heart disease prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>20.34</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>26.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>24.21</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>23.71</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "0           No  16.60     Yes              No     No             3.0   \n",
       "1           No  20.34      No              No    Yes             0.0   \n",
       "2           No  26.58     Yes              No     No            20.0   \n",
       "3           No  24.21      No              No     No             0.0   \n",
       "4           No  23.71      No              No     No            28.0   \n",
       "\n",
       "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
       "0          30.0          No  Female        55-59  White      Yes   \n",
       "1           0.0          No  Female  80 or older  White       No   \n",
       "2          30.0          No    Male        65-69  White      Yes   \n",
       "3           0.0          No  Female        75-79  White       No   \n",
       "4           0.0         Yes  Female        40-44  White       No   \n",
       "\n",
       "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
       "0              Yes  Very good        5.0    Yes            No        Yes  \n",
       "1              Yes  Very good        7.0     No            No         No  \n",
       "2              Yes       Fair        8.0    Yes            No         No  \n",
       "3               No       Good        6.0     No            No        Yes  \n",
       "4              Yes  Very good        8.0     No            No         No  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= pd.read_csv(\"heart_2020_cleaned.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
       "       'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n",
       "       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n",
       "       'Asthma', 'KidneyDisease', 'SkinCancer'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(319795, 18)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HeartDisease</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>292422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>319795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.3254</td>\n",
       "      <td>6.3561</td>\n",
       "      <td>12.02</td>\n",
       "      <td>24.03</td>\n",
       "      <td>27.34</td>\n",
       "      <td>31.42</td>\n",
       "      <td>94.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smoking</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>187887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>298018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stroke</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>307726</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <td>319795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.37171</td>\n",
       "      <td>7.95085</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MentalHealth</th>\n",
       "      <td>319795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.89837</td>\n",
       "      <td>7.95524</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiffWalking</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>275385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>167805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeCategory</th>\n",
       "      <td>319795</td>\n",
       "      <td>13</td>\n",
       "      <td>65-69</td>\n",
       "      <td>34151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race</th>\n",
       "      <td>319795</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>245212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diabetic</th>\n",
       "      <td>319795</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>269653</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>247957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenHealth</th>\n",
       "      <td>319795</td>\n",
       "      <td>5</td>\n",
       "      <td>Very good</td>\n",
       "      <td>113858</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SleepTime</th>\n",
       "      <td>319795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.09707</td>\n",
       "      <td>1.43601</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asthma</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>276923</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KidneyDisease</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>308016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinCancer</th>\n",
       "      <td>319795</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>289976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count unique        top    freq     mean      std    min  \\\n",
       "HeartDisease      319795      2         No  292422      NaN      NaN    NaN   \n",
       "BMI               319795    NaN        NaN     NaN  28.3254   6.3561  12.02   \n",
       "Smoking           319795      2         No  187887      NaN      NaN    NaN   \n",
       "AlcoholDrinking   319795      2         No  298018      NaN      NaN    NaN   \n",
       "Stroke            319795      2         No  307726      NaN      NaN    NaN   \n",
       "PhysicalHealth    319795    NaN        NaN     NaN  3.37171  7.95085      0   \n",
       "MentalHealth      319795    NaN        NaN     NaN  3.89837  7.95524      0   \n",
       "DiffWalking       319795      2         No  275385      NaN      NaN    NaN   \n",
       "Sex               319795      2     Female  167805      NaN      NaN    NaN   \n",
       "AgeCategory       319795     13      65-69   34151      NaN      NaN    NaN   \n",
       "Race              319795      6      White  245212      NaN      NaN    NaN   \n",
       "Diabetic          319795      4         No  269653      NaN      NaN    NaN   \n",
       "PhysicalActivity  319795      2        Yes  247957      NaN      NaN    NaN   \n",
       "GenHealth         319795      5  Very good  113858      NaN      NaN    NaN   \n",
       "SleepTime         319795    NaN        NaN     NaN  7.09707  1.43601      1   \n",
       "Asthma            319795      2         No  276923      NaN      NaN    NaN   \n",
       "KidneyDisease     319795      2         No  308016      NaN      NaN    NaN   \n",
       "SkinCancer        319795      2         No  289976      NaN      NaN    NaN   \n",
       "\n",
       "                    25%    50%    75%    max  \n",
       "HeartDisease        NaN    NaN    NaN    NaN  \n",
       "BMI               24.03  27.34  31.42  94.85  \n",
       "Smoking             NaN    NaN    NaN    NaN  \n",
       "AlcoholDrinking     NaN    NaN    NaN    NaN  \n",
       "Stroke              NaN    NaN    NaN    NaN  \n",
       "PhysicalHealth        0      0      2     30  \n",
       "MentalHealth          0      0      3     30  \n",
       "DiffWalking         NaN    NaN    NaN    NaN  \n",
       "Sex                 NaN    NaN    NaN    NaN  \n",
       "AgeCategory         NaN    NaN    NaN    NaN  \n",
       "Race                NaN    NaN    NaN    NaN  \n",
       "Diabetic            NaN    NaN    NaN    NaN  \n",
       "PhysicalActivity    NaN    NaN    NaN    NaN  \n",
       "GenHealth           NaN    NaN    NaN    NaN  \n",
       "SleepTime             6      7      8     24  \n",
       "Asthma              NaN    NaN    NaN    NaN  \n",
       "KidneyDisease       NaN    NaN    NaN    NaN  \n",
       "SkinCancer          NaN    NaN    NaN    NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18078"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates(inplace=True)\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301717, 18)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeartDisease        0\n",
       "BMI                 0\n",
       "Smoking             0\n",
       "AlcoholDrinking     0\n",
       "Stroke              0\n",
       "PhysicalHealth      0\n",
       "MentalHealth        0\n",
       "DiffWalking         0\n",
       "Sex                 0\n",
       "AgeCategory         0\n",
       "Race                0\n",
       "Diabetic            0\n",
       "PhysicalActivity    0\n",
       "GenHealth           0\n",
       "SleepTime           0\n",
       "Asthma              0\n",
       "KidneyDisease       0\n",
       "SkinCancer          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                         251796\n",
       "Yes                         40589\n",
       "No, borderline diabetes      6776\n",
       "Yes (during pregnancy)       2556\n",
       "Name: Diabetic, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Diabetic'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.replace(\"No\",0)\n",
    "data=data.replace(\"Yes\",1)\n",
    "data=data.replace(\"Male\",0)\n",
    "data=data.replace(\"Female\",1)\n",
    "data=data.replace(\"No, borderline diabetes\",0)\n",
    "data=data.replace(\"Yes (during pregnancy)\",1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65-69          31670\n",
       "60-64          31219\n",
       "70-74          29273\n",
       "55-59          27610\n",
       "50-54          23736\n",
       "80 or older    23352\n",
       "75-79          20713\n",
       "45-49          20518\n",
       "18-24          19998\n",
       "40-44          19837\n",
       "35-39          19526\n",
       "30-34          17953\n",
       "25-29          16312\n",
       "Name: AgeCategory, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['AgeCategory'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Very good    104796\n",
       "Good          91239\n",
       "Excellent     59737\n",
       "Fair          34659\n",
       "Poor          11286\n",
       "Name: GenHealth, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['GenHealth'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    259066\n",
       "1     42651\n",
       "Name: Asthma, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Asthma'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    289941\n",
       "1     11776\n",
       "Name: KidneyDisease, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['KidneyDisease'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    272425\n",
       "1     29292\n",
       "Name: SkinCancer, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SkinCancer'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White                             227724\n",
       "Hispanic                           27107\n",
       "Black                              22810\n",
       "Other                              10891\n",
       "Asian                               7993\n",
       "American Indian/Alaskan Native      5192\n",
       "Name: Race, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Race'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Race'].replace({'White':0,'Hispanic':1,'Black':2,'Asian':3,'American Indian/Alaskan Native':4,'Other':5}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['GenHealth'].replace({'Excellent':0,'Very good':1,'Good':2,'Fair':3,'Poor':4}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data['AgeCategory'].replace({'18-24':0,'25-29':1,'30-34':2,'35-39':3,'40-44':4,'45-49':5,'50-54':6,'55-59':7,'60-64':8,'65-69':9,'70-74':10,'75-79':11,'80 or older':12}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,8))\n",
