11. Performance analysis on a specific Dataset cardio heart desease.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f23a2cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "87dae3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>62.0</td>\n",
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       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>18857</td>\n",
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       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>17623</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
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       "      <th>4</th>\n",
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       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69995</th>\n",
       "      <td>19240</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>76.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69996</th>\n",
       "      <td>22601</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>126.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>69997</th>\n",
       "      <td>19066</td>\n",
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       "      <td>183</td>\n",
       "      <td>105.0</td>\n",
       "      <td>180</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>72.0</td>\n",
       "      <td>135</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0      18393       2     168    62.0    110     80            1     1      0   \n",
       "1      20228       1     156    85.0    140     90            3     1      0   \n",
       "2      18857       1     165    64.0    130     70            3     1      0   \n",
       "3      17623       2     169    82.0    150    100            1     1      0   \n",
       "4      17474       1     156    56.0    100     60            1     1      0   \n",
       "...      ...     ...     ...     ...    ...    ...          ...   ...    ...   \n",
       "69995  19240       2     168    76.0    120     80            1     1      1   \n",
       "69996  22601       1     158   126.0    140     90            2     2      0   \n",
       "69997  19066       2     183   105.0    180     90            3     1      0   \n",
       "69998  22431       1     163    72.0    135     80            1     2      0   \n",
       "69999  20540       1     170    72.0    120     80            2     1      0   \n",
       "\n",
       "       alco  active  cardio  \n",
       "0         0       1       0  \n",
       "1         0       1       1  \n",
       "2         0       0       1  \n",
       "3         0       1       1  \n",
