Brain Stroke Prediction with 89% Accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "06c56f23",
   "metadata": {},
   "source": [
    "BRAIN STROKE PREDICTION USING LOGISTIC REGRESSION(WITH 1000 records and 89% ACCURACY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22f8f0c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "HEAD:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.4</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.8</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>80.43</td>\n",
       "      <td>29.7</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Rural</td>\n",
       "      <td>120.46</td>\n",
       "      <td>36.8</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0    Male  67.0             0              1          Yes        Private   \n",
       "1    Male  80.0             0              1          Yes        Private   \n",
       "2  Female  49.0             0              0          Yes        Private   \n",
       "3  Female  79.0             1              0          Yes  Self-employed   \n",
       "4    Male  81.0             0              0          Yes        Private   \n",
       "5    Male  74.0             1              1          Yes        Private   \n",
       "6  Female  69.0             0              0           No        Private   \n",
       "7  Female  78.0             0              0          Yes        Private   \n",
       "8  Female  81.0             1              0          Yes        Private   \n",
       "9  Female  61.0             0              1          Yes       Govt_job   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "1          Rural             105.92  32.5     never smoked       1  \n",
       "2          Urban             171.23  34.4           smokes       1  \n",
       "3          Rural             174.12  24.0     never smoked       1  \n",
       "4          Urban             186.21  29.0  formerly smoked       1  \n",
       "5          Rural              70.09  27.4     never smoked       1  \n",
       "6          Urban              94.39  22.8     never smoked       1  \n",
       "7          Urban              58.57  24.2          Unknown       1  \n",
       "8          Rural              80.43  29.7     never smoked       1  \n",
       "9          Rural             120.46  36.8           smokes       1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#importing dataset\n",
    "data = pd.read_csv(\"brain_stroke.csv\")\n",
    "print(\"\\nHEAD:\\n\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201224dc",
   "metadata": {},
   "source": [
    "DATA VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e57d5b4",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#determining number of people who got stroke\n",
    "\n",
    "a=sns.countplot(x=\"stroke\",data=data)\n",
    "for p in a.patches:\n",
    "    a.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.25, p.get_height()+0.01))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a026ec30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    792\n",
       "1    208\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['stroke'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c90412e",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#line graph showing how the risk of stroke rises with age\n",
    "sns.lineplot(x=\"age\",y=\"stroke\",data=data)\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed41191e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DAAAAQC6WvIr9888/68iRI+n2S0pK0m+//SZJeuKJJyRJdnb/H/msVmuWz9msWTPZ2dnp2LFjOnz4cJrnSt50rWnTphnO9eabb6pcuXIKCwvTxx9/nKKtRYsWkpTuBm7r169X37591bFjR0VHR2e5/pxG0AYAAACAXKxdu3aqV6+e4uPj9corr2jx4sWpnlcOCQnRgAEDdPToUXl7e+u5556T9P+3gEt3XgeWVWXLllXHjh0lSe+++26KsB0REaGhQ4fq2LFjKly4sPr165fhXC4uLhozZowk6aefftKuXbtsbf369ZOzs7PWr1+v6dOn297hLUm7du2Sn5+fJKlbt24qWLBgluvPaTyjDQAAAAC5mIODg+bOnatBgwZpz549mjhxoqZMmaJy5crJzc1N169ft+3eXbt2bc2ZM0eOjo6S7txCXqpUKV25ckW9evVSuXLlNHnyZFWrVi3T844ePVoXL17Uvn375Ovrq4oVK6pAgQIKDg5WXFyc3N3dNWPGDJUqVSrTuZo3b65nn31Wv/76q8aOHauff/5Zrq6uqlq1qqZMmaJhw4Zp7ty5Wrx4sSpVqqSbN2/q4sWLkqTHH39cQ4YMuYfv4IPHijYAAAAA5HLu7u5asGCBvvrqK3Xu3Flly5bVjRs3dPz4cVksFrVo0UJTpkzR8uXLVbJkyRRjZ86cqdq1ays2NlYXLlzQ+fPns3ROs9msb7/9Vn5+fqpfv77++ecfhYSEqEyZMnrttde0bt06NWnSJMvXMGLECLm5uSk0NFQzZsywHW/btq3Wrl2rrl27yt3dXUFBQbp586Zq166tESNG6Ouvv5aTk1OWz5MbmKzZuVEfWRIUFKTIyEi5ubll+d11AADgwfMZuiinSwDuScC0PjldQr6XvFlYRu+bRt6Qnb/LzDIfK9oAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAAC4b1avXi0vLy/Vq1dPoaGhGfb19vaWl5fXA6rs/nHI6QIAAAAAID/wGboop0vIkoBpfe7LvNHR0Ro9erQWLFhwX+bPTVjRBgAAAAA8EHv27NGKFStyuoz7jqANAAAAALjvSpUqJUmaPHmyrly5ksPV3F8EbQAAAADAfde0aVO1bt1akZGRGjNmTJbHxcTE6IsvvlD79u1Vp04d+fj4qGfPnvr555/vY7X3hqANAAAAAHggxo0bp8KFC2v79u1au3Ztpv3DwsLUrVs3ff7557p+/bqaNWumRx99VEeOHNH777+vDz/88P4XfRcI2gAAAACAB6JEiRK2cDxp0iRdv349w/5jxozRqVOn9PTTT2vr1q2aM2eO5s+fr59//lnlypXT6tWrtXTp0gdRerYQtAEAAAAAD0ynTp3UokULhYeHa9y4cen2Cw0N1W+//SZ3d3dNmTJFZrPZ1lahQgVNmjRJkjR//vz7XXK2EbQBAAAAAA+Un5+f3NzctHnzZm3cuDHNPgcPHpQkNW/eXAUKFEjV3rBhQ5UoUUIXLlzIdZurEbQBAAAAAA9UqVKlNGzYMEnShAkTFBYWlqrPtWvXJEllypRJd56yZctKUqa3oD9oBG0AAAAAwAPn6+urJk2aKCwsTBMmTEi3n8lkSrctKSlJkuTk5GR4ffeCoA0AAAAAyBETJkyQ2WzWxo0btXnz5hRtHh4ekqQLFy6kOz40NFSSVLx48ftX5F0gaAMAAAAAckS5cuU0ePBgSXde/WW1Wm1tPj4+MplM2rlzp6KiolKN/eOPPxQWFqYqVaqoWLFiD6zmrCBoAwAAAAByTK9eveTj46Pr16/LYrHYjpcrV05PPfWUbt26peHDhys6OtrWduHCBY0aNUqS1LNnzwdec2YccroAAAAAAED+ZTKZNGnSJHXo0EGxsbEp2vz8/HT27Fn99ttvatWqlR577DHFxMRo//79io+PV6dOndSjR48cqjx9BG0AAAAAeAACpvXJ6RJyrYoVK+qdd97R1KlTUxwvVqyYli9froULF+qXX37Rjh075OrqKh8fH73wwgt69tlnc6jijJms/74JHoYICgpSZGSk3Nzc5OXlldPlAACAdPgMXZTTJQD3hOCW844fPy5JqlGjRg5XgnuVnb/LzDIfz2gDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAA7ouRI0fKy8tLTz31lKKjozPse+jQIdWoUUM1a9bU0aNHH1CF94dDThcAAAAAAPnBeb/aOV1ClpQfY1zI/eCDD7Rz506FhoZq+vTpGjlyZJr94uPjNXLkSFksFvXv31+1a+eN71V6WNEGAAAAANwXBQsWlJ+fnyTp+++/1+HDh9PsN2fOHIWEhMjb21sDBgx4gBXeH7k+aN+6dUtNmzaVl5dXmu1nzpzR4MGD1aJFC9WtW1ft27fX999/L4vFkmb/iIgITZs2TW3atFGdOnXUqlUrTZ48WZGRkffzMgAAAAAgX2rZsqX+97//yWKxaOTIkYqPj0/Rfvz4cc2bN09OTk6aMmWKHB0dc6hS4+T6oD1+/Hhdv349zbYTJ06oa9eu2rBhgzw9PdWsWTNduXJFEyZM0LBhw1L1j4yMVK9evTRv3jyZTCa1bNlSJpNJCxYskK+vr27fvn2/LwcAAAAA8p2RI0eqePHiCg4O1ty5c23Hk5KSNHLkSCUmJuqdd95R9erVJUmxsbGaO3eu2rdvrzp16qhBgwbq16+f9u/fn+b8a9euVc+ePdW4cWPVrVtXbdu21bRp03Tz5s0Hcn3/lauD9s8//6yNGzem2Wa1WjVs2DBFRkZq6tSpWrp0qWbPni1/f395eXlp/fr18vf3TzFmxowZCgoKUvfu3bVx40bNnDlT/v7+6tChg4KDgzVjxowHcFUAAAAAkL+4u7tr7NixkqRvvvlG58+flyQtXLhQgYGBqlevnl599VVJd+5C7tGjh6ZPn66wsDA9/vjjqlGjhvbu3as+ffpo2bJlKeZetGiRhg8fruPHj6t27dpq1qyZIiMjNW/ePPXo0UNxcXEP9mKVi4P21atXNWHCBNWrV0/29vap2nfv3q2goCA1bNhQHTp0sB0vWrSo7S9w8eLFtuMRERFasWKF3NzcNHz4cNnZ3bl0BwcHjR07VoULF9bKlSsz3QkPAAAAAJB9zzzzjJ599lnFx8dr0qRJunz5smbNmiVXV1dNmTLFltEmTJigwMBAdejQQVu2bNHcuXO1aNEirVixQu7u7po4caJOnTol6c4map999pnc3d3l7++vb775RrNnz9aWLVv02GOP6fTp09qwYcMDv9ZcG7RHjhypuLg4TZkyJc32nTt3SpJat26dqs3Hx0fFihVTQECA7dnrAwcOKDY2Vo0bN5abm1uK/gUKFFCTJk0UGxurAwcOGHwlAAAAAABJGjNmjIoUKaJt27apX79+iomJ0dChQ1WhQgVJdxZcN2zYIA8PD/n5+cnFxcU21tvbWwMHDlRCQoJtUfX27duKiYmRq6ur3N3dbX2dnJw0cuRITZgwQXXr1n2g1yjl0qD9ww8/aOfOnRoyZIjtG/5fwcHBkmS7h/+/KlWqJIvFopCQkBT9q1Wrlmb/ypUrS5KCgoLuqXYAAAAAQNqKFSumUaNGSbqT0Zo0aaIePXrY2g8cOKCkpCQ9+uijKUJ2sqZNm0qS7VntYsWKqXLlyrp8+bK6deum+fPn27Kft7e3unfvripVqtzvy0ol171H+/z585o2bZoaN26snj17ptvv2rVrkqQSJUqk2Z58/J9//pEk24ZqmfW/cePG3RUOAAAAAMjU888/r6lTp+rq1at6/fXXZTKZbG2XL1+WJG3atCndN09J0pUrV2z/Pn36dL311ls6fvy4jh8/rqlTp8rT01NPPfWUevToYVtUfZByVdBOSkrSsGHDZDKZ9PHHH6f4hv9XTEyMJKX5W45/H09+5jr5T1dX1yz1N0JkZKQCAgIMmw8AABjDx8cnp0sADMXPnDnLxcVFUVFR6bYXKFDgAVZjnIyu6V4lP4+dkJCQ4jyxsbGSpKpVq6Z7N7IkmUwm27hy5cpp1apV2rNnj3bs2KEDBw4oNDRUixcv1tKlSzV16lS1bNky05osFotiY2MN+e8pVwXtefPm6dChQ5o4caI8PT0z7Jv8F5NeGLdarSn+zG5/AAAAAMCDVbx4cUlSrVq1NGbMmCyPc3R0VIsWLdSiRQtJd+6U/vbbb7Vu3TrNnDkzS0HbSLkmaJ84cUKzZs1SixYt1K1bt0z7m81mSf//G4//St7CPblfVvunt+J9N9zc3DK83QEAAAAwAndp5Jzjx49Lyrur1hm5n9eUvBDq4uKS4jxPPPGEJOngwYNycHCQs7NzinHbt2/Xxx9/rMaNG2vcuHE6ePCgRo8erQYNGsjPz8/Wr0aNGho/frzWrVunK1euZOla7OzsZDabVaNGjUz7BgUF2TbeTkuuCdrTp09XQkKCEhMTNWTIkBRtFotFkmzHR4wYIQ8PDx0/flz//PNPmg+3//eZbA8PD0n//8x2Zv0BAAAAAA9W+fLl9eSTT2rbtm0aO3asxo4da1sMDQ0N1fjx43Xx4kW9+OKLku5sdn3hwgVdunRJnTt31qOPPmqbK/m1XrVr137g15Frgnbys9G7d+9Ot8/69eslSe+9956qVaum7du3Kzg4WI0aNUrRz2q16vTp07K3t7eF8OT7+5N3oPuv5N3JWYEGAAAAgJwzceJE9e7dW2vWrNH27dtVu3ZtJSUlaf/+/YqPj9fTTz+tXr16SZIKFy6sYcOG6aOPPtKLL76oRx99VCVKlFBoaKgCAwNlNps1fPjwB34NuSZoJ78HLS3e3t5KSkpK8eqtZs2aad68edqyZUuq3cn//PNPhYWFqWHDhrZ3Zjdo0EAuLi7au3evoqOjbbeSS3ce8t+7d6/MZjO33QAAAAC4L8qPOZrTJeQJxYsX14oVK7RgwQL5+/vrjz/+kKurq2rUqKFu3bqpU6dOsre3t/Xv06ePihUrpmXLlun48eM6evSoihYtqk6dOql///6qWLHiA7+GXBO0s6thw4aqVq2adu/erR9//FHdu3eXJIWFhWn8+PGSpL59+9r6m81mdezYUcuWLdP48eP10UcfycHBQYmJifLz81NERIT69u1rC+YAAAAAgPtj69atGba7ublp4MCBGjhwYJbma9eundq1a2dEaYbIs0Hbzs5OkyZN0ksvvaTRo0dr5cqV8vDw0P79+xUeHq7u3burVatWKcYMGjRI+/bt09q1axUQECBvb28dO3ZMFy5ckLe3d5b/EgEAAAAASI9dThdwL+rUqaMVK1aoTZs2OnfunHbv3i1PT0+NHz9e48aNS9Xf3d1dy5YtU+/evZWYmKht27bJzs5O/fr106JFix7KnQIBAAAAAA9WnljRPnbsWLptVatW1cyZM7M8l7u7u0aNGqVRo0YZURoAAAAAACnk6RVtAAAAAAByG4I2AAAAAAAGImgDAAAAAGAggjYAAAAAIF+zWq2GzkfQBgAAAIC7ZDKZJEkWiyWHK8G9SA7ayX+f94qgDQAAAAB3ydHRUZIUGxubw5XgXsTHx0uSHByMeTEXQRsAAAAA7lLBggUlSTdv3jT89mM8OLdv35Ykmc1mQ+YjaAMAAADAXSpUqJBMJpMiIiJ06dIlRUdHy2KxELrzAKvVqqSkJN28eVM3btyQdOfv0wjGrIsDAAAAQD7k4uKismXLKjQ0VBEREYqIiMjpknCXihYtKjc3N0PmImgDAAAAwD1wc3NTpUqVFB4ertu3byshIYEV7TzCZDLJxcVFRYoUMWw1WyJoAwAAAMA9c3Z2loeHhzw8PHK6FOQCPKMNAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNp4aBw9elQDBw5UkyZNVLt2bbVu3VoTJ07U1atXszR+8eLF8vLy0rZt2zLsd/jwYQ0YMECNGzdWrVq11LJlS40aNUoXL168q7pPnTqld999V40bN1bt2rXVpk0bzZw5UzExMXc1HwAAAICcRdDGQ2Hr1q3y9fXVpk2bZLFYVLVqVd28eVOLFy9W+/btdfTo0QzHBwYG6rPPPsv0POvWrVOPHj20ZcsWWSwWValSRbdu3dKKFSvUqVMnHTt2LFt1//333+revbt+/fVXOTg4qFq1arp48aLmzJmjF154QZGRkdmaDwAAAEDOI2gjz7ty5YqGDh2qpKQkDRgwQLt379aaNWu0a9cude7cWeHh4XrvvfeUmJiY5vgjR46oX79+io6OzvA8169f15gxY5SUlKSXX35Zu3fv1k8//aTt27erefPmCg8P1+DBg5WUlJSluuPi4jRgwABFR0fr7bff1o4dO7R69Wpt2bJFNWvW1IkTJzRlypRsfz8AAAAA5CyCNvK8devWKTIyUg0bNtS7774rBwcHSZKrq6vGjx8vd3d3hYaGat++fSnGJSUlafHixerZs6fCwsIyPc+mTZsUExOjKlWqaPjw4XJ0dJQkFS5cWNOmTZOTk5POnDmjv/76K0t1r127VlevXlW9evU0cOBA2dnd+c+xZMmSmjVrlhwdHbV69Wpdu3YtO98OAAAAADmMoI08z8PDQ23atJGvr2+qNicnJ5UvX16SdPnyZdvxuLg4de7cWRMnTlRCQoLeeustlSlTJsPzJAfeqlWr2kJxMnd3d9v4f58nI2vXrpUkderUKVVbmTJl9PjjjysxMVG//fZbluYDAAAAkDsQtJHndezYUTNnztTzzz+fqi06Olpnz56VJFWoUMF2PC4uTidOnFDVqlW1ePFivfPOO5mep2TJkpKkkydPymKxpGiLjIy0BezSpUtnOldSUpICAwMlSfXq1UuzT926dSVJBw8ezHQ+AAAAALkHQRsPrZCQEL399tuKiIhQ/fr11aBBA1ubk5OTpkyZop9++inF8Yw8++yzcnNz05kzZzRt2jTbM99RUVEaMWKEYmNj5e3tnW5w/rcrV64oLi5OktJdSU8+fu7cuSzVBwAAACB3cMjpAgCjzZ49W2vXrlVoaKisVqtatWqlSZMmpejj4uKijh07ZmveokWLav78+Ro+fLi+/fZbrV69Wp6enjp37pyioqLUtGlTTZ48WSaTKdO5kp8Jd3JyUoECBdLsU7hwYUnSrVu3slUnAAAAgJzFijYeOvv379eFCxdktVolSefPn9f+/fsNmbtw4cKqU6eOpDsB+NixY4qKipK9vb08PT2zFLIl2VazXVxc0u2T3BYbG3uPVQMAAAB4kAjaeOhMmjRJR44c0S+//KKePXsqJCRE7777rjZu3HhP8x47dky+vr5at26dXn75ZW3dulVHjx7VqlWr1KhRI/3444/q1atXlnYwz0ogT34OPKvhHQAAAEDuQNDGQ6ds2bJydnZW5cqVNWbMGPXq1UtWq1WffPJJlt9xnRY/Pz+Fh4frhRde0IcffqgyZcrIyclJtWrV0jfffKN69erpzJkzmjt3bqZzJd8unryynZb4+HhJkrOz813XDAAAAODBI2jjoff6669Lki5evKhLly7d1RzXrl3ToUOHJEn9+/dP1e7g4GA7z6+//prpfO7u7pLuBO2YmJg0+yQ/m120aNG7qBgAAABATiFoI88LDw/XkSNHFB0dnWa7h4eHzGazJOnGjRt3dY7kgO7q6pru67sqVqwo6U4oT96RPD2lSpWy1ZRe+L948aKklK8lAwAAAJD7EbSR57Vr107dunXTjh070mwPDw+3rRp7eHjc1Tnc3Nwk3dmY7Pbt22n2SQ7MLi4ucnDIfEP/WrVqSZIOHz6cZvtff/0lSbbN1wAAAADkDQRt5HmNGjWSJK1YsSLN9iVLlshqtap69ery9PS8q3NUrlxZxYsXl9Vq1apVq9Lss3btWknK8nu5n376aUnSypUrU7VdvHhRe/fulaOjo5599tm7qhkAAABAziBoI8977bXXZG9vr127dmnatGm2TcQsFouWLl2qOXPmyGQyaciQIXd9Djs7O73xxhuSpOnTp+unn36y7QqemJioWbNmaf369bKzs0v1DPelS5cUEhKia9eupTjeuXNnlShRQn/++acmT55su9386tWrGjhwoBISEtSxY0eVLFnyrusGAAAA8OCZrMkvG4ZhgoKCFBkZKTc3N3l5eeV0OfnCqlWrNGbMGCUmJsrNzU0VKlTQlStXdOPGDdnb2+vDDz9U7969M5yjVatWunjxoubOnasnn3wyVbvVatXYsWO1fPlySVLx4sXl4eGhs2fPKjo6Wvb29ho7dqx8fX1TjOvdu7f279+vTp06afLkySnadu/erf79+ys+Pl7FihVTqVKldOrUKcXHx8vb21tLliyxPcsNADCez9BFOV0CcE8CpvXJ6RKAfCmzzMeKNh4KXbp00fLly/Xss8/K2dlZJ0+elJ2dndq1a6cff/wx05CdFSaTSX5+fpozZ46aNm2qxMREnTp1SgUKFFC7du20fPnyVCE7M0888YRWrVqlZ599VlarVSdPnpSHh4deeeUVLV68mJANAAAA5EGsaN8HrGgDAJA3sKKNvI4VbSBnZJb5Mt8aGfkKP3Agr+MHDgAAAOQ0bh0HAAAAAMBABG0AAAAAAAxE0AYAAAAAwEAEbQAAAAAADETQBgAAAADAQARtAAAAAAAMRNAGAAAAAMBABG0AAAAAAAzkkNMFpCUpKUlLlizRypUrdebMGbm6uqpWrVrq06ePWrZsmar/mTNnNGvWLAUEBOjWrVsqX768fH191aNHD9nZpf5dQkREhL766itt3rxZly9fVvHixfXMM8/o7bfflpub2wO4QgAAAADAwypXrmh/+OGH+uijj3Tx4kU1adJENWvW1P79+/XGG29ozpw5KfqeOHFCXbt21YYNG+Tp6almzZrpypUrmjBhgoYNG5Zq7sjISPXq1Uvz5s2TyWRSy5YtZTKZtGDBAvn6+ur27dsP6jIBAAAAAA+hXLeivXHjRv3000+qVKmSvv/+exUvXlySdOrUKb344ouaPXu22rVrp4oVK8pqtWrYsGGKjIzU1KlT1aFDB0lSWFiYXn75Za1fv15PP/202rRpY5t/xowZCgoKUvfu3TV+/HjZ2dkpMTFRI0aM0E8//aQZM2Zo9OjROXLtAAAAAIC8L9etaK9bt06SNGTIEFvIlqRq1aqpffv2slgs2r17tyRp9+7dCgoKUsOGDW0hW5KKFi2qsWPHSpIWL15sOx4REaEVK1bIzc1Nw4cPt91W7uDgoLFjx6pw4cJauXKloqOj7/t1AgAAAAAeTrkuaM+cOVPr169X8+bNU7VFRUVJkuzt7SVJO3fulCS1bt06VV8fHx8VK1ZMAQEBioyMlCQdOHBAsbGxaty4capnsQsUKKAmTZooNjZWBw4cMPSaAAAAAAD5R64L2k5OTqpevbqcnJxSHN+2bZt+/fVXmc1mW7AODg6WJFWvXj3NuSpVqiSLxaKQkJAU/atVq5Zm/8qVK0uSgoKC7v1CAAAAAAD5Uq57RvvfYmNjNWzYMAUHByskJESenp6aOnWq7Zbya9euSZJKlCiR5vjk4//8848k6fr161nqf+PGDUPqj4yMVEBAgCFz3W8+Pj45XQJgqLzy3x6AnMHnHh42fO4BuUuuW9H+t0uXLsnf39+2Ii2lXG2OiYmRJLm4uKQ5Pvl48jPXyX+6urpmqT8AAAAAANmVq1e0S5UqpT/++EN2dnbas2ePPvroI02YMEHR0dF6/fXXbZuZmUymNMdbrdYUf2a3/71yc3OTl5eXIXMByB5WqwAA+Qmfe8CDFRQUZNsLLC25ekXbbDarSJEiKly4sNq2bavZs2fLZDLpq6++UlxcnMxms6Q7t5inJS4uzjbPv//MrH96K94AAAAAAGQmVwft/3r00UdVvnx5RUZG6sKFC/Lw8JD0/89g/9d/n8nObn8AAAAAALIrVwVtq9WqqVOnatCgQUpMTEyzT/Ju5ImJibbdw5N3E//vXKdPn5a9vb2qVKkiSRn2l2R7FpzbvQEAAAAAdytXBW2TyaQtW7Zo48aN2r17d6r2Cxcu6MyZMzKbzapUqZKaNWsmSdqyZUuqvn/++afCwsLk4+Nje2d2gwYN5OLior1796ba8CwqKkp79+6V2WzmGRcAAAAAwF3LVUFbkrp37y5Jmjhxoq5cuWI7fvXqVQ0ePFiJiYnq0aOHnJ2d1bBhQ1WrVk27d+/Wjz/+aOsbFham8ePHS5L69u1rO242m9WxY0eFh4dr/PjxtlXzxMRE+fn5KSIiQr6+vrZgDgAAAABAduW6Xcf79Omjffv2afv27Wrbtq3q16+vpKQk/fXXX4qOjlaLFi307rvvSrqzi/ikSZP00ksvafTo0Vq5cqU8PDy0f/9+hYeHq3v37mrVqlWK+QcNGqR9+/Zp7dq1CggIkLe3t44dO6YLFy7I29tbAwcOzInLBgAAAAA8JHLdirajo6O+/PJLjRo1ShUrVtSBAwd0+PBhVatWTePHj9fcuXNtz2lLUp06dbRixQq1adNG586d0+7du+Xp6anx48dr3LhxqeZ3d3fXsmXL1Lt3byUmJmrbtm2ys7NTv379tGjRIhUoUOABXi0AAAAA4GFjshr10mjYJL9TLS++R9tn6KKcLgG4JwHT+uR0CQDyED73kNfxuQfkjMwyX65b0QYAAAAAIC8jaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAbKdtC+dOmSbty4kaW+ISEh2rZtW7aLAgAAAAAgr3LI7oBWrVrpscce0/fff59p3+HDhys0NFR//PHHXRUHAAAAAEBek2HQTkpKUnx8vO1rq9VqOx4bG2v7+r+sVqsuXbqkCxcuKC4uzsByAQAAAADI3TIM2pcuXVK7du2UkJBgO2YymXT48GHVq1cvSyeoXbv2vVUIAAAAAEAekuEz2uXKldMrr7wiq9Vq+0dSiq8z+qdkyZIaNWrUA7kQAAAAAAByg0yf0X777bfVrVs3SXcCduvWrVW7dm3NmDEj3TF2dnYym80qXLiwYYUCAAAAAJAXZBq0HRwcVKZMGdvXnTp1UqVKlVIcAwAAAAAAd2R71/GPP/74ftQBAAAAAMBDIdtBO9mNGzd05MgRRUZGKikpKcO+HTt2vNvTAAAAAACQp2Q7aFutVn388cf64YcfMg3YyQjaAAAAAID8IttBe+nSpVq0aJEkydXVVWXKlJGzs7PhhQEAAAAAkBdlO2ivXLlSJpNJr7zyit577z05Ojrej7oAAAAAAMiTsh20T58+reLFi2vIkCEymUz3oyYAAAAAAPIsu+wOcHZ2VvHixQnZAAAAAACkIdtBu1atWjp79qyioqLuRz0AAAAAAORp2Q7ar7/+umJjYzV58uT7UQ8AAAAAAHlatp/RdnNzU8+ePbVkyRIdPnxYTZs2VcmSJTPcFK1nz573VCQAAAAAAHlFtoN2ly5dZDKZZLVaderUKQUHB2c6hqANAAAAAMgvsh20GzRocD/qAAAAAADgoZDtoL148eL7UQcAAAAAAA+FbG+GBgAAAAAA0kfQBgAAAADAQNm+dfypp57KVn+TyaTNmzdn9zQAAAAAAORJ2Q7aFy9ezFK/5J3JAQAAAADIT7IdtOfOnZtuW0xMjK5fv64tW7Zo3759GjhwoPr06XNPBQIAAAAAkJdkO2i3bNky0z59+vTRp59+qtmzZ6tu3bpq2rTp3dQGAAAAAECec982Qxs4cKDc3Nw0b968+3UKAAAAAABynfsWtJ2cnFS+fHn9/fff9+sUAAAAAADkOvctaMfHxys0NJQN0QAAAAAA+cp9CdpXr17VBx98oPDwcHl7e9+PUwAAAAAAkCtlezO0Jk2apNtmtVoVHx+vmJgYSXde8fXSSy/dfXUAAAAAAOQx2Q7aN2/ezFK/woULa+DAgWrdunW2iwIAAAAAIK/KdtBetGhRhu329vYqXLiwKleuLDu7+/YIOAAAAAAAuVK2g3bDhg3vRx0AAAAAADwUsh20/81qtSowMFBnz55VVFSUzGazKlSooJo1a8re3t6oGgEAAAAAyDPuOmivWrVKM2fO1LVr11K1ubu7691339ULL7xwT8UBAAAAAJDX3FXQ/uSTTzR//nxZrVY5OTmpcuXKMpvNun37ts6cOaObN29q/PjxOnfunIYPH250zQAAAAAA5FrZDtp79+7VvHnz5OTkpPfff1++vr5ycXGxtcfExGj58uX67LPPtHDhQj355JM81w0AAAAAyDeyvS34okWLZDKZNGHCBL300kspQrYkubq66uWXX5afn5+sVqt++OEHw4oFAAAAACC3y3bQPnz4sEqUKKEOHTpk2K9jx44qUaKEDh8+fLe1AQAAAACQ52Q7aN++fVulSpXKUt/SpUvrxo0b2S4KAAAAAIC8KttBu2jRojp37pwsFkuG/ZKSknTu3DkVKVLkrosDAAAAACCvyXbQbtCggSIiIjR//vwM+82fP1/h4eFq0KDBXRcHAAAAAEBek+1dx1999VX9+uuvmj59ui5fvqwXX3xR1apVs7WfPHlSS5cu1fLly2Vvb6++ffsaWjAAAAAAALlZtoO2t7e3RowYoYkTJ2rp0qVaunSpHBwcZDabFR0drcTERFmtVtnZ2WnEiBGqVavW/agbAAAAAIBcKdu3jktSz549tXDhQjVq1Ej29vZKSEhQeHi4EhISZG9vr8aNG2vhwoXq2bOn0fUCAAAAAJCrZXtFO1ndunXVvXt3ffHFF7pw4YKioqJkNpsVEBCgpKQkPfLII0bWCQAAAABAnnBXK9p79uxRixYtNGTIEN2+fVteXl6qX7++HnnkEf3222/6+OOP9eyzz2rv3r1G1wsAAAAAQK6W7aB95MgRvf766woPD1fVqlWVkJCQov25555T3bp1FRYWpgEDBuj06dOGFQsAAAAAQG6X7aD9zTffKDExUX379tW6detUtmzZFO3du3fXsmXL1K9fP8XExOirr74yrFgAAAAAAHK7bAftgIAAFS1aVEOGDMmw33vvvafChQtrz549d10cAAAAAAB5TbaD9u3bt+Xp6Sl7e/sM+zk4OKhcuXK6devW3dYGAAAAAECek+2g7eHhoQsXLigpKSnDfhaLRRcvXpS7u/vd1gYAAAAAQJ6T7aDduHFjRURE6Msvv8yw34IFC3Tz5k01bNjwrosDAAAAACCvyfZ7tF966SWtX79ec+bM0ZkzZ9S5c2dVq1ZNZrNZMTExCg4O1k8//aR169bJwcFB/fr1ux91AwAAAACQK2U7aFevXl1+fn4aM2aMNmzYoI0bN6bqY7Va5eDgoAkTJqhGjRqGFAoAAAAAQF6Q7VvHJaljx4766aef1K1bN5UoUUJWq9X2j7u7u9q3b6+VK1eqU6dORtcLAAAAAECulu0V7WSVKlXShAkTJEnx8fG6efOmXF1dVahQIcOKAwAAAAAgr7nroP1vTk5OKlmypBFTAQAAAACQp93VreMAAAAAACBtBG0AAAAAAAxE0AYAAAAAwEAEbQAAAAAADETQBgAAAADAQARtAAAAAAAMRNAGAAAAAMBABG0AAAAAAAxE0AYAAAAAwEAOOV1AWpKSkrR06VKtWbNGp0+fVlJSksqVK6fnnntO/fr1k7Ozc4r+Z86c0axZsxQQEKBbt26pfPny8vX1VY8ePWRnl/p3CREREfrqq6+0efNmXb58WcWLF9czzzyjt99+W25ubg/qMgEAAAAAD6Fct6KdlJSkAQMGaMKECTp9+rTq1q2rhg0b6tq1a5o5c6Z69+6tmJgYW/8TJ06oa9eu2rBhgzw9PdWsWTNduXJFEyZM0LBhw1LNHxkZqV69emnevHkymUxq2bKlTCaTFixYIF9fX92+fftBXi4AAAAA4CGT64L2ihUr9Pvvv8vLy0u//vqrFi5cqHnz5snf31/16tXTX3/9pS+++EKSZLVaNWzYMEVGRmrq1KlaunSpZs+eLX9/f3l5eWn9+vXy9/dPMf+MGTMUFBSk7t27a+PGjZo5c6b8/f3VoUMHBQcHa8aMGTlw1QAAAACAh0WuC9pr1qyRJI0YMUIlS5a0HS9atKjGjRsnSdqwYYMkaffu3QoKClLDhg3VoUOHFH3Hjh0rSVq8eLHteEREhFasWCE3NzcNHz7cdlu5g4ODxo4dq8KFC2vlypWKjo6+r9cIAAAAAHh45bqgXaRIEVWuXFl16tRJ1VaxYkVJ0rVr1yRJO3fulCS1bt06VV8fHx8VK1ZMAQEBioyMlCQdOHBAsbGxaty4capnsQsUKKAmTZooNjZWBw4cMPKSAAAAAAD5SK4L2nPnztUvv/wis9mcqu3o0aOSpFKlSkmSgoODJUnVq1dPc65KlSrJYrEoJCQkRf9q1aql2b9y5cqSpKCgoHu4AgAAAABAfpbrgnZ6rFarZs6cKUl65plnJP3/ynaJEiXSHJN8/J9//pEkXb9+PUv9b9y4YVDVAAAAAID8Jle+3istn332mfbv36/ixYurX79+kmTbfdzFxSXNMcnHk5+5Tv7T1dU1S/3vVWRkpAICAgyZ637z8fHJ6RIAQ+WV//YA5Aw+9/Cw4XMPyF3yxIr2559/rq+//lpOTk6aMWOGihYtKkm2zcxMJlOa46xWa4o/s9sfAAAAAIDsytUr2omJifLz89Py5cvl7OysWbNmqUGDBrb25Oe4Y2Nj0xwfFxeXol9W+6e34p1dbm5u8vLyMmQuANnDahUAID/hcw94sIKCgmybbqcl165oR0VFqX///lq+fLkKFSqk+fPnq0WLFin6eHh4SPr/Z7D/67/PZGe3PwAAAAAA2ZUrg3Z4eLh69+6tnTt3qnTp0lqyZEmKlexkybuHJ+8m/m9Wq1WnT5+Wvb29qlSpkml/SbbdyVmFBgAAAADcrVwXtOPj4/X6668rMDBQVatW1bJly9J9fVezZs0kSVu2bEnV9ueffyosLEw+Pj62d2Y3aNBALi4u2rt3b6oNz6KiorR3716ZzWZuvQEAAAAA3LVcF7Rnzpypw4cPq3Tp0lq8eLHtndlpadiwoapVq6bdu3frxx9/tB0PCwvT+PHjJUl9+/a1HTebzerYsaPCw8M1fvx4JSYmSvr/Z8EjIiLk6+trC+YAAAAAAGRXrtoM7datW1q8eLEkqWjRopo0aVK6fT/55BPZ2dlp0qRJeumllzR69GitXLlSHh4e2r9/v8LDw9W9e3e1atUqxbhBgwZp3759Wrt2rQICAuTt7a1jx47pwoUL8vb21sCBA+/rNQIAAAAAHm65KmgfOXLEtiN4YGCgAgMD0+37ySefSJLq1KmjFStWaObMmdq3b59OnTqlChUqaPDgwerWrVuqce7u7lq2bJlmz56tzZs3a9u2bSpdurT69eun/v37q0CBAvfn4gAAAAAA+UKuCtrNmzdXUFBQtsdVrVpVM2fOzHJ/d3d3jRo1SqNGjcr2uQAAAAAAyEiue0YbAAAAAIC8jKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAAAAAABiIoA0AAAAAgIEI2gAAAAByBYvFom7duqlRo0aZ9j1+/Li8vb01ZcqUDPudPHlSgwcP1hNPPKG6deuqXbt2+vzzz3Xr1q27qjE8PFwfffSRnnzySdWqVUvNmzfXiBEjdPHixbuaDw8ngjYAAACAXGHGjBk6cuRIpv3Cw8M1dOhQJSUlZdhv8+bN6tKlizZs2KCoqChVqVJFN2/e1BdffKFOnTrp9OnT2aovPDxcL774ohYtWqTw8HBVr15dsbGxWrVqlTp27KgTJ05kaz48vAjaAAAAAHKU1WrV7Nmz9dVXX2Xa959//lG/fv106tSpDPtduHBBQ4cOVXx8vFq3bq0dO3Zo9erV2rlzpwYMGKBLly7ptddeU1xcXJbrHD16tEJCQtSiRYsU83Xu3FkREREaPHhwpuEf+QNBGwAAAECOuX79ut566y3NmjUr07579uxR586ds7TqvXDhQkVHR6tq1aqaPn26ChUqJEmyt7fXu+++q4YNGyo0NFSLFi3KUp0hISHatGmTzGazpk6dKjc3N0mSs7OzJk6cqCpVqigkJES//fZblubDw42gDQAAACBH7Nq1S23atNGWLVtUokQJvf/+++n2HTNmjPr27aurV6/qySefVJs2bTKdW5J69uwpJyenVO09e/aUJK1fvz5Lta5bt05Wq1WtWrWSu7t7ijZ7e3t17txZkrRx48YszYeHG0EbAAAAQI4IDg5WdHS0OnTooPXr16tu3brp9v3rr7/k7u4uPz8/zZ07V2azOcO5L1++LEmqWbNmmu0VK1aUJJ06dUoxMTGZ1pq8il6vXr002x999FFJUkBAQKZz4eHnkNMFAAAAAMif6tSpozVr1qhGjRqZ9n311VfVsmVL2y3gWZWYmJjm8YSEBEl3djq/cuWKKlWqlOE8586dkySVLVs2zXZPT09Jd54hj4qKUoECBbJVJx4urGgDAAAAyBH169fPUsiWpP/973/ZCtnJgfjkyZNptoeEhNj+PSIiItP5bt68KUmpbhtPVrhw4VR9kX8RtAEAAAA8dFq0aCFJ+vbbbxUfH5+izWKxaP78+bavk1e3MxIbGytJcnFxSbP938ezs5M5Hk4EbQAAAAAPnb59+8rd3V3nz59Xv379dOzYMSUkJOj06dMaOHCgzp8/bwvHDg6ZP1Frb2+fYbvFYjGkbjwcCNoAAAAAHjoeHh768ssv5e7urn379qlTp06qVauW2rZtqz179ujTTz+1Be3kV3VlxNXVVVL6q9X/XjVPb9Ub+QeboQEAAAB4KNWvX1+//PKLfvjhB/3111+SJG9vb/n6+qp48eK6ffu2JKlEiRKZzuXu7q6IiAjdunUrzfZ/Hy9atOg91468jaANAAAA4KFVtGhRvf3226mO//3330pKSpKHh0eKjczSU7lyZZ0/f14XL15Ms/3SpUuS7oT25NVv5F/cOg4AAADgoXPw4EF9++23tpXs//r9998lSQ0bNszSfLVq1ZKkdOc7fPiwJGX4LnDkHwRtAAAAAA+dv/76S1OmTNHChQtTtUVGRmrZsmWSpBdffDFL8z399NOSpN9++y3V7eNJSUlas2aNpDuvIQMI2gAAAAAeOq1bt5ajo6N++eUX/fzzz7bj169f14ABA3T9+nW1aNFCjz32WIpxYWFhCgkJ0fnz51Mcf+SRR9SiRQtFRUXpnXfesb0rOy4uTqNGjVJISIgqVapkC+TI33hGGwAAAMBDp0KFCho+fLgmTpyo999/X5999pkKFSqk4OBgJSQkqGbNmvrss89SjVuyZIlmz56tMmXKaOvWrSna/Pz81KNHD+3bt09PPvmkKleurNDQUIWHh6tgwYKaPXu27OxYywQr2gAAAAAeUr1799bs2bPVoEED3bx5U8HBwapYsaIGDRqkH374IUuv9fq3UqVKadWqVerdu7eKFi2qkydPyt7eXs8//7xWrlypqlWr3qcrQV5jslqt1pwu4mETFBSkyMhIubm5ycvLK6fLyRafoYtyugTgngRM65PTJQDIQ/jcQ17H5x6QMzLLfNw6DgAAAORx5/1q53QJwD0pP+ZoTpdgKG4dBwAAAADAQARtAAAAAAAMRNAGAAAAAMBABG0AAAAAAAxE0AYAAAAAwEAEbQAAAAAADETQBgAAAADAQARtAAAAAAAMRNAGAAAAAMBAeSJor169Wl5eXjp48GCa7WfOnNHgwYPVokUL1a1bV+3bt9f3338vi8WSZv+IiAhNmzZNbdq0UZ06ddSqVStNnjxZkZGR9/MyAAAAAAD5QK4P2ocOHdKECRPSbT9x4oS6du2qDRs2yNPTU82aNdOVK1c0YcIEDRs2LFX/yMhI9erVS/PmzZPJZFLLli1lMpm0YMEC+fr66vbt2/fzcgAgQ1euXNGYMWP05JNPqlatWmrUqJH69++vgICAdMdER0dr9uzZeu6551SnTh21aNFC77zzjo4ePXrXdaxdu1Zdu3ZVnTp19Nhjj6l3797asmXLXc8HAACQn+TqoL1p0ya9+uqrio6OTrPdarVq2LBhioyM1NSpU7V06VLNnj1b/v7+8vLy0vr16+Xv759izIwZMxQUFKTu3btr48aNmjlzpvz9/dWhQwcFBwdrxowZD+DKACC1U6dOqWPHjlq+fLlu3LihypUrS5K2bdumXr16aeXKlanG3LhxQ76+vpo1a5ZCQkJUtmxZubm5yd/fX927d9eSJUuyXccnn3yi4cOH6++//1aFChVUtGhR7d+/XwMGDNCcOXPu+ToBAAAedrkyaF+5ckXDhg3TwIEDZbFYVLx48TT77d69W0FBQWrYsKE6dOhgO160aFGNHTtWkrR48WLb8YiICK1YsUJubm4aPny47OzuXL6Dg4PGjh2rwoULa+XKlekGewC4n4YPH66bN2+qUaNG+v3337Vu3Trt2bNH/fv3l8Vi0bhx43ThwoUUYz744AOdPHlSJUqU0I8//qiNGzdqw4YNWrp0qQoXLiw/Pz/t2rUryzVs27ZN33zzjdzd3fXjjz9q/fr12rRpk+bMmSMnJyfNmjUrw9V1AAAA5NKgPWPGDP3000+qVauWli9fblvV+a+dO3dKklq3bp2qzcfHR8WKFVNAQIDt2esDBw4oNjZWjRs3lpubW4r+BQoUUJMmTRQbG6sDBw4YfEUAkLHg4GAFBgbKZDJp2rRpKlq0qCTJ3t5egwYNUu3atZWQkKANGzbYxgQGBmrHjh2S7vz/Zt26dW1t9evX1/DhwyVJH3/8cZbr+OqrryRJ77//vurUqWM73rp1a7399tuyWq36+uuv7/5CAQAA8oFcGbQrV66sKVOmaMWKFfLy8kq3X3BwsCSpevXqabZXqlRJFotFISEhKfpXq1Yt3fNKUlBQ0F3XDgB34+rVq5Ikd3d3lSxZMlW7t7e3JOnSpUu2Y8kr1cnPUf9Xhw4dVKBAAQUHB+vYsWOZ1nDu3DkdOnRIjo6Oev7551O1d+3aVdKdu4kiIiKycFUAAAD5U64M2q+//ro6duxou7U7PdeuXZMklShRIs325OP//POPJOn69etZ6n/jxo3sFw0A96BUqVKSpJs3b9pC978l/6LQ09PTdiw5dCeH8P+ys7NT+fLlJUlHjhzJtIa//vpL0p1fXprN5lTtxYoVU7ly5ZSQkKDDhw9nOh8AAEB+5ZDTBdyLmJgYSZKLi0ua7cnHk5+5Tv7T1dU1S/3vVWRkZJ55ltHHxyenSwAMlVf+2/u36tWr6+TJk3rzzTc1cOBAFSpUSFarVT///LMCAgLk4uKiypUr264t+ZeHV69eTfd6k9+kEBAQkO7dPMn++OMPSXcepUlvvoIFC0q6s5peoECB7F8kkEvwuQcAuVNe/BkuLXk6aCeveJtMpjTbrVZrij+z2x8AHqRBgwZpzpw5+vvvv/XOO++odOnSunXrlsLDw1WmTBm99tprKlasmK2/h4eHJKXaIC1ZfHy87c6fqKioTM+ffDt4cphOS/L+FrwKEQAAIH15Omgn39oYGxubZntcXFyKflntn96Kd3a5ubll+Iw5gPsnL65W3b59W82aNVNwcLBiY2N17tw5W1v58uVVp04d1axZ03asUKFCWrp0qa3/E088kWK+efPmKT4+XtKdZ78z+56sWrVKklS2bNl0+ybf4l60aNE8+T0GAAC5W175+SIoKMi26XZacuUz2lmVvJqT/Az2f/33mezs9geAByUiIkK9e/fWl19+qccee0xr167V0aNHtXnzZvXu3Vt79+5Vr169dOjQIduYatWq2TYtGzx4sNavX6+oqCiFh4dr0aJFmjFjhtzd3SXdeY1hZjLbF0OSLBaLpPTvDAIAAEAeX9GuVq2atm/fruDgYDVq1ChFm9Vq1enTp2Vvb68qVarY+kv/v6nQfyXvTs4qNIAHbd68eTp+/LiqV6+uuXPnytHRUZJUrlw5jRo1Sk5OTpo/f778/Py0Zs0a2zg/Pz/duHFDe/fu1ZAhQ1LM2alTJxUqVEjfffddqlcapiX5rp/ku3vSkpCQICn9vTEAAACQx1e0mzVrJknasmVLqrY///xTYWFh8vHxsf2A2aBBA7m4uGjv3r2pNjyLiorS3r17ZTab88ztCgAeHv7+/pKkV1991Ray/+2NN96Qvb29jh07luKW8gIFCmjBggX69NNP9dxzz6lJkybq0qWLFixYoMmTJ+vWrVuS/v+Onowkr36Hh4en2+fmzZuSpCJFimT10gAAAPKdPB20GzZsqGrVqmn37t368ccfbcfDwsI0fvx4SVLfvn1tx81mszp27Kjw8HCNHz9eiYmJkqTExET5+fkpIiJCvr6+WVr5AQAjJb+qq3Llymm2Fy5cWEWLFk3RN5nJZNLzzz+v6dOna+HChZo0aZIef/xxSdLx48clKdMdx/997osXL2ZaZ4UKFTKdDwAAIL/K07eO29nZadKkSXrppZc0evRorVy5Uh4eHtq/f7/Cw8PVvXt3tWrVKsWYQYMGad++fVq7dq0CAgLk7e2tY8eO6cKFC/L29tbAgQNz6GoA5Gdubm4KCwuz7RXxX3FxcQoLC5Mk22u1rl+/rk2bNsnBwUG+vr6pxly8eFEnT56Uo6OjHn300UxrqFWrliTpxIkTiouLk7Ozc4r2GzduKDQ0VHZ2dqpdu3Z2Lg8AACBfydMr2pJUp04drVixQm3atNG5c+e0e/dueXp6avz48Ro3blyq/u7u7lq2bJl69+6txMREbdu2TXZ2durXr58WLVrEe2EB5IiGDRtKklauXJlm+7p165SUlKSCBQvqkUcekSTZ29trwoQJmjhxou