    "mask = np.triu(np.ones_like(data.corr(), dtype=bool))\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "sns.heatmap(data.corr(), mask=mask, cmap=cmap, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(data,stratify=data['HeartDisease'],test_size=.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((241373, 18), (60344, 18))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape ,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lmNa4eLBqtbsXJzverH5r3TG3ZgD/es25YXfEewhmIh0UJmD/5oOJBBYStFY2KR5AvYxgrMjORXH0KupS2lx7DZJuzaYGBN9K0D1pmLv3IujTbs324a08b7Ow3/8xtDDZA2zxxAyfBo4l6A7cCxjRdLhwny+7+7EE3bHuJRgX0LTPFifA2JJzExERERHpDN0tWbwFOMbMDgtbiVIWTHwytJX6DwDbmNk3LZgApoeZNY272JoB/M0HMlcS9IFeCuTCAcMfL6p/J/AFM9vJgkHT/xuPGI5P+SvBGMem6c6HWMvTGEM7DZLeiE0NCO4BrHD3jJlNJEjANmYpQZfXNg00D4+3E8F5bkMwXqF5nS2dmKEHwcyBywlac39VtM9KM/uMmfUKu7WuKdrnxibAEBEREZGQu39eXVBLR7dKFsNZlI4lGJy7lKDF53u08j6Eg1sPJWihWkQwTvCgcPXWDOBfbyBzeJyvEySFKwkSqPuL4ngQuBx4nGDyhKZJXrLh4w/C8hfC7pH/JZhYpyXtOUh6A20YEPw1gnvyrCVIeu9saT9F+6sjGAf4bNiFc99Wqp5swWyuqwjeu+XAXu7e0jTLWzoxw00E3XbnE0wG8UKz/X4OmBX+DL5KMM38RifAEBEREREpVV1ygpuuLmw5extItmOLoIiIiIiIyP8oWSwTZnY8wayu1cCNQMHdj4s0KJEuon///j5ixIiowxCREvPKK68sc/fWJowrO/qsE5GWbOyzLtHZwcgW+wrBvXvyBPfI+lqk0Yh0ISNGjGDSpElRhyEiJcbMZm+6VvnQZ52ItGRjn3VKFsuEux8edQwiIiIiItJ9dKsJbkRERERERKRtlCyKiIiIiIjIBpQsioiIiIiIyAaULIqIiIiIiMgGlCyKiIiIiIjIBpQsioiIiIiIyAaULJYQM7vOzJaY2dtRxyIiIiIiIt2bksXScgOg+ymKiIiIiEjklCyWEHd/ClgRdRwiIiIiIiKJqAMQESk3Zj+LOgRpI/cLog5BRMrY5nze6/NGuiK1LIqIiIiIiMgG1LIoIrKFvnbn16IOQVpx5aeujDoEESlRW9I75Ct3n9Xqur+ceNXWhCNS0tSyKCIiIiIiIhtQy2IJMbPbgAOB/mY2D7jA3a+NNioRERGRrue0f3611XU3HfPnToxEpHSpZbGEuPup7j7Y3SvcfagSRRERkfJjZjuY2etFyxoz+6aZ9TWzR8xsWvjYp2ib881suplNNbPDisr3MrO3wnWXm5mF5UkzuyMsf9HMRkRwqiLSxSlZFBEREWlH7j7V3Xd3992BvYA64B/AecCj7j4WeDR8jZmNA04Bdia43/KVZhYPd3cVcCYwNlya7sd8BrDS3ccAlwGXdMKpiUg3o2RRWhIH+gDDgZHAqHAZCwwDBgG9gSrUlVlERGRjDgHed/fZwLHAjWH5jcBx4fNjgdvdPevuM4HpwEQzGwz0dPfn3d2Bm5pt07Svu4FDmlodRUTai77odx9xYCj/S/waxsK67aHQH+gF8V6Q6AGV1ZCohGwD1OWg4MHmjnvfmrpaq4vHzWJxi8dixOJxi+dynsk1+op83hebMbcyGZtZURGbCywAFobLAmBdJGcuIiISnVOA28Lng9x9IYC7LzSzgWH5EOCFom3mhWWN4fPm5U3bzA33lTOz1UA/YFlHnISIdE9KFrueJLArsDesmQANO0HFCKjuD2saYFYjTE3Au1Uw22AlsKrZshbwZLivIhl+8b2pVWvX5NYrraqOp3v1rhjSq0/FkJ69K/bs1buCPv0qGvr2T2b79Kso9OhVEa+uiScLeeobGgqTk6nYMxUVsVeA1wiunhY67u0QERGJhplVAp8Azt9U1RbKfCPlG9umeQxnEnRjZbvttttEGCIi61OyWN7iwE7A3rB2f2jcH3qMhLn18GwCXq4KcrEZwCwgu5U/7xj5woZ5XV1tnrraPAvnZ4qLK8Plf3r3ragYOrxqv+1GpvcZOaZ63bARVfF0dTyRzRSmJxL2fDIVf5EggXwbyG5drCIiIpE7AnjV3ReHrxeb2eCwVXEwsCQsn0cwzKPJUIIeOfPC583Li7eZZ2YJoBewonkA7n41cDXAhAkTNkgmRUQ2RslieYkBu0H+47Dqk1CzGyxrDHquPF0DLwOvA3UVHXN4I5/bdK3WrFrRyKoVq3n7tdUxoCdAuirO0OHpnYcOr9p55NjqU4ePqsr37lORzGYLL1TXJO4EHgRmtkv4IiIinetUPuiCCnA/cDpwcfh4X1H5rWb2O2BbgjkCXnL3vJmtNbN9gReB04Armu3reeBE4LFwXKOISLtRslj6+gCHw6qToPJjsMLgXwn4dwqeAlYlN7WD9mMttixujfq6PNOmrGPalHU8/hDVANU1cXYc3/OAXSf02nvn3Xr9H7AiFue+ZDJ+H/AkkNnoTkVERCJmZlXAocBXioovBu40szOAOcBJAO4+2czuBN4BcsDZ7p4PtzkLuAFIE1xAfTAsvxa42cymE7QontKhJyQi3ZKSxdI0AAonw6ozoGocPJuFu3oE/x/mRBiWUdiKlsW2ql2X55UXVvLKCyurzGDo8PS2O+/e6yt7TOz9mW22TSWz2cLLYavjfUT7hoiIiLTI3esIJpwpLltOMDtqS/UvAi5qoXwSML6F8gxhsiki0lGULJaOKuAYWHkWpPaFB/JwY1VwG6ZM5aY27gxmMdq5YXGT3GHurHrmzqqPPXTvop7pqjg77dJj/1336r3Xrnv1ujSX88nVNYk/AfcAazo3OhERERGRrkvJYrTiwIGw+suQ/AS8nIO/9IB7gdpoI9uAUQpDIerr8rz64ipefXFVOpEwdt691177H9L/8rE71lzZ2Fh4qKo68SfgMTTDqoiIiIjIVlGyGI1+kP0aNH4T5lXA1TVwm8GiqOPaiDgtzMgdqVzOeWPSKt6YtKqmuibOhA/1PfagwwYe0qNXoraiMvaHeNyu44OZ5kREREREZDMoWexcO8DaH0DiVPi7w6VpeDPqmNooRik31tWuy/Pkf5bak/9Z2mPEmKoeBxw68Kd7TOx9YWOu8EhVVeLnBFPFioiIiIhIGylZ7HgGHAwrfwKxiXBFBfwxAYs3uWFpiVFqLYutmTW9jlnTZ6XvvDHOvh/te+Thxw0+OB7nlXRV4gcEU4yLiIiIiMgmKFnsODHgZFj9S1gxEH5VDbdY+d71ofS6oW5KfV2exx9aGnv6v8uq9v1ov/2PPnHwf+MJe6uqOvF9gvuOiIiIiIhIK2JRB9AFGXAkrHkP3rwaTh4Fo2rgmjJOFKGcWhaby+WcZx5bZj88962qv/9t/sTVKxv+XV+Xf4Vg+nKLOr6uyMyGmdnjZjbFzCab2TfC8r5m9oiZTQsf+xRtc76ZTTezqWZ2WFH5Xmb2VrjucjOzsDxpZneE5S+a2YiibU4PjzHNzE7vxFMXERER6TKULLav/WH1KzDrTjhtNOxWAw9HHVM7Kb+WxeYKeXj+yeX246+/XX3njXP3XLGs4b76uvwbwOEoaWxvOeA77r4TsC9wtpmNA84DHnX3sQT3hTkPIFx3CrAzwc/jSjOLh/u6CjgTGBsuh4flZwAr3X0McBlwSbivvsAFwD7AROCC4qRURERERNpGyWL72BVWPQZLHoZv7g6jq4P7xXcl5duy2FyhAC89s4KffvPt6luvnbPLsiXZu+rr8i/Twk2PZcu4+0J3fzV8vhaYAgwBjgVuDKvdCBwXPj8WuN3ds+4+E5gOTDSzwUBPd3/eg3u33NRsm6Z93Q0cErY6HgY84u4r3H0l8AgfJJgiIiIi0kZKFrfONrDmTlj9Alx4AAyrghuslGcN3XJd71fFHV59YSUXfntyzf13zN8jk8m/lM3kLwd6RB1bVxJ2D90DeBEY5O4LIUgogYFhtSHA3KLN5oVlQ8LnzcvX28bdc8BqoN9G9iUiIiIim6HrZQCdIw6N50LddPjrsTA0DX+IQUPUcXWg8u+G2hp3eOq/y2IXfmty+q1XV38pm8nPAk5CXVO3mpnVAPcA33T3NRur2kKZb6R8S7cpju1MM5tkZpOWLl26kdBEREREuicli5tvX/e1c+DVX8Pe1fDdSlgXdUydoOt0Q23N2jU5rv/TrPSfLp3ed+ni7PX1dflngO2jjqtcmVkFQaL4N3f/e1i8OOxaSvi4JCyfBwwr2nwosCAsH9pC+XrbmFkC6AWs2Mi+1uPuV7v7BHefMGDAgC09TREREZEuS8li21Vls/nf19flH4P4tvDtangn6pg6UddPFpu8P7WWn39vcvWD9y7cJ5vJv96QLVwMVEUdVzkJxw5eC0xx998VrbofaJqd9HQ+GNx7P3BKOMPpSIKJbF4Ku6quNbN9w32e1mybpn2dCDwWjmt8GPi4mfUJJ7b5OF1npikRERGRTqNksW0Oymby06e8uebLF35ncvrxh9bifrN3r9tUxjddpQsp5OHRfy2J/+y776SnvLXm3GwmPxM4KOq4ysiHgc8BB5vZ6+FyJHAxcKiZTQMODV/j7pOBOwmuwDwEnO3u+XBfZwHXEEx68z7wYFh+LdDPzKYD3yacWdXdVwC/AF4Ol5+HZSIiIiKyGbpTtrMlUpn6/BW5nH/65r/Mrnr7tdUA3HPLfPb96OhCuur7ZvarbpJwxwhbbbrVOL7VKxu5+rIZVeN261n1hbNH/CuRiP2+Mhn7KcGtIaQV7v4Mrf+uHNLKNhcBF7VQPokWZqp19wzB2NKW9nUdcF1b4xURERGRDXWTRGeLjM7U519/7521n7ngW2//L1Fs8uf/WxSHH8ZgZEThdbbu0w21Je+8sYZffP+d9NxZdV+vr8+/yPrj6EREREREuhwliy37ZEM2//o/71ow5i+/m5HO1G94K4z3p9byzpvZgvsNHXKfjD/8AcaPh513ht//fsP17vD1r8OYMbDrrvDqq0H50qWw//7Btvfe+0H9Y4+FBRtM8bE5ulc31JasWZXjsl+8V/3fBxbv2pAtvAMcE3VMIiIiIiIdRcni+iqzmfxVa1Y13vz7X06reeLhpRvNkK7+/axYIb+HwcntGsTbb8Nf/wovvQRvvAEPPADTpq1f58EHg7Jp0+Dqq+Gss4Ly226D00+H55+H3/wmKPvnP2HPPWHbbbcmqu7dstjEHR66d1Hiioun9Vi7pvH2bCZ/FZCMOi4RERERkfamZPEDwzP1+VdmvFd72i++/07V7Bl1m9wg1wC3X7/C3K8CerdbIFOmwL77QlUVJBJwwAHwj3+sX+e+++C008AsqLtqFSxcCBUVUF8P2SzEYpDLBS2T3/ve1kYVV65YZMZ7tfz8u+9UTX933WmZ+vwbBLN3ioiIiIh0GUoWA0c3ZAtvPfiPhTv+8ZLpVXW1+U1vEXruieUsWWR599+1faNNGD8ennoKli+Hujr4979h7tz168yfD8OK7iQ3dGhQ9ulPw8MPw+GHw4UXwpVXBkll1Vbf+CGGK1tcT11tnit/837VfXcsGJvN5l8rFPxTUcckIiIiItJeunuyGMtm8r9Zt6bxjisuntbjv/9askWzw17x67lx+FQc9muXoHbaCX7wAzj00CDp2223oIWxmLeQt5lBr17wr3/BpElB19MHHoATToAvfxlOPDHonrplYqC2xRY99cjS2O9+9l517brc9Q0NhQvpZjPGioiIiEjX1J2TxYpMff6OxQuzZ/3i+1OqZrxXu8U7Wrm8kccfWof7Le1278UzzggmrXnqKejbF8Y26+Q4dOj6rY3z5m04JvHnP4cf/SgYx7jXXnDddfDDH25pRPGWM1QBYN7sen51/pSq5Uuy383U528DKqOOSURERERka3TXZLE6U5//74xptUf+7udTq9et3fpb5t1zy3zq6/oV3L/fLrOjLlkSPM6ZA3//O5x66vrrP/EJuOmmIH974YWgRXHw4A/WT5sWzH56wAFBV9ZYLGh5zGS2NKKYmhU3Yc2qHJf+dGr1+++t+0SmPv8M0DfqmEREREREtlR3TBb7Z+rzL7wxadXEq34zvaqxof1SoPa89+IJJ8C4cXDMMfCnP0GfPvDnPwcLwJFHwqhRwa0zvvzlYGxisR/9CH75y+D5qafCDTcEE+F897tbGlF3/FXZfA3ZAlf95v30C08t3zVTn38F3Y9RRERERMpU+/SZLB/Ds5n800//d+mge29f0O7dBJvuvThu1xswO2Crsqunn96w7Ktf/eC5WZBEtubOOz94PnAgPPfc1kQD6obadu5w103zkqtWNA494vhtXkum4h8FpkQdl4iIiIjI5uhOzUXjs9n8K/+8a+G2HZEoNumoey9GT91QN9cjDyxO3HHD3H4N2cILwD5RxyMiIiIisjm6S7K4fzabf+7Wv87p+/hDS+IdeaCOuvdi9GKaC3ULvPj0Crvm8hk9s9n8Y8DBUccjIiIiItJW3SFZPDybyT989WUzekx6fmWn3NKgI+69GL24csUtNPn1NVx56ftV2Uz+fmDfqOMREZGOZ2a9zexuM3vXzKaY2X5m1tfMHjGzaeFjn6L655vZdDObamaHFZXvZWZvhesuNzMLy5NmdkdY/qKZjYjgNEWki+vqyeKHs5n8PVf8enrVu2+t7dQDt/e9F6MXA9egxS01/d11XHP5zOpsNv8fYLeo4xERkQ73B+Ahd9+R4HN/CnAe8Ki7jwUeDV9jZuOAU4CdgcOBK82sqSfUVcCZwNhwOTwsPwNY6e5jgMuASzrjpESke+nKyeKu2Wz+wb/+fkbVzOlbfg/FLdUR916MVlzz22yld95Yw81/mV2TzeafAHaIOh4REekYZtYT+ChwLYC7N7j7KuBY4Maw2o3AceHzY4Hb3T3r7jOB6cBEMxsM9HT35z24YHtTs22a9nU3cEhTq6OISHvpqsniqGw2//gtV8+pmdLJLYrF2vvei9HSBDft4bUXV9ldN87rmc3mnwFGRB2PiIh0iFHAUuB6M3vNzK4xs2pgkLsvBAgfB4b1hwBzi7afF5YNCZ83L19vG3fPAauBfh1zOiLSXXXFZHGbbCb/zD9und/r1Rc6Z4zixrTnvRejpQlu2svzTy6P3X/Hgj7ZTP45YHDU8YiISLtLAHsCV7n7HkAtYZfTVrT0fcU3Ur6xbdbfsdmZZjbJzCYtXbp041GLiDTT1ZLF3pn6/NOPPLC4/9P/Xdahs562VdO9F91vKPPWRXVDbU9PPLw0/p9/Lu6fqc8/i64Ei4h0NfOAee7+Yvj6boLkcXHYtZTwcUlR/WFF2w8FFoTlQ1soX28bM0sAvYAVzQNx96vdfYK7TxgwYEA7nJqIdCddKVmsytTnH33xmRXDHvzHooqogyn2wb0XPxV1KFshpmSxnT1076KKZx5dNiRTn38K6Bl1PCIi0j7cfREw18yaxqcfArwD3A+cHpadDtwXPr8fOCWc4XQkwUQ2L4VdVdea2b7heMTTmm3TtK8TgcdcE9GJSDvrKsliRX19/p/vvLFmp7tunJuMOpjmPrj34p8JLvyVoxi4OqK2t3/cNr/y1RdXjs7U5++j6/w9iogInAv8zczeBHYHfgVcDBxqZtOAQ8PXuPtk4E6ChPIh4Gx3b7r91lnANQST3rwPPBiWXwv0M7PpwLfZeDdXEZEt0hWm6SRTn//LnBl1+15/5cx0qV5Te+6J5Xzs6F75gdv8DrMzSqKLbOviQHX4mAgfBwbTu/VK0Njo5BoLNDaW6JtdZm67bk5yyLD03oOHpn9RmYz9KOp4RERk67n768CEFlYd0kr9i4CLWiifBIxvoTwDnLR1UYqIbFzZJ4v5nJ9Ruy73qasve7+qkN90/Shd8eu58V/84WTgr8ALEUfTk2CytlHAaGDHetixAUbGYUAa8jko5MEL4Hlwq6y0+M8uG1+wGIl4zCrzec/XrstlV69qLKxc3hhbsawhtWpFQ8XSxVnmza5nxbKGaE+xTBTy8Of/e7/6x5eM+1ZlMvY88EDUMYmIiIiIlHuyuHcuV7jiT5dMT2fqS3/+mKZ7Lx50+N/cbAeDXCcdeQxwMHDoOtg5D8OSUGlQuxDyM6D6bUi/R9C9ZQYwGxLZ5ntJrD8S1GJx69W7b+Xg3n0rBw8fxbbA4Gw2P6KxwXetqLRdgNTCeZnMjGm1VXNn1lXMnlHH4gWZjj/dMrRmdY6rfvt++us/HHtbZTK2JzAt6phEREREpHsr52RxYDaT//eNV81KL164QV5Tsu65ZT77fnR0IV31PTP7dQeNURsAHAEcURcMiUhmofFx6PMvYDJBQrgMKremH6kDq8JlSlNhMhkn+cGo0UEjRlfvvt3Iqj3r6/L7xxM2MdfoVW+/ttrefGV1eurkNZRDkt9ZZk6v5e9/m1d13KlDHk6l47sC66KOSURERES6r3JNFuP1dfl7n350ac83Jq2OOpbN9uf/WxT/1k9+BNwOzGynvY4FjnM4dS2Mq4Tap6Dv34HHCAbFRzHAcDHwcCxmD1fXJACMFNvv85G+h++yZ6+Tk8nYXgvmZTKvvbiy5rWXVsWWLi6fpL+jPP3ostio7asH77pX71tT6fix6O6WIiIiIhKRskwWGxoKFyyan9n1/jsWVEYdy5Z4f2otU97KFHba5QbMDtiK1sUq4PMO36uFATko3Ac97gAeh2Qp9vd0YKqZTa2uSfwBqNpuZNUBA7dJfvKI4wefvGRRhqf/u6zHpOdXdOsWx1uvmZMaNqLq4P6Dkt+vqIhdEnU8IiIiItI9leNU/QfmGgvf/evvZ1SX6synbfGXy2Zvxb0XtwUuaYRFGbjovzDiaKjuBz0+TzCldikmii2pAx5MpeNfrkzG+g0dXvXpY0/Z9sFfX7lr5vSzRtSPGFMVdXyRaGx0rvzN+9W5Rr+AYLCpiIiIiEinK7dkcWA2m7/nuitmplevaow6lq2yZfde3Au4qw6m18NZ10GP8dD748CTQLk3xTUCD1RVJ46srIwN32u/Pj8/97yxi374653W7bxb97tf/YplDfz19zPSDdnCPUDfqOMRERERke6nnJJFq6/L3/7kf5bWTHlrbdSxtIvnnljOkkWWd//dRm76EQOOA15eC08sg2N/BultocdXCWYv7YqWxON2cSodHzpku/QZXzx35MwfX7rTul33amtS3TVMnbyWF55ens7U56+KOhYRERER6X7KJlksFPzkdWsaJ/7zrvIcp9iaK349Nw4nx2HfZmsM+KLDvFq4fjJM+BLUDIaKSwlmIO0O8sCdqXR8zOAh6dNO++qI6Rf8dty6PSb2xizq0DrHvbfOTzZkC0cTTG8rIiIiItJpyiVZ7JtrLPz5hitnVRc20gZXjoJ7L9bi/jf/YL6hvYE3auF3b8Hgw6D3eOBOOu/GjKWmAPwjXRXffuDg1Kmf+fLwd8+7aMfaocPTUcfV4bLZAjdcOasqm8nfBHS//rgiIiIiEpmySBYz9fkrXnh6RWrW+3VRh9Ih7rllHpm6fgX3XxXgpgw8vgp2/hr02gN4Nur4SogDD6Sr4jtvOyz9re9csMPak78wLJtKl8Wv8RabOnktr7+8qiZTn/9D1LGIiIiISPdRDt+yD8o1Fo6797b5yU1XLV/PPpmJwTkGx18D1SMgdhPlP2lNRynEYvbXymRs1MQP973rF38YX7/3h/qU8dy4m3bXTfNS+ZyfDBwYdSwiIiIi0j2UerKYzmTyt9x89ZyqbKZr5k39BlTy7Z9uX7v/Qf2nmqX3gJpzgdVRx1UmlqXS8c9VVScOPuWL2037zoXb1/buWxF1TB2ivi7PTX+Znc5m8rcC1VHHIyIiIiJdX0kni9ls/mfvTV7b++3XumbutOe+ffxHF+9Uv92oql+m0vFdgDeijqlMvZBKx8cNG1516Y8vHVe/y55dc9bUt19bzeQ31vTOZvKXRB2LiIiIiHR9pZws7uoFzrnt2jld7s7siYRx6hnbZT/75e0WJVPxD1dUxC6m+05e017yFZWxn6fT8Y994ewRSz91+tBsItH1pky9/bo56Xzev8iG0+eKiIiIiLSrUk0W45n6/K1//9u81JrVXSuH6jegkvMu2rF2r/36PJpMxXcCXos6pi7muWQqvsPEj/R7/Lxf7Vg7YFDXGupauy7PXTfNS9fX5a8huL+KiIiIiEiHKMlk0Qt+2pJF2RHPPbG8S30Z3nWvXvzw1zvVDxiU/HE6HT8ajU3sKCvT6fiRAwYlzzv/Vzt2uW6pLz+7gtWrGkcAJ0Udi4iIiIh0XaWYLFY2NBQuueumudXehea3/NhRA3OfP3vE8lQ6flCiIvZ7gttASMfxRCL2x2QqfsAXzhmx8uAjBnaZO3S6wx3Xz6nOZvKXA12r6VRERERESkbJJYuFgn9p9oy69Iz3aqMOpd188jNDGo745OD5yWR8D+DFqOPpZl5OJuN7HHXC4DmnfGFY1rpIW/V776xj5vTamnzOz4k6FhERERHpmkotWUw3NhR++fe/zauJOpD2EIvD5782IvPhg/q/m0rF9wLmRh1TNzU7lY7vsfeH+77x5W+Oqu8qE9/cddO86nzBLwR6RxyKiIiIiHRBJZUs5nP+tWnvrqucO6s+6lC2WmUyxtnfH1M3fo9ez6fS8Q8By6OOqZtbnUrHD9hh5x5PfP2HY+sqkyX1q79FFs3P8MakVfHGhsL3oo5FRERERLqeUvrGXJPP+0/vu31+2d9wvDIZ41s/2b52xOjqf6ar4ocBXadPbXnLpNLxY4Zsl77/7O+PqUtUlH8L4z/vXJB25xtAv6hjEREREZGupWSSxcbGwjcnv7E6sWBuJupQtko8bpz13dF1A7dJ3p9Kx08FGqOOSdaTT6Xjnx06PP3Ymd8aVR+LRx3O1lm+tIFXXlgRb8gWfhh1LCIiIiLStSSiDiDU2wucd/8dC6qiDmRrmMEXzx1Zv93IqmdS6fhpaMbTUpVPpeMnjN6+5j9f+NrIidf9cWa6nGfefeDuham99u17FnApsDjqeERERDqb2c+iDkGkSyqJlsWGbOH7r7+8KrZkUTbqULbKKV/cLrvj+B5vp9LxY4Fc1PHIRjWk0vEjxu3W8+1Tz9iurJuzV61o5OXnVlhjQ+HrUcciIiIiIl1HKbQs9sT4xgN3L0hHHcjWOPrEwY0T9uszK5WOHwqUdfLRjdSn0vFD9tq3z/O163Jj77t9QWXUAW2pxx5cktr7Q33PBn4GNEQdj4iISBROevgrra6767C/dGIkIl1D5C2L7v7ZqW+v8eVLy/f77X4H9CscfMTApal0/ABgddTxyGZZm0rHDzzg0AGr95jYu2w7oy6an2He7LoYcGLUsYiIiIhI1xB1smiZ+sJ3H39oadnOgDp8VBWfOn1YfTIVPwSNFytXy5Kp+BGf+8rwzDZDUlHHssUeeWBxj7ranCa6EREpAWY2y8zeMrPXzWxSWNbXzB4xs2nhY5+i+ueb2XQzm2pmhxWV7xXuZ7qZXW5mFpYnzeyOsPxFMxvR6ScpIl1e1MniftlMfuB776yNOIwtU9MzwVnfG11XmYx9Fng36nhkq7xSURE7++zvj6lLpaP+s9gyb7+2mnzeRwIToo5FREQAOMjdd3f3ps/l84BH3X0s8Gj4GjMbB5wC7AwcDlxpZk3zdV8FnAmMDZfDw/IzgJXuPga4DLikE85HRLqZSL8V19flv/Pog0vKciZKM/jS10fWViZjVwL3Rh2PbL1Y3K6vqonffsa5o+qsDG/BWCjAY/9ekqyvy3836ljM7DozW2JmbxeVXWhm88Or7K+b2ZFF69rtirqZnR5etZ9mZqd30imLiLTFscCN4fMbgeOKym9396y7zwSmAxPNbDDQ092fd3cHbmq2TdO+7gYOafqMFBFpL1Emi/0TCTvqxaeWl2Uzzsc/sU1u2Iiqqclk/PyoY5H2k0rFzxq1ffX0w47dpizvj/ns48vi8YQdCwyIOJQb+ODqd7HLwqvsu7v7v6F9r6ibWV/gAmAfYCJwQXE3LxGRTuTAf8zsFTM7Mywb5O4LAcLHgWH5EGBu0bbzwrIh4fPm5ett4+45gjkT+jUPwszONLNJZjZp6dKl7XJiItJ9RJao5XP+hTdfWVWoXZePKoQtNmJMFYcdO6hOt8jokhpS6fhRh31im4Zth5XfBL216/K8/tIqz+UKX44yDnd/CljRxurteUX9MOARd1/h7iuBR2g5aRUR6Wgfdvc9gSOAs83soxup21KLoG+kfGPbrF/gfrW7T3D3CQMGRH0dUUTKTVTJYqyxsfCtxx9aWnbfxuNx4wtnj6xNJuNnsv7VPuk65iUq7FtfPGdEbawM270fe3BxOp/zb1Mat8Zp7hwzezPsptrU4teeV9Rb25eISKdy9wXh4xLgHwS9HRaHF8IIH5eE1ecBw4o2HwosCMuHtlC+3jZmlgB60faLdCIibRLVV+GD165u7DFzem1Eh99yH//EoFx1TeIl4M6oY5GOE4vZNb37Vr51yFGDyq7leO6sepYuzlYCR26ycue6ChgN7A4sBP4vLG/PK+ptutIO6polIh3HzKrNrEfTc+DjwNvA/UDTWOrTgfvC5/cDp4TjsUcSdLt/KeyqutbM9g17T5zWbJumfZ0IPBb2whARaTeRJIt1tbnvPPrvJWV3u4yBg5N8/JhtGtJV8c/TyhdQ6TI8XRX/9JHHD24YODgZdSyb7dnHl9XU1+VPizqOYu6+2N3z7l4A/kpwlR3a94p6a/tqKR51zRKRjjIIeMbM3gBeAv7l7g8BFwOHmtk04NDwNe4+meAi9DvAQ8DZ7t40Tucs4BqCLvrvAw+G5dcC/cxsOvBtwplVRUTaUxTJYu+KytjBLz+3oqxm7DKD088aURuP82NgTtTxSKeYGYvzwy+cPbK23OaXe2PSaksk7AigMupYmjR1vQodT3CVHdr3ivrDwMfNrE/YzfXjYZmISKdx9xnuvlu47OzuF4Xly939EHcfGz6uKNrmIncf7e47uPuDReWT3H18uO6cptZDd8+4+0nuPsbdJ7r7jM4/UxHp6qJIFo9+f+q6bKa+EMGht9x+B/TzbbZNzY4nYpdHHYt0nkQidsWAQcnp+x3Qr6xaklevbGTJokwOOCiK45vZbcDzwA5mNs/MzgAuDW+D8WYY17egfa+oh1+8fgG8HC4/L/4yJiIiIiJt1+kTYNSty5328rMrenT2cbdGMhXjk58Zmkml458Bym/6VtkahXRV/MvHnTrkiVeeX1mVzZbPRY4Xn1nR44jjkp9OV8U7vWXN3U9tofjajdS/CLiohfJJwPgWyjPASa3s6zrgujYHKyIiIiIt6uyWxeqKZOyjb76yupMPu3UOPmJgjqDF4/WIQ5FovByL2cMfO3pQWd178fWXVlk8bscD8U1WFhERERFpprOTxSPmzKjL1tWWT+NcuirOoUcPyqWr4j+IOhaJTroq/u1DjhqYr+lRinejaNnypQ2sWtkA8OGoYxERERGR8tOpyWJdbe7kSc+v6NmZx9xahx4zqLHg3ANMizoWidQsL3DLYcdu0xB1IJvjpWdWVGUz+Za6hIqIiIiIbFRnJouxREXssLdfW9OJh9w6NT0SHHjYgHw6Hf9R1LFI9FLp+AX7H9y/0Kt3RdShtNnrL62KA5+i5fsPioiIiIi0qjOTxb3Wrm5kxbLyaZg57Nhtsl7gZmB21LFISVjg7tcffOTAsvklXjg/Q11tvhKYEHUsIiIiIlJeOi1ZzOUKn3j95VWpzjre1kqlY+x/cH9S6fjPoo5FSkcyFf/d/gf3z1dUlE9D3aTnV6ZyucKxUcchIiIiIuWl05LFbKZw0luvrS6b/nt7f6iv53KFx4H5UcciJWV6oeAv77Vfn6jjaLNpU9YmsvWFw6KOQ0RERETKS2clizXJVGz0zPdqO+lwW++QowbVVlUnfht1HFJ6qqoTF3/8mG3WRh1HW82aXksyFduFCO6rKiIiIiLlq7OSxT2XLsrW5XLeSYfbOiPHVNOjZ2IN8HjUsUhJerhXn4p1I8dWRx1Hm9Suy7NmTa6BFm5uLyIiIiLSmk5JFt194rQp65Kdcaz2cNDhA+oqKmO/AwpRxyIlqVBRGfvNIUcOrIs6kLaaPmVtHPhQ1HGIiIiISPnolGSxrjZ/yIxptWWRLFbXxNllr96xeNxuiDoWKV3xuF0/fo9e8VS6U29VusXee2ddVV1t7mNRxyEiIiIi5aNTvunG4zZh9vvlMV5xz336kGssPAwsjzoWKWmrGrKFF3bZo1fUcbTJjGm1xGL24ajjEBEREZHy0RnJ4qBYzHosXZzthENtvYkf6bumqjpxU9RxSOmrrkncMOHDfddFHUdbLFmYwWL0AraJOhYRERERKQ+dkSzuPXdWXdbLYG6b6po4w0ZUJYGHoo5FysI/dxjXo6KisvTvuegOs9+vywL7RR2LiIiIiJSHDk8Wc7nCvtOmrC2LaSN33r0XjdnCU0DZTFwikVre0FB4Y9yu5dEVderktTUNDYWPRh2HiIiIiJSHDk8WM/WFQ2ZOr4t39HHawx4Te6+rqkncHnUcUj6qaxI3TPhQn7K4uDBzWm2sMVs4OOo4RERERKQ8dHSyaMlkbNdymNwmFoMdxveoAB6MOhYpK/eO371X3Eq/JyqL5meoqIyNjDoOERERESkPHZ0sbpPPu61dk+vgw2y94aOqyed8PrAw6likrCzM533F4KGpqOPYpNWrGonFSAG9o45FREREREpfRyeL261a2djQwcdoFyPGVBGL2+NRxyHlx92fGTW2Juow2mTF8oY6YGzUcYiIiIhI6evoZHH48iXZMuigB2N36rEulYo/HXUcUn6qqhP/HTuupizGLS6an4kB20cdh4iIiIiUvg5PFpcsyqY7+BjtYuSYaoAXo45DytJzY3aoKUQdRFssmFtfXSj4DlHHISIiIiKlr0OTxUx9fuyyJdmKjjxGe6iuiZOujieA96KORcrSO9U9EvGaHomo49ikZUsaYpn6/E5RxyEiIiIipa9Dk8Vczrdfsaz0hywOH11NNlN4GyiL1iEpOYWGTOGNkWNL/3aiK5Y1UCgwKuo4RERERKT0dWiyGIsxfOXy0k8WR4yuLiRTMU1uI1ssmYo9u+2wtEcdx6asXN5APG5Doo5DREREREpfhyaLFZWxQeXQsjhku3RtRUXszajjkPKVqIhNGTIsVfKT3Kxc3kBlMtaPjh+vLCLS7ZlZ3MxeM7MHwtd9zewRM5sWPvYpqnu+mU03s6lmdlhR+V5m9la47nKz4M6+ZpY0szvC8hfNbESnn6CIdHkd+YWxp0Gidl2+Aw/RPgYMqiwAs6OOQ8rae4OHpkv+hqKNjU5jQ6ERGBR1LCIi3cA3gClFr88DHnX3scCj4WvMbBxwCrAzcDhwpZnFw22uAs4kuO3R2HA9wBnASncfA1wGXNKxpyIi3VFHJovD16zOZTpw/+2md9/KBDAn6jikrE3v27+yMuog2iJTn88BvaOOQ0SkKzOzocBRwDVFxccCN4bPbwSOKyq/3d2z7j4TmA5MNLPBQE93f97dHbip2TZN+7obOKSp1VFEpL10ZLK4zZpVjSU/YUwsDumqeAqYH3UsUtYWV1TGKioqSv//dENDoQCU/mw8IiLl7ffA91l/8rxB7r4QIHwcGJYPAeYW1ZsXlg0JnzcvX28bd88Bq4F+7XoGItLtdWSymGpoKPlckT59K2loKKwGSr4LoZS0QkO2sLxPv9JvXGzIFkDJoohIhzGzo4El7v5KWzdpocw3Ur6xbZrHcqaZTTKzSUuXLm1jOCIigQ5NFhsbCiXfzNK3fyX5Rleromy1fN4X9u5b8rcVJZNRsigi0sE+DHzCzGYBtwMHm9ktwOKwaynh45Kw/jxgWNH2Q4EFYfnQFsrX28bMEkAvYEXzQNz9anef4O4TBgwY0D5nJyLdRkcmi8mGhkLJz7jYq08Fvn7XD5EttTaZim+6VsSymbyhZFFEpMO4+/nuPtTdRxBMXPOYu38WuB84Pax2OnBf+Px+4JRwhtORBBPZvBR2VV1rZvuG4xFPa7ZN075ODI9R8rdwEpHykujAfafKIVmsqIgRi7Eu6jikS1hbmSz5X3kydYUYShZFRKJwMXCnmZ1BMLHeSQDuPtnM7gTeIRgWc7a7N00nfxZwA5AGHgwXgGuBm81sOkGL4imddRIi0n10aLLY2OAl/825otIws9qo45DyF4uxJlkOyWImH0fJoohIp3D3J4AnwufLgUNaqXcRcFEL5ZOA8S2UZwiTTRGRjtLR3VBLvk9eIhEjFqfkb6YupS8WtzVl0bJYn0+gZFFERERENqFjJ7jJln6yWFFhxONqWZStl0jYqnIYs5ipL1S4u5JFEREREdmoDksWCwVPNTaW/pjFRGWMRCKmlkXZaolEbE0yFSv5+8U0ZPM0NnqvqOMQERERkdLWYclcPufVjY2lPylXZUUsB2SjjkO6hJK/VQyAO+Ad2qtARERERLqAjmxZTOdzJd/IQsHd6NjuuNJN5HKFnpn6fMn/LiVTcSoqbWXUcYiIiIhIaeuwL7bxuNUmKkr+ezPZTCGez3tN1HFI+cs1ep9spvQvkKTSsUYz0+1iRERERGSjOiybS1TEVidTsZLvh5rNFMg1FvpEHYeUv3zeezVkSz9ZTFfFc6B7i4qIiIjIxnVk019tOh1v7MD9t4tMfZ583vtGHYeUP3d6ZjP5TVeMWCodzwOaAVhERERENqojk8V1qXQ814H7bxe163IUCgyMOg7pEnqWRzfUeB61LIqIiIjIJnRsy2JVvOSbWWrX5QD6Rx2HlD8zemXKoWUxFQMliyIiIiKyCR2ZLK6qrkmUfDPLurV54nFTsihbLZGwbVcsa4g6jE1KpuOgZFFERERENqEjk8XlNT0SHbj79rFiaZbKZKw/UPrBSilLJypiPVatKPlhuiSTMUNjFkVERERkEzo0Wayqjsc7cP/torHRqVuXywAjoo5Fytqotasb67zk5/+FyspYDLUsioiIiMgmdGSyuCxVFa/swP23myWLsnlgh6jjkLI2auniMrhvBpBKxyuAVVHHISIiIiKlrSOTxdWJhCViJd+2CAvm1qeA7aOOQ8ra6EXzM6mog9iUdFWcWPBXvyLiUERERESkxHVkslhoyBZW9ulb+o2LC+dlkvX1+V2jjkPKVzaTH7d4YSYZdRybMmBQkoaGwnygDDrMioiIiEiUOjJZJJfz9wdtW/KNLSxelCGfcyWLssVyOd974fxM1GFsUv9BSQoFpkcdh4iIiIiUvg5NFisq7M2B25R8YwvzZ9eTTMV2Asqg06yUoHgyFdtp9vt1UcexSQMGVnoyFXu7o49jZteZ2RIze7uorK+ZPWJm08LHPkXrzjez6WY21cwOKyrfy8zeCtddbmYWlifN7I6w/EUzG1G0zenhMaaZ2ekdfa4iIiIiXVWHJovJVPytbYemS765Ze2aHLVrc3lgl6hjkbI0bt2aXEN9XT7qODZp0Lap+oqK2HudcKgbgMOblZ0HPOruY4FHw9eY2TjgFGDncJsrzazpws1VwJnA2HBp2ucZwEp3HwNcBlwS7qsvcAGwDzARuKA4KRURERGRtuvQZBGYuu126WwHH6NdvPv22jjw0ajjkLK03/vv1Xb031K72GZIqhF4v6OP4+5PseEkOscCN4bPbwSOKyq/3d2z7j4TmA5MNLPBQE93f97dHbip2TZN+7obOCRsdTwMeMTdV7j7SuARNkxaRURERKQNOvoL7nsDBiXL4mb3UyevTdeuyx0VdRxSfupqc0dOeXNNddRxtEW/AckEnZAstmKQuy8ECB8HhuVDgLlF9eaFZUPC583L19vG3XPAaqDfRvYlIiIiIpupo5PFOemqeLIyWfqNLtOmrKWiMrYfYFHHImXF4nH76PR3S/8e94mEka6KJ1k/mSoFLf3N+UbKt3Sb9Q9qdqaZTTKzSUuXLm1ToCIiIiLdSUdncflsJr9wwKDSn+Rm5fJGspl8DNgh6likrIzN5Ty5dHHp97buN6CShmxhOZCLKITFYddSwsclYfk8YFhRvaHAgrB8aAvl621jZgmgF0G319b2tQF3v9rdJ7j7hAEDBmzFaYmIiIh0TR3e5FcoMHXQ4NJPFgHefXutufuhUcch5SOf9xNff2lVWcyiO2CbFPm8z4wwhPuBptlJTwfuKyo/JZzhdCTBRDYvhV1V15rZvuF4xNOabdO0rxOBx8JxjQ8DHzezPuHENh8Py0RERERkM3V4sphMxV4bODhVFjcAn/Tcyqq62vyXoo5Dykc2kz990vMryuJqyPDRVYVkKvZkZxzLzG4Dngd2MLN5ZnYGcDFwqJlNAw4NX+Puk4E7gXeAh4Cz3b1patmzgGsIJr15H3gwLL8W6Gdm04FvE86s6u4rgF8AL4fLz8MyEREREdlMHT75TEVF7PXRO9SsA3p09LG21rtvrSFRYTsQdF2bt6n60u2NiMVsu3IYrwiw/U491lVUxJ7vjGO5+6mtrDqklfoXARe1UD4JGN9CeQY4qZV9XQdc1+ZgRURERKRFnTHzzNOjtq+u6ITjbLVcznlz0upCoeAtfgkVKVYo+AlvTFrlhULUkbTNsJHpJPBS1HGIiIiISHnojGRxLs6abYakOuFQW++lZ1akM3XqiiqbVl+X//yk51emo46jLQYMSoJTCyyMOhYRka7OzFJm9pKZvWFmk83sZ2F5XzN7xMymhY99irY538ymm9lUMzusqHwvM3srXHd5OIabcJz3HWH5i2Y2otNPVES6vE65p0Wh4E+M2bGmMw611d6dvIZY3EYBw6OORUrasESFjZ369tqo42iTEWOqyeVcrYoiIp0jCxzs7rsBuwOHm9m+BOOrH3X3scCj4WvMbBxwCrAzcDhwpZk1TZ52FXAmweRfY8P1AGcAK919DHAZcEknnJeIdDOdkixWVSce2mmXnrWdcaytVcjDay+tIpcrfCbqWKR0NTQUznnp6RXk82UxdxM77Nyjvrom8VDUcYiIdAceaBrQXhEuDhwL3BiW3wgcFz4/Frjd3bPuPpNgUq+J4W2Gerr78+GMzzc126ZpX3cDhzS1OoqItJdOSRaBp8buVFM2H2BPPbI0lc/5t4CyuCWCdLoUzlmPPbSkLGZBBdhxfI8c8FTUcYiIdBdmFjez1wnuKfuIu78IDApvC0T4ODCsPgSYW7T5vLBsCOtPuNdUvt427p4DVgP9OuRkRKTb6qxkcUYiYQ39BlR20uG2zpyZdSxb0pAEjok6FilJJ8+eUWtLFmajjqNNevRMUNMzkQDejDoWEZHuwt3z7r47wQzrE81sg5mdi7R0Qd03Ur6xbdbfsdmZZjbJzCYtXbp0E1GLiKyvs5JFz+X8mbE7lce4RYD//HNRj7ra3A+jjkNKjtXV5n74yAOLy+aXecyONTRkCy8D+U1WFhGRduXuq4AnCMYaLg67lhI+LgmrzQOGFW02FFgQlg9toXy9bcwsAfQCNrivrLtf7e4T3H3CgAED2uekRKTb6KxkkeqaxL93GN+zrrOOt7VefXElhTw7A3tFHYuUlH0bGwpD3nljTdRxtNlOu/TMpqvi/4o6DhGR7sLMBphZ7/B5GvgY8C5wP3B6WO104L7w+f3AKeEMpyMJJrJ5KeyqutbM9g3HI57WbJumfZ0IPBaOaxQRaTedliwCT+24c48yuSNdMNHNw/cvStXX5S+IOhYpHfV1+R/+919L0uXy79gMdp/YOx+L2f1RxyIi0o0MBh43szeBlwnGLD4AXAwcambTgEPD17j7ZOBO4B3gIeBsd2/qDXIWcA3BpDfvAw+G5dcC/cxsOvBtwplVRUTaU6ITjzUlmY7lBg1OsrhMxno9+/iy2NEnDj4U2B54L+p4JHJ7uPshzz62rDMvsmyVEWOqicVsGcEVbRER6QTu/iawRwvly4FDWtnmIuCiFsonARuMd3T3DHDSVgcrIrIRnfmlt4Bz24QP9c114jG3SjZT4KF7F1XU1+UvjzoWiV59Xe7yf961MJnNlk0DOXvt26ehosJujjoOERERESk/ndpCkkzFb9nnI33rO/OYW+vxh5bEc7nCR4CPRB2LROrgbLawRzm1KgJM2K9PY6IidmfUcYiIiIhI+ensL74v1PRINAwemurkw265xkbnnpvnVdXX5f9M579fUhqsvi7/x7/fMr86ny+TwYrAsBFpKipja4G3oo5FRERERMpPZyc/BYy/TdivfLqiAkx6fiWrVjQMBz4VdSwSiRNWr2oc9uqLK6OOY7PsuW+fxljMbqWF+26JiIiIiGxKp7eUJZPxW/b9aN9MZx93a7jDHTfMrc5m8n8AyqdZVNpDOpPJX3HXjXNrymUG1CZ7f7hvtjIZuz3qOERERESkPEXRrXJSKh2vGzo8HcGht9y0KeuY/u666oZs4SdRxyKdJ5vN/+K9yWt7vvv22qhD2SyDh6aoqopngElRxyIiIiIi5SmKZNHjcbt5wof6NkZw7K3yt7/OqS4U/FvAnlHHIp1iNy/wtduunVMVdSCba4+JffIYd6IuqCIiIiKyhSKZsKWiMva3ffbv2xDFsbfG6lWN3H7dnFSmPn8PkIw6HulQFZn6/B333DIvtWZ1WQ2xBeBDB/arTybjf4s6DhEREREpX1HN7vl6ZTK2Zviosmuw4eXnVtr0d9cNzGbyv446Fuk4DdnCBbNn1A177onlFnUsm2vH8T1IpeNLgeejjkVEREREyldUyaInKuxPBxw2oKzuudjk5qtnV+Xz/lVg36hjkQ6xV6Hg377xylnldzUDOPSYQetS6divURdUEREREdkKkd03MJGIXb3HxD5WXROPKoQttm5Njr/9dU46U5+/G+gZdTzSrvplM/l/3/LX2anVq8puWC39BlQyavsaMzN1QRURERGRrRLlTeaX5nN+/4cP6p+PMIYt9vrLq3jlhZX96oPxi1G+j9J+Epn6/P3PPLas92svriq77qcABx0+sAHnr0Bd1LGIiIiISHmLNMlJV8UvPfjIgdlYmaZad1w/N7VkQWa/bCb/q6hjka2XzeZ/M39O/e733ja/MupYtkRlMsaHDuxXqEzG/hB1LCIiIiJS/qJO016Jx23GLnv2jjiMLZPPO1f+9v3qbLZwbqHgJ0Udj2y5QsFPymYKZ/7ld+9XFQpRR7NlJn64r+fz/gwwK+pYRERERKT8RZ0sUlWduPCoE7YprzueF1m3JsefLple1dhQuAHYPeJwZMuMb2ws3HDlpdOrateVZa9oAA49ZlBtVXXi4qjjEBEREZGuIfJkEbi334DkutHbV0cdxxabN7uem6+enc5m8v8BBkcdj2yWUdls/olbr5mTnjurLCfnBWDsuBqqaxIrgMeijkVEREREuoZSSBbzyVTs50d8cvC6qAPZGq+9uMr+88/FvTP1+WeB/lHHI20yJJvJP3fvbfN7T3puZVlOaNPk0KMHrUulYxej22WIiIiISDsphWQRM7tx9PY1hW2GpKIOZas8dO+iimceXTYkU59/CugVdTyyUQMzmfxzD967qN9Tjywrv/u3FBk2Is3YHWtyZnZT1LGIiIiISNdREskiUG8xfvuJk7Yt++n+/3Hb/MqXn10xKlOffxoljKWqT6Y+/8yTDy8d/Mg/FyeiDmZrnXTasHWJitgPgdqoYxERERGRrqNUkkUqKmK/23HXHtkRo6uiDmWr3X793OTLz63YXgljSeqTqc8/+cJTy7e7/84FFVEHs7V22LkHQ7ZLr4nF7JqoYxERERGRrqVkkkWgtrIi9t2TPz+srMcuNrn9urnJl55dsX2mPv8CmvSmVAzPZPKvvfDU8u3vumleMupgtpYZnHT60HWpdPybQGPU8YiIiIhI11JKySIWsxsHDk4t2W1C12iMu+P6ucn//mvx6Gwm/yawa9TxdHN7ZLP51/5198KhXSFRBNhjnz707lM5D7g76lhEREREpOspqWQRyKfS8bM+dfqwuni8rCen/J8H/7Go4tZr5vTLZvPPAUdEHU83dVg2k3/6pqtm937swSVlPZlNk1gcTvjMkNp0VfwcNAOqiIiIiHSAUksWAf6TTMVf+8gh/QtRB9JeJj2/0q749fTqutrcPblc4Zyo4+lO8jn/Un1d/h9XXDy9+vWXV3WNKxDAhw/qX6hMxV4HHo06FhERERHpmkoxWSRdFf/a0Sdtm01XdYlGIABmTqvlkh+/m16zKndJNpP/C9AlukKWsFQmk79+zerGP1z603fTM6d1nYlCk8kYn/jUttmqqsS5UcciIiIiIl1XSSaLwJtm/OPw47ZpiDqQ9rRsSQO/On9K1bQp6z6bqc9PBnaOOqYuamymPv/m1LfXnvzLH7xTtWRhNup42tXBRw7MmdkjwGtRxyIiIiIiXVepJouk0vHvf/RjA/J9+1dGHUq7qq/Lc9Vv36+655Z5I7OZ/Eu5XOFcoMt0j4yYFfL+xYZs/rX77lgw+urLZqQz9V2mNzMA/QZUcugxgxrSVfFvRh2LiIiIiHRtJZssAvOB3516xnZ1UQfSEZ57Ynns4h+9W7VkYfbX9XX5R4GBUcdU5gbU1+X/uWxp9vJLfzq1+qlHlpby7/YWO+2sEbWxmP0CmBl1LCIi0jIzG2Zmj5vZFDObbGbfCMv7mtkjZjYtfOxTtM35ZjbdzKaa2WFF5XuZ2VvhusvNzMLypJndEZa/aGYjOv1ERaTLK+kv1JXJ2C9Gja1eNHH/vl1ytscli7Jc/KN3q59+dOmHs9n8e17w0yjxn0kJiufzflZDtjDj+SeXfeyi86ZUL5yXiTqmDjFx/74+dLv0/IqK2G+jjkVERDYqB3zH3XcC9gXONrNxwHnAo+4+lmCCsvMAwnWnEAxPORy40syaJm64CjgTGBsuh4flZwAr3X0McBlwSWecmIh0L6WemGRT6fgJJ39hWKZ334qoY+kQ+bxz3+0LKi+/aFqvhfMzV9bX5V8H9o46rjKxd31d/q15s+t+85sL3q2555b5yVxjl7yuQE2PBJ86fVgmlY5/muBLiIiIlCh3X+jur4bP1wJTgCHAscCNYbUbgePC58cCt7t71t1nAtOBiWY2GOjp7s+7uwM3NdumaV93A4c0tTqKiLSXUk8WAV6Pxey3n//aiC7ZHbXJrPfr+NX5U6rvvnne+Lra3JOZ+vzfgEFRx1Wi+mXq8zfW1eaevPPGuTte+pOp1Qvmds3WxCannrFdfSzGNcArUcciIiJtF3YP3QN4ERjk7gshSCj5YAjKEGBu0WbzwrIh4fPm5ett4+45YDXQr0NOQkS6rXJIFqmsjP1i2IiqOR8+uOvce7El7vDCU8vtJ994O/3cE8tPaMgW3s/lCj8AqqOOrUT0aWwo/LwhW5j90rMrTv7pNyenX3pmRZe/irrnPr3ZcXyPZclU/AdRxyIiIm1nZjXAPcA33X3Nxqq2UOYbKd/YNs1jONPMJpnZpKVLl24qZBGR9ZRFsgg0ptLxE0/4zJBsvwFda3bUlmTqC9xzy7zkxT+aUj3lzbU/acgWFuYaCz8CekQdW0T6N2QLlzZkC/Nee2nVd391/pTqO66fm6yvy0cdV4fr2SvBp780vD6Vjp8I1Ecdj4iItI2ZVRAkin9z97+HxYvDrqWEj0vC8nnAsKLNhwILwvKhLZSvt42ZJYBewIrmcbj71e4+wd0nDBgwoD1OTUS6kXJJFgEmx+P2iy+cM7K2u/TIX7wwy5//7/3qS3/ybo+3Xlv9w4aGwsKGbOH/+KALSle3TTaTv7yhoTBn0vMrzv3F99+puvGqWemli7vWfRM35nNfHVEXj9sVwEtRxyIiIm0Tjh28Fpji7r8rWnU/cHr4/HTgvqLyU8IZTkcSTGTzUthVda2Z7Rvu87Rm2zTt60TgsXBco4hIu0lEHcDmSFTELt1mSOqUAz4+YPwTD3fNWyO0ZOH8DNf8YWZVvwGVfOyoQWfv+9F+X8vn/cF0VfzPBLOpdaUmtgRwRF1t7pxEReyAF59ZwcP3LUquWtEYdVyd7sMH9SuMGlu9sDIZ+0nUsYiIyGb5MPA54C0zez0s+yFwMXCnmZ0BzAFOAnD3yWZ2J/AOwSRmZ7t70//2s4AbgDTwYLhAkIzebGbTCVoUT+ngcxKRbqiskkUgn07HT/rEydu+NuWttVWLF3TtSU2aW760gTtumJv8510L2OcjfY/b/+ABH+vTvyJv2I2Vydi1wFtRx7gVdmrIFs4EvrBkUSb25H+W9nj1xZVk6rv0MNVWjRhdxQmfG1qXTMaPAhqijkdERNrO3Z+h5TGFAIe0ss1FwEUtlE8CxrdQniFMNkVEOkq5JYsA7yXisa+fc96Yy3913pSq7jBurbm62jyPP7TUHn9oaY9thqTY9yN9v7bfgf2/HI/bomQq9pdYzB4E3qaFge4lxIDx+bx/IpvJfwYY8dwTyxPPPb6sYvHC7tPNtCU9eiY467uj65LJ+GeBqVHHIyIiIiLdUzkmi8QTdm1VdXzimd8a9dnLfzWtqjv30F80P8O9ty+ouO+OBRVjd6oZtfeH+/5s5916/SRdFS/kcoVHq6oT9wP/Zf2pt6PSHzi4vj5/tBlHNGYL6ddfXpV4fdKq5HuT11Lono2I64nF4azvjq6tTMav4INxKSIiIiIina4sk0WAVCp+zrCRVbse/+khe/79b/O7/hSpm+AO772zjvfeWZcC6Degkh3G9zh+lz17HbrDuB4VhQLLCgV/qao6/qKZvUXQ8jiXjml9NGBbYPdCwfeor8vvH4vZbomE9Z0xrTbz5iurekx9e60tnN+9uhG3xUmfG5YdtG3q5cpk7EdRxyIiIiIi3VvZJotAYzodP3r/g/u/PXdW/aCXn+3699vbHMuXNvDc48t57vHlNWYwZLv0kKHD08cPHV511PBRVfXbDElVVFbGYtls4X0z3k6m4jPjcZsPLCIYKL8yXPIEs+Za+Nj0PA1sEy6Ds9n88MYGHw4MrUzGRhXynpg/pz47c3pt1ZyZdRXzZtezdFGGQoFun9i3Zu8P9/V9PtJ3eTIVPx5QO6uIiIiIRKqck0WA5clU/NBTzxj2wuKFmeo5M+qijqckucO82fXMm10PrKiEIGGrrokzeGh654HbJHfu1bvC+/SvzPTpW9lY3SPuVdUJ0lXxhBnmTjAbtxM8OJ7LFXzN6pyvXN4QX7m8IblqRWPF6lWNrFndyOIFGdasygGkojzvcjJ0eJpTzxhWn0zGDwNWRR1PRzKzWcBaggsROXefYGZ9gTuAEcAs4FPuvjKsfz5wRlj/6+7+cFi+Fx/MEPhv4Bvu7maWBG4C9gKWAye7+6xOOj0RERGRLqPck0WAt5PJ+GfO/v6YWy86752qMEmRNqhdl2f6u+uY/u46+KC1MB1tVN1PdU2cs78/pq6iIvZ5gu7B3cFB7r6s6PV5wKPufrGZnRe+/oGZjSOYDn5ngq7N/zWz7cMp5a8CzgReIEgWDyeYUv4MYKW7jzGzU4BLgJM768REREREuoqucq/C+yqTsd+e/f0xtYkK9UaV8pGoML76ndF1yVTs6ljM7oo6nggdC9wYPr8ROK6o/HZ3z7r7TGA6MNHMBgM93f358CbUNzXbpmlfdwOHhDezFhEREZHN0FWSRSorYz/rPzD55Oe+Mjyjr4VSDmJx+Mq3RtdtOyz9WDIV/27U8XQiB/5jZq+Y2Zlh2SB3XwgQPg4My4cQTMTUZF5YNoT1Z/htKl9vG3fPAauBfh1wHiIiIiJdWpdJFoFCKh3/1Pjde7130mlDdRNzKWlm8MVzRtaP2r76xVQ6fgLBeLzu4sPuvidwBHC2mX10I3VbuvTjGynf2Dbr79jsTDObZGaTli5duqmYRURERLqdrpQsAtSm0vED9vlIv9nHnDS4MepgRFpiBp89c3hmp116vplKx48CutXFDXdfED4uAf4BTAQWh11LCR+XhNXnAcOKNh8KLAjLh7ZQvt42ZpYAehHM8Ns8jqvdfYK7TxgwYED7nJyIiIhIF9LVkkWAVal0fP8DDx+48NCjB2m2Gyk5J502tGH3vXu/l0rHPwbURx1PZzKzajPr0fQc+DjBpD73A6eH1U4H7guf3w+cYmZJMxsJjAVeCruqrjWzfcPxiKc126ZpXycCj4XjGkVERERkM3SF2VBbsiSVin/oiOO3eaWhodD/yf8sjUcdkAjAJz61beM+H+k3K5WOHwisizqeCAwC/hHON5MAbnX3h8zsZeBOMzsDmAOcBODuk83sTuAdIAecHc6ECnAWH9w648FwAbgWuNnMphO0KJ7SGScmIiLty+xnUYcg0u111WQRYH4yFd/32JO3fdGMfk88rIRRovXxTwzKHXjYgIXJVHx/YGXU8UTB3WcAu7VQvhw4pJVtLgIuaqF8EjC+hfIMYbIpIiIiIluuKyeLALOSqfg+n/jUti+YWf/HH1qihFEiccChA/KHH7vN8mQq/iFAs6mIiIi00dGPn9nqugcOuroTIxHpfrrimMXmZiVT8X2OOWnwsoOPGNidZpyUEnHkJwc3HnvKtsuTqfh+wPyo4xERERERaYuu3rLYZHYyFZ949ImDn+vVu2LAvbfPr9R0Fx3njXdvZcr79+I440Yfz247fprpcx7h5beuZuXqmZx42E0M7DeuxW3nLHiOZ175LQXPM270cey58xcAeP61y5m98Fn6996Bj33o5wBMnfkvMtnV7Lbjpzvt3DZHLA6f+fLwzO4Tes9KpuIHAYuijklEREREpK26Q8tikznJVHz3/Q/p//ZXvj2qvqKypVuxydZavmo6U96/lxMOu5GTj7iN2fOfZtWaOfTtNYbDP/Ibth24Z6vbFgp5npp0MUcddDmnHnU302Y/zIrVM8g2rGXRsjc45cg7cM+zfNU0crkM7874J+O3L82haclUjHPPG1u3+4TeL6TS8b1RoigiIiIiZaY7JYsAy1Lp+IfG7tTj39/72Y61PXt1l4bVzrNyzUwG9R9PRSJNLJZg24F7MnPe4/TtNZI+PUdsdNslyyfTq2YYvWqGEo9XMGb4x5k57wnMYuQLjbg7uXyWmCV4bcpN7LrDKcRjFZ1zYpuhZ+8E3//5jrXbjay6O5WOH0r3nPVURERERMpcd0sWAbKpdPykAYOSvz//1zvVDR6aijqeLqVvrzEsWPIamewqGnP1zF7wLOvqFrdp29r6JdRUD/rf65qqQdTWLaWyoprRww7hzgc/Tc+abamsrGHJ8ncYOfTADjqLLTdo2xTnX7RTXd/+lb9JpeOfJ7jdg4iIiIhI2emuTWtemYz9OJ6wd7974Q5X//UPM9LvvrU26pi6hL69RrLHuNO5/7GvUZGool+f7TFr2yS0zoYDScP78bHHuNPZY1xwn/XHX/w5E3f9Ku9M/wdzF71Av95jmTD+S+13EltozI41nPXd0fUVlbGz4nG7Kep4RERERES2RndNFgGIx+2WeDo+6yvfGv2ve/42r+aZR5d1x5bWdjdu9HGMG30cAC+8/kdqqga2abua9CDW1X7QCrmubjFV6f7r1Vm64l0AevcczjOv/JbjD72G/zxzPqvWzKF3z+3a5wS2wIQP9fFPf2m72mQyfhzwaGSBiIiISCTMftbmuu4XdGAkIu1HyRE8U5mM7fXJTw9ZcNJpQxtiuhPjVqvLrABgbe1CZsx7jDEjDm/TdgP7jWP12rmsWTeffL6R6bP/w8ghB6xX56U3r2LiLmdRKORwLwBB62Mun2nfk2ijRIXx6TO2y376jO0WJZPxD6FEUURERES6iG7dslhkejIV322fj/S7Z/txPfa++rIZ1UsXZ6OOqWw9/PT3yGRXE4sl+OiE80hV9mTG3Md4etJvqM+u5F9PfoP+vbfnmIP/RG3dUh5/8RccfdDlxGIJPjLh+/zz8XNwz7PjqGPp23v0//Y7Y+7jDOy3M9VVAwAY1H8Xbv/Xp+jXeyz9+2zf6efZf2AlZ313TG3vvhVPJFPxzwCrOz0IERERKSnfvPXsVtf9/tN/6sRIRLaeksUPrEhXxQ+uqEyec/6vdrz47pvnpZ97Yrnur7EFjj/02g3KRg07mFHDDt6gvLpqAEcfdPn/Xg8fsj/Dh+zf4n5HDTuIUcMO+t/rD+/5LeBbWx/wFthjYm8+e+bw+kTCfpSoiF0OLQy4FBEREREpY0oW1+eJROyKRILHTvjs0Pt2ndB78M1/nlVVuy4fdVxSIpLJGJ/6wrDMHnv3XpVMxT8BvBx1TCIiIiIiHUFjFls2OZWOj99+p5rrf/rbnet22LlH1PFICRg+qoqf/nZc3e4Tet+bTMW3R4miiIiIiHRhShZbl0mm4ufU9Egc/5XvjFr5yc8MaUgk1Cu1O4rF4Yjjt8l988fbr+vdt/LzqXT8VED3WhERERGRLk3J4qb9J5mMb/+hA/s/ef6vd6odOjwddTzSicbuVMMFv9259pAjBz1fmYyNA+6KOiYRESl9ZnadmS0xs7eLyvqa2SNmNi187FO07nwzm25mU83ssKLyvczsrXDd5RbegNjMkmZ2R1j+opmN6NQTFJFuQcli2yxLV8UPGzgo+Y3vXLDDms98ebtMdY3usdGV9eyd4Iyvj6w/67ujl/UfmDwtXRU/AJgbdVwiIlI2bgCa3zvqPOBRdx9LcKul8wDMbBxwCrBzuM2VZtb0ReMq4ExgbLg07fMMYKW7jwEuAy7psDMRkW5LyWLbeSxu11YmYyP23LfPjT///fj6Az4+oBDTO9ilxGJw8BED8xf+3871O+/e80/JVHwE8Hc026mIiGwGd38KWNGs+FjgxvD5jcBxReW3u3vW3WcC04GJZjYY6Onuz7u7Azc126ZpX3cDhzS1OoqItBfNhrr5VqZS8a8CfzzmpG2vOejwgeNvvWZ29XvvrIs6LtlKo3eo5nNnjqit6Zl4M5mKfwGYGnVMIiLSpQxy94UA7r7QzAaG5UOAF4rqzQvLGsPnzcubtpkb7itnZquBfsCyjgtfRLobJYtb7u10VXy/dFX8+K9+Z/RVUyevrbnrpnlVK5Y1RB2XbKYePROc+Lmh9bvs2as+GVwIuBu1JIqISOdpqUXQN1K+sW3W37HZmQTdWNluu+22ND4R6abUiXLrOPD3ZCo+Ysddev72x5fuVHfUiYNzyZTe1nJQ0zPB8Z8e0vCz3++c2WWvXn9JpuLDCSawUaIoIiIdYXHYtZTwcUlYPg8YVlRvKLAgLB/aQvl625hZAujFht1ecfer3X2Cu08YMGBAO56KiHQHymraR31lZeyCZDK+00GHDfzXr/64S/0Rx2+TS6X19paiXn0qOOm0odlf/H58Zv9D+t+cTMZ3SCbj3wLUl1hERDrS/cDp4fPTgfuKyk8JZzgdSTCRzUthl9W1ZrZvOB7xtGbbNO3rROCxcFyjiEi7UTfU9jUnXRU/DtjxkCMH/eLQowcd/fhDSxKP/ntJoq42H3Vs3V7f/pUcfuw2mb337+te8Osqk7Ff8cEVWhERkXZjZrcBBwL9zWwecAFwMXCnmZ0BzAFOAnD3yWZ2J/AOkAPOdvemLw5nEcysmgYeDBeAa4GbzWw6QYviKZ1wWiLSzShZ7BjvpqviJwGjDzhs4M8OPmLQCc8/ucz++68lSY1p7HwDBiU54vht6vfYp4/j/LmyMnYJH3T9ERERaXfufmorqw5ppf5FwEUtlE8CxrdQniFMNkVEOoqSxY71fjod/yzwg/0O6P/t/Q7s/5Upb66xh+5bVDVnRl3UsXV5I8dWc/ARA+vG79HLDf5QURn7P1oYzyEiIiIiIhtSstg55lcmY98BLhy/R68v7Ti+xw+XL21IPvXfpTWvvrDSatepi2p76dk7wT4f6Vc44NABdamq+OrKitgV8YT9GVgddWwiIiIiIuVEyWLnWhuP22XxePyKbYelD/3Ep7b96gmfGfrxaVPW5p55bFnN5NfXkMtpbPrmiseN8Xv04oCPD1g3amx1PJfzv6er4lcCz6OZTUVEREREtoiSxWjkgAerqhMPAr3G7dbrhBGjq78WT9jOk55byfNPLk/NnF4bdYwlb9thKT58UP/svh/t5/m8T66uSVwO3FNRid48EREREZGtpGQxequB66pqEtcBw/f5aN/P7blfn69mM/nezz62LPXmK6vj8+fUo8mwwQy2G1nFLnv1yk/Yt09dz94VDbGYXV1RGbsWeD/q+ESkezL7WdQhSBu5XxB1CCIiZUXJYmmZnUjEfplIcFE6HZ/wsaMGffHgIwZ9woy+Uyevzb316uqaqZPX0p1mVE1Xxdlh5x7sOqFX/a579gZYFk/Y3ZWVsXuBZwEN+BQRERER6QBKFkuTAy8nU/GXCe6vtN1uE3ofMnanmuMSFbGD6tblbPLraxKT31ideu+dddTXdZ18qaLCGDm2mp127ZnbZc9edQMGJpPZbGFSVXX8DjP7FzAj6hhFRFpy59e+FnUI0opPXXll1CGIiJQlJYvlYQ5wfVV14nogVtm3cpf9Dux36O4Te38ymYzttXRJNjNjam3l3Nl1qQVz61kwN1MWCWR1TZyhw6sYOjzNiDHVtcNHVeV796lMZzL595LJ2H2JitjDwAuJilj3aUoVERGRLm9zuq+r+7REScli+SkAb8Ri9kZ1TeK3QHLwkPS+g4ekd6uvy08sFHzPZCo2Kpsp5BfOzzTOfr+2av6c+ooFc+tZND9DY2PnD35MpWP06l3B4KFpho1I50eOrakdul06kUzFYplMYWpFhT2fTMVfAl4H3qmuSWQ7PUgRERHpcBrjK1JelCyWvyzwJPBkuireVBZL1MS2G7NDzfhRY6t3ra/L72PGbslUfEg2k29YuybXuHplIyuWN1SsXNaQXrs6Z3W1Oepq89SGjw3ZAmZgZuFj+Dy2flkiYfToVUHPXgl69a6gT7/KbJ9+ldnefSu8Z6+KeFVNPAkUGht8ZT7v76bT8WfiCXuVIDGcVVMR09Q9IiIi0i2df/05ra779Rf+2ImRiLRMyWLXVABmAbNiMXuguuZ/P+aKqurEoKrqxOBBg1ODgcGFgg9pyBaG5HI+EOhvRp9YzHrFYqQcCjgOePFzoODuHh4nVyiwxIy5yVRsZiIRmw8sDJdF4eO6hH7TREREJHTIc19qdd2jH7qmEyMRkY3RV/jupRGYFy4AxGJGKh1vfQsREREREemWYlEHILKlzOxwM5tqZtPN7Lyo4xERERER6UqULEpZMrM48CfgCGAccKqZjYs2KhERERGRrkPJopSricB0d5/h7g3A7cCxEcckIiIiItJlKFmUcjUEmFv0el5YJiIiIiIi7UDJopQra6FMt+EQEREREWknShalXM0DhhW9HgosiCgWEREREZEuR8milKuXgbFmNtLMKoFTgPsjjklEREREpMvQfRalLLl7zszOAR4G4sB17j454rBERERERLoMJYtSttz938C/o45DRERERKQrUjdUERERERER2YCSRREREREREdmAuqGKiIiIiHQBZj9rc133CzowEukqlCyKiIiIyBbbnARFRMqLkkURERERkS7kN1d8vdV13zv38k6MRMqdkkURERER2WofmnRGq+uem3BtJ0YiIu1FyaKIdElmdjjwB4L7cF7j7hdHHJKISLvrqM86dS0VEVCyKCJdkJnFgT8BhwLzgJfN7H53fyfayERE2s+WfNYpCRSRzaFkUUS6oonAdHefAWBmtwPHAkoWRaQr6fDPuj3e+mKr617b5br2OoxsRCkk+G2Nwf2CDqsr0TB3jzoGEZF2ZWYnAoe7+5fC158D9nH3c1qqP2HCBJ80adJm7D/6f9zSNp3xBUO/D+Vjc38fzOwVd5/QQeFstS35rHvllWM6M0QpYW1N1joyAdzcZHFT9Yv/xttat63vQVeuu7HPOiWLItLlmNlJwGHNvkBNdPdzi+qcCZwZvtwBmNrpgZae/sCyqIOQkqDfhcBwdx8QdRCtacfPus35eatu9MdXXdVt77qtftapG6qIdEXzgGFFr4cCC4oruPvVwNWdGVSpM7NJpdyKIp1Hvwtlo10+6zbn56260R9fdVW3s+oCxNpaUUSkjLwMjDWzkWZWCZwC3B9xTCIi7U2fdSLSodSyKCJdjrvnzOwc4GGC6eSvc/fJEYclItKu9FknIh1NyaKIdEnu/m/g31HHUWbULVea6HehTLTTZ93m/LxVN/rjq67qdlZdTXAjIiIiIiIiG9KYRREREREREdmAkkURkW7OzA43s6lmNt3Mzos6HomOmV1nZkvM7O2oYxERkegpWRQR6cbMLA78CTgCGAecambjoo1KInQDcHjUQYiISGnQBDciIt3bRGC6u88AMLPbgWOBdyKNSiLh7k+Z2Yio45DSYmaDgCGAAwvcffHW1FNdKWdmtiPB/8n//YyB+919ypbUK5W6rVGyKCLSvQ0B5ha9ngfsE1EsItJJ2pLQmNnuwJ+BXsD8sHioma0Cvubur25OPdX9oG5YP/IEQXU3r66Z/QA4FbgdeCksHgrcZma3u/vFm1OvVOpujGZDFRHpxszsJOAwd/9S+PpzwER3PzfayCQqYcviA+4+PupYpP21ltAAq9gw+Xkd+Iq7v9hsH/sCf3H33TannuquV7f4i/y8sHgocAqwsS/9qhtt3feAnd29kSJmVglMdvexm1OvVOpulLtr0aJFi5ZuugD7AQ8XvT4fOD/quLRE+jsxAng76ji0dNjP93VgnxbK9wXeaFY2bSP7mb659VR3vdfvARUt1Ktsvh/VLam67wLDW6g7HJi6ufVKpe7GFnVDFRHp3l4GxprZSIJWhlOAT0cbkoh0oGpv1vIF4O4vmFl1s+IHzexfwE180F19GHAa8NAW1FPdDxSAbYHZzcoHh+tUtzTrfhN41Mym8cHPeDtgDHDOFtQrlbqtUjdUEZFuzsyOBH4PxIHr3P2iaCOSqJjZbcCBQH9gMXCBu18baVDSrszscmA0LSc0M939nGb1j+CDsVxG0E3vfnf/95bUU93/1Tsc+CPQ4hd5d39IdUuvblg/RjA5XPHP+GV3z29JvVKp2xoliyIiIiLdyOYkP9JxSiFBUN3Nr9vdKFkUERERkc1iZme6+9XtVU91pdyZ2QPufnR71SuVurG2VBIRERGRrs3Mztyc6u1cT3WbKpo9oLrlVxf4cjvXK4m6alkUEREREczsK+7+l2ZlOxJ0zXvR3dcVlR/ebNzXRMDd/WUzGwccDrzblq6tZnaTu5/Whnr7E3QVfNvd/9Ns3T7AFHdfY2Zp4DxgT+Ad4Ffuvrqo7teBf7h78T1mWztmJcHEXwvc/b9m9mngQ8AU4Grf8LYEo4HjCcaB5gjGwd1WfPw2HHOwuy9U3fKqWw7MbKC7L9msbZQsioiIiIiZfcHdry96/XXgbILEaHfgG+5+X7juVXffM3x+AXAEkAAeAfYBngA+RnBrnouK9nl/88MCBwGPAbj7J4rqvuTuE8PnXw5j+QfwceCfvv797yYDu7l7zsyuBuqAu4FDwvJPFtVdDdQC7wO3AXe5+9JW3pO/hedVRXAvyhrg7+F+cffPN3u/jgGeBI4kuE3JSoLk8Wvu/kRLxyhXW5J4tHG//dx9eXvvtz2YWS+CW0wdBwwIi5cA9wEXu/uqNuzjQXc/ouh1z3CfQ4EH3f3WonVXuvvXil5vA1xAMEvrT4FzgRMI/ka/UZzYmlnf5ocGXgH2IMgBV7TpnJUsioiIiIiZzXH37YpevwXs5+7rzGwEQfJ1s7v/wcxec/c9iurtDiSBRcDQoha+F91916J9vkrQ2ncN4ARfYG8jaL3D3Z8sqlt8jJeBI919qQW3+HjB3XcpqjvF3XdqOkZTIhu+ft3ddy/eL7AXQTJ7MvAJgi/RtwF/d/e1RXXfdPddzSxBcHuhbd09b2ZGcF/K4nN7C9g9XF8F/NvdDzSz7YD7ms4lrLvVSUe4n5JLPIpbncPz/B2wN/A28C13X1xU92Lgt+6+zMwmAHeG8VQApzX7fXiVIFG/zd3f38T7MgH4DcHP7HzgOoJW6feAM939taK6NcD3w3MfCjQQXEj4s7vf0Gy/DxNc2LjR3RcVvY+nAx9z90PDsj1pmQEPuPvgon3eQ9AC/QLwRaAR+LS7Z1v4XX4I+BdQTXCbq78R/N4eGx7/2KK6BTa8HchQgsl73N1Htfb+FdN9FkVEpGSY2Tp3ryl6/Xlggjebzn8L9z0C+FDTlyczO5Dgi9kMglaDxcCl7v5AuP6rQJ2737S1xxYpFWb2ZmurgEHNyuJNXU/dfVb4N3O3mQ1n/TF4uXDWyDoze9/d14Tb1IdfWItNAL4B/Aj4nru/bmb1xUlBkZiZ9SGYY8OaWv/cvdbMcs3qvl3UMvqGmU1w90lmtj3Bl+9i7u4F4D/Af8ysgqBl9FTgt3yQvDXFUEnw5bwK6AWsIEiMK1qIOQHkw/U9woPNCY9R7E6CpOPAFpKOu4BDmypuIvHYvVnZ9QSJxz3AF83sBMLEA9i3Wd0b+CDxeJwg8TiKIPH4c/jYZBkbJh5DgFcJkv7ixONXfHBfyf8DFhK0uH4S+AtBgtzkKHc/L3z+G+DksCvz9sCtBL8vTfoAvYHHzWwRQZJ0h7svYENXEiTCvYHnCJLUQ83skHDdfkV1/0bQYn0Y8Knw/bgd+LGZbe/uPyyqO8LdLyk+UPjzu8TMvlhU/DJBC3NLY1V7N3s92t1PCJ/fa2Y/Ah4zs0+woUHufgWAmX2tKJYrzOyMZnW/T3BB5Hvu/la4zUx3H9nCflvn7lq0aNGiRUtJLMC6Zq8/D/yxHfabILh/4ANFZc1f7w7MAg6J+n3QoqWjFoKLIrsDw5stIwjG5RXXfYygpay4LEFwj8Z8UdmLQFX4PFZU3gt4tZU4hhIkRX8E5rRSZxbBxZyZ4eM2YXkN8Hqzur0Ikp/3w3gaw22eJOiGWlz3tY28P+lmr78V7mc28HXgUeCvwFsE9yEtrvsN4E3gauBd4Ath+QDgqWZ1p24khqnNXufDn8XjLSz1zeo2f19+BDwL9Gv+syh+H5r/DFrYz3cJEsBdispmthL/qxvZT/PX7wKJ8PkLzda9tZH9foQg6VsUvg9nbsa5vdbs9RvNXr/c9LtMMO62eN1/CJKwQUVlg4AfAP8tKnsbGNvK+zO32espFP3dhGWnA5OB2a3FCvxyY++Xr/939juCixczWvu9a21Ry6KIiJQFMxtAcLW7qZvcN9392XBijd8DaaCe4Ava1LBV8iggxQetAjuZ2evAjcBrxfv3oIXj58A5wKNmdiFB8vrbcCzSVwkmrHjH3U8Ju8JdAexC8AX6Qne/L2zBvDk8JgQ3dX7OzAYDdwA9w/pnufvTZvZx4GcELRHvh/H/byIRkXb2AFDj7q83X2FmTzQrOo3gd/5/3D0HnGZmxRPhfNSDlis8aLFrUkHwpXcD7j4POMnMjgLWtFJnRCvnUCAYB1hcdzXweTPrQdDKlQDmeVGXxyInt7Jf3L2+2evLzOyO8PkCM7uJoLXmr+7+UrO6fzCz/wI7Ab9z93fD8qXAR5sdaraZfZ+gO+NiADMbRHCBrPnEO1OAr7j7tObxmlnzukkzizX9HNz9IjObBzxFkGQXK74rQvMeFPFm5/ZbM7sduCw85gUELYotGWhm3yZoVetpZuZh5sKGd2L4E/DvsDvqQ2b2ez4YE/p6K/vH3Z8GnjazcwlaYU8mSNKbZMLP1l6Am9lx7n6vmR1AkHwXqzWz/d39GTM7hqDlGHcvhN2Ni51MMHnSk+HPywkuwNxP0CrZ5MIWzrXJuc1e/xM4GPhv0fndaGaLCf7HFLvPzGrcfZ27/7ip0MzGAFObH6jo7+wYgvHEVa3E1LrNzS61aNGiRYuWjloI/om/XrTMIWxZJOiStH/4fDuCmQ8hTL7C5x8D7gmff55gbEbf8PWBbKRlMSzbvWi/FwLfDZ8vAJLh897h46+AzzaVEYyFaUpKU2H5WGBS+Pw7wI/C53GCq7z9Cb7EVYflPwB+GvXPQYsWLR27EHSpvISgZW1FuEwJy/o0q3sisEMr+zmu2etLCcauNa93ODCtWdnPCS4cNK87Brh7I7EfQzC+blEr6y9otgwIy7cBbmqh/oEEF9JeI2ix/TdwZtPnelG92zfj/d0NeBh4ENgR+APBBEWTCYYjFNfdFXgpXP8MsH1YPgD4egv73jH8X1PTrPzwFuodsql6m6h7xGbU3eh+CS6ojm+tbqvvZZR/KFq0aNGiRUvxwka6oRJM/vB60TI/TLiGEYw3eTv8ovFu0bbXF+3rQDadLO5By8niQwSTe3y26R80MCk8ZlM8cwhaFHoRtCy+FZbXhfU/CkwP97t7WHY0wVigpn28A1wb9c9BixYt0S2E3VdLuW6zxKPk422vugRdkacC9xJ0kz62aN2rm1svfH1uB9VtcwwbW1prHhURESk1MYKZGXcPlyEezFr4C+Bxdx9PcMU7VbRN7WYeYw+Cq/vNHUXQXWov4JVwZkQDTiiKZzt3n0IwxmkxwZXtCUAlgLs/RZAwzgduNrPTwn08UrSPce7efJICEeleflbqdd293t3fjjKGiOp+GdjL3Y8juOD4EzP7RrjOtqAeBK2oHVF3c2JolcYsiohIufgPwXjC3wCY2e4ejLvqRZCAQdCa2Jq1hLMTtsTMdgV+AnypWXkMGObuj5vZMwTTldcQdHE618zOdXc3sz08mI69F8FYqYKZnU449iecQXK+u/81HO+4J3AR8CczG+Pu08Pp9oe6+3ttf1tEpNxszqy0qls6dWn7DMFtrVcqdVulZFFERMrF1wkSqzcJ/n89RTDpzKXAjeGECo9tZPs3gZyZvUEwa+JrwEcsuOdaFUE316+7+6PNtosDt1hwvzADLnP3VWb2C4KJdd4MJ0GYRdCt9ErgHjM7iWCWvqbWzQOB75lZI7CO4B5iS8OJeG4zs2RY78cE4x9FpOsaRHCrhpXNyo3gVg+qW5p1FxVdqMSDe5AeTXAfx122oF6p1G2VkkURESkZXnSPxfD1DQSJHe6+jBZmMXT354Hti4p+0nzb8HUjwUD/Yr02EsuFRS/3b2F9PfCVFsqnEUyY0OT8sPxGgllYm9d/jOCG1SLSfWzOrLSqWzp12zpDcFvrlUrdVlk4yFFERERERETkfzTBjYiIiIiIiGxAyaKIiIiIiIhsQMmiiIiIiIiIbEDJooiIiIiIiGxAyaKIiIiIiIhsQMmiiIiIiIiIbEDJooiIiIiIiGzg/wFvPjIv47kRhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,3,figsize=(16,5))\n",
    "plt.subplot(131)\n",
    "x = train['HeartDisease'].value_counts()\n",
    "colors = [\"slateblue\", \"yellow\"]\n",
    "plt.pie(x.values, startangle=122, labels=[0,1], wedgeprops={\"edgecolor\":\"white\", \"width\":0.4}, radius=1.1, colors=colors, autopct=\"%1.1f%%\", pctdistance=0.8)\n",
    "plt.title('Percentage of Heart Disease')\n",
    "plt.subplot(132)\n",
    "sns.countplot(train['HeartDisease'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.title('Distribution of Heart Disease')\n",
    "plt.subplot(133)\n",
    "sns.countplot(train['SleepTime'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('Distribution of Sleep Time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(4,4,figsize=(15,15))\n",
    "plt.subplot(441)\n",
    "sns.distplot(train['BMI'], color='yellow', kde=True, kde_kws={'color':'navy'})\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(442)\n",
    "x = train['Smoking'].value_counts()\n",