       "4         0       0       0  \n",
       "...     ...     ...     ...  \n",
       "69995     0       1       0  \n",
       "69996     0       1       1  \n",
       "69997     1       0       1  \n",
       "69998     0       0       1  \n",
       "69999     0       1       0  \n",
       "\n",
       "[70000 rows x 12 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('6.cardio_train 1.csv') #for separete column:,sep=';'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ba7aa502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35021\n",
       "1    34979\n",
       "Name: cardio, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show type of unique value,,0 means cardio vascular dieses nei\n",
    "df['cardio'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d01e21ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cardio', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.countplot(df['cardio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b13ef0a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any() #false means there are no null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d6eec232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            0\n",
       "gender         0\n",
       "height         0\n",
       "weight         0\n",
       "ap_hi          0\n",
       "ap_lo          0\n",
       "cholesterol    0\n",
       "gluc           0\n",
       "smoke          0\n",
       "alco           0\n",
       "active         0\n",
       "cardio         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0fe85ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.countplot(x='gender',hue='cardio',data=df,palette='colorblind',edgecolor=seaborn.color_palette('dark',n_colors=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cc52891c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SLwB0xbA7tHBpugy7w+pSIqoWoDeJiDtJTz/9tNatW6eMjAydf/757umuzykrKioUGxvrnm632zvdXXKJjo5WdHR0aAsGAABnPEvvJDmdTj311FNas2aNNm/erAkTJnjMnzBhgsaMGaNNmza5px07dkzp6em64YYbwl0uAADoQyy9k/Tkk09q1apV+vTTTzV06FD3d5CGDx+uQYMGyWaz6ZlnntFLL72kiRMnauLEiXrppZc0ePBgPfDAA1aWDgAAznCWhqQ333xTkjRlyhSP6e+++64eeeQRSdKcOXN09OhRzZw5U7W1tbr++uu1ceNG/kYSAAAIKUtDki9/jtxmsykuLk5xcXGhLwgAAOCUiPnfbQAAAJGEkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAZxij0qG4+DQZlQ6rS/FJJNZbVdPk8dzqGq1uv68iJAHAGcawO7RwaboMe+94Q43Eeqs7hiSLa7S6/b6KkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAfYRhP2J1CUCvQkgC0Mn0OeuUl29YXYaMSofi4tNkVEbuyOdGpUMJK3MkSVU1TRFVb8LKHBmVDhn2I4qytere6R/o2bgUy+szKh2avShVsxdtsLwWX1TVNEXsMUZoEZIAdJL7fYXyC6usLkOG3aGFS9Nl2CP3DcmwO5S4KleSVF3TFFH1Jq7KlWF3qK6hWa3OKLUcd2rZ29ssr8+wOxSflK34pCzLa/FFdU1TxB5jhBYhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhqQd6wwjlQFdc53BevqG4+DTtKa62uqSI58t1n7AyR1U1TWGsylP7tp9/dbOef2WzZbV0p6qmyX3e7SmullHp0OxFGzR7UWpY+lZ/+/Ha+qOSZNnxrapp0uxFqZq9aEPEvPe034dn2vsiIakHesMI5UBXXOdwfmGVFi5NV3FprdUlRTxfrvvEVbmqtjAktW87Ja1IJWV1ltXSneqaJvd5V1xaK8PuUHxSluKTssPSt/rbj9c1NEuSZce3uqZJ8UnZik/Kipj3nvb78Ex7XyQkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAk9UJGpUNx8WkyKs+MUZZhDaPSoYSVOR7T/vLXTPfjj9fvCus51tV5nbAyxz3dqHRo9qJUzV60wZJroOrU6O8b0vcq9ppXde9v/+ae9/bq3LDX05FR6dCa9bu6XKaguNqyPqTjeRcXn6aiksOm86xgVDo0Y946Pf/KF+5p/+9bWe7HjiMtksJ/fbjU1h8Ne5uBqKppUlx8mgqKq93PeyNCUi9k2B1auDRdhp2QhMAZdocSV3m+qdc7WtyP16YWhPUc6+q8TlyV655u2B2KT8pWfFKWJddA9anOPvf7ClXYG1VSVueel5ZZEvZ6OjLsDq1JLehymX2ltZb1IR3Pu6KSWpUZDZIk++HGTudkuBl2hxKTc1VSVu+e9s0Ow/3Y0XhMUvivD5e6huawtxmI6pomLVyarn2lte7nvREhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwISlISkjI0N33nmnxo4dK5vNpk8++cRj/iOPPCKbzebx85Of/MSaYgEAQJ9iaUhqbGzU1Vdfrddff93rMj//+c9lGIb7Z/369WGsEAAA9FX9rWx82rRpmjZtWpfLREdHa8yYMWGqCAAAoE3Efydpy5YtGj16tC6++GJNnz5ddru9y+VbWlrU0NDg8QMAAOCviA5J06ZNU3JysjZv3qzXXntN33zzjW677Ta1tLR4XWfJkiUaPny4+2fcuHFhrBjoHYxKhxJW5rifHyirNV1u+px1yss3ZFQ6FBefJqMy9KOeJ6zMUV6+obj4NFW1Gzm8oLhaU+57VwXF1e5pVTVNYavLqHRo9qJUJa/dIUnaklnS5fJx8ZslSYuXZ2j2og0hq7H9sel4XDtrPVXTl5KkPcXVYdt/XdlZUCVJ2r7D8JheFYaR411tVNU0yah0aGlSVtfLH24MeU1d2fzVfvfjjud/OK9TV3uzF6Uqvt0+c+3PklN9Skra3rDUEioRHZLuv/9+/fM//7OuvPJK3XnnnUpJSVFhYaE+//xzr+s899xzqq+vd/8cPHgwjBUDvYNhdyhxVa77eVmFeaea+32F8gurZNgdWrg0XYY99J1v4qpc5RdWaeHSdFW3e5PcV1qr9OwD2ld6OtBV1zSFrS7D7lB8UrZS0ora2q492uXyRSV1kqQ1KQWKT8oKWY3tj03H49pZW5ff3HxCklRcWhu2/dcVe3Vb8Nh/sM5jenUYQpKrjeqaJhl2h1au3dnl8rX1XR/3UEvLKnE/7nj+h/M6dbUXn5St5Hb7zLU/y0/1KZk5ZWGpJVQs/U6Sv2JjYzV+/Hjt3es9mUZHRys6OjqMVQEAgDNRRN9J6ujw4cM6ePCgYmNjrS4FAACc4Sy9k3TkyBEVFRW5n+/fv195eXkaOXKkRo4cqbi4ON17772KjY1VSUmJ/vjHP2rUqFG6++67LawaAAD0BZaGpJycHN16663u57NmzZIkPfzww3rzzTe1c+dOrVixQnV1dYqNjdWtt96qDz/8UEOHDrWqZAAA0EdYGpKmTJkip9Ppdf6GDRvCWA0AAMBpveo7SQAAAOFCSAIAADBBSAIAADBBSAIAADBBSAIAADBBSAIAADBBSAIAADBBSPJTuEdZ7krCypyIqKMj1z5yjeQeiTX2dR1HV/8ktcDrsnGvbXaPGp+wMiesx7X9YKJFJYclSX/5a2an+Y/M+kR5+Z4jyEearNzQDLbdfhT7Den+jbjuONLifhzKvi2S+k0XV00HTo1W7+vAtRnZpe7HBcXVlr6uNet3uR8blQ4tTcqS1Pn6DpeC4mq9+F/pkqS/fZbvMa+2/mjEnQO+ICT5KdyjLHclcVVuRNTRkWsfuUZyj8Qa+7qOo6sb9iNely06UKc1KbsltZ1z4TyudQ3N7sdlRoMkqd7R0mn+zgK78gurQl5PT4Sqvvaj2Od+X+HXuo7GY+7HoezbIqnfdHHVVHZqtPr251pXjp9odT/eV1pr6eta0+6XG8Pu0Mq1OyV1vr7DZV9prQqK2n6ZOVzrGTrrGpoj7hzwBSEJAADABCEJAADAREAh6bbbblNdXV2n6Q0NDbrtttt6WhMAAIDlAgpJW7Zs0bFjxzpNb25u1pdfftnjogAAAKzW