3VXP/2zTffSJI6dOiQpf9vK1u2rLy9vRUfH69169alal+xYoUkqUWLFipcuHDWLgwAACAfyhMr2osXL86wvWrVqpo5c2aW53N3d9eoUaM0atSoey0NAAzx2muvafPmzdq6daumTp2qt99+27Y52a+//qrJkydLkl5//XU5OTlJuvOKrUaNGumPP/7QqFGjNHnyZJnNZiUmJmrRokVaunSpzGaz+vfvn+JcCQkJOn/+vCTJ09MzxSsNX3/9db333nuaPHmyypcvb9tocsuWLZozZ45MJpP69et3378fAAAAeVmeCNoA8LCrVauWJk6cqNGjR2v+/PlaunSpKlWqpMuXL9tuGe/UqZNee+21FOM++ugjde7cWf7+/tqzZ4/Kly9vG+Ps7KwvvvhC5cqVSzHm6tWreu655yRJixYtSvHWhrZt22rnzp1atWqV+vTpo6pVqyoxMVFnz56VdOfxm8cee+w+ficAAADyPoI2AOQSnTp1Uo0aNTR//nzt27dPJ0+elNls1uOPP67u3burbdu2qcaULVtWq1at0pw5c7Rr1y4FBQWpSJEi+t///qf+/fvbXm+YHR999JF8fHy0bNkynTp1SlarVfXq1VPv3r3Vrl07Iy4VAADgoUbQBoBc5JFHHtG0adOyNaZcuXK2W8uzomzZsgoKCkq33WQyqUuXLurSpUu26gAAAMAdBG0AD6XzfuyKjbyr/JijOV0CAAC4B3l+13EAAAAAAHITgjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgoHwdtPfs2aM+ffqoUaNGql+/vnr37q2dO3fmdFkAAAAAgDws3wbt1atXq2/fvjp06JDq1KmjevXq6dChQ+rXr5+WL1+e0+UBAAAAAPIoh5wuICdcu3ZNY8eOVcGCBfXDDz+oevXqkqQjR46ob9+++uijj9SyZUuVLFkyhysFAAAAAOQ1+XJF+/vvv1d8fLxefvllW8iWpDp16qhfv36Ki4tjVRsAAAAAcFfyZdBOfg67devWqdqefvppSdKOHTseaE0AAAAAgIdDvgvaVqtVwcHBsrOzU+XKlVO1V6xYUXZ2dgoODpbVas2BCgEAAAAAeVm+C9rh4eGKj4+Xu7u7nJycUrU7ODioSJEiiomJUVRUVA5UCAAAAADIy0zWfLZse/nyZbVs2VJlypTR1q1b0+zTqlUrXbx4UTt27LirDdGOHDmihISEey31gXJzc8vpEgAA/xEZGZnTJTy0+NwDgNwpr332OTo6qk6dOqmO57tdx+3sMl/Ev9ffPSQlJd3T+JyQ1/4HDQDAveBzDwBghPSyX74L2mazWZIUFxeXbp/kNldX17s6h7Ozs+Li4mRvby9nZ+e7mgMAAAAAkDvFxcUpKSkp3byX74K2m5ubzGazbt68qcTERDk4pPwWJCYm6ubNm3J2dlahQoXu6hze3t5GlAoAAAAAyIPy3WZoJpNJVatWVVJSks6ePZuq/cyZM7JYLCnerw0AAAAAQFblu6AtSc2aNZMkbd68OVVb8rEWLVo80JoAAAAAAA+HfBm0O3fuLGdnZ33zzTf6+++/bcePHj2qefPmycXFRT169MjBCgEAAAAAeVW+e71XsiVLlsjPz0+Ojo5q3LixrFar9u3bp8TERE2ZMkUdOnTI6RIBAAAAAHlQvg3akrRt2zbNmzdPx44dk5OTk7y8vPTmm2+qSZMmOV0aAAAAACCPytdBGwAAAAAAo+XLZ7QBAAAAALhfCNoAAAAAABiIoA0AAAAAgIEI2gAAAAAAGIigDQAAAACAgQjaAAAAAAAYiKANAAAAAICBCNoAcszq1avl5eUlLy8vNW3aVBaLJcP+v/76q63/Bx98cFfn3Ldvn7y8vPTyyy/f1XgAAKT//zzJyj+rV6/O6XKz5YMPPpCXl5d++umnnC4FyLMccroAAJCk69evKyAgQA0aNEi3zy+//PIAKwIAIHNms1lPPfVUhn3Kly//gKoBkFsQtAHkuEKFCikiIkL+/v7pBu3o6Ght375djo6OSkhIeMAVAgCQtiJFiuiTTz7J6TIA5DLcOg4gxzVt2lTOzs7atGmTrFZrmn22bdummJgYNWvW7AFXBwAAAGQPK9oAcpzZbFbz5s3122+/6dChQ6pfv36qPhs3bpTZbFbLli21devWFG2JiYlauXKlNmzYoJMnTyoyMlJubm6qVauWXn755SyH87/++ktff/21AgICFBUVJU9PTz377LN67bXX5ObmZsi1AgDyt5CQEM2dO1d79+7VrVu35OHhoVatWunNN99UsWLFUvT18vJS3bp1NXfuXM2YMUNbtmxRVFSUqlWrpvfee09PPPGEgoKC9OmnnyogIEDOzs7y8fHRBx98oDJlyqSY68qVK/r222+1a9cuXb58WYmJifLw8FDTpk01YMAAlSxZMtPaExMTtXz5cq1atUqnT5+WnZ2dvL291adPHz3zzDOGfp+AvI4VbQC5Qtu2bSVJ/v7+qdoiIyO1c+dOtWrVSi4uLinarFar3nrrLY0dO1anTp1S3bp11aJFC7m5uWnXrl167bXXtHnz5kzPv3r1ar344ovaunWrypUrpyeffFJxcXGaO3euXnzxRd26dcuQ6wQA5F+7du1Sly5dtG7dOhUrVkytWrWSk5OTFi9erC5duujChQupxkRERMjX11e//PKL6tatq0qVKunIkSN6/fXXtWLFCvn6+urMmTNq0qSJnJyctGnTJvXq1UtxcXG2OUJCQtShQwd99913sre3V7NmzeTj46OwsDAtW7ZML7zwgiIjIzOsPSEhQf3795efn59CQ0P12GOPqV69ejpy5IgGDhyo6dOnG/79AvIyVrQB5AotW7aUi4uLNm3apA8//DBF2+bNmxUXF6e2bdsqKioqRduvv/6q33//XfXq1dPChQttQdxisWjy5Mn67rvvtGTJErVu3Trdc4eEhGjMmDEym8366quv5OPjI+nODxUTJkzQ8uXLNWHCBH366acGXzUAIL8ICwvT4MGDlZCQoNmzZ+vpp5+WdOcXxskr1kOHDtWyZctSjDtz5oweeeQRrVixQu7u7rJarXr33Xfl7++vUaNGqUePHho1apTs7e0VFRWlzp076+zZs9q5c6fts2/KlCm6deuWRowYoZdeesk2940bN/TCCy/o/Pnz2rp1q/73v/+lW/+cOXO0c+dOPfHEE/rss8/k7u4uSQoNDVXfvn01d+5cNWjQQE2bNjX4OwfkTaxoA8gVChQooObNm+vSpUs6cuRIirZffvlFBQsWVPPmzVONs1gsatWqlYYMGZJitdvOzk7dunWTJF26dCnDc3/33XdKSEjQO++8YwvZkuTo6KhRo0apZMmS2rhxo65evXovlwgAeAhdvHgxw1d77du3T5K0cuVKhYeHq1evXraQLUkmk0lvvvmmateurUOHDunQoUOpzjFo0CBbsDWZTHr22Wcl3Xn0asiQIbK3t5d057M0+XGpc+fO2cZ7enrqmWeeUe/evVPMW6xYMVsYv3z5crrXGB8fr++//17Ozs6aOnWqrRZJKlu2rEaOHClJWrBgQZa+Z0B+wIo2gFyjbdu22rRpk3799VfVqVNHkhQeHq7du3fr+eefl5OTU6ox7dq1U7t27VIci46OVnBwsHbs2CFJme5SnvxDUKNGjVK1OTk5qWHDhlq/fr0OHjyY6lwAgPwts9d7FS9eXFLGnzWS9MQTT+jo0aM6cOCA6tWrl6Ktbt26Kb4uWrSoJKlChQoqUKBAirZChQpJUopbx8eNG5fqfNeuXdPx48d14sQJSRl/VgYGBur27duqWbOm7Xr+rUmTJnJwcFBAQICSkpJswR/IzwjaAHKNf98+PmzYMEnSb7/9poSEBD333HPpjouIiNCyZcu0c+dOnT59Wv/884+kO7/1z4orV65IUoa3zEkZ/7YfAJA/ZfX1XsmfIW+++WaW+iUzmUwqXLhwqmOSUqws/7ftv44fP64ffvhBR44c0fnz5xUdHZ2if3pv/fh3TYGBgfLy8kq3X2JiosLDw22/CADyM4I2gFzDbDarRYsW8vf3V2BgoGrWrKlffvlF7u7uevzxx9Mcc/LkSb300ksKCwtT8eLFVbt2bVWpUkXe3t6qUKGCunTpkul5k5KSJEnPP/98huG8QoUKd3dhAIB8L/mz5qmnnpLZbE633yOPPJLiazs7O9nZ3dvTnl9//bVtn5Hq1avr6aefVtWqVVWnTh3t27dPX3zxRYbjLRaLpDu3if93tR1A2gjaAHKVZ599Vv7+/tq0aZM8PT31xx9/qEuXLnJwSPv/riZMmKCwsDC99dZbGjhwYIqgHBQUlKVzenh46OLFixo6dKhKlSplyHUAAPBvHh4eOnv2rF555RU99thjD+y8Fy5c0PTp0+Xu7q5vvvnG9mhWst9//z3TOUqUKCFJKleuXJZW7wGwGRqAXObJJ5+Uq6ur/P39tWXLFiUmJtpe/ZWW5I3T+vfvn2o1evfu3ZL+/zfx6Un+gWf79u1ptr/66qvy9fVNtUkbAABZlfxZk7x/yH99+OGH6tKli7Zs2WLoeY8ePSqLxaInnngiVci2WCzau3ev7d/TU7t2bbm4uOjo0aMKCwtL1R4UFKSnn35aAwcOzPAWdCA/IWgDyFVcXV3VvHlznTlzRvPnz1fx4sXT3ThGkm0F+r8/mPz++++aNWuWpJQbwqSld+/esrOz02effaaDBw/ajlutVs2ePVu7du1SaGhoqtv5AADIKl9fX7m6uurbb7/Vb7/9lqJt9erVWrNmjU6ePJlq47N7Vbp0aUnSn3/+qVu3btmOx8XFyc/Pz7YZWkaflWazWd26dVNkZKSGDRummzdv2tpu3rypDz/8UOfPn1fp0qWzvD8K8LDj1nEAuU7btm3l7++v06dPq1evXhk+m/byyy9r3LhxGjRokL7//nsVK1ZMISEhCg4Otn3gR0REKD4+Ps1dy6U7v6kfPny4Jk+erF69esnb21tlypTRyZMndfbsWbm4uOjzzz9PdzwAAJkpVaqUPv74Yw0dOlRvv/22qlevrooVK+rcuXMKCgqSnZ2dpk6dmuau3veiTp06qlevng4dOqQ2bdqofv36slgsOnTokMLDw1W1alUFBwfbNhJNz/vvv6/AwEDt3LlTTz/9tOrUqSMHBwcdPHhQUVFRevTRR/Xee+8ZWjuQl7GiDSDXadmypW2jmIx2G5ekF198UVOnTpW3t7eOHz+uP/74Qw4ODurXr5/Wrl2rRo0aKTExMd1b9ZK9/PLLWrRokZ588kldunRJv//+uywWizp16qS1a9c+0OfpAAAPp7Zt22rFihV6/vnndfPmTW3btk23b99WmzZttGLFigwflbpb9vb2mjt3rnr37q2CBQtq165dCgoK0iOPPKJPPvlE33//vUwmk3bs2KHExMR053F1ddV3332nDz/8UOXLl9eff/6pgIAAVahQQcOHD9fChQsz3OQNyG9MVh6kAAAAAADAMKxoAwAAAABgIII2AAAAAAAGImgDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABiJoAwAAAABgIII2AAB44L7//nt5eXmpd+/eOV0KAACGI2gDAAAAAGAggjYAAAAAAAYiaAMAAAAAYCCCNgAAAAAABnLI6QIAAMC92bt3r+bPn69jx44pJiZGjzzyiPr16yc3Nzf16dNHDRs21OLFi2394+Pj9cMPP2j9+vU6ffq0rFarKlWqpOeff169evWSs7Nzivk/+OADrVmzRp9//rkqVqyoOXPm6MCBA4qKilL58uXVvn17vfLKK3JyckpV2/Hjx/Xll1/qzz//VGRkpGrUqKE333wzw+uxWCxau3atVq1apaCgIMXHx6ts2bJ65pln9Oqrr6pgwYJp1jdjxgwFBwfr+++/V1xcnCpXrqxFixbJzc3tHr67AABkH0EbAIA87Ntvv9WUKVMkSR4eHqpUqZICAwM1YMAAPfXUU6n637p1S6+99pqOHDkiOzs7lStXTi4uLgoKCtKxY8e0YcMGzZ8/X0WKFEk19o8//tCQIUMkSZUqVZKrq6uCg4M1ffp0/fXXX/ryyy9T9P/tt980ePBgxcfHq0iRIqpSpYpOnDih1157TT4+PmleT3x8vAYOHKjff/9dklSmTBkVLlxYp06d0pdffqmff/5ZCxYsULly5VKNXbhwoQ4fPqyKFSsqMTFRLi4uhGwAQI7g1nEAAPKoI0eOaOrUqbKzs9O4ceO0Y8cOrV69Wtu3b1eLFi20ZcuWVGM++OADHTlyRPXq1ZO/v782bdqkdevWacuWLXrssccUGBiokSNHpnm+pUuX6oknntDvv/+u9evXa9u2bba+W7du1ZEjR2x9b9y4oQ8++EDx8fF67bXXtGvXLq1atUq7du1Su3btFBAQkOY5pk6dqt9//11VqlTRmjVrtHXrVq1Zs0Y7d+7UM888owsXLujdd9+VxWJJNfbw4cP68MMP5e/vry1btmj27Nl3820FAOCeEbQBAMijvvzyS1mtVvXp00cvvviiTCaTJKlIkSL6/PPP5enpmaL/0aNHtW3bNrm7u+uLL75Q+fLlbW2lS5fWzJkzVaBAAW3ZskUnTpxIdT53d3d9/vnnKl68uO1Ynz59bPMcPnzYdnzp0qWKjIxUw4YNNWTIEDk43LmJrkCBApo8ebIqVKiQav6rV69q2bJlcnR01KxZs+Tt7W1rK1KkiD755BN5enoqMDBQW7duTTW+ZMmS6tOnj+3rokWLZvj9AwDgfiFoAwCQB8XFxWnPnj2SpO7du6dqd3V1VceOHVMcS17hfvzxx9MMocWKFVPjxo0lSTt27EjV3rBhQ7m4uKQ6XqlSJUlSZGSk7diuXbskSf/73/9S9Xdyckrz+I4dO5SQkCBvb29VqVIlVbuzs7Nat26dbn1169aVnR0/2gAAch7PaAMAkAddvHhRsbGxcnR0VOXKldPs8+8VYUkKCQmRJB08eFAvvvhimmNCQ0MlSWfOnEnVVrJkyTTHJIfvf9/OffbsWUlKMzBL0iOPPJLqWHJ9586dS7e+69evp1tfiRIl0hwDAMCDRtAGACAPunXrlqQ7K9fJt4z/V4ECBVJ8nbzifO3aNV27di3D+W/fvp3qmKOjY4ZjrFZrqnOZzeY0+/535/B/j7l165b+/PPPDM/179XzZP/dLR0AgJxC0AYAIA9KDrAxMTGyWCxp3jIdFRWV4mtXV1dJ0vDhw/XKK6/c1/oKFSqkGzdupKohWWxsbKpjyfX16tVLo0ePvq/1AQBwP/EgEwAAeVDFihXl6OiohISENG+jlqSTJ0+m+Dp5A7LkW7TTcuzYMR0/fjzNFePsSH5uO61N1STp9OnTqY5VrFgx0/pCQkJ09OhRhYeH31N9AADcTwRtAADyIBcXFzVp0kSStGrVqlTtCQkJWrduXYpjLVu2lCRt2rRJYWFhqcbcvn1bL7/8sjp27KhffvnlnupLfof3qlWrUr2Ky2Kx6Keffko1pnnz5rKzs9P+/fvTDOKJiYkaMGCAunbtqgULFtxTfQAA3E8EbQAA8qg333xTJpNJ3333nVauXGk7HhkZqeHDh9s2JEvWqFEjNWjQQBEREXrjjTd07tw5W9vVq1c1YMAAhYeHq0SJEmrfvv091da9e3eVLFlSgYGBGj16tO1W8bi4OI0bN07Hjx9PNaZcuXJq3769kpKS1L9/fwUGBtraIiIiNHToUJ09e1ZmszndzdIAAMgNeEYbAIA8qn79+ho0aJA+++wzjRw5UjNnzlSJEiV0+vRpxcTEqGbNmgoMDJS9vb1tzKeffqpXX31VR44cUZs2bVS1alXZ2dnp9OnTSkhIkJubm7755ps0X+OVHW5ubpo+fbreeOMNrVy5Ups2bVKFChV07tw5RURE6Mknn9S2bdtSjRszZowuXbqkAwcOqHPnzqpYsaLMZrPOnDmjmJgYOTo6aubMmenugA4AQG7AijYAAHnYG2+8oS+//FINGzZUVFSUQkJC5O3trXnz5qlNmzaSlCI0lyxZUitWrNDQoUNVs2ZNXbx4UadPn5aHh4d8fX31008/qUaNGobU5uPjo9WrV6tz584ym806efKkypQpo08++UQ9evRIc4ybm5sWLFggPz8/+fj46MaNGzp58qQKFSqk9u3ba+XKlWrWrJkh9QEAcL+YrP9+FwcAAHhoTJs2TfPmzVO3bt00ceLEnC4HAIB8gxVtAADyqJdfflndunXTkSNH0mzftWuXJBm2Qg0AALKGoA0AQB5VuXJlHTlyRNOmTdONGzdsx6OjozVx4kSdOHFC7u7uatu2bQ5WCQBA/sOt4wAA5FFXr16Vr6+vLl++LEdHR1WsWFH29vY6f/68oqOjZTabNWPGDLVo0SKnSwUAIF8haAMAkIdFRERo6dKl8vf318WLFxUXF6eSJUvqiSeeUJ8+fVSxYsWcLhEAgHyHoA0AAAAAgIF4RhsAAAAAAAMRtAEAAAAAMBBBGwAAAAAAAxG0AQAAAAAwEEEbAAAAAAADEbQBAAAAADDQ/wEakPLWoHywZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gender-based countplot indicating whether or not patients had strokes\n",
    "plt.figure(figsize=(16,9))\n",
    "a=sns.countplot(x ='gender', hue = \"stroke\", data = data)\n",
    "for p in a.patches:\n",
    "    a.annotate('{:.1f}'.format(p.get_height()),(p.get_x()+0.1, p.get_height()+0.01))\n",
    "plt.legend(['No','Yes'],title=\"Stroke\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "869a49ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gender-based countplot indicating whether or not patients have hypertension\n",
    "plt.figure(figsize=(12,9))\n",
    "a=sns.countplot(x ='gender', hue = \"hypertension\", data = data)\n",
    "for p in a.patches:\n",
    "    a.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+0.01))\n",
    "plt.legend(['No','Yes'],title=\"hypertension\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28f237a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gender-based countplot indicating their smoking status\n",
    "plt.figure(figsize=(12,9))\n",
    "a=sns.countplot(x ='gender', hue = \"smoking_status\", data = data)\n",
    "for p in a.patches:\n",
    "    a.annotate('{:.1f}'.format(p.get_height()), (p.get_x(), p.get_height()+0.01))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c80c1695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gender-based countplot indicating whether or not patients have heart disease\n",
    "plt.figure(figsize=(12,9))\n",
    "a=sns.countplot(x ='gender', hue = \"heart_disease\", data = data)\n",
    "for p in a.patches:\n",
    "    a.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+0.01))\n",
    "plt.legend(['No','Yes'],title=\"heart_disease\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2c3e423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gender-based countplot reflecting their type of residence\n",
    "plt.figure(figsize=(12,9))\n",
    "a=sns.countplot(x ='gender', hue = \"Residence_type\", data = data)\n",
    "for p in a.patches:\n",
    "    a.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+0.01))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42d613c",
   "metadata": {},
   "source": [
    "DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a47e176d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 11)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "458b63f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "INFO:\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             1000 non-null   object \n",
      " 1   age                1000 non-null   float64\n",
      " 2   hypertension       1000 non-null   int64  \n",
      " 3   heart_disease      1000 non-null   int64  \n",
      " 4   ever_married       1000 non-null   object \n",
      " 5   work_type          1000 non-null   object \n",
      " 6   Residence_type     1000 non-null   object \n",
      " 7   avg_glucose_level  1000 non-null   float64\n",
      " 8   bmi                1000 non-null   float64\n",
      " 9   smoking_status     1000 non-null   object \n",
      " 10  stroke             1000 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 86.1+ KB\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nINFO:\\n\")\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61e78643",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DESCRIBE:\n",
      "\n"
     ]
    },
    {
     "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>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.618120</td>\n",
       "      <td>0.124000</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>110.428490</td>\n",