    "sns.countplot(train['Smoking'],palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(443)\n",
    "sns.countplot(train['AlcoholDrinking'],palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(444)\n",
    "sns.countplot(train['Stroke'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(445)\n",
    "sns.distplot(train['PhysicalHealth'], color='yellow', kde=True, kde_kws={'color':'navy'})\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(446)\n",
    "sns.distplot(train['MentalHealth'], color='yellow', kde=True, kde_kws={'color':'navy'})\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(447)\n",
    "sns.countplot(train['DiffWalking'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(448)\n",
    "sns.countplot(train['Sex'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(4,4,9)\n",
    "sns.countplot(train['AgeCategory'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(4,4,10)\n",
    "sns.countplot(train['Race'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(4,4,11)\n",
    "sns.countplot(train['Diabetic'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(4,4,12)\n",
    "sns.countplot(train['PhysicalActivity'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(4,4,13)\n",
    "sns.countplot(train['GenHealth'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(4,4,14)\n",
    "sns.countplot(train['Asthma'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(4,4,15)\n",
    "sns.countplot(train['KidneyDisease'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.subplot(4,4,16)\n",
    "sns.countplot(train['SkinCancer'], palette=\"cool\", linewidth=2.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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geOD+iAhJ9wA/z1sh72jgnKKP1qxI77//PrW1tbzzzjvlrkpF6969O1VVVWy55Zblrkqb8XuhebrCe8HMrJz8fdR8Lf1OajJAAg4BTgaelDQ/pX2fLDC6UdJ4YBnwBYCIWCDpRuBpYAMwMSI2pnynA9OBHmSLM9yV0q8Erk4LOqwGxqSyVks6H3g47ffT3IINZu2ptraW7bffnoEDB5JWp7d6IoJVq1ZRW1vLoEGDyl2dNuP3QtO6ynvBKpeks4CvkA3LfxL4N2Ab4AZgILAE+GJuVVxJ5wDjgY3ANyPinpR+IB+et9wJnJku4G4NXAUcCKwCToiIJe1zdGYZfx81TzHfSc1Zxe4vFJ4LBDCigTyTgEkF0muAIQXS3yEFWAW2TQOmNVVPs7b0zjvvuAFqgiR22mknOvtCKX4vNK2rvBesMknqC3wTGBwRb6eLtmOAwcDsvNuTnA3kbk8yBtiXdHsSSXuni7uXAxP48PYkx5Bd3B0PrImIPdPtSS4ETmjXA7Uuz99HzVPMd1KLVrEz68rcADWtq/yNuspxtob/RlZm3YAeaV7zNmTzl0eR3ZaE9Ht0erzp9iQR8SKQuz3JbqTbk0R208ir6uXJlXUTMEJ+01sZ+G3XPC39OzlAMjMzs04jIl4GLiIb/r8cWBsR99I+tycxs07AAZJZkbbbbrs6z6dPn84ZZ5xRkrKXLFnCtddeu+n5nDlz2GGHHTjggAPYZ599OOyww7j99ts3bb/iiiu46qqrSvLa1nJ+L5hVjrSw0yhgENmQuW0lfbmxLAXSir09Sf26+D6N1q78fVQazVmkoVMbOGAyS5c1dlunyjSgf0+WLD2r3NWwNrBhw4ZNjdCXvvSlTemHHnropoZn/vz5jB49mh49ejBixAhOO+20clXX2pDfCx1XY98tbr/b3KeBFyNiJYCkW4BPkm5PEhHLS3h7ktp6tyepIyKmAlMBqqurNwugOqJSnjf5s9BxdLXvoy4fIC1dto4188aVuxot1mvY9HJXwRqxcuVKTjvtNJYtWwbAr371Kw455BDmzZvHt771Ld5++2169OjB73//e/bZZx+mT5/OHXfcwTvvvMObb77JW2+9xcKFCxk6dChjx47lgAMOqFP+0KFD+fGPf8yUKVMYMWIE5513Httttx3f/e53ueSSS7jiiivo1q0bgwcP5vrrr+fNN9/kG9/4Bk8++SQbNmzgvPPOY9SoUSxZsoSTTz6ZN998E4ApU6bwyU9+kuXLl3PCCSewbt06NmzYwOWXX86hhx7Kvffey7nnnsu7777LRz/6UX7/+99vdrXK6vJ7oWtq7LvF7XebWwYMl7QN8DbZglI1wJu08e1J2uPgyq2U503+LLQvfx81X5cPkMyK9fbbbzN06NBNz1evXs1xxx0HwJlnnslZZ53Fpz71KZYtW8bIkSNZuHAhH/vYx3jggQfo1q0b9913H9///ve5+eabAZg7dy5PPPEEvXv3Zs6cOVx00UWbrsrMmTNns9f/xCc+wS9/+cvN0i+44AJefPFFtt56a15//XUAJk2axJFHHsm0adN4/fXXGTZsGJ/+9KfZZZddmDVrFt27d2fRokWceOKJ1NTUcO211zJy5Eh+8IMfsHHjRt566y1ee+01fvazn3Hfffex7bbbcuGFF3LxxRfz4x//uLR/2A7I7wW/F6xyRMRDkm4CHiW73chjZL0429HGtycxKzd/H5Xm+8gBklmRevTowfz58zc9nz59OjU1NQDcd999PP3005u2rVu3jvXr17N27VrGjh3LokWLkMT777+/aZ+jjjqK3r17N/v1G7pYud9++3HSSScxevRoRo8eDcC9997LzJkzueiii4BsadBly5ax++67c8YZZzB//ny22GILnnvuOQAOOuggTj31VN5//31Gjx7N0KFD+fOf/8zTTz/NIYccAsB7773HwQcf3Oz6dmZ+L/i90FY8VK84EXEucG695Hdph9uTmJWTv49K833kAMmsDXzwwQfMnTuXHj161En/xje+wRFHHMGtt97KkiVLOPzwwzdt23bbbVv0Go899hgf//jHN0u/4447eOCBB5g5cybnn38+CxYsICK4+eab2Wefferse95557Hrrrvy+OOP88EHH9C9e3cADjvsMB544AHuuOMOTj75ZP793/+dXr16cdRRR3Hddde1qJ5dnd8L1hoeqmdmpeLvo+bzKnZmbeDoo49mypQpm57nruasXbuWvn2zVWKnT5/eYP7tt9+e9evXN7j9iSee4Pzzz2fixIl10j/44ANeeukljjjiCH7xi1/w+uuv88YbbzBy5EguvfTSTVd2HnvssU312W233fjIRz7C1VdfzcaN2aiSpUuXsssuu/DVr36V8ePH8+ijjzJ8+HD++te/snjxYgDeeuutTVd1rGF+L5iZWSXw91HzOUAyawOXXHIJNTU17LfffgwePJgrrrgCgP/4j//gnHPO4ZBDDtn0gS9kv/32o1u3buy///5MnjwZgP/7v//btJTmxIkTueSSSxgxou5okY0bN/LlL3+Zf/iHf+CAAw7grLPOYscdd+RHP/oR77//Pvvttx9DhgzhRz/6EQBf//rXmTFjBsOHD+e5557bdKVozpw5DB06lAMOOICbb76ZM888kz59+jB9+nROPPFE9ttvP4YPH84zzzzTFn++TsXvBTMzqwT+Pmo+dbZFV6qrqyM31rI5pJ902FXssiHW1h4WLlxYsMvYNlfobyXpkYioLlOVilaoPfF7ofm68t+qse+WlrTfpSqnM+lM7UlHVMrzpq76Hi6VrtzGFqMl5yfuQTIzMzMzM0scIJmZmZmZmSUOkMzMzMzMzBIHSGZmZmZmZokDJDMzMzMzs8QBkpmZmZmZWdKt3BUw64oGDpjM0mXrSlbegP49WbL0rCb3u/vuuznzzDPZuHEjX/nKVzj77LNLVgcrXjneD34vmJlZff4+yjQZIEmaBnwWWBERQ1LaDcA+aZcdgdcjYqikgcBC4Nm07cGIOC3lORCYDvQA7gTOjIiQtDVwFXAgsAo4ISKWpDxjgR+msn4WETNac7BmlWLpsnUlvf9Wr2HTm9xn48aNTJw4kVmzZlFVVcVBBx3Ecccdx+DBg0tWDytOe78f/F4wM7NC/H2Uac4Qu+nAMfkJEXFCRAyNiKHAzcAteZufz23LBUfJ5cAEYK/0kytzPLAmIvYEJgMXAkjqDZwL/CMwDDhXUq+WHZ6Z5cybN48999yTPfbYg6222ooxY8Zw2223lbtaVgZ+L5iZWSWo1O+jJgOkiHgAWF1omyQBXwSua6wMSbsBPSNibkQEWY/R6LR5FJDrGboJGJHKHQnMiojVEbEGmEW9QM3Mmu/ll1+mX79+m55XVVXx8ssvl7FGVi5+L5iZWSWo1O+j1i7ScCjwakQsyksbJOkxSX+WdGhK6wvU5u1Tm9Jy214CiIgNwFpgp/z0AnnqkDRBUo2kmpUrV7bykMw6p+zaRF3ZtQjravxeMDOzSlCp30etXaThROr2Hi0H+kfEqjTn6H8l7QsUOtLcX6ShbY3lqZsYMRWYClBdXV1wH7Ourqqqipde+vCaQ21tLbvvvnsZa/QhSf3Iepb/H/ABMDUifp2G2t4ADASWAF9MPcpIOodsiO5G4JsRcU9Kb/F8x66mkt8LlaCxScrNXRDFzMyaVqnfR0UHSJK6Af9KdrIBQES8C7ybHj8i6Xlgb7Len6q87FXAK+lxLdAPqE1l7kA2pK8WOLxenjnF1tesqzvooINYtGgRL774In379uX666/n2muvLXe1cjYA34mIRyVtDzwiaRYwDpgdERdIOhs4G/iepMHAGGBfYHfgPkl7R8RGPpzv+CBZgHQMcBd58x0ljSGb73hCux5lhajw90LZNTZJuTkLopiZWfNU6vdRa3qQPg08ExGbhs5J6gOsjoiNkvYgW4zhhYhYLWm9pOHAQ8ApwKUp20xgLDAXOB64P13tvQf4ed7CDEcD57SivmYVY0D/niU90RrQv2eT+3Tr1o0pU6YwcuRINm7cyKmnnsq+++5bsjq0RkQsJ+uBJiLWS1pINqR2FB9eKJlBdpHkeyn9+nRR5kVJi4FhkpaQ5jsCSMrNd7wr5TkvlXUTMEWSolD/fjtr7/dDJb8XzFpL0j5kPc85ewA/JutBdo+0WSP8fZTq1dQOkq4jO0HZWVItcG5EXEl29bb+4gyHAT+VtIGskTktInILPJzOh43MXekH4Erg6nSCszqVSwqqzgceTvv9NK8ssw6tXEN0jj32WI499tiyvHZzpdsFHEB2MWXXFDwREcsl7ZJ260vWQ5STm6P4Ps2c7ygpN9/xtXqvP4GsB4r+/fuX7LgaU473Q0d4L5gVIyKeBYYCSNoCeBm4lawH2j3SZo3w91GmyQApIk5sIH1cgbSbyZb9LrR/DTCkQPo7wBcayDMNmNZUHc2sc5C0HVkb8q2IWNfIRM1i5i42a16j5zSadSojyG4/slRSl+iRNrPWa+0qdmZmJSFpS7Lg6JqIyN1b7dV0m4Dc7QJWpPTc3MWc3LzG5sx3zM2hzM13NLPOK3+0S50eaSC/R7rQqrnFrMBrZp2AAyQzK7t077MrgYURcXHeptwcRdLv2/LSx0jaWtIgsvmO89JJz3pJw1OZp9TLkytr03zHNjsoMysrSVsBxwF/aGrXAmkl6ZH2bUjMOiYHSGZWCQ4BTgaOlDQ//RwLXAAcJWkRcFR6TkQsAG4EngbuBiam+QKQzXf8HbAYeJ668x13SsNnvk02/8DMOq/PAI9GxKvpebv3SEfE1IiojojqPn36lOSgzKzttfY+SGZmrRYRf6HwFVnI5hAUyjMJmFQgvcXzHc06uy56b6f692rM9SJfwOY90tdKuphskYZcj/TGlq7A28bHY2btxAGSmZlZJ9fV7u0kaRuyXuev5SVfANwoaTywjHTBJCIWSMr1SG9g8x7p6TRzBV4z6xwcIJmVQf8BF/PSsvUlK69f/+1ZtvTbTe536qmncvvtt7PLLrvw1FNPlez1rXXK8X7we8E6s4h4i3qLJkTEKtwjbdYofx9lHCCZlcFLy9bztxc/VrLyPjnomWbtN27cOM444wxOOeWUkr22tV453g9+L5iZWX3+Psp4kQazLuSwww6jd+/e5a6GVQC/F8zMrBJU4veRAyQzMzMzM7PEAZKZmZmZmVniAMnMzMzMzCxxgGRmZmZmZpZ4FTuzMujXf/tmrzzX3PKa48QTT2TOnDm89tprVFVV8ZOf/ITx48eXrB5WnHK8H/xeMDOz+vx9lHGAZFYGzblnUVu47rrrmt7J2l053g9+L5hZVzJwwGSWLltXkrIG9O/JkqVnlaSsSuPvo4wDJDMzMzPr1JYuW8eaeeNKUlavYdNLUo5VLs9BMjMzMzMzSxwgmTVTRJS7ChWvq/yNuspxtob/RmZmbc9tbfO09O/kAMmsGbp3786qVavcEDUiIli1ahXdu3cvd1XalN8LTesq7wUzs3Ly91HzFPOd1OQcJEnTgM8CKyJiSEo7D/gqsDLt9v2IuDNtOwcYD2wEvhkR96T0A4HpQA/gTuDMiAhJWwNXAQcCq4ATImJJyjMW+GF6jZ9FxIxmH5lZCVVVVVFbW8vKlSub3rkL6969O1VVVeWuRpvye6F5yvFeaGwSdmeeVG1mXZO/j5qvpd9JzVmkYTowhSyIyTc5Ii7KT5A0GBgD7AvsDtwnae+I2AhcDkwAHiQLkI4B7iILptZExJ6SxgAXAidI6g2cC1QDATwiaWZErGn20ZmVyJZbbsmgQYPKXQ2rAH4vVK7GJmF7UrWZdTb+Pmo7TQ6xi4gHgNXNLG8UcH1EvBsRLwKLgWGSdgN6RsTcyPoBrwJG5+XJ9QzdBIyQJGAkMCsiVqegaBZZUGVmZmZmZtYmWjMH6QxJT0iaJqlXSusLvJS3T21K65se10+vkyciNgBrgZ0aKWszkiZIqpFU425GMzMzMzMrVrEB0uXAR4GhwHLgv1K6CuwbjaQXm6duYsTUiKiOiOo+ffo0Um0zMzMzM7OGFRUgRcSrEbExIj4AfgsMS5tqgX55u1YBr6T0qgLpdfJI6gbsQDakr6GyzMzMzMzM2kRRAVKaU5TzOeCp9HgmMEbS1pIGAXsB8yJiObBe0vA0v+gU4La8PGPT4+OB+9M8pXuAoyX1SkP4jk5pZmZmZg2StKOkmyQ9I2mhpIMl9ZY0S9Ki9LtX3v7nSFos6VlJI/PSD5T0ZNp2STqHIZ3n3JDSH5I0sAyHaWZtpMkASdJ1wFxgH0m1ksYDv0gNxhPAEcBZABGxALgReBq4G5iYVrADOB34HdnCDc+TrWAHcCWwk6TFwLeBs1NZq4HzgYfTz09TmpmZmVljfg3cHREfA/YHFpKdX8yOiL2A2el5/RV4jwEuk7RFKie3Au9e6Se3WNSmFXiByWQr8JpZJ9HkMt8RcWKB5Csb2X8SMKlAeg0wpED6O8AXGihrGjCtqTqamZmZAUjqCRwGjAOIiPeA9ySNAg5Pu80A5gDfI28FXuDFdMF2mKQlpBV4U7m5FXjvSnnOS2XdBEyRpPAdO806hdasYmdmZmZWafYgu5H97yU9Jul3krYFdk1D/km/d0n7l3IF3jq8yq5Zx+QAyczMzDqTbsAngMsj4gDgTdJwugaUcgXeugleZdesQ3KAZGZmZp1JLVAbEQ+l5zeRBUyv5haZSr9X5O1fqhV4zawTcIBkZmZmnUZE/B14SdI+KWkE2eJR+avmjqXuarqlWoHXzDqBJhdpMDMzM+tgvgFcI2kr4AXg38guCt+YVuNdRlogKiIWSMqtwLuBzVfgnQ70IFucIX8F3qvTgg6ryVbBM7NOwgGSmZmZdSoRMR+oLrBpRAP7l2wFXjPr+DzEzszMzMzMLHGAZGZmZmZmljhAMjMzMzMzSzwHycysCQMHTGbpsnUFtw3o35MlS89q5xqZmZlZW3GAZGbWhKXL1rFm3riC23oNm96udemMHICamVklcYBkZmZl5QDUzMwqiQMkMzMrmnt/zMyss3GAZGZmRXPvj5mZdTZexc7Myk7SNEkrJD2Vl3aepJclzU8/x+ZtO0fSYknPShqZl36gpCfTtkskKaVvLemGlP6QpIHteoBmZmbWYbgHycwqwXRgCnBVvfTJEXFRfoKkwcAYYF9gd+A+SXtHxEbgcmAC8CBwJ3AMcBcwHlgTEXtKGgNcCJzQdodT+Tw0zszMrDAHSGZWdhHxQAt6dUYB10fEu8CLkhYDwyQtAXpGxFwASVcBo8kCpFHAeSn/TcAUSYqIKNlBdDAeGmdmZlaYh9iZWSU7Q9ITaQher5TWF3gpb5/alNY3Pa6fXidPRGwA1gI7FXpBSRMk1UiqWblyZemOxMzMzDqEJgOkBuYG/FLSM+nE5VZJO6b0gZLezpszcEVenhbPDZA0VtKi9DO2lAduZhXvcuCjwFBgOfBfKV0F9o1G0hvLs3lixNSIqI6I6j59+rSowmZmZtbxNacHaTrZOP58s4AhEbEf8BxwTt625yNiaPo5LS89Nzdgr/STK3PT3ABgMtncACT1Bs4F/hEYBpybdwXZzDq5iHg1IjZGxAfAb8naAch6hvrl7VoFvJLSqwqk18kjqRuwA7C67WpvZmZmHVWTAVJEPEC9E4mIuDcNU4FsMnTVZhnzSNqNNDcgjfnPzQ2AbG7AjPT4JmBE6l0aCcyKiNURsYYsKKsfqJlZJ5XajZzPAble7JnAmNT7PIjsgsu8iFgOrJc0PLUhpwC35eXJ9UIfD9zflecfmZmZWcNKsUjDqcANec8HSXoMWAf8MCL+jxbMDZCUmxvQ0DyDzUiaQNY7Rf/+/Vt7PGbWziRdBxwO7Cyplqz3+HBJQ8mGwi0BvgYQEQsk3Qg8DWwAJqYV7ABOJ+v17kG2OMNdKf1K4Oq0oMNqslXwzMzMzDbTqgBJ0g/ITlCuSUnLgf4RsUrSgcD/StqX4uYGtGjOADAVoLq62leFzTqYiDixQPKVjew/CZhUIL0GGFIg/R3gC62po5mZmXUNRa9ilxZN+CxwUm6oSkS8GxGr0uNHgOeBvSlubkBD8wzMzMzMGiRpSVoYar6kmpTWW9KstPDTrPx5zb75tJnlK6oHSdIxwPeAf4qIt/LS+wCrI2KjpD3I5ga8EBGrJa2XNBx4iGxuwKUpW25uwFzy5gZIugf4eV4DdjR1F4MwMzOzDqYdb1J8RES8lvf8bGB2RFwg6ez0/Hu++bSZ1ddkgNTA3IBzgK2BWeliyoNpxbrDgJ9K2gBsBE6LiNwCDy2aG5CCqvOBh9N+P80ry8ysS2rHk0uzNlHGmxSPIjufgWxxqDlkF3t982kzq6PJAKklcwMi4mbg5ga2tXhuQERMA6Y1VUczs66ijCeXZh1JAPdKCuC/01zlXdNql0TEckm7pH37kvUQ5eQWhXqfli8wld9j5UWkzDqoUqxiZ2ZmZlZJDomIV1IQNEvSM43s22Y3n/YiUmYdU9GLNJiZmZlVooh4Jf1eAdxKdqPpV3P3V0u/V6TdffNpM6vDAZKZmZl1GpK2lbR97jHZIk9PUfeG0WOpeyNp33zazDbxEDszMzPrTHYFbk2LSHUDro2IuyU9DNwoaTywjDT/2TefNrP6HCCZmZlZpxERLwD7F0hfBYxoII9vPm1mm3iInZmZmZmZWeIeJDMzM2uS78FlZl2FAyQzMzNrku/BZWZdhQMkMzMzs3bWWI9cS7kHz6y0HCCZmbUTD1Eys5zGeuRayj14ZqXlAMnMrJ14iJKZmVnl8yp2ZmZmZmZmiQMkMzMzMzOzxAGSmZmZmZlZ4gDJzMzMzMwscYBkZmZmZmaWOEAyMzMzMzNLHCCZmZmZmZklTQZIkqZJWiHpqby03pJmSVqUfvfK23aOpMWSnpU0Mi/9QElPpm2XSFJK31rSDSn9IUkD8/KMTa+xSNLYkh21mZmZmZlZAc3pQZoOHFMv7WxgdkTsBcxOz5E0GBgD7JvyXCZpi5TncmACsFf6yZU5HlgTEXsCk4ELU1m9gXOBfwSGAefmB2JmZmZmZmal1mSAFBEPAKvrJY8CZqTHM4DReenXR8S7EfEisBgYJmk3oGdEzI2IAK6qlydX1k3AiNS7NBKYFRGrI2INMIvNAzUzMzMzM7OSKXYO0q4RsRwg/d4lpfcFXsrbrzal9U2P66fXyRMRG4C1wE6NlLUZSRMk1UiqWblyZZGHZGZmZmZmXV2pF2lQgbRoJL3YPHUTI6ZGRHVEVPfp06dZFTUzM7POSdIWkh6TdHt63i5zp82scyg2QHo1DZsj/V6R0muBfnn7VQGvpPSqAul18kjqBuxANqSvobLMzMzMGnMmsDDveZvPnTazzqPYAGkmkFtVbixwW176mHR1ZRBZgzIvDcNbL2l4ugJzSr08ubKOB+5P85TuAY6W1Ctd6Tk6pZmZmZkVJKkK+Gfgd3nJ7TF32sw6iW5N7SDpOuBwYGdJtWQry10A3ChpPLAM+AJARCyQdCPwNLABmBgRG1NRp5OtiNcDuCv9AFwJXC1pMVnP0ZhU1mpJ5wMPp/1+GhH1F4swMzMzy/cr4D+A7fPS6sydlpQ/d/rBvP1y853fp5lzpyXl5k6/Vr8ikiaQ9ULRv3//Vh2UmbWfJgOkiDixgU0jGth/EjCpQHoNMKRA+jukAKvAtmnAtKbqaGZmZibps8CKiHhE0uHNyVIgrdi505snRkwFpgJUV1cX3MfMKk+TAZKZmZlZB3EIcJykY4HuQE9J/0OaO516j0o1d7q23txpM+skSr2KnZmZmVlZRMQ5EVEVEQPJhuzfHxFfpn3mTptZJ+EeJDMzM+vs2nzutJl1Hg6QzMzMrNOJiDnAnPR4Fe0wd9rMOgcPsTMzMzMzM0scIJmZmZmZmSUOkMys7CRNk7RC0lN5ab0lzZK0KP3ulbftHEmLJT0raWRe+oGSnkzbLsndvDFNwL4hpT8kaWC7HqCZmZl1GA6QzKwSTAeOqZd2NjA7IvYCZqfnSBpMNil635TnMklbpDyXk92Uca/0kytzPLAmIvYEJgMXttmRmJmZWYfmAMnMyi4iHmDz+4iMAmakxzOA0Xnp10fEuxHxIrAYGJbubdIzIuamJXevqpcnV9ZNwIhc75KZmZlZPgdIZlapdk33IiH93iWl9wVeytuvNqX1TY/rp9fJExEbgLXATm1WczMzM+uwHCCZWUdTqOcnGklvLM/mhUsTJNVIqlm5cmWRVTQzM7OOygGSmVWqV9OwOdLvFSm9FuiXt18V8EpKryqQXiePpG7ADmw+pA+AiJgaEdURUd2nT58SHYqZmZl1FA6QzKxSzQTGpsdjgdvy0seklekGkS3GMC8Nw1svaXiaX3RKvTy5so4H7k/zlMzMzMzq6FbuCpiZSboOOBzYWVItcC5wAXCjpPHAMtKd6yNigaQbgaeBDcDEiNiYijqdbEW8HsBd6QfgSuBqSYvJeo7GtMNhmZmZWQfkAMnMyi4iTmxg04gG9p8ETCqQXgMMKZD+DinAMjMzM2uMh9iZmZmZmZklDpDMzMzMzMwSB0hmZmZmZmZJ0QGSpH0kzc/7WSfpW5LOk/RyXvqxeXnOkbRY0rOSRualHyjpybTtktwd7tMqVTek9IckDWzV0ZqZmZmZmTWi6AApIp6NiKERMRQ4EHgLuDVtnpzbFhF3AkgaTLZy1L7AMcBlkrZI+18OTCBbrnevtB1gPLAmIvYEJgMXFltfMzMzMzOzppRqiN0I4PmIWNrIPqOA6yPi3Yh4EVgMDEs3gOwZEXPTfUmuAkbn5ZmRHt8EjMj1LpmZmZmZmZVaqQKkMcB1ec/PkPSEpGmSeqW0vsBLefvUprS+6XH99Dp5ImIDsBbYqf6LS5ogqUZSzcqVK0txPGZmZtYBSeouaZ6kxyUtkPSTlN5b0ixJi9LvXnl5PAXAzDZpdYAkaSvgOOAPKely4KPAUGA58F+5XQtkj0bSG8tTNyFiakRUR0R1nz59ml95MzMz62zeBY6MiP3JzkWOkTQcOBuYHRF7AbPTc08BMLPNlKIH6TPAoxHxKkBEvBoRGyPiA+C3wLC0Xy3QLy9fFfBKSq8qkF4nj6RuwA7A6hLU2czMzDqhyLyRnm6ZfoK6w/ZnUHc4v6cAmNkmpQiQTiRveF1qUHI+BzyVHs8ExqRu6UFkV2LmRcRyYL2k4alxOQW4LS/P2PT4eOD+1EiZmZmZFSRpC0nzgRXArIh4CNg1nXOQfu+SdvcUADOro1trMkvaBjgK+Fpe8i8kDSW7WrMkty0iFki6EXga2ABMjIiNKc/pwHSgB3BX+gG4Erha0mKynqMxramvmZmZdX7p/GKopB2BWyUNaWT3Np0CAEwFqK6u9gVesw6iVQFSRLxFvSsmEXFyI/tPAiYVSK8BNmu8IuId4AutqaOZmZl1TRHxuqQ5ZHOHXpW0W0QsT6NdVqTdWjMFoNZTAMw6n1KtYmdmZmZWdpL6pJ4jJPUAPg08Q91h+2OpO5zfUwDMbJNW9SCZmZmZVZjdgBlpJbqPADdGxO2S5gI3ShoPLCONUPEUADOrzwGSmZmZdRoR8QRwQIH0VWQ3ti+Ux1MAzGwTD7EzMzMzMzNLHCCZmZmZmZklDpDMzMzMzMwSB0hmZmZmZmaJAyQzMzMzM7PEAZKZmZmZmVniAMnMzMzMzCxxgGRmZmZmZpY4QDIzMzMzM0scIJmZmZmZmSUOkMzMzMzMzBIHSGZmZmZmZokDJDMzMzMzs8QBkpmZmZmZWdKt3BUwMzMzM7PNDRwwmaXL1pWkrAH9e7Jk6VklKauza1WAJGkJsB7YCGyIiGpJvYEbgIHAEuCLEbEm7X8OMD7t/82IuCelHwhMB3oAdwJnRkRI2hq4CjgQWAWcEBFLWlNnMzMzM7OOYOmydayZN64kZfUaNr0k5XQFpRhid0REDI2I6vT8bGB2ROwFzE7PkTQYGAPsCxwDXCZpi5TncmACsFf6OSaljwfWRMSewGTgwhLU18zMzMzMrKC2mIM0CpiRHs8ARuelXx8R70bEi8BiYJik3YCeETE3IoKsx2h0gbJuAkZIUhvU2czMzDoBSf0k/UnSQkkLJJ2Z0ntLmiVpUfrdKy/POZIWS3pW0si89AMlPZm2XZI7B5G0taQbUvpDkga2+4GaWZtpbYAUwL2SHpE0IaXtGhHLAdLvXVJ6X+ClvLy1Ka1velw/vU6eiNgArAV2ql8JSRMk1UiqWblyZSsPyczMzDqwDcB3IuLjwHBgYhrF4hEuZtYsrQ2QDomITwCfIWuADmtk30I9P9FIemN56iZETI2I6oio7tOnT1N1NjMzs04qIpZHxKPp8XpgIdkFV49wMbNmaVWAFBGvpN8rgFuBYcCrqVEh/V6Rdq8F+uVlrwJeSelVBdLr5JHUDdgBWN2aOpuZmVnXkIa+HQA8hEe4mFkzFR0gSdpW0va5x8DRwFPATGBs2m0scFt6PBMYk8btDiLrqp6XGqn1koanqy+n1MuTK+t44P50FcfMzMysQZK2A24GvhURja2T7BEuZlZHa5b53hW4NfUodwOujYi7JT0M3ChpPLAM+AJARCyQdCPwNNn44IkRsTGVdTofLvN9V/oBuBK4WtJisp6jMa2or5mZmXUBkrYkC46uiYhbUvKrknaLiOUlHOFS6xEuZp1P0QFSRLwA7F8gfRUwooE8k4BJBdJrgCEF0t8hBVhm1jW19f3W2vNYzKztpdEoVwILI+LivE25USkXsPkIl2slXQzszocjXDZKWi9pONkQvVOAS+uVNRePcDHrdNpimW8zs1Jry/utmVnncghwMnCkpPnp51iywOgoSYuAo9JzImIBkBvhcjebj3D5HdnCDc9Td4TLTmmEy7dJbZCZdQ6tGWJnZlYuo4DD0+MZwBzge+StRgW8mE5ehqVeqJ4RMRdAUm41qrsws04lIv5C4TlC4BEuZtYM7kEys0rX1vdbq8OrTpmZmXVt7kEys0p3SES8ImkXYJakZxrZt5jVqOomRkwFpgJUV1d7ToGZmVkX4x4kM6to7XC/NTMzM7NNHCCZWcVqp/utmZmZmW3iIXZmVsna435rZmZmZps4QDKzitUe91szMzMzy+cAyczMzCrCVlsJ6ScFt/Xrvz3Lln67nWtkZl2RAyQzMzOrCO+9F/ztxY8V3PbJQY0tYGlmVjpepMHMzMzMzCxxgGRmZmZmZpZ4iJ2ZmVkX5nk/ZmZ1OUAyMzPrwjzvx8ysLg+xMzMzMzMzSxwgmZmZmZmZJQ6QzMzMzMzMEgdIZmZmZmZmSdGLNEjqB1wF/D/gA2BqRPxa0nnAV4GVadfvR8SdKc85wHhgI/DNiLgnpR8ITAd6AHcCZ0ZESNo6vcaBwCrghIhYUmydrXMbOGAyS5etK3c1WmxA/54sWXpWuathZh1MJa0+V0l1MTNrrdasYrcB+E5EPCppe+ARSbPStskRcVH+zpIGA2OAfYHdgfsk7R0RG4HLgQnAg2QB0jHAXWTB1JqI2FPSGOBC4IRW1Nk6saXL1rFm3rhyV6PFeg2bXu4qmFkHVEmrz1VSXSRNAz4LrIiIISmtN3ADMBBYAnwxItakbb54a2Z1FD3ELiKWR8Sj6fF6YCHQt5Eso4DrI+LdiHgRWAwMk7Qb0DMi5kZEkDU6o/PyzEiPbwJGSFKxdTYzM7NObzrZhdZ8ZwOzI2IvYHZ6Xv/i7THAZZK2SHlyF2/3Sj+5MjddvAUmk128NbNOpCRzkCQNBA4AHkpJZ0h6QtI0Sb1SWl/gpbxstSmtb3pcP71OnojYAKwFdirw+hMk1UiqWblyZf3NZmZm1kVExAPA6nrJ+RdcZ1D3Qqwv3ppZHa0OkCRtB9wMfCsi1pFdcfkoMBRYDvxXbtcC2aOR9Mby1E2ImBoR1RFR3adPn5YdgJmZmXV2u0bEcshGwAC7pPQ2u3gLvoBr1lG1Zg4SkrYkC46uiYhbACLi1bztvwVuT09rgX552auAV1J6VYH0/Dy1kroBO7D5VSEzM7MOwwsaVJQ2u3gL2QVcYCpAdXV1wX3MrPK0ZhU7AVcCCyPi4rz03XJXaYDPAU+lxzOBayVdTLZIw17AvIjYKGm9pOFkQ/ROAS7NyzMWmAscD9yfurrNzMw6pEpa0KALeTV3fpKGz61I6b54a2abac0Qu0OAk4EjJc1PP8cCv5D0pKQngCOAswAiYgFwI/A0cDcwMa1gB3A68Duysb/Pk61gB1kAtpOkxcC3SZMqzczMzFogd8GV9Pu2vPQxkraWNIgPL94uB9ZLGp4uCJ9SL0+uLF+8NeuEiu5Bioi/ULib+c5G8kwCJhVIrwGGFEh/B/hCsXU0MzOzrkXSdcDhwM6SaoFzgQuAGyWNB5aRzi0iYoGk3MXbDWx+8XY62TLfd1H34u3V6eLtarJV8MysE2nVHCQzMzOzShIRJzawaUQD+/virZnV4QDJzMzMzKyZGltopSW8KEvlcoBkZmZmZtZMjS200hJelKVyleRGsWZmZmZmZp2BAyQzMzMzM7PEQ+zMzMyayTd5NTNr2sABk1m6bF1JyhrQvydLlp5VkrKaywFSB1WqCYLtzScQZtaR+SavZtZRtefiEkuXrWPNvHGtfi2AXsOml6SclnCA1EGVaoJge/MJhJmZmVn78+ISzec5SGZmZmZmZokDJDMzMzMzs8RD7MzMzMys3fmGq1apHCCZmVmn59XnzCqP58RYpXKAZFZmXpHQrO159TkzM2suB0hmZeYVCc3MzMwKK8dQTAdIZmZmZmZWkcoxFNMBkpmZVSzPHTIzs/bmAMnMzCqW5w6ZmVl7c4BkZmZtwr0/ZmbWEXWIAEnSMcCvgS2A30XEBWWukpl1QG5L2pd7f6wzq6T2xPcTMiutig+QJG0B/AY4CqgFHpY0MyKeLm/NzKwj6QptSal6bNzzY9a4SmtPfD8hs9Kq+AAJGAYsjogXACRdD4wCOs1JjZm1izZpSyopKClVj417fsya5HMTs05MEVHuOjRK0vHAMRHxlfT8ZOAfI+KMvH0mABPS032AZ9u9ou1vZ+C1clfCSqIr/C8HRESfclagOW1JSm9pe1Kq/18pyqmkupSqnEqqS6nKqaS6lKqc9qxLZ25PitVRv0dc7/blem+uYHvSEXqQVCCtTlQXEVOBqe1TncogqSYiqstdD2s9/y/bTZNtCbS8PSnV/68U5VRSXUpVTiXVpVTlVFJdSlVOJdWlnbRJe1J0ZTrO360O17t9ud7N95H2fLEi1QL98p5XAa+UqS5m1nG5LTGzUnF7YtaJdYQA6WFgL0mDJG0FjAFmlrlOZtbxuC0xs1Jxe2LWiVX8ELuI2CDpDOAesqU0p0XEgjJXqxJ0qSGFnZz/l+2gDduSUv3/SlFOJdWlVOVUUl1KVU4l1aVU5VRSXdpcBZ6bdIi/WwGud/tyvZup4hdpMDMzMzMzay8dYYidmZmZmZlZu3CAZGZmZmZmljhA6mAkHSPpWUmLJZ1d7vpY8SRNk7RC0lPlrou1XKk+i6V4H0jqJ+lPkhZKWiDpzCLL6S5pnqTHUzmF71zbvLK2kPSYpNtbUcYSSU9Kmi+pphXl7CjpJknPpL/RwS3Mv0+qQ+5nnaRvFVmXs9Lf9ilJ10nqXmQ5Z6YyFjS3LoXea5J6S5olaVH63avIcr6Q6vKBpGYtx9tAOb9M/6cnJN0qacfmlNVVddRzgo74/Veqdra9lbJdL4dSfJcUwwFSByJpC+A3wGeAwcCJkgaXt1bWCtOBY8pdCWu5En8Wp9P698EG4DsR8XFgODCxyPq8CxwZEfsDQ4FjJA0vsk5nAguLzJvviIgY2sp7YPwauDsiPgbs39J6RcSzqQ5DgQOBt4BbW1oJSX2BbwLVETGEbHL/mCLKGQJ8FRhGdjyflbRXM7JOZ/P32tnA7IjYC5idnhdTzlPAvwIPNCN/Y+XMAoZExH7Ac8A5LSivS+ng5wTT6Xjff6VqZ9tbKdv1cijVd0mLOEDqWIYBiyPihYh4D7geGFXmOlmRIuIBYHW562FFKdlnsRTvg4hYHhGPpsfryb5M+hZRTkTEG+nplumnxSv5SKoC/hn4XUvzlpqknsBhwJUAEfFeRLzeiiJHAM9HxNIi83cDekjqBmxDcffO+TjwYES8FREbgD8Dn2sqUwPvtVHAjPR4BjC6mHIiYmFEPNt01Zss5950TAAPkt1fyArrsOcEHfH7r1TtbHsrVbteDuX8LnGA1LH0BV7Ke15LB/hwmnVCFftZlDQQOAB4qMj8W0iaD6wAZkVEMeX8CvgP4INi6pAngHslPSJpQpFl7AGsBH6fhmn8TtK2rajTGOC6YjJGxMvARcAyYDmwNiLuLaKop4DDJO0kaRvgWOretLQldo2I5al+y4FdiiynLZwK3FXuSlSwim2HOrvWtrPtrUTtejn8itJ8l7SYA6SORQXSOsRVALNOpiI/i5K2A24GvhUR64opIyI2pqFkVcCwNJyrJXX4LLAiIh4p5vXrOSQiPkE2hGiipMOKKKMb8Ang8og4AHiT5g0j24yyG4IeB/yhyPy9yK7wDwJ2B7aV9OWWlhMRC4ELyYaj3Q08Tjb8p9OQ9AOyY7qm3HWpYBXZDnV2pWhn21tr2/VyKPF3SYs5QOpYaql7lbCK4oZnmFnrVNxnUdKWZF/a10TELa0tLw1Dm0PL5wkcAhwnaQnZkJ8jJf1PkXV4Jf1eQTbnZ1gRxdQCtXlXTG8iC5iK8Rng0Yh4tcj8nwZejIiVEfE+cAvwyWIKiogrI+ITEXEY2VClRUXW6VVJuwGk3yuKLKdkJI0FPgucFL5ZY2Mqrh3q7Erdzra3VrTr5VCy75JiOEDqWB4G9pI0KF3JHAPMLHOdzLqiivosShLZHJuFEXFxK8rpk1s1TFIPshP6Z1pSRkScExFVETGQ7O9yf0S0uJdE0raSts89Bo4mG1rWIhHxd+AlSfukpBHA0y0tJzmRIofXJcuA4ZK2Sf+zERQ5+VjSLul3f7LFEYqt10xgbHo8FrityHJKQtIxwPeA4yLirXLWpQOoqHaosytVO9veStGul0OpvkuK5QCpA0kTV88A7iH7Ur0xIhaUt1ZWLEnXAXOBfSTVShpf7jpZ85Tys1ii98EhwMlkV9hyS1EfW0Q5uwF/kvQE2cnXrIho16VV8+wK/EXS48A84I6IuLvIsr4BXJOOayjw85YWkOb6HEXW61OU1It1E/Ao8CTZd/DUIou7WdLTwB+BiRGxpqkMDbzXLgCOkrSI7PguKKYcSZ+TVAscDNwh6Z4i6zMF2B6Yld7HVzRVTlfVkc8JOuj3X6na2fZWSe16hyH3XpuZmZmZmWXcg2RmZmZmZpY4QDIzMzMzM0scIJmZmZmZmSUOkMzMzMzMzBIHSGZmZmZmZokDJGsRSRvT0pYLJD0u6duSPpK2VUu6JD3eWtJ9ad8TJB2a8syXdKuk0XllPivph3nPb5b0r43UYY6k6vT4jQLbd5d0UwkP28xKJC0HHZI+1spyvivpGUlPpbbolCb2Hydp99a8pplVPkm7SrpW0guSHpE0V9Lniixr0/lGej5QUovvx5aX/428cr6Ulz5O0pRiy7XSc4BkLfV2RAyNiH3J7plxLHAuQETURMQ3034HAFumfW8ATgIuioihwN9Id4+XtBPwBtm9M3IOTvsUJSJeiYjji81vZm3qROAvZDf+K4qk08jan2ERMQQ4DFAT2cYBbRogSerWluWbWePSzVz/F3ggIvaIiAPJ2pqqslZscwOBLzW1k5WPAyQrWkSsACYAZyhzuKTb0x3e/wcYmnqMvgZ8EfixpGuAv5ICpPT7dqBPKmMQWRD2d0mXS6pJPU8/aawuknZOV4n+Of8KT7oqc4ukuyUtkvSLvDzjJT2XrhD91ldvzNqWpO3IbrY4nhQgSfqIpMvS5/x2SXdKOj5tO1DSn9NV4Hsk7ZaK+j7w9YhYBxARayNiRsrzY0kPp56lqaldOR6oJrtZ7HxJPRoqW9JBkp5I7ckv89qS7pJ+L+lJSY9JOiKlj5P0B0l/BO6VdLWkUXnHfI2k49r+r2tmwJHAexGx6QbDEbE0Ii6VtEX6TD+cPuNfA0jnLnMk3ZR6pa9JgVajGilvO0mzJT2a2otRBbJfABya2qOzUtruhc5VrDx8tctaJSJeUDbEbpe8tBWSvgJ8NyI+CyDpYOD2iLhJ0tbAEElbkQVIfwb2AD5O1vP011TUDyJitaQtgNmS9ouIJ+rXQdKuwEzghxExS9LAersMTeW+Czwr6VJgI/Aj4BPAeuB+4PHW/0XMrBGjgbsj4jlJqyV9guyzPxD4B7J2ZCEwTdKWwKXAqIhYKekEYJKkM4HtI+L5Bl5jSkT8FEDS1cBnU7tzBlmbVNNQ2cCpwO+BCRHxN0kX5JU7ESAi/kHZ8MB7Je2dth0M7Jfaq38CzgJuk7QDWRs3trV/ODNrln2BRxvYNh5YGxEHpfOQv0q6N207IOV9hewc5BCynm7ILqy8nR5vBXzQRHkvAZ+LiHWSdgYelDQzIiKvLmdT9xxpHAXOVSLipaL/EtYqDpCsFJq80pIvIt6VtIAsOBkO/ILsJOmTZI1DbnjdFyVNIHuf7gYMBuoHSFsCs4GJEfHnBl5ydkSsBZD0NDAA2Bn4c0SsTul/APZuIL+ZlcaJwK/S4+vT8y2BP0TEB8DfJf0pbd8HGALMShdztwCWk7U3+Sca9R0h6T+AbYDewALgj/X2KVi2pB3Jgq9cG3Qt8Nn0+FNkQRUR8YykpXzYZszKtSUR8WdJv1HWk/6vwM0RsaHpP42ZlZqk35B9dt8DlgL75XqogR2AvdK2eRFRm/LMJ7tokwuQToqImrRtINmoF4CjGyivFvi5pMPIgqm+wK7A35uobqFzFQdIZeIAyVpF0h5kvTEryHqAmutvZPMGto+INZIeBM4gC5CuUDbU7rvAQWn7dKB7gXI2AI8AI8l6ogp5N+/xRrL3fYuCOjNrHWXzDY8k6z0OsqAkgFsbygIsiIiDN9sgvSlpj4h4oV56d+AyoDoiXpJ0HoXbjYJlS+rV2CE0su3Nes+vJpt3OYasV8rM2scC4PO5JxExMfXi1ADLgG9ExD35GSQdTuHzhKaogfLGAX2AAyPifUlLKNwO1VdMHayNeA6SFU1SH+AKsiEtjV3RLeSvwNf4cFjbE2S9Sf3JGrieZCcda9MQus80UE6QnYB8TNLZLXj9ecA/SeqlbGL155vKYGatcjxwVUQMiIiBEdEPeBF4Dfi8srlIuwKHp/2fJZubeDCApC0l7Zu2/SfwG0k907aeqbc5dxLymrL5TvmLtawHtm+s7IhYA6yXNDztl7+QxANkQQ9paF3/VE4h04FvAUTEgmb9dcysFO4Huks6PS9tm/T7HuD0NMQWSXtL2rYVr9VQeTsAK1JwdARZT1B9+e2RVSBHp9ZSPVL385ZkvTdXAxcXUc7fyIbV/SdARGyQtAJ4KQ21eVzSY2TB0gt8OC9pMxGxUdIY4I+S1gF3NvXiEfGypJ8DD5GNOX4aWFvEcZhZ85xINjE5381kPc+1wFPAc2SfybUR8V4aunJJmsvTjWx43gLgcmA74GFJ7wPvA/8VEa9L+i3wJLAEeDjvtaaT9U6/TTZnqKGyxwO/lfQmMIcP24XLUv4nydq+cWm48GYHGhGvSlpItpqWmbWTiAhltxGZnIbariS72Po94A9kQ+ceVfbBXUk2L7JYv2ugvGvIzkdqgPnAMwXyPgFskPQ4Wdu0phX1sDagll/4N+scJG0XEW+kHqRbgWkR0dBwHzNrI3mfxZ3IencPiYimxuu3aV3S47OB3SLizBaWsQ1ZkPaJ3JwCMzPrONyDZF3ZeZI+TTYs5158tdesXG5PCyRsBZxfruAo+WdJ55B9Py4lu39Ss6U2ZRpwsYMjM7OOyT1IZmZmZmZmiRdpMDMzMzMzSxwgmZmZmZmZJQ6QzMzMzMzMEgdIZmZmZmZmiQMkMzMzMzOz5P8DroWSHdMjiB8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,3,figsize=(14,4))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "sns.countplot(train['DiffWalking'], hue=train['HeartDisease'],palette=\"PiYG\", linewidth=1.0, edgecolor=\"navy\")\n",
    "plt.title('0:No ← → 1:Yes')\n",
    "plt.ylabel('')\n",
    "plt.subplot(1,3,2)\n",
    "sns.countplot(train['AgeCategory'], hue=train['HeartDisease'],palette=\"PiYG\", linewidth=1.0, edgecolor=\"navy\")\n",
    "plt.title('0:Young ← → 12:Old')\n",
    "plt.ylabel('')\n",
    "plt.subplot(1,3,3)\n",
    "sns.countplot(train['GenHealth'], hue=train['HeartDisease'],palette=\"PiYG\", linewidth=1.0, edgecolor=\"navy\")\n",
    "plt.title('0:Excellent ← → 4:Poor')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,4,figsize=(19,5))\n",
    "plt.subplot(1,4,1)\n",
    "sns.boxplot(x=train['Diabetic'],y=train['BMI'],linewidth=1,palette='Set3')\n",
    "plt.ylim(ymin=10,ymax=50)\n",
    "plt.title('BMI')\n",
    "plt.ylabel('')\n",
    "plt.subplot(1,4,2)\n",
    "sns.boxplot(x=train['GenHealth'],y=train['BMI'],linewidth=1,palette='Set3')\n",
    "plt.title('BMI')\n",
    "plt.ylim(ymin=10,ymax=50)\n",
    "plt.subplot(1,4,3)\n",
    "sns.countplot(train['AgeCategory'], hue=train['DiffWalking'],palette=\"PiYG\", linewidth=1.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.title('DiffWalking')\n",
    "plt.subplot(1,4,4)\n",
    "sns.countplot(train['Diabetic'], hue=train['DiffWalking'],palette=\"PiYG\", linewidth=1.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.title('DiffWalking')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'AgeCategory')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,5,figsize=(22,5))\n",
    "plt.subplot(1,5,1)\n",
    "sns.boxplot(x=train['DiffWalking'],y=train['GenHealth'],linewidth=1,palette='Set3')\n",
    "plt.title('GenHealth')\n",
    "plt.ylabel('')\n",
    "plt.subplot(1,5,2)\n",
    "sns.boxplot(x=train['SkinCancer'],y=train['AgeCategory'],linewidth=1,palette='Set3')\n",
    "plt.ylabel('')\n",
    "plt.title('AgeCategory')\n",
    "plt.subplot(1,5,3)\n",
    "sns.countplot(train['Diabetic'], hue=train['GenHealth'],palette=\"PiYG\", linewidth=1.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.title('GenHealth')\n",
    "plt.subplot(1,5,4)\n",
    "sns.countplot(train['Diabetic'], hue=train['DiffWalking'],palette=\"PiYG\", linewidth=1.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.title('DiffWalking')\n",
    "plt.subplot(1,5,5)\n",
    "sns.countplot(train['SkinCancer'], hue=train['AgeCategory'],palette=\"PiYG\", linewidth=1.0, edgecolor=\"navy\")\n",
    "plt.ylabel('')\n",
    "plt.title('AgeCategory')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,4,figsize=(16,4))\n",
    "\n",
    "plt.subplot(141)\n",
    "sns.boxplot(train['SleepTime'],linewidth=1,palette='Set2')\n",
    "plt.subplot(142)\n",
    "sns.boxplot(train['BMI'],linewidth=1,palette='Set2')\n",
    "plt.subplot(143)\n",
    "sns.boxplot(train['PhysicalHealth'],linewidth=1,palette='Set2')\n",
    "plt.subplot(144)\n",
    "sns.boxplot(train['MentalHealth'],linewidth=1,palette='Set2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier(data, columns=None):\n",
    "    if columns == None:\n",
    "        columns = data.columns\n",
    "    for col in columns:\n",
    "        mean = data[col].mean()\n",
    "        std = data[col].std()\n",
