrB/azS3JEmSJEmSJGl86vSI7f8GPgqsaeTtkJlLAerj9jV/CnBHo9zimjelpvvnr1UnM1cBDwDbjugeSJIkSZIkSZLGlI4FtiPiIGBZZv5iqFXa5OUg+YPV6d+X2RGxICIWLF++fIjdkSRJkiRJkiSNRZ0csf1i4A0RcRtwFvDKiPgqcFedXoT6uKyWXwzs1Kg/FVhS86e2yV+rTkRMBLYC7u3fkcw8JTOnZ+b0yZMnj8zeSZIkSZIkSZK6omOB7cw8JjOnZuY0yk0hf5yZbwPmA7NqsVnABTU9H5gZEZMiYhfKTSKvrNOVrIiIfev82Yf3q9Nq69C6jceN2JYkSZIkSZIkjR+dnmO7nU8Br46IW4BX1+dk5kJgHnAD8H3gqMxcXescSbkB5SLgN8CFNf9UYNuIWAR8CDh6tHZCkjZmEfGsiLimsfwhIj4YEdtExEURcUt93LpR55iIWBQRN0fEAd3svyRJkiRJ6m0TR2MjmXkpcGlN3wPsP0C5OcCcNvkLgD3b5D8MHDaCXZUkDUFm3gzsBRARE4DfA+dTLjBenJmfioij6/N/iojdKb/e2QN4GvCjiHhm4wKmJEmSJEnSkHVjxLYkaXzZH/hNZv4OOBiYW/PnAofU9MHAWZm5MjNvpfwCZ5/R7qgkSZIkSRofDGxLkjbUTOAbNb1DvTcC9XH7mj8FuKNRZ3HNkyRJkiRJGjYD25Kk9RYRTwDeAHxzXUXb5D3uZr8RMTsiFkTEguXLl49EFyW18ZXzlvGV85Z1uxuSJEmStN4MbEuSNsRrgasz8676/K6I2BGgPrYiZ4uBnRr1pgJL+jeWmadk5vTMnD558uQOdluSBrfsxB+w7MQfdLsbkiRJkgZgYFuStCHeSt80JADzgVk1PQu4oJE/MyImRcQuwG7AlaPWS0mSJEmSNK5M7HYHJEm9KSKeBLwaeHcj+1PAvIg4ArgdOAwgMxdGxDzgBmAVcFRmrh7lLksbhX87f+lj6Se1nQVIkiRJknqfgW1J0nrJzD8B2/bLuwfYf4Dyc4A5o9A1SZIkSZI0zjkViSRJkgZ14dl3c+HZd3e7G5IkSZL0GAPbkiRJkiRJkqSeYmBbkiRJkiRphETEThFxSUTcGBELI+IDNX+biLgoIm6pj1s36hwTEYsi4uaIOKCRv3dEXFfXnRARUfMnRcTZNf+KiJg26jsqSV1mYFuSJKmHzDzvVmaed2u3uyFJkga2CvhwZj4H2Bc4KiJ2B44GLs7M3YCL63PqupnAHsAM4KSImFDbOhmYDexWlxk1/wjgvszcFTgeOG40dkySxhID25IkSZIkSSMkM5dm5tU1vQK4EZgCHAzMrcXmAofU9MHAWZm5MjNvBRYB+0TEjsCWmXl5ZiZwRr86rbbOAfZvjeaWpI2FgW1JkiRJkqQOqFOEPB+4AtghM5dCCX4D29diU4A7GtUW17wpNd0/f606mbkKeADYtiM7IUljlIFtSZIkSZKkERYRmwPnAh/MzD8MVrRNXg6SP1id/n2YHRELImLB8uXL19VlSeopBrYlSZIkSZJGUERsSglqfy0zz6vZd9XpRaiPy2r+YmCnRvWpwJKaP7VN/lp1ImIisBVwb/9+ZOYpmTk9M6dPnjx5JHZNksYMA9uSJEmSJEkjpM51fSpwY2Z+trFqPjCrpmcBFzTyZ0bEpIjYhXKTyCvrdCUrImLf2ubh/eq02joU+HGdh1uSNhoTu90BSZIkFYeeezUA57z5L7qy/XPPvRuAN795u65sX5KkceLFwNuB6yLimpr3MeBTwLyIOAK4HTgMIDMXRsQ84AZgFXBUZq6u9Y4ETgc2Ay6sC5TA+ZkRsYgyUntmh/dJksYcA9uSJEmSJEkjJDN/Rvs5sAH2H6DOHGBOm/wFwJ5t8h+mBsYlaWNlYFuSJGkMOuzc6wD45pv/fJ1ljz7/94+lN3emOUmSJEkbAf/zkSRJkiRJkiT1FAPbkiRJkiRJkqSeYmBbkiRJkiRJktRTDGxLkiRtxL5+7nK+fu7ybndDkiRJkobFwLYkSZIkSZIkqacY2JYkSdLjzP/m3cz/5t3d7oYkSZIktTWx2x2QJEnS8L3n/DseSz/FsQqSJEmSNjL+FyRJkiRJkiRJ6ikGtiVJksa4vzr3Zv7q3Ju73Q1JkiRJGjMMbEuSJEmSJEmSeoqBbUmSJEmSJElSTzGwLUmSJEmSJEnqKQa2JUmSJEmSJEk9xcC2JEmSJEmSJKmnGNiWJEmSJEmSJPUUA9uSJEmSJEmSpJ7SscB2RDwxIq6MiF9FxMKI+Oeaf2xE/D4irqnLgY06x0TEooi4OSIOaOTvHRHX1XUnRETU/EkRcXbNvyIipnVqfyRJkiRJkiRJY0MnR2yvBF6Zmc8D9gJmRMS+dd3xmblXXb4HEBG7AzOBPYAZwEkRMaGWPxmYDexWlxk1/wjgvszcFTgeOK6D+yNJkiRJkiRJGgM6FtjO4sH6dNO65CBVDgbOysyVmXkrsAjYJyJ2BLbMzMszM4EzgEMadebW9DnA/q3R3JIkSZIkSZKk8amjc2xHxISIuAZYBlyUmVfUVe+NiGsj4rSI2LrmTQHuaFRfXPOm1HT//LXqZOYq4AFg207siyRJkjZOy078LstO/G63uyFJkiSpoaOB7cxcnZl7AVMpo6/3pEwr8gzK9CRLgc/U4u1GWucg+YPVWUtEzI6IBRGxYPny5cPaB0mSJEmSJEnS2NLRwHZLZt4PXArMyMy7asB7DfAlYJ9abDGwU6PaVGBJzZ/aJn+tOhExEdgKuLfN9k/JzOmZOX3y5MkjtVuSJEkbnR99Yzk/+oYDBSRJkiR1V8cC2xExOSKeUtObAa8CbqpzZre8Ebi+pucDMyNiUkTsQrlJ5JWZuRRYERH71vmzDwcuaNSZVdOHAj+u83BLkiRJkiRJksapiR1se0dgbkRMoATQ52XmdyLizIjYizJlyG3AuwEyc2FEzANuAFYBR2Xm6trWkcDpwGbAhXUBOBU4MyIWUUZqz+zg/kiSJKnh0q+Wkdv7vc1fxEmSJEkaXR0LbGfmtcDz2+S/fZA6c4A5bfIXAHu2yX8YOGzDeipJkrRhDj7n+4+lLzh0Rhd70h3/e0bf1CQvPtwgtyRJkqTOG5U5tiVJkiRJkiRJGimdnIpEkiRJG5krv7LssfQ+79y+iz2RJEmSNJ45YluSJEmSJEmS1FMMbEuS1ktEPCUizomImyLixoh4YURsExEXRcQt9XHrRvljImJRRNwcEQd0s++SJEmSJKm3GdiWJK2v/wG+n5nPBp4H3AgcDVycmbsBF9fnRMTuwExgD2AGcFJETOhKryVJkiRJUs8zsC1JGraI2BJ4GXAqQGY+kpn3AwcDc2uxucAhNX0wcFZmrszMW4FFwD6j2WdJkiRJkjR+GNiWJK2PPwOWA1+JiF9GxJcj4snADpm5FKA+tu4cNwW4o1F/cc2TJA1RROwUEZfU6Z8WRsQHav6wp4GKiL0j4rq67oSIiJo/KSLOrvlXRMS0Ud9RSZIkaQgMbEuS1sdE4C+AkzPz+cAfqdOODCDa5OXjCkXMjogFEbFg+fLlI9NTSRo/VgEfzsznAPsCR9WpntZnGqiTgdnAbnWZUfOPAO7LzF2B44HjRmPHJEmSpOEysC1JWh+LgcWZeUV9fg4l0H1XROwIUB+XNcrv1Kg/FVjSv9HMPCUzp2fm9MmTJ3es85JGx9WnLnts0YbLzKWZeXVNr6Dc22AKw5wGqp6ft8zMyzMzgTP61Wm1dQ6wf2s0tyRJkjSWGNiWJA1bZt4J3BERz6pZ+wM3APOBWTVvFnBBTc8HZtafuO9CGR145Sh2WZLGlTpFyPOBKxj+NFBTarp//lp1MnMV8ACwbUd2QpIkSdoAE7vdAUlSz3of8LWIeALwW+CdlAum8yLiCOB24DCAzFwYEfMowe9VwFGZubo73Zak3hYRmwPnAh/MzD8MMqB6oGmgBpseashTR1GmMmHnnXdeV5clSZKkEWdgW5K0XjLzGmB6m1X7D1B+DjCnk32Shuqgc74GwHcO/ZtR2+Ybz70MgPPf/LJR26bGn4jYlBLU/lpmnlez74qIHTNz6RCngVpc0/3zm3UWR8REYCvg3v79yMxTgFMApk+f/rjAtyRJktRpTkUiSZKkjrvulGWPLVo/da7rU4EbM/OzjVXDmgaqTleyIiL2rW0e3q9Oq61DgR/XebglSZKkMcUR25IkSVJveDHwduC6iLim5n0M+BTDnwbqSOB0YDPgwrpACZyfGRGLKCO1Z3Z4nyRJkqT1YmBbkiSpi958brmP6rlv3qfLPdFYl5k/o/0c2DDMaaAycwGwZ5v8h6mBcUmSJGkscyoSSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZJG2ZvO/T/edO7/dbsbkiRJktSzDGxLkiRJkiRJknqKgW1JkiRJkiRJUk8xsC1JktQhh5x7CYece0m3uyFJkiRJ446BbUmSJEmSJElSTzGwLUmSJEmSJEnqKQa2JUmSJEmSJEk9xcC2JEmSJEmSJKmnTOx2ByRJkjrloHNPB+A7b37HiLf9hnO++1g6mDDi7UuSJEmSBuaIbUmSJEmSJElST3HEtiRJ0gg65JyL+p6EX7UkSZIkqRP8b0uSJPW81537pcfS333z33WxJ5IkSZKk0eBUJJIkSZIkSZKknmJgW5IkSaPqxpPu4saT7up2NyRJkiT1MAPbkiRJkiRJkqSeYmBbkiRJkiRJktRTDGxLkiRJkiRJknqKgW1JkiRJkiRJUk/pWGA7Ip4YEVdGxK8iYmFE/HPN3yYiLoqIW+rj1o06x0TEooi4OSIOaOTvHRHX1XUnRETU/EkRcXbNvyIipnVqfyRJkqRln5/Pss/P73Y3JEmSpI1eJ0dsrwRemZnPA/YCZkTEvsDRwMWZuRtwcX1OROwOzAT2AGYAJ0XEhNrWycBsYLe6zKj5RwD3ZeauwPHAcR3cH0mS1AMOOvc0Djr3tA1u5/XnnMvrzzl3BHokSZIkSRppHQtsZ/FgfbppXRI4GJhb8+cCh9T0wcBZmbkyM28FFgH7RMSOwJaZeXlmJnBGvzqtts4B9m+N5pYkSZIkSZIkjU8dnWM7IiZExDXAMuCizLwC2CEzlwLUx+1r8SnAHY3qi2velJrun79WncxcBTwAbNuRnZEkSVJH/e74O/nd8Xd2uxuSJEmSesDETjaemauBvSLiKcD5EbHnIMXbjbTOQfIHq7N2wxGzKVOZsPPOOw/WZUmSpAG94Zzm3MoTBiwnSZIkSeqsjo7YbsnM+4FLKXNj31WnF6E+LqvFFgM7NapNBZbU/Klt8teqExETga2Ae9ts/5TMnJ6Z0ydPnjwyOyVJ0hjyxTMP4ItnHrDugnqcg845i4POOavb3ZAkSZIkDUPHAtsRMbmO1CYiNgNeBdwEzAdm1WKzgAtqej4wMyImRcQulJtEXlmnK1kREfvW+bMP71en1dahwI/rPNySJEmSJEmSpHGqk1OR7AjMjYgJlAD6vMz8TkRcDsyLiCOA24HDADJzYUTMA24AVgFH1alMAI4ETgc2Ay6sC8CpwJkRsYgyUntmB/dHkiRJI2zR5+4CYNf37dDlnkiSJEnqJR0LbGfmtcDz2+TfA+w/QJ05wJw2+QuAx83PnZkPUwPjkiRJkiRJkqSNw6jMsS1JktRtB51zJgedc2a3uyFJkiRJGgEGtiVJUlcdeP6/c+D5/97tbkiSJEmSeoiBbUmSJEmSJElSTzGwLUmSJEmSJEnqKQa2JUmSJEmSRkhEnBYRyyLi+kbesRHx+4i4pi4HNtYdExGLIuLmiDigkb93RFxX150QEVHzJ0XE2TX/ioiYNqo7KEljhIFtSZIkSZKkkXM6MKNN/vGZuVddvgcQEbsDM4E9ap2TImJCLX8yMBvYrS6tNo8A7svMXYHjgeM6tSOSNJYZ2JYkSZIkSRohmXkZcO8Qix8MnJWZKzPzVmARsE9E7AhsmZmXZ2YCZwCHNOrMrelzgP1bo7klaWNiYFuSJEmSJKnz3hsR19apSraueVOAOxplFte8KTXdP3+tOpm5CngA2LaTHZekscjAtiRpvUTEbXXOv2siYkHN2yYiLoqIW+rj1o3ybecOlAbyuvM+y+vO+2y3uyFJkjQSTgaeAewFLAU+U/PbjbTOQfIHq/M4ETE7IhZExILly5cPq8OSNNZN7HYHJEk97RWZeXfj+dHAxZn5qYg4uj7/p35zBz4N+FFEPDMzV49+l9VrXnfeCY+lv/um93exJ5IkSesnM+9qpSPiS8B36tPFwE6NolOBJTV/apv8Zp3FETER2IoBpj7JzFOAUwCmT5/eNvgtSb3KEduSpJHUnO9vLmvPA/i4uQNHv3saKw48/1gOPP/YbnfjcQ46Zx4HnTOv292QJEnjTJ0zu+WNwPU1PR+YGRGTImIXyk0ir8zMpcCKiNi3zp99OHBBo86smj4U+HGdh1uSNiqO2JYkra8EfhgRCXyxjgbZoX4JJzOXRsT2tewU4OeNus05AqXHHHj+fwLwvTd+dJ1lX3fuFxrPJnSoR5IkScMTEd8A9gO2i4jFwCeB/SJiL8p36NuAdwNk5sKImAfcAKwCjmr8qvFI4HRgM+DCugCcCpwZEYsoI7VndnynJGkMMrAtSVpfL87MJTV4fVFE3DRI2SHNAxgRs4HZADvvvPPI9FIaAa8/53wAwgC6JElah8x8a5vsUwcpPweY0yZ/AbBnm/yHgcM2pI+SNB44FYkkab1k5pL6uAw4nzK1yF2tn1nWx2W1+EBzB/Zv85TMnJ6Z0ydPntzJ7muUHPitD3Pgtz7c7W5IkiRJksYZA9uSpGGLiCdHxBatNPAayjyBzfn+ZrH2PICPmztwdHstSZIkSZLGC6cikSStjx2A88t9bJgIfD0zvx8RVwHzIuII4HbqTyTXMXegJEmSJEnSsBjYliQNW2b+Fnhem/x7gP0HqNN27kBJkiRJkqThMrAtSZJ6xuvO+3zjmTdylCRJkqSNlXNsS5IkSZIkSZJ6ioFtSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZIkSZIkST3Fm0dKkjTGffX0Ax5Lv+0dP+hiTyRJkiRJGhscsS1JkiRJkiRJ6ikGtiVJ0oh67QV/z2sv+Ptud0OSJEmSNI4Z2JYkSZIkSZIk9RQD25IkaVQc+K2PceC3PtbtbkiSJEmSxgED25IkSZIkSZKknmJgW5IkSZIkSZLUUwxsS5IkaUxb+unbWfrp27vdDUmSJEljiIFtSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZI05iz9z6Us/c+l3e6GJEmSpDHKwLYkSZIkSZIkqacY2JYkSZIkSZIk9ZSOBbYjYqeIuCQiboyIhRHxgZp/bET8PiKuqcuBjTrHRMSiiLg5Ig5o5O8dEdfVdSdERNT8SRFxds2/IiKmdWp/JEmSpKZlnz+PZZ8/b9S2FxGnRcSyiLi+ked3a0mSJG2UOjliexXw4cx8DrAvcFRE7F7XHZ+Ze9XlewB13UxgD2AGcFJETKjlTwZmA7vVZUbNPwK4LzN3BY4Hjuvg/kiSpIYZ8w98bJE0Kk6n73twk9+tJUmStNHpWGA7M5dm5tU1vQK4EZgySJWDgbMyc2Vm3gosAvaJiB2BLTPz8sxM4AzgkEaduTV9DrB/a8SJJEmSNJ5k5mXAvUMs7ndrSZIkjWujMsd2/Rnj84EratZ7I+La+nPKrWveFOCORrXFNW9KTffPX6tOZq4CHgC27cQ+SJIkSWOU360lSZK00el4YDsiNgfOBT6YmX+g/PTxGcBewFLgM62ibarnIPmD1enfh9kRsSAiFixfvnx4OyBJkqQx6c7P3MSdn7mp293otlH/bg1+v5YkSVL3Texk4xGxKSWo/bXMPA8gM+9qrP8S8J36dDGwU6P6VGBJzZ/aJr9ZZ3FETAS2os3PMzPzFOAUgOnTp7f9ci5Jktbtbd9qTO+7yaj88EvSILrx3bpu1+/XkiRJ6qqO/Uda5+M7FbgxMz/byN+xUeyNQOuu7vOBmfVu7LtQbmRzZWYuBVZExL61zcOBCxp1ZtX0ocCP61yBkiRJGofu/K/fcOd//abb3Rgz/G4tSZKkjVUnR2y/GHg7cF1EXFPzPga8NSL2ovys8Tbg3QCZuTAi5gE3AKuAozJzda13JOUu8JsBF9YFSuD8zIhYRBlNMrOD+yNJkiR1TUR8A9gP2C4iFgOfBPbzu7UkSZI2Rh0LbGfmz2g/T9/3BqkzB5jTJn8BsGeb/IeBwzagm5IkSVJPyMy3tsk+dZDyfreWJEnSuNXRObYlSdLY8V/fOACAj7z1B13uiTT+LDvp7G53QZIkSdqoeNcnSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZIkSZIkST3FwLYkSZIkSZIkqad480hJkrTBXnvB2xvPtuhaPyRJkiRJGwdHbEuSJEmSJEmSeoqBbUmSJEmSJElST3EqEkmSNKgjz5vR98RL4pIkSZKkMcB/TyVJkiRJkiRJPcXAtiRJknrenZ+9njs/e323uyFJkiRplBjYliRJkiRJkiT1lCEFtiPi4qHkSZJ6i+d3SeoOz7+SNPZ5rpaksW3Qm0dGxBOBJwHbRcTWQNRVWwJP63DfJEkdMhLn94iYACwAfp+ZB0XENsDZwDTgNuCvMvO+WvYY4AhgNfD+zPzByO2NJPUOv19L0tjnuVqSesOggW3g3cAHKSfuX9B3Mv8D8PnOdUuS1GEjcX7/AHAj5Qs+wNHAxZn5qYg4uj7/p4jYHZgJ7FG396OIeGZmrh6JHZGkHuP3a0ka+zxXS1IPGDSwnZn/A/xPRLwvMz83Sn2SJHXYhp7fI2Iq8DpgDvChmn0wsF9NzwUuBf6p5p+VmSuBWyNiEbAPcPmG7IMk9SK/X0vS2Oe5WpJ6w7pGbAOQmZ+LiBdRfl4+sZF/Rof6JUkaBRtwfv9v4KPAFo28HTJzaa2/NCK2r/lTgJ83yi2ueVoPp899zWPpd8z6YRd7ImlD+P1aksY+z9WSNLYNKbAdEWcCzwCuocyPCpCAJ3NJ6mHrc36PiIOAZZn5i4jYbyibaZOXA7Q9G5gNsPPOOw+haUnqTX6/lqSxz3O1JI1tQwpsA9OB3TOzbSBCktSz1uf8/mLgDRFxIPBEYMuI+CpwV0TsWEdr7wgsq+UXAzs16k8FlrRrODNPAU4BmD59up85Xfbhc2aUxCbd7Yc0Tvn9WpLGPs/VkjSGDfVf1euBp3ayI5Kkrhj2+T0zj8nMqZk5jXJTyB9n5tuA+cCsWmwWcEFNzwdmRsSkiNgF2A24ciQ6L0k9zO/XkjT2ea6WpDFsqCO2twNuiIgrgZWtzMx8Q0d6JUkaLSN5fv8UMC8ijgBuBw6rbS2MiHnADcAq4KjMXD1wM5K0UfD7tSSNfZ6rJWkMG2pg+9hOdkKS1DXHbkjlzLwUuLSm7wH2H6DcHGDOhmxLksaZY7vdAUnSOh3b7Q5IkgY2pMB2Zv6k0x2RJI0+z++S1B2efyVp7PNcLUlj25AC2xGxgnLnX4AnAJsCf8zMLTvVMUlS53l+l6Tu8PwrSWOf52pJGtuGOmJ7i+bziDgE2KcTHZIkjR7P75LUHZ5/JWns81wtSWPbJutTKTO/BbxyZLsiSeo2z++Set1dx1/z2NJLPP9K0tjnuVqSxpahTkXypsbTTYDp9P0cR5LUozy/S1J3eP6VpLHPc7UkjW1DCmwDr2+kVwG3AQePeG8kSaPN87skdYfnX0ka+zxXS9IYNtQ5tt/Z6Y5Ikkaf53dJ6g7Pv5I09nmulqSxbUhzbEfE1Ig4PyKWRcRdEXFuREztdOckSZ3l+V2SusPzrySNfZ6rJWlsG+rNI78CzAeeBkwBvl3zJEm9zfO7JHWH519JGvs8V0vSGDbUwPbkzPxKZq6qy+nA5A72S5I0Ojy/S1J3eP6VpLHPc7UkjWFDDWzfHRFvi4gJdXkbcE8nOyZJGhWe3yWpOzz/StLY57laksawoQa23wX8FXAnsBQ4FPAmCpLU+zy/S1J3eP6VpLHPc7UkjWETh1juX4FZmXkfQERsA/wX5SQvSepdnt8lqTs8/0rS2Oe5WpLGsKGO2H5u60QOkJn3As8frEJE7BQRl0TEjRGxMCI+UPO3iYiLIuKW+rh1o84xEbEoIm6OiAMa+XtHxHV13QkRETV/UkScXfOviIhpw9h3SdJ6nN8lSSPC868kjX2eqyVpDBtqYHuTfgHobVj3aO9VwIcz8znAvsBREbE7cDRwcWbuBlxcn1PXzQT2AGYAJ0XEhNrWycBsYLe6zKj5RwD3ZeauwPHAcUPcH0lSsT7nd0nShvP8K0ljn+dqSRrDhnpC/gzwfxFxDpCUOabmDFYhM5dS5qAiM1dExI3AFOBgYL9abC5wKfBPNf+szFwJ3BoRi4B9IuI2YMvMvBwgIs4ADgEurHWOrW2dA5wYEZGZOcT9kqSN3bDP75KkEeH5V5LGPs/VkjSGDSmwnZlnRMQC4JVAAG/KzBuGupE6RcjzgSuAHWrQm8xcGhHb12JTgJ83qi2ueY/WdP/8Vp07alurIuIBYFvg7qH2TZI2Zht6fpekXnTXCT97LL3D+1/SlT54/pWksc9ztSSNbUP+CU09eQ/7BB4RmwPnAh/MzD/U6bHbFm232UHyB6vTvw+zKVOZsPPOO6+ry5K0UVnf87u678tnHND3ZICP1//5einzgb/+wSj0SNJweP6VpLHPc7UkjV1DnWN7vUTEppSg9tcy87yafVdE7FjX7wgsq/mLgZ0a1acCS2r+1Db5a9WJiInAVsC9/fuRmadk5vTMnD558uSR2DVJknraf5x1AP9x1gHrLihJkiRJ0hjUscB2lKHZpwI3ZuZnG6vmA7NqehZwQSN/ZkRMiohdKDeJvLJOW7IiIvatbR7er06rrUOBHzu/tiRJkgDu+u8Fjy2SJEmSxpdO3s33xcDbgesi4pqa9zHgU8C8iDgCuB04DCAzF0bEPMpPfFYBR2Xm6lrvSOB0YDPKTSMvrPmnAmfWG03eC8zs4P5IkiRJkiRJksaAjgW2M/NnDDjjJ/sPUGcObe4wnJkLgD3b5D9MDYxLkiRJkiRJkjYOHZ1jW5IkSZIkaWMSEadFxLKIuL6Rt01EXBQRt9THrRvrjomIRRFxc0Qc0MjfOyKuq+tOqNOzUqdwPbvmXxER00Z1ByVpjDCwLUmSJAHLPncxyz53cbe7IUnqfacDM/rlHQ1cnJm7ARfX50TE7pRpVfeodU6KiAm1zsnAbMo9yHZrtHkEcF9m7gocDxzXsT2RpDHMwLYkSZIkSdIIyczLKPcBazoYmFvTc4FDGvlnZebKzLwVWATsExE7Altm5uWZmcAZ/eq02joH2L81mluSNiYGtiVJkiRJkjprh8xcClAft6/5U4A7GuUW17wpNd0/f606mbkKeADYtt1GI2J2RCyIiAXLly8foV2RpLGhYzePlCRJksaKu/7n531PHNMmSRo72n0q5SD5g9V5fGbmKcApANOnT29bRpJ6lSO2JUmStNG664RLueuES7vdDUnS+HdXnV6E+ris5i8GdmqUmwosqflT2+SvVSciJgJb8fipTyRp3DOwLUmSJEmS1FnzgVk1PQu4oJE/MyImRcQulJtEXlmnK1kREfvW+bMP71en1dahwI/rPNyStFFxKhJJkiRJkqQREhHfAPYDtouIxcAngU8B8yLiCOB24DCAzFwYEfOAG4BVwFGZubo2dSRwOrAZcGFdAE4FzoyIRZSR2jNHYbckacwxsC1Jkh7z8W/OAGDOYd/vck8kSZJ6U2a+dYBV+w9Qfg4wp03+AmDPNvkPUwPjkrQxcyoSSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZIkSZIkST3FwLYkSZIkSZIkqacY2JYkSZIkSZIk9RQD25IkSZIkSZKknmJgW5IkSZIkSZLUUwxsS5IkSZIkSZJ6ioFtSZI2cv887wD+ed4B3e6GJEmSJElDZmBbkiRJkiRJktRTDGxLkoYtIp4YEVdGxK8iYmFE/HPN3yYiLoqIW+rj1o06x0TEooi4OSIcHixJkiRJktabgW1J0vpYCbwyM58H7AXMiIh9gaOBizNzN+Di+pyI2B2YCewBzABOiogJ3ei4JPWqiDgtIpZFxPWNvGFfUIyIvSPiurruhIiImj8pIs6u+VdExLRR3UFJkiRpGAxsS5KGLYsH69NN65LAwcDcmj8XOKSmDwbOysyVmXkrsAjYZ/R6LEnjwumUi4NN63NB8WRgNrBbXVptHgHcl5m7AscDx3VsTyRJkqQNZGBbkrReImJCRFwDLAMuyswrgB0ycylAfdy+Fp8C3NGovrjm9W9zdkQsiIgFy5cv72j/JanXZOZlwL39sod1QTEidgS2zMzLMzOBM/rVabV1DrB/azS3JEmSNNYY2JYkrZfMXJ2ZewFTKcGSPQcp3i4wkm3aPCUzp2fm9MmTJ49QTyVpXBvuBcUpNd0/f606mbkKeADYtmM9lyRJkjaAgW1J0gbJzPuBSyk/Zb+rjgakPi6rxRYDOzWqTQWWjF4vJWmjM9AFxcEuNA7pIiT4CxtJkiR1n4FtSdKwRcTkiHhKTW8GvAq4CZgPzKrFZgEX1PR8YGa9MdkulDldrxzVTkvS+DTcC4qLa7p//lp1ImIisBWPn/oE8Bc2kiRJ6j4D25Kk9bEjcElEXAtcRZlj+zvAp4BXR8QtwKvrczJzITAPuAH4PnBUZq7uSs8laXwZ1gXFOl3JiojYt86ffXi/Oq22DgV+XOfhliRJksacid3ugCSp92TmtcDz2+TfA+w/QJ05wJwOd02Sxq2I+AawH7BdRCwGPkm5gDgvIo4AbgcOg3JBMSJaFxRXsfYFxSOB04HNgAvrAnAqcGZELKKM1J45CrslSZIkrRcD25IkSVIPyMy3DrBqWBcUM3MB8Lgb/mbmw9TAuCRJkjTWORWJJEmSJEmSJKmnGNiWJEmSJEmSJPUUA9uSJEmSJEmSpJ5iYFuSJEmSJEmS1FMMbEuSJEmSJEmSeoqBbUmSJEmSJElST+lYYDsiTouIZRFxfSPv2Ij4fURcU5cDG+uOiYhFEXFzRBzQyN87Iq6r606IiKj5kyLi7Jp/RURM69S+SJIkSZIkSZLGjk6O2D4dmNEm//jM3Ksu3wOIiN2BmcAetc5JETGhlj8ZmA3sVpdWm0cA92XmrsDxwHGd2hFJkiRJkiRJ0tjRscB2Zl4G3DvE4gcDZ2Xmysy8FVgE7BMROwJbZublmZnAGcAhjTpza/ocYP/WaG5JkiRJkiRJ0vjVjTm23xsR19apSraueVOAOxplFte8KTXdP3+tOpm5CngA2LbdBiNidkQsiIgFy5cvH7k9kSRJkiRJkiSNutEObJ8MPAPYC1gKfKbmtxtpnYPkD1bn8ZmZp2Tm9MycPnny5GF1WJIkSZIkSZI0toxqYDsz78rM1Zm5BvgSsE9dtRjYqVF0KrCk5k9tk79WnYiYCGzF0Kc+kSRJkiRJkiT1qFENbNc5s1veCFxf0/OBmRExKSJ2odwk8srMXAqsiIh96/zZhwMXNOrMqulDgR/XebglSZIkSZIkSePYxE41HBHfAPYDtouIxcAngf0iYi/KlCG3Ae8GyMyFETEPuAFYBRyVmatrU0cCpwObARfWBeBU4MyIWEQZqT2zU/siSZIkSZIkSRo7OhbYzsy3tsk+dZDyc4A5bfIXAHu2yX8YOGxD+ihJkiRJkiRJ6j2jffNISZIkSZIkSZI2iIFtSZIkSZIkSVJPMbAtSZIkSZIkSeopBrYlSZIkSZIkST3FwLYkSZIkSZIkqacY2JYkaZz5/FcP4PNfPaDb3ZAkSZIkqWMMbEuSJEmSJEmSeoqBbUmSJEmSJElSTzGwLUmSJEmSJEnqKQa2JUmSJEmSJEk9xcC2JEmSJEmSJKmnGNiWJEmSJEmSJPUUA9uSJEmSJEmSpJ5iYFuSJEmSJEmS1FMMbEuSJEmSJEmSeoqBbUmSJEmSJElSTzGwLUmSJEmSJEnqKQa2JUmSJEmSJEk9xcC2JEmSJEmSJKmnGNiWJEmSJEmSJPUUA9uSJEmSJEmSpJ5iYFuSJEmSJEmS1FMMbEuSJEmSJEmSeoqBbUmSJEmSJElSTzGwLUkatojYKSIuiYgbI2JhRHyg5m8TERdFxC31cetGnWMiYlFE3BwRB3Sv95IkSZIkqdcZ2JYkrY9VwIcz8znAvsBREbE7cDRwcWbuBlxcn1PXzQT2AGYAJ0XEhK70XJIkSZIk9TwD25KkYcvMpZl5dU2vAG4EpgAHA3NrsbnAITV9MHBWZq7MzFuBRcA+o9ppSZIkSZI0bkzsdgckSb0tIqYBzweuAHbIzKVQgt8RsX0tNgX4eaPa4ponSZLUM37yspd3uwsb5OWX/aTbXZAkacQ4YluStN4iYnPgXOCDmfmHwYq2ycs27c2OiAURsWD58uUj1U1JkiRJkjTOGNiWJK2XiNiUEtT+WmaeV7Pviogd6/odgWU1fzGwU6P6VGBJ/zYz85TMnJ6Z0ydPnty5zkuSJEmSpJ5mYFuSNGwREcCpwI2Z+dnGqvnArJqeBVzQyJ8ZEZMiYhdgN+DK0eqvJEmSJEkaX5xjW5K0Pl4MvB24LiKuqXkfAz4FzIuII4DbgcMAMnNhRMwDbgBWAUdl5upR77UkSZIkSRoXDGxLkoYtM39G+3mzAfYfoM4cYE7HOiVJkiRJkjYaTkUiSZIkSZIkSeopHQtsR8RpEbEsIq5v5G0TERdFxC31cevGumMiYlFE3BwRBzTy946I6+q6E+q8rtR5Ws+u+VdExLRO7YskSZIkSZIkaezo5Ijt04EZ/fKOBi7OzN2Ai+tzImJ3YCawR61zUkRMqHVOBmZTbjS2W6PNI4D7MnNX4HjguI7tiSRJkiRJkiRpzOjYHNuZeVmbUdQHA/vV9FzgUuCfav5ZmbkSuDUiFgH7RMRtwJaZeTlARJwBHAJcWOscW9s6BzgxIiIzszN7JEmSJGmo9v7HM7rdhQ3yi08f3u0uSJIkaRCjPcf2Dpm5FKA+bl/zpwB3NMotrnlTarp//lp1MnMV8ACwbcd6LkmSJEmSJEkaE8bKzSOjTV4Okj9Yncc3HjE7IhZExILly5evZxclSZIkSZIkSWPBaAe274qIHQHq47KavxjYqVFuKrCk5k9tk79WnYiYCGwF3Ntuo5l5SmZOz8zpkydPHqFdkSRJkiRJGrqIuC0irouIayJiQc3bJiIuiohb6uPWjfLHRMSiiLg5Ig5o5O9d21kUESdERLvBf5I0ro12YHs+MKumZwEXNPJnRsSkiNiFcpPIK+t0JSsiYt96kj68X51WW4cCP3Z+bUmSJEmSNMa9IjP3yszp9fnRwMWZuRtwcX1OROwOzAT2AGYAJ0XEhFrnZGA2JX6yW10vSRuVjgW2I+IbwOXAsyJicUQcAXwKeHVE3AK8uj4nMxcC84AbgO8DR2Xm6trUkcCXgUXAbyg3jgQ4Fdi23mjyQ9QTvyRJkiRJUg85GJhb03OBQxr5Z2Xmysy8lRIX2af+An7LzLy8DvA7o1FHkjYaEzvVcGa+dYBV+w9Qfg4wp03+AmDPNvkPA4dtSB8lSZIkSZJGUQI/jIgEvpiZpwA71F+sk5lLI2L7WnYK8PNG3cU179Ga7p8vSRuVjgW2JUmSJEmStJYXZ+aSGry+KCJuGqRsu3mzc5D8xzcQMZsyZQk777zzcPsqSWPaaM+xLUmSJEmStFHKzCX1cRlwPrAPcFedXoT6uKwWXwzs1Kg+FVhS86e2yW+3vVMyc3pmTp88efJI7ookdZ2BbUmSJEmSpA6LiCdHxBatNPAa4HpgPjCrFpsFXFDT84GZETEpInah3CTyyjptyYqI2DciAji8UUeSNhpORSJJkiRJktR5OwDnl1g0E4GvZ+b3I+IqYF5EHAHcTr2fWGYujIh5wA3AKuCozFxd2zoSOB3YDLiwLpK0UTGwLUmSJEmS2jrxw9/udhc2yHs/8/pud+Exmflb4Hlt8u8B9h+gzhxgTpv8BcCeI91HSeolTkUiSZIkSZIkSeopBrYlSZKkHhcRt0XEdRFxTUQsqHnbRMRFEXFLfdy6Uf6YiFgUETdHxAGN/L1rO4si4oQ6d6skSZI05hjYliRJksaHV2TmXpk5vT4/Grg4M3cDLq7PiYjdgZnAHsAM4KSImFDrnAzMptygbLe6XpIkSRpzDGxLkiRJ49PBwNyangsc0sg/KzNXZuatwCJgn4jYEdgyMy/PzATOaNSRJEmSxhQD25IkSVLvS+CHEfGLiJhd83bIzKUA9XH7mj8FuKNRd3HNm1LT/fMlSZKkMWditzsgSZIkaYO9ODOXRMT2wEURcdMgZdvNm52D5D++gRI8nw2w8847D7evkiRJ0gZzxLYkSZLU4zJzSX1cBpwP7APcVacXoT4uq8UXAzs1qk8FltT8qW3y223vlMycnpnTJ0+ePJK7IkmSJA2JgW1JkiSph0XEkyNii1YaeA1wPTAfmFWLzQIuqOn5wMyImBQRu1BuEnllna5kRUTsGxEBHN6oI0mSJI0pTkUiSZIk9bYdgPNLLJqJwNcz8/sRcRUwLyKOAG4HDgPIzIURMQ+4AVgFHJWZq2tbRwKnA5sBF9ZFkiRJGnMMbEuSJEk9LDN/CzyvTf49wP4D1JkDzGmTvwDYc6T7KEmSJI00pyKRJEmSJEmSJPUUA9uSJEmSJEmSpJ5iYFuSJEmSJEmS1FMMbEuSJEmSJEmSeoqBbUmSJEmSJElSTzGwLUmSJEmSJEnqKQa2JUmSJEmSJEk9xcC2JEmSJEmSJKmnGNiWJEmSJEmSJPUUA9uSJEmSJEmSpJ5iYFuSJEmSJEmS1FMMbEuSJEmSJEmSeoqBbUmSJEmSJElSTzGwLUmSJEmSJEnqKQa2JUmSJEmSJEk9xcC2JGnYIuK0iFgWEdc38raJiIsi4pb6uHVj3TERsSgibo6IA7rTa0mSJEmSNF4Y2JYkrY/TgRn98o4GLs7M3YCL63MiYndgJrBHrXNSREwYva5KkiRJkqTxxsC2JGnYMvMy4N5+2QcDc2t6LnBII/+szFyZmbcCi4B9RqOfkiRJkiRpfDKwLUkaKTtk5lKA+rh9zZ8C3NEot7jmSZIkSZIkrRcD25KkTos2edm2YMTsiFgQEQuWL1/e4W5JkiRJkqRe1ZXAdkTcFhHXRcQ1EbGg5g37pmMRsXdtZ1FEnBAR7YInkqTRcVdE7AhQH5fV/MXATo1yU4El7RrIzFMyc3pmTp88eXJHOytJkiRJknpXN0dsvyIz98rM6fX5+tx07GRgNrBbXfrfyEySNHrmA7NqehZwQSN/ZkRMiohdKOfrK7vQP0mSJEmSNE6MpalIhnXTsToacMvMvDwzEzijUUeS1EER8Q3gcuBZEbE4Io4APgW8OiJuAV5dn5OZC4F5wA3A94GjMnN1d3ouSZIkSZLGg4ld2m4CP4yIBL6YmafQ76ZjEdG86djPG3VbNx17tKb750uSOiwz3zrAqv0HKD8HmNO5HkmSJEmSpI1JtwLbL87MJTV4fVFE3DRI2YFuOjasm5FRpixh5513Hm5fJUmSJEmSJEljSFemIsnMJfVxGXA+sA/Dv+nY4prun99ue96MTJIkSZIkSZLGiVEPbEfEkyNii1YaeA1wPcO86VidtmRFROwbEQEc3qgjSZIkSZIkSRqnujEVyQ7A+SUWzUTg65n5/Yi4CphXb0B2O3AYlJuORUTrpmOrWPumY0cCpwObARfWRZIkSZIkSZI0jo16YDszfws8r03+PQzzpmOZuQDYc6T7KEmSJEmSJEkau7oyx7YkSZIkSZIkSevLwLYkSZIkSZIkqacY2JYkSZIkSZIk9RQD25IkSZIkSZKknmJgW5IkSZIkSZLUUwxsS5IkSZIkSZJ6ioFtSZIkSZIkSVJPMbAtSdooLT7xXSw+8V3d7oYkSZIkSVoPBrYlSRolF556IBeeemC3uyFJkiRJUs+b2O0OSJLUbbedcAgA097/rbbrrzn59Y+l9zry2yOyzW+f9loAXv+uC9fKP+crMwA49J3fH5HtSJIkSZI0HjliW5K00Vjy+X9gyef/odvdkCRJkiRJG8jAtiRJXXb+aa/l/DqCW5IkSZIkrZuBbUmSRtglX34dl3z5dd3uhiRJkiRJ45aBbUlSz1r+hc+z/Auf73Y3APjpl17HT79kMFuSJEmSpNFgYFuS1FOWf+FLLP/Cl7rdDUmSJEmS1EUTu90BSZJ6yVVffP1j6Re8+9td7IkkSZIkSRsvA9uSpHFt6Un/D4Ad//7f1ln21yce/Fj6me+9oGN9GoqzTj+gq9uXJEmSJGkscyoSSZLW0+WnHMTlpxw0aJmLvnwgF335wFHqkSRJkiRJGwcD25IkSZIkSZKknmJgW5IkSZIkSZLUUwxsS5IkSZIkSZJ6ijePlMaI2//lz7vdBXXYzp+4rttd2GjcdfK/A7DDkR/rck8kSZIkSVInGNiWJI0Ly77wWQC2f8+HRqS9hSe9oe9JjEiTkiRJkiRphDgViSRJkiRJkiSppxjYliRJkiRJkiT1FAPbkiRJkiRJkqSe4hzbkqQxb/nJp/c9cb5rSZIkSZI2eo7YliRJkiRJkiT1FAPbkiRJkiRJkqSeYmBbkiRJkiRJktRTDGxLkiRJkiRJknqKN4+UJI1Jq5bfy/KTv9rtbkiSJEmSpDHIEduSJEmSJEmSpJ5iYFuSJEmSJEmS1FMMbEuSJEmSJEmSekrPB7YjYkZE3BwRiyLi6G73R5LUnudrSeoNnq8lqTd4vpa0sevpm0dGxATg88CrgcXAVRExPzNv6G7PJElNnq8lqTd4vtZwvPhzL+52FzbY/77vf7vdBWm9eL6WpN4fsb0PsCgzf5uZjwBnAQd3uU+SpMfzfC1JvcHztST1Bs/XkjZ6vR7YngLc0Xi+uOZJksYWz9eS1Bs8X0tSb/B8LWmjF5nZ7T6st4g4DDggM/+2Pn87sE9mvq9fudnA7Pr0WcDNo9rR3rYdcHe3O6Fxy/fX0D09Myd3uxPrawPO1/fQ9x7Zrsvpbm/fvozN7dsX+9I//eSN9Hzdje/XG+P3CPd5/NvY9he6t88b6/dr4yFDtzH+PWp0+N4anoHP15nZswvwQuAHjefHAMd0u1/jaQEWdLsPLuN38f218Szre75uvke6ne729u3L2Ny+fbEvA6V7daGHvl+Ph+PtPrvPG/v+bqz7PELHrWfO1726+N506dTie2vkll6fiuQqYLeI2CUingDMBOZ3uU+SpMfzfC1JvcHztST1Bs/XkjZ6E7vdgQ2Rmasi4r3AD4AJwGmZubDL3ZIk9eP5WpJ6g+drSeoNnq8lqccD2wCZ+T3ge93uxzh2Src7oHHN99dGZD3P16eMoXS3t29fxub27Yt9GSjds3ro+/W4ON7D5D6Pfxvb/sLGuc8joofO173K96Y6xffWCOnpm0dKkiRJkiRJkjY+vT7HtiRJkiRJkiRpI2NgW21FxIyIuDkiFkXE0d3uj8aPiDgtIpZFxPXd7ou6q9955rQB0gsj4pG6LI2IlRHx44hYHRFZlzX1+aqIeDQiFkfEvf3K3BMR10TEHf3q3d8ot7pfnaztXV/70ay3JiLOqttslm2tWxER90XEw3Vptbuq9uOaiHioUXdV7cvK+vz2Rrq13BEREyLil7W91rZWRsSD9XF1PVYPNY7JqkbZ1jF6pF9/W/uxpj6ubBybZr3m/q+p7azpV7d1LJp9bK1f0a9c87g3+/JII72mzXabx2ZNvzbXtFlWx+P72cpvvgatbf6pTX67bbT6/3C/47EqIpa02c81Ud6brW3f06j/YJv2VtQ2m/nt+tDcl/59bZffPC6r+rU5UNn+x7b/+6f/Npt/T83X+IHGPjVfm5VtjnG7fq1rPweq2/9vu//7sN3S3LdmX1tttfbl3vpaLY2I5TH4Oc3vVG1ExOkRcWib/KdFxDk1vV9EfGeA+rdFxHad7ueGqu+da6J8rnwzIp40QLn/W8/2p0XEX29YLzdeEfHxKJ/319bX6S8HKfvYezYiXlrrXRMRm41SXx/scPtt/yalXtfvPPztiHjKCLbd0b9LjU31s/f6fnnHRsRH2pT13NoBBrb1OBExAfg88Fpgd+CtEbF7d3ulceR0YEa3O6Hu6nee2RP4G+B9/dIfAZ4DHAS8GXgKMBeYDqwBng+sqk3Or3lLKfOVbQX8CfhDzV8NHAtMBd4GbF7rXQV8HLgJuB9I4I/AQuBvKfei2AaYBDwM3A7cWtNPAx4A3gE8Wut+G/gNcC/wNWAlcDPwYO3HGuCvgbcCmwIfqmWWAOcBfwcsAL5Q214D7AH8Ze37mcBDlM/vnYEP1z7OA44GVgDbA8fVMrsDZ9d9XQp8saYfrPv+x9rXm+oxuhd4V01fVNffAny1bmcNcEY97suA62r6d8BdwG31+SaU1/C2ur3fA5+rx/184PU1/17gP4AAZte2Ae6mvD+i7u8fG2XeBTwBOJLyfqHm39FI/6aW/yDwo9qfD9b9D2ARsLju56G179T19wNPrNtcUvf5Y5TX93fAPrXsBynvh02AC+sx3RS4tO7XU4ErKO8X6jZ/AGxZt30b5X36W8oNnzatxy4o58lL6/GaS99reDrlfQ3wV3U/W+kfNdKfq+mFlPdIK//ERvojte/XU95rrfzWsXh93b9HgV/WdNZjcn8ts4DyPkjK6/oo8Ajw6br+C7UvUdMfAbYA7mxs8wuUz4QnUP52v1qPw5vrNtdQ3oOtfxZ/UbfxKOVv5qG6/S9SXs9FwDdq2bcD/9g4Fg808g+s6XcCT67p99c2WmnqMfpu7Qd1m7+r2zwD2LrWv5DyvrkbOIz25zS/Uw1TZi7JzPX6BzCKsfa/zkOZuVdm7kl5H7+nubJ+NpKZL1rP9qdRPmM2WL1485nG849ExLEj0XY3tAs89Fv/Qsr3jb/IzOcCr6Lvc2VdAau/Af6rvrYPjVSfx7KI2CEivh4Rv42IX0TE5RHxxvVoZ7+IGPT9HhHviYjD11Hm0oiYPtzta6PUPA/fCxw11IoR0fP3qFP3+P7pnLH2ZU9jwz7Aosz8bWY+ApwFHNzlPmmcyMzLKF8itHF77DxDCVD/Dti7X3oWJci2d2Z+mxLYegElWL2KEsBt/dM5kRIsexIlILeCEnj6PSUIvZISVEtKAOoJlEDVU4DnUoJyT6QEGlo3n/h1TW9BCQo/TAkqrqrbW16ftwJ7E4FzKJ+tj9R9ubf287K6vUeBKcAhlMDpTXVb21HOtTMoQer9KYHPNbX8lrXc3vQF+J5Y+x7AD4Fn1/5sBryklplQt98Kqk+s+7ElJSj6J0qQcioloPcgcEmtdzMlaPgo8NK6LurzxfXYnV7TWwDX1r5PBO6hBG2fVuv9oeZRX4t76rab/XghcEHtX6uvSQkGb1qP6QspQcmHKe+FLRt1d2iU2am28WxKYDFr+qKafnJdfyflwsHq2q9n1DbvrMdgUk3/Xd3m9yiB80dqe0+lvB9eUF+PrPuzRd3X6+gLpt4AbFvr3lSPTytAuwnlvZ61vVfV4/sw8ArgZzX9qnosAJ5X9zNres9G/kU1vXnd/qqa//tG+d1qmSvqvq6p+VvVfX4u5e/kjpo3lRJo3qHu663ArpT37q31+E2s23h2bW8xJdBLTe9OeQ+dX7eZNf9j9TW4k/Lev7VxfO8BdgHuo7yXnl3b+13j2D1c938FcBrwzNreTsB+dTuX1P1ZVfP/ox7/Hel7fZ9W+/Vo3d/WhZUX0XeB7Jbar0fq+ufWvmxZy55BeY887pzmd6o+EXF4HRX7q4g4s2a/LCL+rwbKWiNh2wYjI2LbiPhhlF+wtC5YtcrfGBEnAVcDO0XEP0bEVXV7/9yv3JfqSNsfjtZI24afArvWwN4lEfF1yjnjsSBqRJwdEa2LMK2RXm+u/f9pRFxdl1Zg8FPAS+toxH+I8iufTzf2/93D6N9K4E2jNRK+k//wty4YrMOOwN2ZuRIgM+/OzCURsXdE/ATYLCJ+EBE79mv7bykXBT8REV9rs+23RcSV9TX5YqsvUX6lc1wNCv8oIvapwdnfRsQbapl3RMQFEfH9KL/6+GSb9qO+xtdHxHUR8Zaaf2ZEHNwo97WIeMNA74nazokRcUNEfJfy3WGg4xnAt4DLMvPPMnNvYCblvDlc+1HOsQPKzC9k5hmDlZHW0+WU79lrXRyJiO0i4raafkeUX9h8G/hhRGweERfXc+91zb8zqb/6vvr3+jnygZr9qvoZ/uuIOKiWa/u5Xr8jXBoR50TETfVcHt3anzErM11c1looo9e+3Hj+duDEbvfLZfwslBFF13e7Hy5dfQ88dp6p6Uspo0mb6QWUkaIn1nILKYG1X1O+iD5ECTS1AsZrKMHi4+rjQ8D3KYGsNZQA38O1zipKYOq+ml5c276dvuB2UgKyK4D/pQTMVtf1fwL+m/IF5U+17CO1fGtU65spQb5HKQHAVh+vowStf1Hby7qNM+kb/fxi4N2NfmQt+7K674829n01ZaT4A5Rg+0vqftxb+/ZILXMrJTD7x1rvO5Rg9KJa5kFKEHNaTd9Qj8uqul8Lar27G218q/Z9VU1/rm7r4Zr/KCXQu6Kub42sPZK+APZ/1PQXKaOOH63LPfXx7+gbEX9P7dsqSiD417Xv8+kL2q5ulF1e02tq+qHG80tqW619bx3nWxvP/9Robxkl0JOUAOz99F0waB2PP1GCtq0A9fn1dVgD/LimF1AulDxS23u4lv3/6mNS3petbd9d+/RIbaMVAF/d2M6yuo1WP39D33tycX1c1ujnavreQ8v6tdV6fLQ+/qlxPFrlHqrHoHXM7myUeYC+913r4kjrb7S1zZsbbbX+JlvvmzX18f7aZivvIcp7f01t+1LKe3RlLXsX8H+U7ywP1boravp+yjml1Ye76+O9te/3Uf5mzmu8xmsaS9Y6rX1/tLZ5Nn3nrGWU99TbKRfPLqXfOa2exzb671SUi0k3A9vV59tQLpJ9k75fmiyq66ZRvy9QAmDfqekTgE/U9Ovqa7NdLb8G2Leuew3llwBR2/4O5Tw6jfL+3quWmwe8bRT2/cH6OJFyIe/Iul9/BHZpU+6NwNyabl1o2oxyEfeJNX83YEH/Y1Sfzwb+X01Popx/dhlqX4FjgDn1+UeAY2t6MnAu5Zc/V1E+szahfLY9pdHGIsrFsMeVr+uPra/PD4GvD9CP7wHPrelfNl73f6X8sioovxK5nvL5+pbGsbgE+DrlXNF8L/1ZbesFlAua36/PH6Scb08C3gL8nHIe+Uxd9xbgtNrG6cCh/dP9+v4cyufzpvX5ScDhNZ3Aa2v6/HoMNqVcYLym5r+D8nmxbX3drwem93uPvJlyMXNCPda3U4L0Lwe+VctsVfdr4kDvCeBNjXaeRjnHPW6far39gZ8MsO6JwFfqa/FL4BU1/wpgj0a5SykX/+6kfH5fA7x0gDaPBT5S03vV1+Xaety2brT335TPgeuBfbp1jnMZ20vjb2cC5XNnRn1+aePvazvgtpp+B+W71Db1+URgy0a5RUA023bZuBbaxDZa5636vjqpkX865TNnE8rn9+J63hzsc/0ByoXDTSjfhV/S7X0ea4sjttVOuytAOeq9kDSeRZt0DpJumgA8nRJEbY2avI3yT9umlJ9hr6EEqx6gBKGuoQSxJlH+oT2wlp1ACTCvoIzi3YoSSLuRMjLzybX9iZR/gH9NGT0blBGiB1P+uc5a5lRKgG0Lyj+VO1D+oX5C3db3MvPPKSN296KM8nqUMrJ2u9qXByijzv+DEiBsjRjfpG5jQs1/KiVAsAklKPizut/vrOs2re38iBKsm0qZ0qT1M+l2r0HLprWty+sx/n5tM4F/oo4qpPxTurCW2R/4i1pmEeWL21n1eK+hBJOo6z9Sy0AJ9C2qx2Nvyj/3i+prsbiWn1CP2Tcp/9y3gsZPruufQQmO3UEJHHydMkXEJvX4Utt9Ty2/nDKVy42U98Yayj/3D1Jeixtr+nbKqM9HanvHAldSgjTLKBcwVlNehzsoX0xf3djPHeibLmfbmt6G8n64n77R0qvpG6G/kjIdR9TlG5Qg4MS63Ydr306qx7c5jQmUUaD/XtP3Uy7IPFr7exPl9f8W5WLF6lr+vpp/AX0j839AuTgwodH2Ikqg4ol1P1rTdqwBftVI/7r2aWv6poX5DSV4twl9F0uo+a3pdVoXJybWY966WPQI5TV7QW2r9Xe/S6Nv21CmdTmY8nf+K8r7YBLl7zvqvvySEpBujcg/t74ekymjwTel/O2fVrfTCmxvXvf3tNpWa+R8UN5La+ibGqZloPPYxv6d6pXAOZl5N0Bmtn7F9a3MXJOZN1D+dgbzMsrfCZn5Xcp7uOV3mfnzmn5NXX5J+Vt+Nn2/Vrg1M6+p6V9Q/jHttM0i4hrK+eZ2ymcGwJWZeWub8hcCr4yISZSpuy7LMtXFpsCXIuI6ynlxoOltXgMcXrd5BeU8tNsAZdv5PPA3EbFVv/z/AY7PzBdQAqtfzsw1lHPIGwGizE99W2be1a58o629gYMzc6ApVC6jjELfkvL3/uKa/xLK+etNlM+P51F+1fLpxqjqfYCPZ+ZjxycinkX5u39nZl5FCay/LzOfTzl/30c538yl75dbb6F89vw/hjcqef+6f1fV12B/ymci9H22QvlM/UlmPlrT0xptXJSZ99TX/Tz6fpHV8hLgG5m5uh7rnwAvyMyfUH4RsD1l+rNzM3MVA78nXtZoZwnlQupA9qD8PbVzFED9rvNWYG5EPJHyefVXAPX1eVpm/oIyHdXxWaaG+Okg22w5A/inLNPFXAd8srHuyVmm8fl7yrlaaqd1Hr6H8t3hosGLA+XvsPVZFcC/R8S1lO/YU1j3Z5bGt4G+17Xyz+6XP69+37mF8t219f1zoM/1KzNzcf2cvYbR+b7SUwxsq53FlJ/ptkyl/PMuSSOleZ5ZTBkdtKRf+nZKMLV1/tme8iV0S0pQ6q8p/3y2Rr5uTQmITaEEoXak/BP1RMqoqUdqO3+kjPRZQ99UE5Nr/S1r3m6Uf7ZW1DqbUQJ+q2v7qylfKu6n/BPXGmG8Xe3bkygjjSdQpipYSPkifGBE/Ectu4q+EcQPU4LCd9Z9/kfKVBQTKf9Y30DfHN7Pq/t3PCXgmbVPi+v29qYEAzep9e6o/XuQMlXKE+u+30vfF/E/Ur5QraYEHTal/LO4CyXot1/dxibAnHpsW3Npb1/3YwIluLBJrXNnPY5JCfZ/u6ZfQxk12frZ/0/rcXsu5Wf0i+uxfZRyAePkWm5XStChFeR/Zd2fAJ5F35QUS+ibS/oKykjb1ZTX9x76pjm5ttbbgRLk2KW2sU19LZ5U93mXWi8pU7JcXdNPr/u+CSXQ0pq6orVfE+trMbnmP6emd6G8fpMpAYmJ9XidUY/7JMpo/SfQN6/4y+m7mLITJaD/Esqo1aRcgLi7viab1eO9pr4mt9L3HvlwPUZb0BcQfy3lvTaJcjHhltr/TeprNqHm/7EemwvpGw2+oh7b1rzhWdu5o25/c+ATtcxE+oK8z6RvupYnUL7UU+u0pvbZpu7LFrW9t9T6qyiv8a6U98Ldtdy9lL+Nl9X1kynv09X1tZlVt/H8uq41J/oWlNF9EygXSFo2o1zg2KTu5+01/wHK3/0TKOefP6/b+TbltZlKuUjR7pwGfqeC8jq2+ydwZb8y6zLQP5J/7NfOf9Sg2V6ZuWtmtoLJze213qOd9lCjL+/LMj1N/z4/JjMfpoz2OoDyN3BWXfUPlL/z51HuO/GEAbYXlKBta5u7ZOYPh9rZzPwD5dz0/n6rXgWcWIND84EtI2ILyj/vb6llZtL3z/xA5QHm5+DzUv+U8nf9Esp895tHuenmtMy8mQECu7Vu/wsGkynB97dl5jURsTllGoxv1r6dDGyWmZ+kfAbtTPn83p3y2v15Zr5moI5GxE7Rd4Po91CO/9zG8X9WZh5biz+ama33cOuiJjVw0Xwv9n+f938+2N/KmZT5v99JGUXdKj/Qe2K9LrpFxOejTCt0FeX1OBMgM2+i/ArtmZRfRRxWq/wVJXAz3O1sRflFwE9q1lz6LppDvb9ClmkPt4wRvCmgxpWHMnMvyve4J9A3x3brey2U78pNzXP031DOJXvXdu5qU14bl3so/2c0bUPfdHz9P+PbndcH+1zvxveVnmJgW+1cBewWEbtExBMoX0znd7lPksaXx84zlCvPT6eMmGumz6AEHX8RES+hBPMup3x53J4yL+9TKZ9l/0cJoj1E+SnqKsqXiV9TvkwcQ7mZI5RgY+vLa1AC5F+lBKtao3sfoIxY3aKmN6UEsJ5E3w3+llN+An9crdMcJf7H2v/bKKNQv1Lb/mxmHkP5R3lT+qbY2JQSPPxrSrDul/TNHz4P+Gjd5j/UtpIS8Lyh5t9CCXJvTgksXl2P0wM1f7N6bO6q6QcoX8A2q3WupgT2nkC5KeLK2sftKaNQP0XfFBbT63F/tLYzhRIsnURf4HEaJRh+b81bSN+XsD9RbnS5S613FeV1/jElOHhYbeM/6/ZWUIKdm1BG4V5Zj9f1lFF/m9I3lcQ0ynvn+XW7l1MCmBNr3ZfU/Xywltm8tvGcevy/Sd8UI/fWY7dVbXsi5T2woLaxhBI0DuBfKAF/6r49WNv4eG0DSkCmNfXJ8ZQA8aOUIMtqymjq+2q9gyjT30AZdf/3NX0d5f0EZeT8nnX7/0u5mDGRMnL/RfV4tX66+GTKCO9WgP9Syk/rAf6tHt+gBJBaFw6upox2nEB5P0Q9Fqvq0np/PULfzTapfbiBvp9L/rq2cQvwz7XMjygXPqCMAl/USC+pbdxb25xEGW3/lMY+XU95Dy6mb8T1QsqFiidSRjU+nRJwP4e+Efn31/T2tT8TKYGrb9M3j3vrotXPKcEYat0/q8dgB8rfUWvO3sNqv86lXMg5HPgSbc5pfqd6zMXAX0XEtgARsc16tHEZJbhARLyWx/9D2fID4F01gElETKkjWHvJWZTA5Esp+wPlb3FpDYK+nb734wr6LhpRyx8ZEZsCRMQzI+LJDM9/A0fQd5NVKO/5FzaCo1MycwXlb37XiJhMuZfEeesoDwME9RuuonzuvJTyuv+ScuH4F3X9YIHd/m0/QPlsbY363gS4vwan3gIclpnPqesmUC5uTaYGyiNi04jYY6CNZeYdjX38AuW9fmjrPRcR20TE09exv/29utbbjHJM/7ff+suAt9S5sydTAr1X1nWnUz7TycyFNW+g98RlwMzazo6U+zsMZCHlAnBrv4+ijEZvXch9nMz8PXBPRDyXtS/SjKR1XQSQHpOZD1Au2n2k/j3cRvkfAco0YgPZCliWmY9GxCson/HaiGXmg8DSiNgfHvteM4PynbydwyJik4h4BuX7Zev/jXaf6xqKTs5z4tK7C+Vn+r+mBDQ+3u3+uIyfhTKaYil9N6A7ott9cunae6F5nmn95Hc5fcGw5fTNRbyKEgRt3Tgy2yyt+a9bcxK38lsjoe6kBMta+Q/X583pBgZqd3W/vNYcxAPVac3pvabRRmvdcsoI1f71W/uXlH8a7+m3fmU9Zqey9ty/D9W6j1ICo6spwcElA+xb/30Z6jLQMRrs2LXb7lDKP9oo37+/a9qkB3o91gxzu63XujnP+qoBttmar3lVfW1ac5638ltzZ/ffRit4mpT3X2uu+Lsbr2tr7u3VNd2qc1d9jdsdh+Z+ttvX/uXXNNrvX2ZNv3QrmL2m39J8jYa6zYfrkv3aWtVvu6sa5ZqvwX30/dJh5SDbbfXtT5TzSPP1aeX/qdFOay7vB2nf9/770dzevbWN1gW12yifb/3Pb36n6jv/z6JcoPgVJfh2Oo35fOmbA3Ua7efY3pZyweNqyoWi39E3x3b/eS4/QLkodB3l8+UZ/cvRmD+6w/v9uPlXm/vVrhzloss9wFcaebtRfnXyc8q0VQ82yl5cj+s/UAK3/173/XrKnNNbDbevlAuNt7eOEWWan39srN+rkf40ZcTu9xp5bcvTmDt5HX25tP79PIkSFL0D+EBd9yZKsHYCJbD6O8qFr7WOa+s1pwTofwb8dc3/P8oFqr1r+jf12C6lTF+1F+VC2GrKZ/Pf1Xqns445tuu6t1AucF1LCca35n9vHt+1jkPj9XwH5eL2dymBj0+2KRO0mWO8Ue77wHsaz9u+J2o7J1IuTH6rLgPtU1B+EXVkI29nyrnvQ8CpNe+Z9fWYVJ8fRRlIsLBR78PAP6/j9X/s+FDe2y9t5B/feI98oaZfAlzX6b9nl95c6HceplzcfjtlOohr63ng31h7ju0TG+W3o3yWLKAMjriR8guSx7XtsvEslF/2XFLP99cAf1PzL6XO3V6fn0753vJTynfDg2r+QJ/r+7H2Z9mJwDu6vb9jbWlNci9JkiRJEhHxYGa2RrrvQLlg/J+ZeWxEbEeZf/s5lF8+XJaZ76llp1NGWb8jM+fWvLblI+JYyj/v/7WOvvwrsH9mviginkb55cremXl1RAQl8P5ayoWmf8vMsyNiP0ow9KDaxjRKcGDPOkXFRZTg1bWUKUh2pFwYOCsz/6X+ouzrtb/nUm64uPn6H9Hhi4h3UAIi713P+k+iBLD/Isvo1JHs246U4MxfUi7Y/5EyhdsF9bH167gPZeYltc4OlNfuXzPzn2veM+n7Zc37ss082/V9siIzPxMRe9X2n0QZJPDOzLwvIi6lBBtfTvm1zrsy88r+bUmSxh8D25IkSZIkjSEbEtiOiFdRbqD42cz87xHu2qiKiM8BV2fmV7rdF0nS2GNgW5IkSZIkjSl1tP4BwGsz855u90eSNPZ4N01JkiRJUtdExAH03Yy55dbMfGM3+qPRFxEfp8x33vTNzNynG/2RJPUGR2xLkiRJkiRJknrKJt3ugCRJkiRJkiRJw2FgW5IkSZIkSZLUUwxsa1yIiGkRcX0H2t0rIg4cZp3bImK7mv6/ke6TJEmSJEna+ETE6oi4JiKuj4hvR8RT1qON6RFxwgDrHotndENEfKxb21ZvMrAtDSAiJgJ7AcMKbDdl5otGrEOSpCGLiG9FxC8iYmFEzK55R0TEryPi0oj4UkScWPMnR8S5EXFVXV7c3d5L0sZjqEGUiHhKRPz9aPRJksawhzJzr8zcE7gXOGq4DWTmgsx8/8h3bUQY2NawGNjWeDKhBioWRsQPI2KziHhGRHy/Bjd+GhHPBoiI10fEFRHxy4j4UUTsUPOPjYhTIuKHwBnAvwBvqVdE39JuoxGxbd3eLyPii0A01j1YH3eMiMsaV1ZfWvNfExGXR8TVEfHNiNi85n+iBleur/2Jmv/+iLghIq6NiLNq3pMj4rRa/pcRcXCnDrAk9ZB3ZebewHTg/RExBfj/gH2BVwPPbpT9H+D4zHwB8Gbgy6PdWUnaGEXEhGEUfwpgYFuS+lwOTAEYJPZxWI0r/CoiLqt5+0XEd2p6sHjG2yLiyhrH+GLrnB0RD0bEnNrmzxvxlB0i4vya/6uIeNFg7fQXEZ8CNqvlvhYR/xoRH2isn1NjIvvV+Mr5NT7yhYjYpJZpG2PR+GVgW+PJbsDnM3MP4H5KcOIU4H01uPER4KRa9mfAvpn5fOAs4KONdvYGDs7MvwY+AZxdr4iePcB2Pwn8rLY1H9i5TZm/Bn6QmXsBzwOuqSNT/h/wqsz8C2AB8KFa/sTMfEG9CrsZcFDNPxp4fmY+F3hPzfs48OMakHkF8OmIePI6jpUkjXfvj4hfAT8HdgLeDvwkM+/NzEeBbzbKvgo4MSKuoZzHt4yILUa7w5LUSyLioxHx/po+PiJ+XNP7R8RXI+KtEXFdDagc16j3YET8S0RcAbywkb9ZDcr83QCb/BTwjBrw+HREnNkc0FGDIG+IiHdExAW1rZsj4pONMkMKrkjSWFfPX/tTvrvCwLGPTwAHZObzgDe0aaptPCMingO8BXhxjWOsBv6m1nky8PPa5mVA67x9AuX79vOAvwAWrqOdtWTm0fSNSP8b4FRgVu3PJsBM4Gu1+D7Ah4E/B54BvGkdMRaNUxO73QFpBN2amdfU9C+AacCLgG/WAc8Ak+rjVODsiNgReAJwa6Od+Zn50DC2+zLgTQCZ+d2IuK9NmauA0yJiU+BbmXlNRLwc2B3439q/J1CuuAK8IiI+CjwJ2AZYCHwbuBb4WkR8C/hWLfsa4A0R8ZH6/ImUD6Mbh7EPkjRuRMR+lGD1CzPzTxFxKXAz8JwBqmxSyw7n3C9JG7vLKEGFEyi/jplUv+u+BLgFOI4yYOQ+4IcRcUhmfosSELk+Mz8BUL8Hb04ZbHJGZp4xwPaOBvasgRHqd+l/AC6IiK0o3/tnAW+jBDz2BP4EXBUR3wX+SF9w5dGIOIkSXBloe5I0Fm1WB2NMo8Q9LqqjkgeKffwvcHpEzAPOa9PeQPGM/Snn8Ktqm5sBy+q6R4Dv1PQvKL+GBHglcHhtazXwQES8fZB2BpWZt0XEPRHxfGAH4JeZeU9t58rM/C1ARHyD8tnzMAPHWDROGdjWeLKykV5NOfHd3/ry28/ngM9m5vwaADm2se6P67HtHHRl5mUR8TLgdcCZEfFpypf8izLzrc2yEfFEytXV6Zl5R0QcSwlWU+u/jHKl9f+LiD0oPxV6c2bevB79lqTxaCvgvhrUfjZl+pEvAS+PiK2BFZRf9VxXy/8QeC/waSg3Dm5cKJUktfcLYO/6C5eVwNWUAPdLKQMyLs3M5VBGU1O+w36L8j393H5tXQD8Z2Z+jSHKzJ9ExOcjYntKUObczFxVgxkXZeY9ddvnUQIeq1jP4IokjSEPZeZe9YLedyhzbJ/OALGPzHxPRPwlJZZwTUQ8rgzt4xkBzM3MY9qsezQzW3VWM3hscbB2huLLwDuApwKnNfL79znrth4XY9H45lQkGs/+ANwaEYcBRPG8um4r4Pc1PWuQNlYA6/o5+mXUn9JExGvh/2/vfkKsKsM4jn8fLSqJsD+boIhSLCaQEmsT1IAFGS1aKENFoLQpzAyyoE0oQZtCaCe1KKgM2syu7I9oiuUQOOmoi9pI0KYcUCj64+Jp8by3uVzmjjpZM3f8fmAW99xz3vswi8Phd973ebm294SIuAX4OTPfoZbTrKKWx98XEcvbOUsiYgVTIfap9uZ1Xft+EXBzZu6lWqcspWa3fAZsjvinD/fd56hXkha63cBlEXEUeI263/4EvA6MAV8CJ4Az7fzngdVR+xecYKrVkySpj9bW6SSwEfgaOEC1xVsG/DjDpX+0mXzdDgJrO8+zF+B96jl8I/Bud3m95TIVrtzV/m7PzG0X+HuSNC9k5hnqGXYr8Dt9so+IWJaZY22VzCmqRV+3fnnGHmBde3lIRFzXco2Z7AGebecvjohrZjHO2bb6p2MUeBi4h8o+Ou6NiFtbTjJCtZvtl7FoATPY1kL3JPB0VJ/V40CnD982apnOAerm3s9eYChm2DwS2A7cHxGHqbYg0z3ID1NvR8epWYJvtRksG4CPWvhyCLgjM09TMwsnqFkt37YxFgMfRMQEME5tdHaaCm0uB45GxLH2WZIuWZn5Z2auzcyVmbk+M4czcx+wKzNXUG1KbqL67pGZpzJzpJ0/lJkG25J0fvZTocp+Kth+BviOeq59ICJuaH1gHwe+mmGcV4FJpnrCTme6CSfvAS8AZObxruMPtfDkKuAxKjifTUgjSfNWZo4DR6je0/2yjzei7XdA3auP9AwzbZ6RmSeoftWft7ziC+DGc5S0hWqrOkGt6rlzFuO8TWUbH7Y6/qJymY97Xop+Q+29cIxqLTvaL2M5R80acDG1ekCSJGnhiog3qVD7Sqr9yJb0QUiSZi0i1lCrZJZm5m8R8T2wMzN3RMQTwCvUTOlPMvPlds2vmXl11xgnqRYmk9Qy8186507ze7uAlcCnmflSO7ab2sNmZ/u8AXiE6uW9nHqpub19N9JqWgScBTZl5qGL+C+RJF1EbUb2YWB9Zv7Qjg0DWzPz0TksTfOEwbYkSZIkaeBExBJqleOqtiy/E2yvzszn5rI2SdK/ExFDVB/x0cx8sev4MAbbatw8UjpPEbGRWlrT7WBmbpqLeiRJkqRLVUQ8SM3w3tEJtSVJgyEixoAreg4/lZmdzd077VBu6722tRjc91/Wp8HhjG1JkiRJ0rwQEddT/bB7rcnMyf+7HkmSNH8ZbEuSJEmSJEmSBsqiuS5AkiRJkiRJkqQLYbAtSZIkSZIkSRooBtuSJEmSJEmSpIFisC1JkiRJkiRJGigG25IkSZIkSZKkgfI30ArqP/t4e8QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax5, ax6, ax7, ax8) = plt.subplots(1,4,figsize=(25,7))\n",
    "fig.suptitle(\"Counting cols\", fontsize=35)\n",
    "sns.countplot(x=\"heart_disease\", data=df,ax=ax5)\n",
    "sns.countplot(x=\"age\", data=df,ax=ax6)\n",
    "sns.countplot(x=\"work_type\", data=df,ax=ax7)\n",
    "sns.countplot(x=\"Residence_type\", data=df,ax=ax8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "feaffa57-27e6-42b8-bade-55472bad40f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='smoking_status', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax9, ax10, ax11) = plt.subplots(1,3,figsize=(25,7))\n",
    "fig.suptitle(\"Counting cols\", fontsize=35)\n",
    "sns.countplot(x=\"avg_glucose_level\", data=df,ax=ax9)\n",
    "sns.countplot(x=\"bmi\", data=df,ax=ax10)\n",
    "sns.countplot(x=\"smoking_status\", data=df,ax=ax11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0f7ddf11-c077-49d6-8682-92e0c581b6b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stroke', ylabel='age'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ifReAUyimt0zVM5vsN/713Lsfz1X/2mIKz10/CmyE9p/iBmNf61RG6NS/1obvhTb1b+PaCytJwcx8Huue60bGFpSOAb4cEcc03Kmws+7+cc3k2x+Z+WBmXpmZ787MX6RYBP+P2btQdwAfbtXzS5LULItJkqS5aPzaKQ9P0Kf+SlzNXtFqfL8bJupUrk3z53Wb/jYz3we8v27beyKimSuA1Ttp310e1297Zu5PMekm9ha/oPnfUf1r+na5ds949VOf2uFqb9fX3T8xIvY5Za0cWba0wTFmg/Fr70x0dbIxU9RafGW+ln0ex8vMGyjWiPpRuelYioLS4kl2+0bd/dP2c2ThlGVmLTM/BLy1bvOzveqbJKlqFpMkSXPRqePaE132+5q6+68tr361L2+ou//tzBy/2DQR8STg08D80X7A/yrvfwD4j/J+N/CpiDikiecd9eom+59dd/8rUzj+T2Xmw8CWuk2/ta99yqty1a/pck2Drg/W3Z/foM9Mqs95CPCaJvZZztgpjI1ea7v62XHteybo8+C4div/W7Xk89hIZl5PMUJptKD0TIopb40W1r6i7n4P8Lpmn2ua/PO49lEz/PySJI1hMUmSNKeUU1/eXrfpUeDfJ+j6CSDL+z0UU0kmO+7PA2fVbfpYg64fBE6se+7fLgszZOYeii/Ao6NCnk2xsG6zngKct4+cZwPH1226aArHH+8TdfdfFRGn7aP/GuDounaj39H36+4vmXqs6ZWZ32FvkQ/gTyYbnVQWzT5Yt+mmzPxWq/JNJiJeEhEv2HfPx6lfGD6Bqybo82OgfmRZK/9bterz2FBZUFrO3mmxDQtKmflfwL/WbfpQRDyhgs4UR3odOq49G6aPSpLmMItJkqQ5IQqvoBiJs6juofUTXf47M28HPl+36T0R8dsNjv0c4J/Ye968h7GFltF+vwy8s27Tu8o1Wuqf93uM/SL/5oj4DZp3/iQ5Xwb0123aAgxO4djjfQr4Xl374og4fqKO5aLAf1q36fJyOtFExk8r+6UnkHG6/N+6+8+lWOj4oPGdyiLTRcCLG+w7014C3BQRn4uI0/e1UHpEHBwR/cAr6jZfmpnfH9+3LH7eVLdp9RQWYp+SVnwem3ze6xhbUHoWxZS3oyfofj57p8weXfZ7/mTHj4h55X+XjWXuer8dEZ8uC2OTHaMb+LO6TTuA/5psH0mSWq276gCSJDXpqIjYOMH2eRR/tX8u8NRxj10DvGeSY76V4kpvC8vj/GNEvB74HHBnebxlwO+yd4rPCHBOZo65AlVEHA5sYO8aQJuBv5zoSTNzQ0S8mr1TZT4aEV/PzO2TZKXM9ZtlztcBFwN3AUcArwL62Htu3w2sfCKXo8/MhyLizcCm8rgLgG9FxCcppv3UKAp3vwH8Wt2uNeD3JjnurRFxI8UIrgD+PSJuKl9L/TpNK/dzvacpy8x/i4h/YG/u1wJby8LLDRRX1ToeOBeoLwp8JjM/T7XmUbyXXgdsj4gvA9dSvId/RPHfbhHFelavY+z0vF2MHck33qfZuwbXORRTLW9m7BS4z2bmZ5/4y5i+z+NUZOaWshA9WD7HsykKRafVfyYzc2tErGTv5/x5FIW8y4CNFIXXh8pjHEOxplYvexc3Hz8SqRt4PfD6iBii+ExdR1Ece4Di/2vHU3yun1e3358+kc+1JEnTwWKSJGm2eDLFJbObMQL8DcXIoIcadcrMH0TEyym+RI5ezenVjL0KW73HgBWZOVFRq5+9I6J+CPRlZk7Qb9RbgF+gGOFwOLAhIpbtY5+/pxgZ8SaKdX0are3zGPC6SUYGNS0zr46IX6f4Qv9k4EkUBZVzG+yyA3hFZk60Bk+936P4vT+tbB/P2Ol5MHmRoxX+gOI1vrFsPwu4cJL+X6T4b1GlPePaiyimUr5hgr7j3Q68NjO3TdLn74AzgNHRYz/DuKsjAjc28Vz7NM2fx6k+97fKgtKVFMWgJRRT3l5e/17OzH+MiAeA/0exvlYXcGZ5eyKW0Nw0wr+l+H+bJEmVcpqbJGm2S4q/4t9JsabJ+cAxmbkmM8cvIPz4nYvpNSdQXG57/BWuRo2Uxz5xohEY5eidX6/b9Hv7GmWUmT8CVrD3ilm/DPxRE3nfDLyDxpdBvxZ4aWZeuq9jNas81vHAlxg7cqjeQxRfcl+UmTc3ccwtwAspFiX/BkUBrtGxZ0RmDmfmCorRX9+ZpOt3Kf7bnZWZj85IuAYy85MUI4f+FPgWzf0Ob6KYjvmizLxlH8d/jGIa2JuAf6EYPTbR1RGnxXR8Hp/Ac3+TYvrf6CinJRQjlJ4+rt8/U4xOW1/Xt5EdwCcpinHj31NfBj4E3Mze9aIa+RZwRma+bR8FZ0mSZkR4PpIkqVBeie1/AMdRLAL8ALAduCozaxVl2sbeURq/lJlXldsPAE6jmJJzGLAT+EZmfrvFeZ5WPu/RFNNwfkhRXLkmMx9pvOfsExHPA36evVfOuhe4PjO3VpdqchFxMPB8ivfwkRSjZx4F7qO4quGNmbmjuoTNa8fP43gR0UUxne35FNNND6Aogt1FcYW5ptY2iojDKKZ9PovitT6JvUXy6zJzoitSSpJUGYtJkiS1sUbFJEmSJKkqTnOTJEmSJElS0ywmSZIkSZIkqWkWkyRJkiRJktQ0i0mSJEmSJElqmsUkSZIkSZIkNc2ruUmSJEmSJKlpjkySJEmSJElS0ywmSZIkSZIkqWkWkyRJkiRJktQ0i0mSJEmSJElqmsUkSZIkSZIkNc1ikiRJkiRJkppmMUmSJEmSJElNs5gkSZIkSZKkpllMkiRJkiRJUtMsJkmSJEmSJKlpFpMkSZIkSZLUNItJkiRJkiRJaprFJEmSJEmSJDXNYpIkSZIkSZKaZjFJkiRJkiRJTeuuOsATdcQRR+Sxxx5bdQxJajvXXXfdDzJzQdU5quZ5QpIm5nmi4HlCkiY22XmipcWkiHgycA1wYPlcX8jMCyLicOBzwLHANuB1mfmjcp/zgXOAPcDqzLxisuc49thj2bJlS8tegyTNVhFxR9UZ2oHnCUmamOeJgucJSZrYZOeJVk9z2w38cmaeAJwI9EbEycB5wObMPA7YXLaJiOcDZwMvAHqBv4uIrhZnlCRJkiRJUpNaWkzKwgNl84DylsAZwEC5fQA4s7x/BvDZzNydmd8DhoCTWplRkiRJkiRJzWv5AtwR0RURNwL3AoOZeS1wVGbuACh/Hll2XwTcVbf73eW28cdcGRFbImLLrl27WppfktQ6EfGJiLg3Im6u23Z4RAxGxO3lz8PqHjs/IoYi4jsR8cpqUkuSJEmdreXFpMzck5knAkcDJ0XECyfpHhMdYoJj9mfm0sxcumBBx68ZKEmz2UUU05rrORVakiRJamMtLyaNyswfA1dRfAHYGRELAcqf95bd7gaeUbfb0cA9M5VRkjSzMvMa4IfjNjsVWpIkSWpjLS0mRcSCiHhaeX8+sAy4DbgU6Cu79QGXlPcvBc6OiAMj4pnAccA3W5lRktR2ntBUaEmSJEmt1d3i4y8EBsppCPOAizPzXyLi68DFEXEOcCdwFkBm3hIRFwPfBoaBt2bmnhZnlCTNDk1NhYZibT1gJcDixYtbmUmSJEnqOC0tJmXmTcCLJ9heA05vsM8HgA+0Mpckqa3tjIiFmbljf6dCZ2Y/0A+wdOnSCQtOkiRJkvbPjK2ZJElSk5wKLUmSJLWxVk9zkySpoYj4DHAacERE3A1cAFyIU6ElSZKktmUxSZJUmcx8fYOHnAotSZIktSmnuUmSJEmSJKlpFpNErVZj9erV1Gq1qqNIktqQ5wlJkiTVs5gkBgYG2Lp1Kxs2bKg6iiSpDXmekCRJUj2LSR2uVquxceNGMpONGzf6V2dJ0hieJyRJkjSeC3B3uIGBAUZGRgDYs2cPGzZs4B3veEfFqSRJ7cLzhCS1t/Xr1zM0NFRphu3btwOwaNGiSnMsWbKEVatWVZqh0/l+3Guuvx8dmdThNm3axPDwMADDw8MMDg5WnEiS1E48T0iS9uXhhx/m4YcfrjqGBPh+nCmOTOpwy5Yt4/LLL2d4eJju7m6WL19edSRJUhvxPCFJ7a0dRj6sWbMGgLVr11acRFXz/dg5HJnU4fr6+pg3r3gbdHV1sWLFiooTSZLaSV9fHxEBwLx58zxPSJIkyWJSp+vp6aG3t5eIoLe3l56enqojSZLaSE9Pz0/XHHj605/ueUKSJEkWk1T81flFL3qRf22WJD1OrVbjnnvuAeCee+7xam6S2kZEvCMibomImyPiMxHx5Ig4PCIGI+L28udhVeeUpLnIYpLo6elh3bp1/rVZkvQ49VdzGxkZYcOGDRUnkiSIiEXAamBpZr4Q6ALOBs4DNmfmccDmsi1JmmYWkyRJUkNezU1SG+sG5kdEN3AQcA9wBjBQPj4AnFlNNEma2ywmSZKkhpYtW0Z3d3HxV6/mJqldZOZ24MPAncAO4CeZeSVwVGbuKPvsAI6sLqUkzV0WkyRJUkNe9VNSOyrXQjoDeCbwdODgiHjDFPZfGRFbImLLrl27WhVTkuYsi0miVquxevVqF1WVJD2OV/2U1KaWAd/LzF2Z+RjwJeAXgJ0RsRCg/HnvRDtnZn9mLs3MpQsWLJix0JI0V1hMEv39/dx000309/dXHUWS1Ia86qekNnQncHJEHBQRAZwO3ApcCvSVffqASyrKJ0lzWnfVAVStWq3208VUBwcHWblypX91liSNMXrVT0lqF5l5bUR8AbgeGAZuAPqBQ4CLI+IcioLTWdWllGbe+vXrGRoaqjpGpUZf/5o1aypOUr0lS5awatWqlhzbYlKH6+/vH3PJ5/7+fs4///yKU0mSJEmTy8wLgAvGbd5NMUpJ6khDQ0PcfssNLD5kT9VRKvOkx4oJWLvv2FJxkmrd+UBXS49vManDbd68+XFti0mSJEmSNDstPmQP73rJfVXHUMU+eP2hLT2+ayZ1uMyctC1JkiRJklTPYlKHW7hw4aRtSZIkSZKkehaTOtwPfvCDSduSJEmSJEn1LCZ1uJ/5mZ+ZtC1JkiRJklTPYlKH27lz56RtSZJqtRqrV6+mVqtVHUWSJEltwGJSh1u+fDkRAUBE8IpXvKLiRJKkdjMwMMDWrVvZsGFD1VEkSZLUBiwmdbi+vr4xxaQVK1ZUnEiS1E5qtRobN24kM9m4caOjkyRJkmQxSZCZY35KkjRqYGCAkZERAPbs2ePoJEmSJFlM6nQDAwNjikl+SZAk1du0aRPDw8MADA8PMzg4WHEiSZIkVc1iUoe78sorx7SvuOKKipJIktrRsmXL6O7uBqC7u5vly5dXnEiSJElVs5jU4bq6uiZtS5I6W19fH/PmFf9c6Orqcm09SZIkWUzqdA8++OCkbUlSZ+vp6aG3t5eIoLe3l56enqojSZIkqWLdVQeQJEntra+vj23btjkqSZIkSYDFpI43f/58Hn744TFtdbb169czNDRU2fNv374dgEWLFlWWAWDJkiWsWrWq0gxSu+jp6WHdunVVx5AkSVKbsJjU4eoLSRO1pZnme1CSJEmS2pvFJEljVD0aZ82aNQCsXbu20hyS9qrVarzvfe/jggsucM0kSZIkuQC3JEma3Ec/+lFuuukm+vv7q44iSZKkNmAxSZIkNVSr1RgcHARgcHCQWq1WcSJJkiRVzWKSJElq6KMf/SiZCcDIyIijkyRJkmQxSZIkNbZ58+Yx7U2bNlWURJIkSe3CYpIkSWpoZGRk0rYkSZI6j8UkSZLU0Lx58yZtS5IkqfP4L0JJktTQ6aefPqa9bNmyipJIkiSpXVhMkiRJDZ177rk/HY00b948Vq5cWXEiSZIkVc1ikiRJaqinp+eno5GWL19OT09PxYkkSZJUte6qA0iSpPZ27rnn8v3vf99RSZIkSQIsJkmSpH3o6elh3bp1VceQJElSm3CamyRJkiRJkppmMUmSJEmSJElNs5gkSZIkSZKkpllMkiRJkiRJUtNcgFuSJEmSpDlg+/btPHh/Fx+8/tCqo6hid9zfxcHbt7fs+I5MkiRJkiRJUtMcmSRJkiRJ0hywaNEidg/v4F0vua/qKKrYB68/lAMXLWrZ8R2ZJEmSJEmSpKZZTJIkSZIkSVLTLCZJkiRJkiSpaRaTJEmSJEmS1DSLSZIkSZIkSWqaxSRJkiRJkiQ1zWKSJEmaVK1WY/Xq1dRqtaqjSJIkqQ1YTJIkSZMaGBhg69atbNiwoeookiRJagMtLSZFxDMi4ssRcWtE3BIRa8rt742I7RFxY3l7Vd0+50fEUER8JyJe2cp8kiRpcrVajY0bN5KZbNy40dFJkiRJavnIpGHgDzPzecDJwFsj4vnlY3+VmSeWt8sBysfOBl4A9AJ/FxFdLc4oSZIaGBgYYGRkBIA9e/Y4OkmSJEmtLSZl5o7MvL68fz9wK7Bokl3OAD6bmbsz83vAEHBSKzNKkqTGNm3axPDwMADDw8MMDg5WnEiSJElVm7E1kyLiWODFwLXlprdFxE0R8YmIOKzctgi4q263u5m8+CRJklpo2bJldHd3A9Dd3c3y5csrTiRJkqSqzUgxKSIOAb4IvD0z7wM+AjwbOBHYAfzFaNcJds8JjrcyIrZExJZdu3a1JrQkSaKvr49584p/LnR1dbFixYqKE0mSJKlqLS8mRcQBFIWkT2XmlwAyc2dm7snMEeAf2DuV7W7gGXW7Hw3cM/6YmdmfmUszc+mCBQta+wIkSepgPT099Pb2EhH09vbS09NTdSRJkiRVrNVXcwvg48CtmfmXddsX1nV7LXBzef9S4OyIODAingkcB3yzlRklSdLk+vr6eNGLXuSoJEmSJAHQ3eLjnwK8EdgaETeW294FvD4iTqSYwrYNOBcgM2+JiIuBb1NcCe6tmbmnxRklSdIkenp6WLduXdUxJEmS1CZaWkzKzK8y8TpIl0+yzweAD7QslCRJmpJarcb73vc+LrjgAqe5SZIkaeau5iZJ0lRExDsi4paIuDkiPhMRT46IwyNiMCJuL38etu8j6Ynq7+/npptuor+/v+ookiRJagMWkyRJbSciFgGrgaWZ+UKgCzgbOA/YnJnHAZvLtlqoVqsxODgIwODgILVareJEkiRJqprFJElSu+oG5kdEN3AQxdU9zwAGyscHgDOridY5+vv7GRkZAWBkZMTRSZIkSbKYJElqP5m5HfgwcCewA/hJZl4JHJWZO8o+O4Ajq0vZGTZv3jxpW5IkSZ3HYpIkqe2UayGdATwTeDpwcES8YQr7r4yILRGxZdeuXa2K2REyc9K2JEmSOo/FJElSO1oGfC8zd2XmY8CXgF8AdkbEQoDy570T7ZyZ/Zm5NDOXLliwYMZCz0Wnn376mPayZcsqSiJJkqR2YTFJktSO7gROjoiDIiKA04FbgUuBvrJPH3BJRfk6xrnnnsu8ecU/F+bNm8fKlSsrTiRJkqSqWUySJLWdzLwW+AJwPbCV4nzVD1wILI+I24HlZVst1NPT89PRSMuXL6enp6fiRJIkSapad9UBJEmaSGZeAFwwbvNuilFKmkGve93r+NrXvsZZZ51VdRRJkiS1AUcmSZKkSV166aU89NBDXHbZZVVHkSRJUhuwmCRJkhqq1Wps3LiRzGTjxo3UarWqI0mSJKliFpMkSVJDAwMDjIyMALBnzx42bNhQcSJJkiRVzWKSJElqaNOmTQwPDwMwPDzM4OBgxYkkSZJUNYtJkiSpoWXLltHdXVyvo7u7m+XLl1ecSJIkSVWzmCRJkhrq6+tj3rzinwtdXV2sWLGi4kSSJEmqmsUkSZLUUE9PD729vUQEvb299PT0VB1JkiRJFeuuOoAkSWpvfX19bNu2zVFJkiRJAiwmSZKkfejp6WHdunVVx5AkSVKbcJqbJEmSJEmSmmYxSZIkSZIkSU2zmCRJkiRJkqSmWUySJEmSJElS0ywmSZIkSZIkqWkWkyRJkiRJktQ0i0mSJEmSZp2IeFpEfCEibouIWyPiZRFxeEQMRsTt5c/Dqs4pSXORxSRJkiRJs9FaYGNmPhc4AbgVOA/YnJnHAZvLtiRpmnVXHUCSJEmSpiIiDgVOBd4EkJmPAo9GxBnAaWW3AeAq4I9nPqFUnTsf6OKD1x9adYzK7HyoGDNz1EEjFSep1p0PdHFcC49vMUmSJEnSbPMsYBfwyYg4AbgOWAMclZk7ADJzR0QcOdHOEbESWAmwePHimUkszYAlS5ZUHaFyjw4NAXDgMZ39uziO1r4fLCZJkiRJmm26gZcAqzLz2ohYyxSmtGVmP9APsHTp0mxNRGnmrVq1quoIlVuzZg0Aa9eurTjJ3OaaSZIkSZJmm7uBuzPz2rL9BYri0s6IWAhQ/ry3onySNKdZTJIkSZI0q2Tm94G7IuI55abTgW8DlwJ95bY+4JIK4knSnOc0N0mSNKlarcb73vc+LrjgAnp6eqqOI0mjVgGfiognAd8F3kzxx/KLI+Ic4E7grFYGWL9+PUPl+iydbPR3MDq9qJMtWbLEqWbqCBaTJEnSpPr7+7npppvo7+/n/PPPrzqOJAGQmTcCSyd46PSZyjA0NMSNN9/KnoMOn6mnbEvzHi2WnbruuzsrTlKtrod+WHUEacZYTJIkSQ3VajWuuOIKAK644gpWrlzp6CRJqrPnoMN5+LmvqjqG2sD82y6vOoI0Y1wzSZIkNdTf3z9pW5IkSZ3HYpIkSWpocHBw0rYkSZI6j8UkSZLU0MjIyKRtSZIkdR6LSZIkSZIkSWqaxSRJkiRJkiQ1zWKSJElq6OSTTx7TftnLXlZREkmSJLULi0mSJKmhpz71qWPahx56aEVJJEmS1C4sJkmSpIauueaaSduSJEnqPBaTJElSQ0ccccSkbUmSJHUei0mSJKmhe+65Z9K2JEmSOo/FJEmSJEmSJDXNYpIkSWpo4cKFk7YlSZLUeSwmSZKkhmq12qRtSZIkdR6LSZIkqaFTTz110rYkSZI6j8UkSZLU0COPPDKmvXv37oqSSJIkqV1YTJIkSQ199atfHdP+yle+UlESSZIktQuLSZIkSZIkSWqaxSRJktTQ05/+9EnbkiRJ6jwWkyRJUkM/+MEPJm1LkiSp81hMkiRJDS1fvnxM+xWveEVFSSRJktQuLCZJkqSGTj311EnbkiRJ6jwWkyRJUkN/8zd/M6a9fv36ipJIkiSpXVhMkiRJDW3btm3StiRJkjqPxSRJktRQREzaliRJUuexmCRJkhrKzEnbkiRJ6jwWkyRJkiRJktQ0i0mSJEmSJElqmsUkSZIkSZIkNc1ikiRJkiRJkppmMUmSJEmSJElNa2kxKSKeERFfjohbI+KWiFhTbj88IgYj4vby52F1+5wfEUMR8Z2IeGUr80mSJEmSJGlquvfVISK2AhNdBziAzMzjJ9l9GPjDzLw+Ip4CXBcRg8CbgM2ZeWFEnAecB/xxRDwfOBt4AfB0YFNE/Gxm7pnSq5IkSZIkSVJL7LOYBPzK/h48M3cAO8r790fErcAi4AzgtLLbAHAV8Mfl9s9m5m7gexExBJwEfH1/M0iSJEmSJGn67HOaW2beMXorNx1X3r8X+GGzTxQRxwIvBq4FjioLTaMFpyPLbouAu+p2u7vcJkmSJEmSpDbQ9JpJEfF7wBeAj5abjgb+ucl9DwG+CLw9M++brOsE2x43xS4iVkbElojYsmvXrmYiSJIkSZIkaRpMZQHutwKnAPcBZObt7B1R1FBEHEBRSPpUZn6p3LwzIhaWjy+kGOUExUikZ9TtfjRwz/hjZmZ/Zi7NzKULFiyYwkuQJEmSJEnSEzGVYtLuzHx0tBER3Uy8MDd1fQL4OHBrZv5l3UOXAn3l/T7gkrrtZ0fEgRHxTOA44JtTyChJkiRJkqQWamYB7lFXR8S7gPkRsRz4A+CyfexzCvBGYGtE3FhuexdwIXBxRJwD3AmcBZCZt0TExcC3Ka4E91av5CZJkiRJktQ+plJMOg84B9gKnAtcDnxssh0y86tMvA4SwOkN9vkA8IEp5JIkSZIkSdIMmUox6QxgQ2b+Q6vCSJKk9nLggQeye/fuMW1JkiR1tqmsmfQa4L8i4v9FxKvLNZMkSdIcVl9ImqgtSZKkztN0MSkz3wwsAT4P/Bbw3xEx6TQ3SZIkSZIkzS1TGl2UmY9FxL9RXMVtPsXUt99tRTBJkiRJkiS1n6ZHJkVEb0RcBAwBv0Gx+PbCFuWSJEmSJElSG5rKyKQ3AZ8Fzs1MF0yQWmD9+vUMDQ1VHaNSo69/zZo1FSep3pIlS1i1alXVMSRJkiRpjKaLSZl5dkQcA/wisCki5gPdmXl/y9JJHWZoaIjbb7mBxYfsqTpKZZ70WDFgcvcdWypOUq07H+iqOoIkSZIkTajpYlJE/B6wEjgceDZwNPD3wOmtiSZ1psWH7OFdL7mv6hiq2AevP7TqCJWLiKdRTKl+IcVafb8DfAf4HHAssA14XWb+qJqEkiRJUmdqes0k4K3AKcB9AJl5O3BkK0JJkgSsBTZm5nOBE4BbgfOAzZl5HLC5bEuSJEmaQVMpJu3OzEdHGxHRTfGXYkmSplVEHAqcCnwcIDMfzcwfU1xFdKDsNgCcWUU+SZIkqZNNpZh0dUS8C5gfEcuBzwOXtSaWJKnDPQvYBXwyIm6IiI9FxMHAUZm5A6D8OeEI2YhYGRFbImLLrl27Zi61JEmS1AGmUkw6j+If9luBc4HLgXe3IpQkqeN1Ay8BPpKZLwYeZApT2jKzPzOXZubSBQsWtCqjJEmS1JGmcjW3EeAfypskSa10N3B3Zl5btr9AUUzaGRELM3NHRCwE7q0soSRJktSh9llMioitTLI2UmYeP62JJEkdLzO/HxF3RcRzMvM7FFcO/XZ56wMuLH9eUmFMSZIkqSM1MzLpV1qeQpKkx1sFfCoingR8F3gzxfTsiyPiHOBO4KwK80mSJEkdaZ/FpMy8o5kDRcTXM/NlTzySJEmQmTcCSyd46PQZjiJJkiSpzlQW4N6XJ0/jsSRJkiRJktSGprOY1HBdJUmSJEmSJM0N01lMkiRJkiRJ0hw3ncWkmMZjSZIkSZIkqQ1NqZgUEcdExLLy/vyIeErdw2+c1mSSJEnSOLVajdWrV1Or1aqOIklSx2q6mBQRvwd8Afhouelo4J9HH8/Mm6c1mSRJkjTOwMAAW7duZcOGDVVHkSSpY01lZNJbgVOA+wAy83bgyFaEkiRJksar1Wps3LiRzGTjxo2OTpIkqSJTKSbtzsxHRxsR0Y1XcJMkSdIMGRgYYGRkBIA9e/Y4OkmSpIpMpZh0dUS8C5gfEcuBzwOXtSaWJEmSNNamTZsYHh4GYHh4mMHBwYoTSZLUmbqn0Pc84BxgK3AucDnwsVaEkiRJksZbtmwZl112GZlJRLB8+fKqI6nDbd++na6HfsL82y6vOoraQNdDNbZvH646hjQjmh6ZlJkjmfkPmXkWsBK4NjOd5iZJkqQZ8ZrXvIbRf35mJr/6q79acSJJkjpT0yOTIuIq4DXlPjcCuyLi6sx8Z2uiSZIkSXtdeumlRMRPRyZddtllvOMd76g6lqZJRBycmQ9WnWMqFi1axPd3d/Pwc19VdRS1gfm3Xc6iRUdVHUOaEVNZM+mpmXkf8GvAJzPz54BlrYklSZotIuLi8ufWiLip7rY1Im6qOp+kuWPTpk1jRia5ZtLcEBG/EBHfBm4t2ydExN9VHEuSNImprJnUHRELgdcB/6dFeSRJs8+a8uevVJpC0py3bNkyLr/8coaHh+nu7nbNpLnjr4BXApcCZOZ/RsSp1UaSJE1mKiOT/gS4AvjvzPxWRDwLuL01sSRJs0Vm7ih/3pGZdwA/Au6vu0nStOjr62PevOKfr11dXaxYsaLiRJoumXnXuE17KgkiSWrKVBbg/nxmHp+Zv1+2v5uZv966aJKk2SQizo2IncBNwHXlbUu1qSTNJT09PfT29hIR9Pb20tPTU3UkTY+7IuIXgIyIJ0XEH1FOeZMktaepLMB9NLAeOAVI4KvAmsy8u0XZJEmzyx8BL8jMH1QdRNLc1dfXx7Zt2xyVNLe8BVgLLALuBq4E3lppIknSpKayZtIngU8DZ5XtN5TbnKwuSQL4b+ChqkNImtt6enpYt25d1TE0jco/Qvx21TkkSc2bSjFpQWZ+sq59UUS8fZrzSJJmr/OB/4iIa4Hdoxszc3V1kSRJ7S4iJqoO/gTYkpmXzHQeSdK+TWUB7h9ExBsioqu8vQGotSqYJGnW+Sjw78A32Ltm0nWVJpIkzQZPBk6kuLjP7cDxwOHAORHx15PtWH4vuSEi/qVsHx4RgxFxe/nzsNZGl6TONJWRSb8D/A3FpTsT+I9ymyRJAMOZ+c6qQ0iSZp0lwC9n5jBARHyEYt2k5cDWfey7hmKx7kPL9nnA5sy8MCLOK9t/3JLUktTBpnI1tzsz8zWZuSAzj8zMM8tLQEuSBPDliFgZEQvLvwwfHhGHVx1KktT2FgEH17UPBp6emXuomzY9XnmBoFcDH6vbfAYwUN4fAM6c1qSSJGBqV3MboLh624/L9mHAX2Smo5MkSQC/RTFy9bxx259VQRZJ0uzxIeDGiLgKCOBU4IMRcTCwaZL9/hr438BT6rYdlZk7ADJzR0Qc2ZLEktThprJm0vGjhSSAzPwR8OJpTyRJmq2eD/wt8J/AjcB64AVVBpIktb/M/DhwCnAb8E/Au4H/yswHM/N/TbRPRPwKcG9m7tfafOVI2i0RsWXXrl37G12SOtZU1kyaFxGHlUUkyqkLU9lfkjS3DQD3AaNX5Xl9ue11lSWSJLW9iPhdirWPjqb4Y8TJwNeBX55kt1OA10TEqygW8D40Iv4R2BkRC8tRSQuBeyfaOTP7gX6ApUuX5nS9FknqFFMZmfQXFJd8fn9EvJ9iAe4PtSaWJGkWek5m/m5mfrm8rQSeU3UoSVLbWwP8PHBHZv4SxeyHSYcLZeb5mXl0Zh4LnA38e2a+AbgU6Cu79QGXtCy1JHWwqSzAvQH4dWAnRYX/1zLz/7UqmCRp1rkhIk4ebUTES4GvVZhH0hxUq9VYvXo1tVqt6iiaPo9k5iMAEXFgZt7G/v8x4kJgeUTcTnE1uAunKaMkqc5UFuBeDDxAUe3/6bbMvLMVwSRJs0NEbKVYePsAYEVE3Fm2jwG+XWU2SXNPf38/N910E/39/Zx//vlVx9H0uDsingb8MzAYET8C7ml258y8CriqvF8DTp/2hJKkMaay5tG/Unw5AJgPPBP4Di6uKkmd7leqDiCpM9RqNQYHBwEYHBxk5cqV9PT0VJxKT1Rmvra8+96I+DLwVGBjhZEkSfvQdDEpM19U346IlwDnTnsiSdKskpl3VJ1BUmfo7+9nZGQEgJGREUcnzUGZeXXVGSRJ+zaVBbjHyMzrKRbKkyRJklpu8+bNk7YlSdLMmMqaSe+sa84DXsI+rrIgSZIkTZfMnLQtSZJmxlTWTHpK3f1hijWUvji9cTrP+vXrGRoaqjrGGGvWrKnkeZcsWcKqVasqeW5JktT+Tj/9dK688sqftpctW1ZhGkmSOtdU1kx6XyuDSJIkSZM599xz2bRpEyMjI8ybN4+VK1dWHUmSpI60z2JSRFzG3qu4PU5mvmZaE3WYqkfinHbaaY/btnbt2pkPIkmStA89PT2ceuqpXHXVVZx66qleyU2SpIo0MzLpwy1PococeeSR3HvvvT9tL1y4sMI0kiRJk7vvvvsAuP/++ytOIklS59pnMcnLc85tF1988ZjRSZ/5zGeqCyNJkjSJWq3G9ddfD8B1111HrVZzdJIkSRWY12zHiNgaETeNu30lIv4qIjyLz2JdXV2Ao5IkSVJ7+/M///Mx7Q9/2AH0kiRVYSpXc/s3YA/w6bJ9NhDAT4CLgF+d1mSaMS984QsB10qSJEnt7Rvf+MaY9te//vWKkkiS1NmmUkw6JTNPqWtvjYivZeYpEfGG6Q4mSZIkSZKk9tP0NDfgkIh46WgjIk4CDimbw9OaSpIkSRrn4IMPnrQtSZJmxlSKSb8LfCwivhcR3wM+BvxuRBwM/GlL0kmSJEmllStXjmm/5S1vqSiJJEmdreliUmZ+KzNfBJwIvDgzjy+3PZiZF0dEX8tSSpIkqeN94QtfGNO++OKLK0oiSVJnm8rIJAAy8yeZ+eMJHlrzxONIkiRJE7vrrrsmbUuSpJkxlQW49yWm8ViSJAlYv349Q0NDVccYY82aav5+tGTJElatWlXJc0uSJGmvKY9MmkRO47EkSZKkMV7+8pePaZ922mnVBJEkqcM5MkmSpDZW9Uicib6sr127duaDSMAb3/hGrr766p+23/CGN1SYRpKkzjWdI5O+No3HkiRJbeApT3nKmPZTn/rUipJIcOmll45pX3bZZRUlkSSpszVdTIqId05wOyciTgTIzLe1LKUkSarE+C/rl1xySUVJJLjyyivHtK+44oqKkkiS1NmmMjJpKfAWYFF5WwmcBvxDRPzviXaIiE9ExL0RcXPdtvdGxPaIuLG8varusfMjYigivhMRr9yfFyRJklrDUUmqWldX16RtSZI0M6ayZlIP8JLMfAAgIi4AvgCcClwHfGiCfS4C/gbYMG77X2Xmh+s3RMTzgbOBFwBPBzZFxM9m5p4pZJQkSdPshBNOAFwrSdV78MEHJ21LkqSZMZWRSYuBR+vajwHHZObDwO6JdsjMa4AfNnn8M4DPZubuzPweMAScNIV8kiRJkiRJarGpFJM+DXwjIi4oRyV9DfhMRBwMfHuKz/u2iLipnAZ3WLltEXBXXZ+7y22PExErI2JLRGzZtWvXFJ9akiRJkiRJ+6vpYlJmvh/4PeDHwE+At2Tmn2Tmg5n521N4zo8AzwZOBHYAf1Fuj4metkGW/sxcmplLFyxYMIWnliRJkiRJ0hPR9JpJEbEW+FxmPqEFEzJzZ90x/wH4l7J5N/CMuq5HA/c8keeSJEmSJEnS9JrKNLfrgXeXV1v784hYuj9PGBEL65qvBUav9HYpcHZEHBgRzwSOA765P88hSZIkSZKk1mh6ZFJmDgADEXE48OvAn0XE4sw8rtE+EfEZ4DTgiIi4G7gAOC0iTqSYwrYNOLc8/i0RcTHF+kvDwFu9kpskSZIkSbPD+vXrGRoaqjTD6POvWbOm0hxLlixh1apVlWZopaaLSXWWAM8FjmUfC29n5usn2PzxSfp/APjAfmSSJElSi7XDl4TxqvqyMNe/JEjSbDV//vyqI3SEqayZ9GfArwH/DXwOeH9m/rhFuSRJkqQxIoLMHNOWJLUPi+ydYyojk74H/ALwLOBA4PjyhH5NS5JJkiSprVT9JWHLli380R/90U/bH/7wh/m5n/u5ChNJktSZplJM2gP8O8VV1m4ETga+Dvzy9MeSJEmSxlq6dOlPRycddNBBFpIkSarIVK7mthr4eeCOzPwl4MXArpakkiRJkiZw7LHHAvD+97+/2iCSJHWwqRSTHsnMRwAi4sDMvA14TmtiSZIkSY936KGHcsIJJzgqSZKkCk1lmtvdEfE04J+BwYj4EXBPK0JJkiRJkiSpPTVdTMrM15Z33xsRXwaeCmxsSSpJkiRJkiS1palMc/upzLw6My/NzEenO5AkSZIkSdL+qNVqrF69mlqtVnWUOW2/ikmSJEmSJEntZmBggK1bt7Jhw4aqo8xpFpMkSZIkSdKsV6vV2LhxI5nJxo0bHZ3UQhaTJEmSJEnSrDcwMMDIyAgAe/bscXRSC1lMkiRJkiRJs96mTZsYHh4GYHh4mMHBwYoTzV0WkyRJbSsiuiLihoj4l7J9eEQMRsTt5c/Dqs4oSZKk9rBs2TK6u4uL1nd3d7N8+fKKE81dFpMkSe1sDXBrXfs8YHNmHgdsLtuSJEkSfX19zJtXlDm6urpYsWJFxYnmLotJkqS2FBFHA68GPla3+QxgoLw/AJw5w7EkSZLUpnp6eujt7SUi6O3tpaenp+pIc1Z31QEkSWrgr4H/DTylbttRmbkDIDN3RMSRE+0YESuBlQCLFy9ucUxJkiS1i76+PrZt2+aopBZzZJIkqe1ExK8A92bmdfuzf2b2Z+bSzFy6YMGCaU4nSZKkdtXT08O6descldRijkySJLWjU4DXRMSrgCcDh0bEPwI7I2JhOSppIXBvpSklSZKkDuTIJElS28nM8zPz6Mw8Fjgb+PfMfANwKdBXdusDLqkooiRJktSxLCZJkmaTC4HlEXE7sLxsS5IkSZpBTnOTJLW1zLwKuKq8XwNOrzKPJEmS1OkcmSRJkiRJkqSmWUySJEmSJElS0ywmSZIkSZIkqWkWkyRJkiRJktQ0i0mSJEmSJElqmsUkSZIkSZIkNc1ikiRJkiRJkppmMUmSJEmSJElNs5gkSZIkSZKkpllMkiRJkiRJUtO6qw4gSZIkSbNV10M/ZP5tl1cdo1LzHrkPgJEnH1pxkmp1PfRD4KiqY0gzwmKSJEmSJO2HJUuWVB2hLQwN3Q/Akmd1eiHlKN8T6hgWkyRJkiRpP6xatarqCG1hzZo1AKxdu7biJJJmimsmSZIkSZIkqWkWkyRJkiRJktQ0i0mSJEmSJElqmsUkSZIkSZIkNc1ikiRJkiRJkppmMUmSJEmSJElNs5gkSZIkSZKkpllMkiRJkiRJc0KtVmP16tXUarWqo8xp3VUHkCRJUnPWr1/P0NBQ1TEqNfr616xZU3GS6i1ZsoRVq1ZVHUOS2srAwABbt25lw4YNvOMd76g6zpxlMUmSpEn45d0v7/Wq/vI+NDTE7bfcwOJD9lSWoWpPeqwYWL/7ji0VJ6nWnQ90VR1BktpOrVZj48aNZCYbN25kxYoV9PT0VB1rTrKYJEnSJIaGhrjx5lvZc9DhVUepzLxHE4Drvruz4iTV6nroh1VHAGDxIXt410vuqzqGKvbB6w+tOoIktZ2BgQFGRkYA2LNnj6OTWshikiRJ+7DnoMN5+LmvqjqGKjb/tsurjiBJkiaxadMmhoeHARgeHmZwcNBiUou4ALckSZKkWSUinhERX46IWyPilohYU24/PCIGI+L28udhVWeVNHOWLVtGd3cxZqa7u5vly5dXnGjuspgkSZIkabYZBv4wM58HnAy8NSKeD5wHbM7M44DNZVtSh+jr62PevKLM0dXVxYoVKypONHdZTJIkSZI0q2Tmjsy8vrx/P3ArsAg4Axgouw0AZ1YSUFIlenp66O3tJSLo7e118e0W6vg1k7xKj1fpqVf1VXokSZI0NRFxLPBi4FrgqMzcAUXBKSKObLDPSmAlwOLFi2coqaSZ0NfXx7Zt2xyV1GIdX0zyKj1epWdUu1ylR5IkSc2JiEOALwJvz8z7IqKp/TKzH+gHWLp0abYuoaSZ1tPTw7p166qOMed1fDEJvEqPCl6lR5IkafaIiAMoCkmfyswvlZt3RsTCclTSQuDe6hJK0tzlmkmSJEmSZpUohiB9HLg1M/+y7qFLgb7yfh9wyUxnk6RO4MgkSZIkSbPNKcAbga0RcWO57V3AhcDFEXEOcCdwVjXxJGlus5gkSZIkaVbJzK8CjRZIOn0ms0hSJ3KamyRJkiRJkppmMUmSJEmSJElNc5qbJEnSLLF9+3YevL+LD15/aNVRVLE77u/i4O3bq44hSepQjkySJEmSJElS0xyZJEmSNEssWrSI3cM7eNdL7qs6iir2wesP5cBFi6qOIUnqUBaTpDbi9AWNcvqCJEmSpHblNDdJkiRJkiQ1zZFJUhtx+oJGOX1BkiRJUruymCRJ0iS2b99O10M/Yf5tl1cdRRXreqjG9u3DVceQJEmqXEunuUXEJyLi3oi4uW7b4RExGBG3lz8Pq3vs/IgYiojvRMQrW5lNkiRJkiRJU9fqkUkXAX8DbKjbdh6wOTMvjIjzyvYfR8TzgbOBFwBPBzZFxM9m5p4WZ5QkqaFFixbx/d3dPPzcV1UdRRWbf9vlLFp0VNUxJEmSKtfSkUmZeQ3ww3GbzwAGyvsDwJl12z+bmbsz83vAEHBSK/NJkiRJkiRpaqq4mttRmbkDoPx5ZLl9EXBXXb+7y22SJEmSJElqE1UUkxqJCbblhB0jVkbElojYsmvXrhbHkiRJkiRJ0qgqikk7I2IhQPnz3nL73cAz6vodDdwz0QEysz8zl2bm0gULFrQ0rCRJkiRJkvaqoph0KdBX3u8DLqnbfnZEHBgRzwSOA75ZQT5JkiRJkiQ10NKruUXEZ4DTgCMi4m7gAuBC4OKIOAe4EzgLIDNviYiLgW8Dw8BbvZKbJEnSWHc+0MUHrz+06hiV2flQ8bfQow4aqThJte58oIvjqg4hSW2oVqvxvve9jwsuuICenp6q48xZLS0mZebrGzx0eoP+HwA+0LpEkiRJs9eSJUuqjlC5R4eGADjwmM7+XRyH7wdJmsjAwABbt25lw4YNvOMd76g6zpzV0mKSJEmSps+qVauqjlC5NWvWALB27dqKk0iS2k2tVmPjxo1kJhs3bmTFihWOTmqRdrqamyRJkiRJ0n4ZGBhgZKSYBr1nzx42bNhQcaK5y2KSJEmSJEma9TZt2sTw8DAAw8PDDA4OVpxo7rKYJEmSJEmSZr1ly5bR3V2s5tPd3c3y5csrTjR3WUySJEmSJEmzXl9fH/PmFWWOrq4uVqxYUXGiuctikiRJkiRJmvV6enro7e0lIujt7XXx7Rbq+Ku5bd++na6HfsL82y6vOooq1vVQje3bh6uOIUmSJEnaT319fWzbts1RSS3W8cUkSZIkSZI0N/T09LBu3bqqY8x5HV9MWrRoEd/f3c3Dz31V1VFUsfm3Xc6iRUdVHUNSG+p66IcdPYJ13iP3ATDy5EMrTlKtrod+CHiekCRJ6vhikiRJk1myZEnVESo3NHQ/AEue1emFlKN8P0iSJGExSZKkSa1atarqCJVbs2YNAGvXrq04iSRJktqBV3OTJEmSJElS0ywmSZIkSZIkqWkWkyRJkiRJktQ0i0mSJEmSJElqmsUkSZIkSZIkNc1ikiRJkiRJkppmMUmSJEmSJElNs5gkSWo7EfGMiPhyRNwaEbdExJpy++ERMRgRt5c/D6s6qyRJktRpLCZJktrRMPCHmfk84GTgrRHxfOA8YHNmHgdsLtuSJEmSZpDFJElS28nMHZl5fXn/fuBWYBFwBjBQdhsAzqwkoCRJktTBuqsOIGmsOx/o4oPXH1p1jMrsfKiocR910EjFSap15wNdHFd1iDYREccCLwauBY7KzB1QFJwi4sgG+6wEVgIsXrx4hpJKkiRJncFiktRGlixZUnWEyj06NATAgcd09u/iOHw/AETEIcAXgbdn5n0R0dR+mdkP9AMsXbo0W5dQkiRJ6jwWk6Q2smrVqqojVG7NmjUArF27tuIkqlpEHEBRSPpUZn6p3LwzIhaWo5IWAvdWl1CSJEnqTK6ZJElqO1EMQfo4cGtm/mXdQ5cCfeX9PuCSmc4mSZIkdTpHJkmS2tEpwBuBrRFxY7ntXcCFwMURcQ5wJ3BWNfEkSZKkzmUxSZLUdjLzq0CjBZJOn8kskiRJksaymAR0PfRD5t92edUxKjPvkfsAGHly515BDIr3ARxVdQxJkiRJktpaxxeTvFoSDA3dD8CSZ3V6IeUo3w+SJEmSJO1DxxeTvHqWV8+SJEmSJEnN82pukiRJkiRJaprFJEmSJEmSJDXNYpIkSZIkSZKaZjFJkiRJkiRJTbOYJEmSJEmSpKZZTJIkSZIkSVLTLCZJkiRJkiSpaRaTJEmSJEmS1DSLSZIkSZIkSWqaxSRJkiRJkiQ1rbvqAJIkSZod1q9fz9DQUKUZ/uu//ovdu3fzB3/wBxxwwAGV5ViyZAmrVq2q7PklSaqSI5MkSZI0a4yMjDAyMsLOnTurjiJJUsdyZJIkSZKaUvVInFqtxutf/3oAHnjgAd7znvfQ09NTaSZJkjqRI5MkSZI0KwwMDDAyMgLAnj172LBhQ8WJJEnqTBaTJEmSNCts2rSJ4eFhAIaHhxkcHKw4kSRJnclikiRJkmaFZcuW0d1drNLQ3d3N8uXLK04kSVJnspgkSZKkWaGvr49584p/vnZ1dbFixYqKE0mS1JksJkmSJGlW6Onpobe3l4igt7fXxbclSaqIV3OTJEnSrNHX18e2bdsclSRJUoUsJkmSJGnW6OnpYd26dVXHkCSpoznNTZIkSZIkSU2zmCRJkiRJkuaEWq3G6tWrqdVqVUeZ0ywmSZIkSZKkOWFgYICtW7eyYcOGqqPMaRaTJEmSJEnSrFer1di4cSOZycaNGx2d1EIWkyRJkiRJ0qw3MDDAyMgIAHv27HF0UgtZTJIkSZIkSbPepk2bGB4eBmB4eJjBwcGKE81dFpMkSZIkSdKst2zZMrq7uwHo7u5m+fLlFSeauywmSZIkSZozIqI3Ir4TEUMRcV7VeSTNnL6+PubNK8ocXV1drFixouJEc5fFJEmSJElzQkR0AX8L/E/g+cDrI+L51aaSNFN6enro7e0lIujt7aWnp6fqSHNWd9UBJLWX9evXMzQ0VNnzjz73mjVrKssAsGTJElatWlVpBkmSNGUnAUOZ+V2AiPgscAbw7UpTSZoxfX19bNu2zVFJLWYxqWJVf3EHv7yrvcyfP7/qCFJb8Tyxl+cJSU1YBNxV174beGlFWWaE54m9PE8IitFJ69atqzrGnGcxSX551xiegCWN53lC0iwSE2zLx3WKWAmsBFi8eHGrM815niekzmMxqWJ+cZckTcbzhCRNyd3AM+raRwP3jO+Umf1AP8DSpUsfV2yaTTxPSKqCC3BLkiRJmiu+BRwXEc+MiCcBZwOXVpxJkuacykYmRcQ24H5gDzCcmUsj4nDgc8CxwDbgdZn5o6oySpIkSZo9MnM4It4GXAF0AZ/IzFsqjiVJc07VI5N+KTNPzMylZfs8YHNmHgdsLtuSJEmS1JTMvDwzfzYzn52ZH6g6jyTNRVUXk8Y7Axgo7w8AZ1YXRZIkSZIkSeNVWUxK4MqIuK68mgLAUZm5A6D8eWRl6SRJkiRJkvQ4VV7N7ZTMvCcijgQGI+K2Znf0Up6SJEmSJEnVqGxkUmbeU/68F/gn4CRgZ0QsBCh/3ttg3/7MXJqZSxcsWDBTkSVJkiRJkjpeJcWkiDg4Ip4yeh94BXAzxWU7+8pufcAlVeSTJEmSJEnSxKqa5nYU8E8RMZrh05m5MSK+BVwcEecAdwJnVZRPkiRJkiRJE6ikmJSZ3wVOmGB7DTh95hNJkiRJkiSpGVVezU2SJEmSJEmzjMUkSZIkSZIkNc1ikiRJkiRJkppmMUmSJEmSJElNs5gkSZIkSZKkpkVmVp3hCYmIXcAdVeeYA44AflB1CKnk+3F6HJOZC6oOUTXPE9PGz6Xaie/H6eF5As8T08jPpdqJ78fp0fA8MeuLSZoeEbElM5dWnUMC349SO/JzqXbi+1FqP34u1U58P7ae09wkSZIkSZLUNItJkiRJkiRJaprFJI3qrzqAVMf3o9R+/Fyqnfh+lNqPn0u1E9+PLeaaSZIkSZIkSWqaI5MkSZIkSZLUNItJIiJ6I+I7ETEUEedVnUedKyI+ERH3RsTNVWeRtJfnCbULzxNS+/EcoXbieWLmWEzqcBHRBfwt8D+B5wOvj4jnV5tKHewioLfqEJL28jyhNnMRniektuE5Qm3oIjxPzAiLSToJGMrM72bmo8BngTMqzqQOlZnXAD+sOoekMTxPqG14npDajucItRXPEzPHYpIWAXfVte8ut0mSBJ4nJEmNeY6QOpTFJMUE27zEnyRplOcJSVIjniOkDmUxSXcDz6hrHw3cU1EWSVL78TwhSWrEc4TUoSwm6VvAcRHxzIh4EnA2cGnFmSRJ7cPzhCSpEc8RUoeymNThMnMYeBtwBXArcHFm3lJtKnWqiPgM8HXgORFxd0ScU3UmqdN5nlA78TwhtRfPEWo3nidmTmQ6pVWSJEmSJEnNcWSSJEmSJEmSmmYxSZIkSZIkSU2zmCRJkiRJkqSmWUySJEmSJElS0ywmSZIkSZIkqWkWk6QmRcTbI+KgKe5zbETc3KpMkqT24XlCktSI5wjNNRaTpOa9HZjwBBARXTMbRZLUht6O5wlJ0sTejucIzSEWk6QJRMTBEfGvEfGfEXFzRFwAPB34ckR8uezzQET8SURcC7wsIt5Z9r05It4+wTGfFRE3RMTPR8SzI2JjRFwXEV+JiOfO7CuUJD0RnickSY14jlAn6K46gNSmeoF7MvPVABHxVODNwC9l5g/KPgcDN2fmeyLi58rHXwoEcG1EXA38qNz/OcBngTdn5o0RsRl4S2beHhEvBf4O+OUZfH2SpCfG84QkqRHPEZrzIjOrziC1nYj4WeAK4GLgXzLzKxGxDVg6egKIiGHgwMzcExFrgJ7MfE/52PuBXcClwLUUJ4Jfz8xbIuKQ8rHv1D3lgZn5vBl6eZKkJ8jzhCSpEc8R6gSOTJImkJn/Vf6F4FXAn0bElRN0eyQz95T3Y5LD/QS4CzgFuIVieumPM/PEaYwsSZpBnickSY14jlAncM0kaQIR8XTgocz8R+DDwEuA+4GnNNjlGuDMiDgoIg4GXgt8pXzsUeBMYEVE/FZm3gd8LyLOKp8rIuKE1r0aSdJ08zwhSWrEc4Q6gSOTpIm9CPjziBgBHgN+H3gZ8G8RsSMzf6m+c2ZeHxEXAd8sN30sM2+IiGPLxx+MiF8BBiPiQeC3gY9ExLuBAyjmQP/nDLwuSdL08DwhSWrEc4TmPNdMkiRJkiRJUtOc5iZJkiRJkqSmWUySJEmSJElS0ywmSZIkSZIkqWkWkyRJkiRJktQ0i0mSJEmSJElqmsUkSZIkSZIkNc1ikiRJkiRJkppmMUmSJEmSJElN+/8BRwo/yP8hRwcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(20,7))\n",
    "fig.suptitle(\"Boxplot for Strokes\", fontsize=35)\n",
    "\n",
    "sns.boxplot(x=\"stroke\", y=\"avg_glucose_level\", data=df,ax=ax1)\n",
    "sns.boxplot(x=\"stroke\", y=\"bmi\", data=df,ax=ax2)\n",
    "sns.boxplot(x=\"stroke\", y=\"age\", data=df,ax=ax3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "55210f4a-64c0-4796-b430-eb5b7336e368",
   "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>Residence_type</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>risk of stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rural</td>\n",
       "      <td>384</td>\n",
       "      <td>21638</td>\n",
       "      <td>1.774656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Urban</td>\n",
       "      <td>399</td>\n",
       "      <td>21751</td>\n",
       "      <td>1.834398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Residence_type  sum  count  risk of stroke\n",
       "0          Rural  384  21638        1.774656\n",
       "1          Urban  399  21751        1.834398"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df\n",
    "temp['stroke'] = temp['stroke'].astype(int)\n",
    "residence_type_df = df.groupby([\"Residence_type\"])['stroke'].agg(['sum','count']).reset_index()\n",
    "residence_type_df['risk of stroke'] = residence_type_df['sum'] / residence_type_df['count'] * 100\n",
    "residence_type_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7a9becb7-121f-449b-8c91-3f36f2844943",
   "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                   1458\n",
       "smoking_status       13290\n",
       "stroke                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3838fe56-f6b6-43d6-87f7-8b95cfea54a2",
   "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>81</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>407</th>\n",
       "      <td>Female</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>Male</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>219.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>Male</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>217.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1613</th>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>189.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42530</th>\n",
       "      <td>Male</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>182.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42839</th>\n",
       "      <td>Female</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>234.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43007</th>\n",
       "      <td>Female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>89.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43100</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>136.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43339</th>\n",
       "      <td>Female</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>100.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "81     Female  61.0             0              0          Yes  Self-employed   \n",
       "407    Female  59.0             0              0          Yes        Private   \n",
       "747      Male  78.0             0              1          Yes        Private   \n",
       "1139     Male  57.0             0              1           No       Govt_job   \n",
       "1613     Male  58.0             0              0          Yes        Private   \n",
       "...       ...   ...           ...            ...          ...            ...   \n",
       "42530    Male  66.0             0              0          Yes  Self-employed   \n",
       "42839  Female  67.0             1              0          Yes       Govt_job   \n",
       "43007  Female  69.0             0              1          Yes  Self-employed   \n",
       "43100    Male  67.0             0              0          Yes  Self-employed   \n",
       "43339  Female  76.0             0              0           No        Private   \n",
       "\n",
       "      Residence_type  avg_glucose_level  bmi smoking_status  stroke  \n",
       "81             Rural             202.21  NaN   never smoked       1  \n",
       "407            Rural              76.15  NaN            NaN       1  \n",
       "747            Urban             219.84  NaN            NaN       1  \n",
       "1139           Urban             217.08  NaN            NaN       1  \n",
       "1613           Rural             189.84  NaN            NaN       1  \n",
       "...              ...                ...  ...            ...     ...  \n",
       "42530          Urban             182.89  NaN   never smoked       1  \n",
       "42839          Urban             234.43  NaN   never smoked       1  \n",
       "43007          Rural              89.19  NaN         smokes       1  \n",
       "43100          Urban             136.79  NaN         smokes       1  \n",
       "43339          Rural             100.55  NaN   never smoked       1  \n",
       "\n",
       "[140 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['bmi'].isnull() & df[\"stroke\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bcd07646-e1e1-49d8-bbea-470612c2d3b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Female</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Rural</td>\n",
       "      <td>76.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>Male</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>219.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>Male</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>217.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>Male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>208.30</td>\n",
       "      <td>32.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42110</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>222.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42569</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>88.57</td>\n",
       "      <td>44.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43051</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>114.61</td>\n",
       "      <td>21.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43130</th>\n",
       "      <td>Female</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>118.61</td>\n",
       "      <td>29.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43268</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>97.36</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "407    Female  59.0             0              0          Yes        Private   \n",
       "426    Female  78.0             0              0          Yes        Private   \n",
       "747      Male  78.0             0              1          Yes        Private   \n",
       "1139     Male  57.0             0              1           No       Govt_job   \n",
       "1315     Male  82.0             0              1          Yes        Private   \n",
       "...       ...   ...           ...            ...          ...            ...   \n",
       "42110  Female  80.0             0              0           No        Private   \n",