       "      <td>29.151400</td>\n",
       "      <td>0.20800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.752331</td>\n",
       "      <td>0.329746</td>\n",
       "      <td>0.261902</td>\n",
       "      <td>48.744997</td>\n",
       "      <td>6.764528</td>\n",
       "      <td>0.40608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.250000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.635000</td>\n",
       "      <td>24.375000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.295000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.115000</td>\n",
       "      <td>33.200000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age  hypertension  heart_disease  avg_glucose_level  \\\n",
       "count  1000.000000   1000.000000    1000.000000        1000.000000   \n",
       "mean     47.618120      0.124000       0.074000         110.428490   \n",
       "std      22.752331      0.329746       0.261902          48.744997   \n",
       "min       0.240000      0.000000       0.000000          55.250000   \n",
       "25%      30.000000      0.000000       0.000000          77.635000   \n",
       "50%      51.000000      0.000000       0.000000          94.295000   \n",
       "75%      66.000000      0.000000       0.000000         119.115000   \n",
       "max      82.000000      1.000000       1.000000         271.740000   \n",
       "\n",
       "               bmi      stroke  \n",
       "count  1000.000000  1000.00000  \n",
       "mean     29.151400     0.20800  \n",
       "std       6.764528     0.40608  \n",
       "min      14.400000     0.00000  \n",
       "25%      24.375000     0.00000  \n",
       "50%      28.400000     0.00000  \n",
       "75%      33.200000     0.00000  \n",
       "max      48.900000     1.00000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\nDESCRIBE:\\n\")\n",
    "data1 = data\n",
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d28ae12e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     gender    age  hypertension  heart_disease  ever_married  work_type  \\\n",
      "0     False  False         False          False         False      False   \n",
      "1     False  False         False          False         False      False   \n",
      "2     False  False         False          False         False      False   \n",
      "3     False  False         False          False         False      False   \n",
      "4     False  False         False          False         False      False   \n",
      "..      ...    ...           ...            ...           ...        ...   \n",
      "995   False  False         False          False         False      False   \n",
      "996   False  False         False          False         False      False   \n",
      "997   False  False         False          False         False      False   \n",
      "998   False  False         False          False         False      False   \n",
      "999   False  False         False          False         False      False   \n",
      "\n",
      "     Residence_type  avg_glucose_level    bmi  smoking_status  stroke  \n",
      "0             False              False  False           False   False  \n",
      "1             False              False  False           False   False  \n",
      "2             False              False  False           False   False  \n",
      "3             False              False  False           False   False  \n",
      "4             False              False  False           False   False  \n",
      "..              ...                ...    ...             ...     ...  \n",
      "995           False              False  False           False   False  \n",
      "996           False              False  False           False   False  \n",
      "997           False              False  False           False   False  \n",
      "998           False              False  False           False   False  \n",
      "999           False              False  False           False   False  \n",
      "\n",
      "[1000 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "#checking for null value\n",
    "print(data.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a6b776ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               0\n",
       "age                  0\n",
       "hypertension         0\n",
       "heart_disease        0\n",
       "ever_married         0\n",
       "work_type            0\n",
       "Residence_type       0\n",
       "avg_glucose_level    0\n",
       "bmi                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17b8fea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "0       1  67.0             0              1             1          1   \n",
       "1       1  80.0             0              1             1          1   \n",
       "2       0  49.0             0              0             1          1   \n",
       "3       0  79.0             1              0             1          2   \n",
       "4       1  81.0             0              0             1          1   \n",
       "\n",
       "   Residence_type  avg_glucose_level   bmi  smoking_status  stroke  \n",
       "0               1             228.69  36.6               1       1  \n",
       "1               0             105.92  32.5               2       1  \n",
       "2               1             171.23  34.4               3       1  \n",
       "3               0             174.12  24.0               2       1  \n",
       "4               1             186.21  29.0               1       1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LabelEncoder can be used to normalize labels. It can also be used to transform non-numerical labels to numerical labels.\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "enc = LabelEncoder()\n",
    "\n",
    "gender=enc.fit_transform(data['gender'])\n",
    "data['gender']=gender\n",
    "\n",
    "ever_married=enc.fit_transform(data['ever_married'])\n",
    "data['ever_married']=ever_married\n",