    "        \n",
    "        border = np.abs(data[col] - mean) / std\n",
    "        \n",
    "        data = data[(border < 3.0)]\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_2(data, columns=None):\n",
    "    if columns == None:\n",
    "        columns = data.columns\n",
    "    for col in columns:\n",
    "        mean = data[col].mean()\n",
    "        std = data[col].std()\n",
    "        \n",
    "        border = np.abs(data[col] - mean) / std\n",
    "        \n",
    "        data = data[(border < 1.9)]\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_3(data, columns=None):\n",
    "    if columns == None:\n",
    "        columns = data.columns\n",
    "    for col in columns:\n",
    "        mean = data[col].mean()\n",
    "        std = data[col].std()\n",
    "        \n",
    "        border = np.abs(data[col] - mean) / std\n",
    "        \n",
    "        data = data[(border < 1.2)]\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(241373, 18)\n",
      "(237726, 18)\n",
      "(225992, 18)\n",
      "(202502, 18)\n",
      "(182454, 18)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "train = outlier(train, ['SleepTime'])\n",
    "print(train.shape)\n",
    "train = outlier_2(train, ['BMI'])\n",
    "print(train.shape)\n",
    "train = outlier_3(train, ['PhysicalHealth'])\n",
    "print(train.shape)\n",
    "train = outlier_3(train, ['MentalHealth'])\n",
    "print(train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60344, 18)\n",
      "(59449, 18)\n",
      "(56487, 18)\n",
      "(50686, 18)\n",
      "(45628, 18)\n"
     ]
    }
   ],
   "source": [
    "print(test.shape)\n",
    "test = outlier(test, ['SleepTime'])\n",
    "print(test.shape)\n",
    "test = outlier_2(test, ['BMI'])\n",
    "print(test.shape)\n",
    "test = outlier_3(test, ['PhysicalHealth'])\n",
    "print(test.shape)\n",
    "test = outlier_3(test, ['MentalHealth'])\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,4,figsize=(16,4))\n",
    "\n",
    "plt.subplot(141)\n",
    "sns.boxplot(train['SleepTime'],linewidth=1,palette='Set2')\n",
    "plt.subplot(142)\n",
    "sns.boxplot(train['BMI'],linewidth=1,palette='Set2')\n",
    "plt.subplot(143)\n",
    "sns.boxplot(train['PhysicalHealth'],linewidth=1,palette='Set2')\n",
    "plt.subplot(144)\n",
    "sns.boxplot(train['MentalHealth'],linewidth=1,palette='Set2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Physical_MentalHealth'] = train['PhysicalHealth'] + train['MentalHealth']\n",
    "test['Physical_MentalHealth'] = test['PhysicalHealth'] + test['MentalHealth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['PhysicalHealth_log'] = np.log1p(train['PhysicalHealth'])   #Take the logarithm after adding 1\n",
    "train['MentalHealth_log'] = np.log1p(train['MentalHealth'])\n",
    "\n",
    "plt.subplots(1,2,figsize=(12,4))\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['PhysicalHealth_log'])\n",
    "sns.distplot(train['PhysicalHealth'])\n",
    "plt.ylim(ymin=0,ymax=3.3)\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.distplot(train['MentalHealth_log'])\n",
    "sns.distplot(train['MentalHealth'])\n",
    "plt.ylim(ymin=0,ymax=2.7)\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['PhysicalHealth_log'] = np.log1p(test['PhysicalHealth'])\n",
    "test['MentalHealth_log'] = np.log1p(test['MentalHealth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>...</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "      <th>Physical_MentalHealth</th>\n",
       "      <th>PhysicalHealth_log</th>\n",
       "      <th>MentalHealth_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>286929</th>\n",
       "      <td>0</td>\n",
       "      <td>22.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112305</th>\n",
       "      <td>0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33692</th>\n",
       "      <td>0</td>\n",
       "      <td>32.55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280863</th>\n",
       "      <td>0</td>\n",
       "      <td>24.81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135463</th>\n",
       "      <td>1</td>\n",
       "      <td>32.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HeartDisease    BMI  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  \\\n",
       "286929             0  22.67        1                0       0             0.0   \n",
       "112305             0  22.50        0                0       0             0.0   \n",
       "33692              0  32.55        0                0       0             0.0   \n",
       "280863             0  24.81        0                0       0             0.0   \n",
       "135463             1  32.02        1                0       0             3.0   \n",
       "\n",
       "        MentalHealth  DiffWalking  Sex  AgeCategory  ...  Diabetic  \\\n",
       "286929           1.0            0    1            2  ...         0   \n",
       "112305           0.0            0    1            3  ...         0   \n",
       "33692            0.0            0    0            6  ...         0   \n",
       "280863           0.0            0    0            8  ...         0   \n",
       "135463           0.0            0    0            6  ...         1   \n",
       "\n",
       "        PhysicalActivity  GenHealth  SleepTime  Asthma  KidneyDisease  \\\n",
       "286929                 1          0        7.0       0              0   \n",
       "112305                 1          0        8.0       0              0   \n",
       "33692                  1          1        7.0       1              0   \n",
       "280863                 1          2        7.0       0              0   \n",
       "135463                 1          2        8.0       0              0   \n",
       "\n",
       "        SkinCancer  Physical_MentalHealth  PhysicalHealth_log  \\\n",
       "286929           0                    1.0            0.000000   \n",
       "112305           0                    0.0            0.000000   \n",
       "33692            0                    0.0            0.000000   \n",
       "280863           0                    0.0            0.000000   \n",
       "135463           0                    3.0            1.386294   \n",
       "\n",
       "        MentalHealth_log  \n",
       "286929          0.693147  \n",
       "112305          0.000000  \n",
       "33692           0.000000  \n",
       "280863          0.000000  \n",
       "135463          0.000000  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['HeartDisease']\n",
    "X_train = train.drop('HeartDisease',axis=1)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, stratify=train['HeartDisease'], test_size=.2, random_state=1205)\n",
    "\n",
    "y_test = test['HeartDisease']\n",
    "X_test = test.drop('HeartDisease', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Sex',data=data,hue='HeartDisease')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr=['UnderWeight','Healthy','OverWeight','Obesity']\n",
    "# # dict={arr[0]:18.49,arr[1]:}\n",
    "# df[df['BMI'] <= 18.49]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Bmi_groups']=pd.cut(x=data['BMI'],bins=[0,18.5,25,30,50],labels=arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
       "       'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n",
       "       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n",
       "       'Asthma', 'KidneyDisease', 'SkinCancer', 'Bmi_groups'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Bmi_groups', ylabel='count'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Bmi_groups',hue='HeartDisease',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Smoking', ylabel='count'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Smoking',hue='HeartDisease',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Stroke', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Stroke',hue='HeartDisease',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AgeCategory', ylabel='count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.countplot('AgeCategory',hue='HeartDisease',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Race', ylabel='count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.countplot('Race',hue='HeartDisease',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Race', ylabel='count'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.countplot('Race',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr=LogisticRegression(max_iter=1000)\n",
    "model_lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9251796688201804"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9252651880424301"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1=model_lr.score(X_test,y_test)\n",
    "model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_lr=model_lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[42024,  3219],\n",
       "       [  191,   194]], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_predict_lr,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.93      0.96     45243\n",
      "           1       0.06      0.50      0.10       385\n",
      "\n",
      "    accuracy                           0.93     45628\n",
      "   macro avg       0.53      0.72      0.53     45628\n",
      "weighted avg       0.99      0.93      0.95     45628\n",
      "\n"
     ]
    }
   ],
   "source": [
    "matrix = classification_report(y_predict_lr,y_test)\n",
    "print('Classification report : \\n',matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=StandardScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=10, max_features='sqrt', n_estimators=28,\n",
       "                       random_state=0)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_forest=RandomForestClassifier(n_estimators=28, max_depth=10, max_features='sqrt',criterion='gini',random_state=0)\n",
    "model_forest.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9277556641066572"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=model_forest.score(X_train,y_train)\n",
    "rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9256815990181467"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2=model_forest.score(X_test,y_test)\n",
    "model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.float64' object has no attribute 'predict'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-89-ca597503146f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.float64' object has no attribute 'predict'"
     ]
    }
   ],
   "source": [
    "y_pred = rf.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=10, max_features='sqrt', random_state=123)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_decision= DecisionTreeClassifier(max_depth=10, max_features='sqrt',criterion='gini',random_state=123)\n",
    "model_decision.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9278104725170078"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_decision.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9236872096081353"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3=model_decision.score(X_test,y_test)\n",
    "model3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=123)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model_svc=SVC(random_state= 123)\n",
    "model_svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9246315847166747"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9236872096081353"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4=model_decision.score(X_test,y_test)\n",
    "model4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model_KNN= KNeighborsClassifier( n_neighbors=5)\n",
    "model_KNN.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.931092126086748"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_KNN.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9188875252038222"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5= model_KNN.score(X_test,y_test)\n",
    "model5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model_nb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_nb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model_nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8500441892808451"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_nb.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8500262996405715"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model6= model_nb.score(X_test,y_test)\n",