       "42569    Male  60.0             0              0          Yes        Private   \n",
       "43051  Female  80.0             0              0          Yes  Self-employed   \n",
       "43130  Female  82.0             0              1          Yes  Self-employed   \n",
       "43268    Male  69.0             0              0          Yes  Self-employed   \n",
       "\n",
       "      Residence_type  avg_glucose_level   bmi smoking_status  stroke  \n",
       "407            Rural              76.15   NaN            NaN       1  \n",
       "426            Urban              58.57  24.2            NaN       1  \n",
       "747            Urban             219.84   NaN            NaN       1  \n",
       "1139           Urban             217.08   NaN            NaN       1  \n",
       "1315           Rural             208.30  32.5            NaN       1  \n",
       "...              ...                ...   ...            ...     ...  \n",
       "42110          Urban             222.87   NaN            NaN       1  \n",
       "42569          Urban              88.57  44.6            NaN       1  \n",
       "43051          Rural             114.61  21.4            NaN       1  \n",
       "43130          Urban             118.61  29.4            NaN       1  \n",
       "43268          Rural              97.36  28.0            NaN       1  \n",
       "\n",
       "[145 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['smoking_status'].isnull() & df[\"stroke\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3477cc18-39bb-4ff5-8dd9-113b1c5bebc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>work_type</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>risk of stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Govt_job</td>\n",
       "      <td>89</td>\n",
       "      <td>5438</td>\n",
       "      <td>1.636631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Never_worked</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>441</td>\n",
       "      <td>24827</td>\n",
       "      <td>1.776292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Self-employed</td>\n",
       "      <td>251</td>\n",
       "      <td>6793</td>\n",
       "      <td>3.694980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>children</td>\n",
       "      <td>2</td>\n",
       "      <td>6154</td>\n",
       "      <td>0.032499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       work_type  sum  count  risk of stroke\n",
       "0       Govt_job   89   5438        1.636631\n",
       "1   Never_worked    0    177        0.000000\n",
       "2        Private  441  24827        1.776292\n",
       "3  Self-employed  251   6793        3.694980\n",
       "4       children    2   6154        0.032499"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df\n",
    "temp['stroke'] = temp['stroke'].astype(int)\n",
    "work_type_df = df.groupby([\"work_type\"])['stroke'].agg(['sum','count']).reset_index()\n",
    "work_type_df['risk of stroke'] = work_type_df['sum'] / work_type_df['count'] * 100\n",
    "work_type_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c05dd04e-955c-4e19-8964-a17695c6c0ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>smoking_status</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>risk of stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>221</td>\n",
       "      <td>7487</td>\n",
       "      <td>2.951783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>never smoked</td>\n",
       "      <td>284</td>\n",
       "      <td>16051</td>\n",
       "      <td>1.769360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>smokes</td>\n",
       "      <td>133</td>\n",
       "      <td>6561</td>\n",
       "      <td>2.027130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    smoking_status  sum  count  risk of stroke\n",
       "0  formerly smoked  221   7487        2.951783\n",
       "1     never smoked  284  16051        1.769360\n",
       "2           smokes  133   6561        2.027130"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df\n",
    "temp['stroke'] = temp['stroke'].astype(int)\n",
    "smoking_status_df = temp.groupby([\"smoking_status\"])['stroke'].agg(['sum','count']).reset_index()\n",
    "smoking_status_df['risk of stroke'] = smoking_status_df['sum'] / smoking_status_df['count'] * 100\n",
    "smoking_status_df\n",
    "\n",
    "#suprisingly according to those who had stroke and their smoking status was logged formerly smoked status had a higher\n",
    "#risk than currently smokes\n",
    "\n",
    "#would love to know what the true results would be if many stroke patients had their smoking_status measured "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6cc85de-e9eb-4015-bc62-924298d74fec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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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>ever_married</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>risk of stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>80</td>\n",
       "      <td>15456</td>\n",
       "      <td>0.517598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>703</td>\n",
       "      <td>27933</td>\n",
       "      <td>2.516736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ever_married  sum  count  risk of stroke\n",
       "0           No   80  15456        0.517598\n",
       "1          Yes  703  27933        2.516736"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df\n",
    "temp['stroke'] = temp['stroke'].astype(int)\n",
    "ever_married_df = temp.groupby([\"ever_married\"])['stroke'].agg(['sum','count']).reset_index()\n",
    "ever_married_df['risk of stroke'] = ever_married_df['sum'] / ever_married_df['count'] * 100\n",
    "ever_married_df\n",
    "\n",
    "# question is how correlated is this to age (older individuals have higher chance of having been married)\n",
    "# -> is age really what is an important feature here or is marital status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e62ed268-2a6c-47ee-9437-7a0cb8358b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop these columns to prevent answer leaking into data\n",
    "df = df.drop('smoking_status', axis = 1)\n",
    "df = df.drop('bmi', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3815e3d3-47b8-4602-ad49-4c8f109524af",
   "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>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>95.12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>87.96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>110.89</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>69.04</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Never_worked</td>\n",
       "      <td>Rural</td>\n",
       "      <td>161.28</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",
       "0    Male   3.0             0              0           No      children   \n",
       "1    Male  58.0             1              0          Yes       Private   \n",
       "2  Female   8.0             0              0           No       Private   \n",
       "3  Female  70.0             0              0          Yes       Private   \n",
       "4    Male  14.0             0              0           No  Never_worked   \n",
       "\n",
       "  Residence_type  avg_glucose_level  stroke  \n",
       "0          Rural              95.12       0  \n",
       "1          Urban              87.96       0  \n",
       "2          Urban             110.89       0  \n",
       "3          Rural              69.04       0  \n",
       "4          Rural             161.28       0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "22259954-f537-4ca4-ab2b-7b0d50705438",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9db14198-de4b-452e-af04-d0e99f5ba49a",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = OneHotEncoder(handle_unknown='ignore')\n",
    "\n",
    "ct = ColumnTransformer([\n",
    "    ('encoder', encoder, [0, 4, 5, 6])\n",
    "], remainder='passthrough')\n",
    "\n",
    "newDF = ct.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ef2e19c0-f780-4161-a6f9-c6ed91ef1f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = np.delete(newDF, -1, axis=1), newDF[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7d95c37b-2fcd-4513-b367-c2eb3e9ffb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "from imblearn.combine import SMOTETomek\n",
    "\n",
    "smote_enn = SMOTEENN(random_state=0)\n",
    "\n",
    "\n",
    "X_resampled, y_resampled = smote_enn.fit_resample(X, y)\n",
    "\n",
    "\n",
    "smote_tomek = SMOTETomek(random_state=0)\n",
    "X_resampled, y_resampled = smote_tomek.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ccbd1b82-8552-4af9-8b76-a0e46fbdd2be",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "71e07539-5505-411a-b604-c9bef817cb17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(newDF[0])"
   ]
  }
 ],
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