    "\n",
    "work_type=enc.fit_transform(data['work_type'])\n",
    "data['work_type']=work_type\n",
    "\n",
    "Residence_type=enc.fit_transform(data['Residence_type'])\n",
    "data['Residence_type']=Residence_type\n",
    "\n",
    "smoking_status=enc.fit_transform(data['smoking_status'])\n",
    "data['smoking_status']=smoking_status\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afeb82cb",
   "metadata": {},
   "source": [
    "SPLITTING DATA FOR TEST AND TRAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed513c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "0       1  67.0             0              1             1          1   \n",
       "1       1  80.0             0              1             1          1   \n",
       "2       0  49.0             0              0             1          1   \n",
       "3       0  79.0             1              0             1          2   \n",
       "4       1  81.0             0              0             1          1   \n",
       "\n",
       "   Residence_type  avg_glucose_level   bmi  smoking_status  \n",
       "0               1             228.69  36.6               1  \n",
       "1               0             105.92  32.5               2  \n",
       "2               1             171.23  34.4               3  \n",
       "3               0             174.12  24.0               2  \n",
       "4               1             186.21  29.0               1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.iloc[:,0:10]\n",
    "y = data.iloc[:,10:11]\n",
    "\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8be2cbcb",
   "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>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stroke\n",
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bffbe909",
   "metadata": {},
   "source": [
    "LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9099c28b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction score is:  0.89\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=0)\n",
    "\n",
    "#Applying ML model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression(max_iter=4000) #max_iter is for maximum number of iterations,by default max_iter value is 1000\n",
    "\n",
    "#training the data\n",
    "logreg.fit(x_train,y_train['stroke'])\n",
    "\n",
    "#predciting score\n",
    "score = logreg.score(x_test,y_test)\n",
    "print(\"Prediction score is: \",score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aaca5826",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy:  89.0 %\n"
     ]
    }
   ],
   "source": [
    "a = logreg.score(x_test,y_test)\n",
    "print(\"Test Accuracy: \",(a*100), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a76eee5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values:  [0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]\n",
      "Actual Values:       stroke\n",
      "993       0\n",
      "859       0\n",
      "298       0\n",
      "553       0\n",
      "672       0\n",
      "..      ...\n",
      "679       0\n",
      "722       0\n",
      "215       0\n",
      "653       0\n",
      "150       1\n",
      "\n",
      "[200 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "y_pred=logreg.predict(x_test)\n",
    "print(\"Predicted values: \",y_pred)\n",
    "print(\"Actual Values: \",y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "42de65c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  0.6666666666666666\n",
      "Recall:  0.42857142857142855\n"
     ]
    }
   ],
   "source": [
    "#recall and precision\n",
    "\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision = precision_score(y_test,y_pred,zero_division=1)\n",
    "recall = recall_score(y_test,y_pred)\n",
    "print(\"Precision: \",precision)\n",
    "print(\"Recall: \",recall)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe89a71c",
   "metadata": {},
   "source": [
    "CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a894eca7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[166   6]\n",
      " [ 16  12]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cmat=confusion_matrix(y_test,y_pred)\n",
    "print(cmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a0952059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "cmat = pd.DataFrame(data = cmat,columns = ['Predicted:0', 'Predicted:1'],index =['Actual:0',\n",
    "'Actual:1'])\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.heatmap(cmat, annot = True, fmt = 'd', cmap = \"Blues\", linecolor=\"Blue\", linewidths=1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ccca12ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Negatives:  166\n",
      "False Positives:  6\n",
      "False Negatives:  16\n",
      "True Positives:  12\n"
     ]
    }
   ],
   "source": [
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "print(\"True Negatives: \",tn)\n",
    "print(\"False Positives: \",fp)\n",
    "print(\"False Negatives: \",fn)\n",
    "print(\"True Positives: \",tp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d99067b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                        precision    recall  f1-score   support\n",
      "\n",
      "Person does not have chances of stroke       0.91      0.97      0.94       172\n",
      "          Person has chances of stroke       0.67      0.43      0.52        28\n",
      "\n",
      "                              accuracy                           0.89       200\n",
      "                             macro avg       0.79      0.70      0.73       200\n",
      "                          weighted avg       0.88      0.89      0.88       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#classification report\n",
    "from sklearn.metrics import classification_report\n",
    "target_names = ['Person does not have chances of stroke', 'Person has chances of stroke']\n",
    "print(classification_report(y_test,y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b44f003",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}