    "model6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>92.568160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>92.526519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>92.368721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVM</td>\n",
       "      <td>92.368721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNN</td>\n",
       "      <td>91.888753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>85.002630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model      Score\n",
       "2         RandomForest  92.568160\n",
       "0  Logistic Regression  92.526519\n",
       "1        Decision Tree  92.368721\n",
       "3                  SVM  92.368721\n",
       "4                  KNN  91.888753\n",
       "5          Naive Bayes  85.002630"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model' : ['Logistic Regression','Decision Tree', 'RandomForest', 'SVM','KNN', 'Naive Bayes'],\n",
    "    'Score' : [model1*100,model3*100,model2*100,model4*100,model5*100, model6*100]\n",
    "})\n",
    "\n",
    "\n",
    "models.sort_values(by = 'Score', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HeartDisease\n",
       "0        134962\n",
       "1         11001"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Undersampling ⬇️\n",
    "#The classes (of HeartDisease) are distributed as the following in the train set:\n",
    "pd.DataFrame(y_train.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "proportion 0 is more than ten times of 1. This time, we will use an undersampled ⬇️ version of the train set, where the classes are equally balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HeartDisease\n",
       "1         11001\n",
       "0         11001"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Under_s = X_train.copy()\n",
    "Under_s['HeartDisease'] = y_train\n",
    "Under_0 = Under_s[Under_s['HeartDisease'] == 0].copy()\n",
    "Under_1 = Under_s[Under_s['HeartDisease'] == 1].copy()\n",
    "\n",
    "Under_Sample = pd.concat([Under_1, Under_0.sample(frac = len(Under_1)/len(Under_0))])\n",
    "\n",
    "pd.DataFrame(Under_Sample['HeartDisease'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "execution_time_fit = []\n",
    "execution_time_predict = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.730078022267029\n",
      "Precision Score:  0.18859741168240643\n",
      "Recall Score:  0.7899208907119836\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "Classifier_1_Under = LogisticRegression(max_iter = 300)\n",
    "\n",
    "time_1_fit_start = time.time()\n",
    "\n",
    "Classifier_1_Under.fit(Under_Sample.drop('HeartDisease', axis = 1), Under_Sample['HeartDisease'])\n",
    "\n",
    "time_1_fit_end = time.time()\n",
    "time_1_fit = time_1_fit_end - time_1_fit_start\n",
    "execution_time_fit.append(time_1_fit)\n",
    "\n",
    "time_1_predict_start = time.time()\n",
    "\n",
    "Predictions_1_Under = Classifier_1_Under.predict(X_test)\n",
    "\n",
    "time_1_predict_end = time.time()\n",
    "time_1_predict = time_1_predict_end - time_1_predict_start\n",
    "execution_time_predict.append(time_1_predict)\n",
    "\n",
    "\n",
    "Confusion_Under = confusion_matrix(y_test, Predictions_1_Under)\n",
    "Display = ConfusionMatrixDisplay(confusion_matrix=Confusion_Under)\n",
    "Display.plot()\n",
    "\n",
    "print('Accuracy Score: ', accuracy_score(y_test, Predictions_1_Under))\n",
    "print('Precision Score: ', precision_score(y_test, Predictions_1_Under))\n",
    "print('Recall Score: ', recall_score(y_test, Predictions_1_Under))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.6929955290611028\n",
      "Precision Score:  0.15762941898791444\n",
      "Recall Score:  0.7146205684148843\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Classifier_2 = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "time_2_fit_start = time.time()\n",
    "\n",
    "Classifier_2.fit(Under_Sample.drop('HeartDisease', axis = 1), Under_Sample['HeartDisease'])\n",
    "\n",
    "time_2_fit_end = time.time()\n",
    "time_2_fit = time_2_fit_end - time_2_fit_start\n",
    "execution_time_fit.append(time_2_fit)\n",
    "\n",
    "time_2_predict_start = time.time()\n",
    "\n",
    "Predictions_2 = Classifier_2.predict(X_test)\n",
    "\n",
    "time_2_predict_end = time.time()\n",
    "time_2_predict = time_2_predict_end - time_2_predict_start\n",
    "execution_time_predict.append(time_2_predict)\n",
    "\n",
    "Confusion_2 = confusion_matrix(y_test, Predictions_2)\n",
    "Display = ConfusionMatrixDisplay(confusion_matrix=Confusion_2)\n",
    "\n",
    "Display.plot()\n",
    "\n",
    "print('Accuracy Score: ', accuracy_score(y_test, Predictions_2))\n",
    "print('Precision Score: ', precision_score(y_test, Predictions_2))\n",
    "print('Recall Score: ', recall_score(y_test, Predictions_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.7030989743140177\n",
      "Precision Score:  0.16730137885751806\n",
      "Recall Score:  0.7465572809844712\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Classifier_3 = RandomForestClassifier(n_estimators=25)\n",
    "\n",
    "time_3_fit_start = time.time()\n",
    "\n",
    "Classifier_3.fit(Under_Sample.drop('HeartDisease', axis = 1), Under_Sample['HeartDisease'])\n",
    "\n",
    "time_3_fit_end = time.time()\n",
    "time_3_fit = time_3_fit_end - time_3_fit_start\n",
    "execution_time_fit.append(time_3_fit)\n",
    "\n",
    "time_3_predict_start = time.time()\n",
    "\n",
    "Predictions_3 = Classifier_3.predict(X_test)\n",
    "\n",
    "time_3_predict_end = time.time()\n",
    "time_3_predict = time_3_predict_end - time_3_predict_start\n",
    "execution_time_predict.append(time_3_predict)\n",
    "\n",
    "Confusion_3 = confusion_matrix(y_test, Predictions_3)\n",
    "Display = ConfusionMatrixDisplay(confusion_matrix=Confusion_3)\n",
    "Display.plot()\n",
    "print('Accuracy Score: ', accuracy_score(y_test, Predictions_3))\n",
    "print('Precision Score: ', precision_score(y_test, Predictions_3))\n",
    "print('Recall Score: ', recall_score(y_test, Predictions_3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.6971815551854125\n",
      "Precision Score:  0.16942043721403152\n",
      "Recall Score:  0.7811309698212716\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Classifier_4 = DecisionTreeClassifier(max_depth=10, max_features='sqrt',criterion='gini',random_state=123)\n",
    "\n",
    "time_4_fit_start = time.time()\n",
    "\n",
    "Classifier_4.fit(Under_Sample.drop('HeartDisease', axis = 1), Under_Sample['HeartDisease'])\n",
    "\n",
    "time_4_fit_end = time.time()\n",
    "time_4_fit = time_4_fit_end - time_4_fit_start\n",
    "execution_time_fit.append(time_4_fit)\n",
    "\n",
    "time_4_predict_start = time.time()\n",
    "\n",
    "Predictions_4 = Classifier_4.predict(X_test)\n",
    "\n",
    "time_4_predict_end = time.time()\n",
    "time_4_predict = time_4_predict_end - time_4_predict_start\n",
    "execution_time_predict.append(time_4_predict)\n",
    "\n",
    "Confusion_4 = confusion_matrix(y_test, Predictions_4)\n",
    "Display = ConfusionMatrixDisplay(confusion_matrix=Confusion_4)\n",
    "Display.plot()\n",
    "print('Accuracy Score: ', accuracy_score(y_test, Predictions_4))\n",
    "print('Precision Score: ', precision_score(y_test, Predictions_4))\n",
    "print('Recall Score: ', recall_score(y_test, Predictions_4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.7030551415797317\n",
      "Precision Score:  0.1766619880056144\n",
      "Recall Score:  0.811309698212716\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Classifier_5 = SVC(random_state=123)\n",
    "\n",
    "time_5_fit_start = time.time()\n",
    "\n",
    "Classifier_5.fit(Under_Sample.drop('HeartDisease', axis = 1), Under_Sample['HeartDisease'])\n",
    "\n",
    "time_5_fit_end = time.time()\n",
    "time_5_fit = time_5_fit_end - time_5_fit_start\n",
    "execution_time_fit.append(time_5_fit)\n",
    "\n",
    "time_5_predict_start = time.time()\n",
    "\n",
    "Predictions_5 = Classifier_5.predict(X_test)\n",
    "\n",
    "time_5_predict_end = time.time()\n",
    "time_5_predict = time_5_predict_end - time_5_predict_start\n",
    "execution_time_predict.append(time_5_predict)\n",
    "\n",
    "Confusion_5 = confusion_matrix(y_test, Predictions_5)\n",
    "Display = ConfusionMatrixDisplay(confusion_matrix=Confusion_5)\n",
    "Display.plot()\n",
    "print('Accuracy Score: ', accuracy_score(y_test, Predictions_5))\n",
    "print('Precision Score: ', precision_score(y_test, Predictions_5))\n",
    "print('Recall Score: ', recall_score(y_test, Predictions_5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.7853511002016306\n",
      "Precision Score:  0.19295544220960448\n",
      "Recall Score:  0.5874597128625842\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Classifier_6 = GaussianNB()\n",
    "\n",
    "time_6_fit_start = time.time()\n",
    "\n",
    "Classifier_6.fit(Under_Sample.drop('HeartDisease', axis = 1), Under_Sample['HeartDisease'])\n",
    "\n",
    "time_6_fit_end = time.time()\n",
    "time_6_fit = time_6_fit_end - time_6_fit_start\n",
    "execution_time_fit.append(time_6_fit)\n",
    "\n",
    "time_6_predict_start = time.time()\n",
    "\n",
    "Predictions_6 = Classifier_6.predict(X_test)\n",
    "\n",
    "time_6_predict_end = time.time()\n",
    "time_6_predict = time_6_predict_end - time_6_predict_start\n",
    "execution_time_predict.append(time_6_predict)\n",
    "\n",
    "Confusion_6 = confusion_matrix(y_test, Predictions_6)\n",
    "Display = ConfusionMatrixDisplay(confusion_matrix=Confusion_6)\n",
    "Display.plot()\n",
    "print('Accuracy Score: ', accuracy_score(y_test, Predictions_6))\n",
    "print('Precision Score: ', precision_score(y_test, Predictions_6))\n",
    "print('Recall Score: ', recall_score(y_test, Predictions_6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'FPR')"
      ]
     },
     "execution_count": 124,
     "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": [
    "y_pred_prob1 = Classifier_1_Under.predict_proba(X_test)[:,1]\n",
    "fpr1 , tpr1, thresholds1 = roc_curve(y_test, y_pred_prob1)\n",
    "\n",
    "y_pred_prob2 = Classifier_2.predict_proba(X_test)[:,1]\n",
    "fpr2 , tpr2, thresholds2 = roc_curve(y_test, y_pred_prob2)\n",
    "\n",
    "\n",
    "y_pred_prob3 = Classifier_3.predict_proba(X_test)[:,1]\n",
    "fpr3 , tpr3, thresholds3 = roc_curve(y_test, y_pred_prob3)\n",
    "\n",
    "y_pred_prob4 = Classifier_4.predict_proba(X_test)[:,1]\n",
    "fpr4 , tpr4, thresholds4 = roc_curve(y_test, y_pred_prob4)\n",
    "\n",
    "\n",
    "\n",
    "y_pred_prob6 = Classifier_6.predict_proba(X_test)[:,1]\n",
    "fpr6 , tpr6, thresholds6 = roc_curve(y_test, y_pred_prob6)\n",
    "\n",
    "plt.plot([0,1],[0,1], 'k--')\n",
    "plt.plot(fpr1, tpr1, label= \"Logistic\", color = 'cornflowerblue')\n",
    "plt.plot(fpr2, tpr2, label= \"Nearest Neighbors\", color = 'salmon')\n",
    "plt.plot(fpr3, tpr3, label= \"Random Forest\", color = 'lightgreen')\n",
    "plt.plot(fpr4, tpr4, label= \"Decision Tree\", color = 'sandybrown')\n",
    "plt.plot(fpr6, tpr6, label= \"Naive bayes\", color = 'olive')\n",
    "plt.legend()\n",
    "plt.xlabel(\"FPR\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.7297054440255983\n",
      "Precision Score:  0.18806826129528606\n",
      "Recall Score:  0.7878699091708175\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Streamlit = ['BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
    "       'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n",
    "       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n",
    "       'Asthma', 'KidneyDisease', 'SkinCancer']\n",
    "\n",
    "Classifier_Streamlit = LogisticRegression(max_iter = 300)\n",
    "\n",
    "Classifier_Streamlit.fit(Under_Sample[Streamlit], Under_Sample['HeartDisease'])\n",
    "\n",
    "Predictions_Streamlit = Classifier_Streamlit.predict(X_test[Streamlit])\n",
    "\n",
    "Confusion_Streamlit = confusion_matrix(y_test, Predictions_Streamlit)\n",
    "Display = ConfusionMatrixDisplay(confusion_matrix=Confusion_Streamlit)\n",
    "Display.plot()\n",
    "\n",
    "print('Accuracy Score: ', accuracy_score(y_test, Predictions_Streamlit))\n",
    "print('Precision Score: ', precision_score(y_test, Predictions_Streamlit))\n",
    "print('Recall Score: ', recall_score(y_test, Predictions_Streamlit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}