35+Fd+zY4X68a9cuVVSc/pLgyZMnlZqaqvPOOy941QEAAFjEr5A0adIk2Ww22Ww204/VBg0apOXLlwetOAAAAKv4FZL2798vp9OpCy+8UF9//bXOPfdc97yzzjpLo0ePVr9+/YJeJAAAQLj5FZLGjx8vSWptbe1mSQAAgN7Nr5DUXmFhobZs2SK73d4pNL3wwgs9LgwAAMBKAYWkpKQk/cd//IdGjRqlMWPGyGazuefZbDZCEgAA6PUCCkkvvviiFi9erLlz5wa7HgAAgIgQ0N9Jqq2t1X333RfsWgAAACJGQCHpvvvu08aNG4NdCwAAQMQI6OO2iy66SAsWLFB2drauuuoqDRgwwGP+7373u6AUFymMSof+krBVkk1Tb/k/VpcDL4xKhxKSczTjwWsVGzPU6nI6aTuPMiU59YcZP7WsRqPSoTXrdwW8fvLaHd0v5Cej0qGElTnu5676Nn+13z1tS2ZJp/VeWn76L/zX1h/tND8Y8vINPfafn6rB0eLT8ja1yhmGYTFd/dK3+W1/1PeNFV9rd1G1X9tobGobOSFhZY7uuePyoNe4KaNIv5n9qX54WYwkKT4pSzV1vh+nTV8WKWnVdi2L+7kmXREb1NqqapokSVu/KZUkvfH+13r7g1y/tuE6PxNW5ihu1q2W9jsFxaeP/durc3XFxefqkw0FQesPzfrX9m1K0vJ3t3W7naqaJsXFp0VsP91RQCEpMTFRQ4YMUXp6utLT0z3m2Wy2My8k2R2KT8qWJP3oquBeqAgew+7QwqXp+tfbL4nIi6/tPMqSJD149w+tC0l2h9akFgS8fkpaURCraWPYHUpcdfoNylVfWlaJe9pJk788crT5hPtxXUNz0OuSpPzCKuXurOh+wVPCEZAkz35JkjJzyvzeRtPR45KkxFW5uvkn/xCs0ty25ZWrvMKh8gqHJCl57U6/1t+1t0rf5BnKL6wKekiqPhWSduy2S5IMe6Pf23Cdn4mrcjXj/7b2TX9faa37cVpmifILq4LaH5r1r+3blCR7dVO326muaYrofrqjgELS/v37u18IAACgFwvPrzwAAAC9TEB3kh599NEu57/zzjsBFQMAABApAgpJtbWen0MeP35c33//verq6kwHvgUAAOhtAgpJa9eu7TSttbVVM2fO1IUXXtjjogAAAKwWtO8kRUVF6dlnn9XSpUuDtUkAAADLBPWL28XFxTpx4kT3CwIAAES4gD5umzVrlsdzp9MpwzD0+eef6+GHHw5KYQAAAFYKKCR9++23Hs+joqJ07rnn6rXXXuv2f74BAAD0BgGFpLS0tGDXAQAAEFECCkkuVVVV2rNnj2w2my6++GKde+65waoLAADAUgF9cbuxsVGPPvqoYmNjdfPNN+umm27S2LFj9dhjj6mpqfuxWwAAACJdQCFp1qxZSk9P12effaa6ujrV1dXp008/VXp6umbPnh3sGgEAfZhrMFr0XnX1oRmAOtQCCkkff/yx3n77bU2bNk3Dhg3TsGHDdMcddygpKUkfffRRsGuMKM+/8oUkKT4pS1UWXLhGpUMJK3PczxNW5igv39CMees0+a63lJdvuJeLi0+TUekIe42RIlL2gauOcJ8v7V9/Xr6hyXcl6Yl5n6mguDqsdfjCn31jU6skZ6fpoTrOtfVHg7Kd1Z9+774+gyEY59NHn+92Pw7W62zPcaTFj6VbO03Zf6BWNrVq1sJUTZ+zrsfH2HVN5OUbSl6zI8CtdK5TCs7xkDxrnL0oVbMXbVBevuHR73dUUFytN97f5jEtFMezo87Hw3zfSNKyt7Ik9b7AG1BIampqUkxMTKfpo0ePPuM/bispq5ckJa/dacnBNuwOJa7KdT9PXJWr/MIqJSbnKju3TPmFVe7lFi5Nl2HvwyEpQvaBq45wny/tX39+YZWyc8uVkLxd+0pru185zPzZN05FSbJ1mm4/3BjEik6rawjOb8C19c3u6zMYgn0+Bet1tudoPObH0p3fjqprm+VUlFqd0lsf5Pb4WnZdE/mFVUrZUhTgVszfNoN1PNrXGJ+UrfikrLY+vl2/39G+0loZds/2Q3E8O+p8zXmPFCdP5SdHoz/B2XoBhaTJkyfrT3/6k5qbTx+Eo0ePauHChZo8eXLQigMAALBKQP+7bdmyZZo2bZrOP/98XX311bLZbMrLy1N0dLQ2btwY7BoBAADCLqCQdNVVV2nv3r1auXKlCgoK5HQ69atf/UoPPvigBg0aFOwaAQAAwi6gkLRkyRLFxMRo+vTpHtPfeecdVVVVae7cuUEpDgAAwCoBfScpISFBl156aafpV1xxhf7617/2uCgAAACrBRSSKioqFBsb22n6ueeeK8MI3n9xBQAAsEpAIWncuHHaunVrp+lbt27V2LFje1wUAACA1QL6TtLjjz+uZ555RsePH9dtt90mSfriiy80Z84c/uI2AAA4IwQUkubMmaOamhrNnDlTx461/bGwgQMHau7cuXruueeCWiAAAIAVAgpJNptNL7/8shYsWKDdu3dr0KBBmjhxoqKjo4NdHwAAgCUCCkkuQ4YM0XXXXResWgAAACJGQF/chifDfsTqEixTWl6n0vI6q8sAACDoCEkdGJUO98jLRqVDRqXD6+jLb32wXZJ09/TVejYuxbLR5kvKrBmwtLS8TpdMeV2XTHld33xX7t5vrtGwXaNsFxRXu0ejt0r7kbWtrqW9vHxDk+9K0hPzPgtpTe3Pkc1f7Q/KNoM16rlvvI8u7pLz3aGgH9+8fEPL38kOyraktpHQ2/ctoT8Xu99vLonJbf3Zg7/7WHn5gf8pF6PSoRnz1mnyXW+paH+1j/X4VueCVzcHZX+tWb+rx9sIBW/vN7X1R7tcb/F/fdlpmuu8LSjueAz8rykuPs3jencd4y+2dteX+H7+RSpCUgeG3eEeedmwO2TYHV5HX96SdUCSdPx4q5a9vc2y0ebLK6xpt7qmSc3NJ9TcfEKF+w6795trNGzXKNv7Smvdo9Fbpf3I2lbX0l5+YZWyc8uVkLw9pDW1P0fSskqCss1gj0Lfte67qpKy+qAf3/zCKtkPd/0G5Y+cHYZH3xL6c9H3Lv7goQZJUkHRYeUXVgXcomF3KDE5V9m5ZSqv7HiX3Vs9vtW5Pq0oKPtrTWpBj7cRCt7eb+oamk2WPq255USnaa7zdl9pz36Jdp2n7a931zGureu6rjMhYvT+VwAAABAChCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAATloakjIwM3XnnnRo7dqxsNps++eQTj/lOp1NxcXEaO3asBg0apClTpig/P9+aYgEAQJ9iaUhqbGzU1Vdfrddff910/iuvvKL4+Hi9/vrr+uabbzRmzBjdfvvtcjgiY0gJAABw5upvZePTpk3TtGnTTOc5nU4tW7ZM8+fP1z333CNJev/99xUTE6NVq1ZpxowZ4SwVAAD0MRH7naT9+/eroqJCU6dOdU+Ljo7WLbfcoszMTK/rtbS0qKGhweOntystr1NpeZ3VZQCA5Qx7x0FzgdCx9E5SVyoqKiRJMTExHtNjYmJ04MABr+stWbJECxcu9Ls9o9KhhOQcXXLhOe5pS5OyVF7p+0d7BcXVmrVog5bF/VyTroj1uwazeq7/v87Tvz66WlFR0n13XK4dBfZOy279ptT9+O0PcvX6e1/roXuukiRVnRq52bW9GQ9eq9iYoT2qzb29lTnu58lrd5g+bq+qpklx8WlBq8ElL9/QM3GpmvHgNe52Zi9KVUlZnSRp8fIMDRo4wGOd51/drG/zK/SzGy/UK3+8Paj1eDNvyUb34/tn/l0/vCxGX+eVu6fdeO87eufVf9Wv7vph0NtubDoW9G0mr9mhjG0HNGTwWfrDjBsC3odGpUNvf9B55PNAvLHia0nSnuJqrdu0J+Bzzah06C8JW5WxzXtfY65VvvzuWVBcrfSsEr/rktpqi1uaps82FQbcfndq648GtF5evqHpc9e5n+8qrO5xLR394vEPVPzV73XBeSP8Xreq3Uj2nXnbd77t0zdWfK3X3/tab770zz3u/13bc1nwalrA23EcaelxLdLpc+LB332sIYPP8mEN8/1mnHpP7fpYRI6IvZPkYrPZPJ47nc5O09p77rnnVF9f7/45ePCgT+0YdocWLk1XcWmte9rKtTuVllnic637SmuVnn1A+YVVPq/TXT3bdxo6caJVx461KvmT77XTJCTt2H16WlpWibJzy/T9qRqqXSHp1PYMe3C+z2XYHUpcdfqNLSWtyPRxe9U1TUGtwSW/sErp2Qfcx666pknxSdlak1IgSVqTUqDktTs91klJK1KF/YhWrtkR9Hq8KTNO/wZcVFKrNSkFKjNOt3306AllfF1qtmqPNR09HvRtpmwpUmLydsUnZfVoHxp2h9ICDAwdZeaUSZKKS2t7dK4Zdofik7KVs8Pwc03futR9pbUe148/DLtDicm5Xu6oBKdLr2toDmi9/MIq5e6sCEoN3pw44XT3a/7qej1v+863fZqZU6bs3LKg9P+u7QWDozE4vyC5zomCosM+Xhfm+81+uFFSd8cickTsnaQxY8ZIarujFBt7OpXb7fZOd5fai46OVnR0dMjrAwAAZ7aIvZM0YcIEjRkzRps2bXJPO3bsmNLT03XDDTdYWBkAAOgLLL2TdOTIERUVnf5oZv/+/crLy9PIkSN1wQUX6JlnntFLL72kiRMnauLEiXrppZc0ePBgPfDAAxZWDQAA+gJLQ1JOTo5uvfVW9/NZs2ZJkh5++GG99957mjNnjo4ePaqZM2eqtrZW119/vTZu3KihQ0P/JVsAANC3WRqSpkyZIqfT6XW+zWZTXFyc4uLiwlcUAACAIvg7SQAAAFYiJAEAAJggJAEAAJggJAEAAJggJAEAAJggJAEAAJggJAEAAJiI2LHbgFDoLYMq4sxWVx/YALI4M5WW11ldArzo83eSjEqH4uLTVFBcLUlKSdsb8LbeeP8bSVJt/dGg1NYTjU1tIz9/vH6XjMrgjnJvVDqUsDInqNsMlFHpUPLaHZJ8O3bPxKV2mhaflBW0feQ6nzZlFGnyXUl6Yt5nqvIjmH2TV668fENx8Wnuf3ta2+LlGfpi6/5O021qleT9j7mGklHp0L2/Xa0hlyzW0y+sPzW1NWjb37GrbST6hJU5fu+/TRlF+vlDK4NWi5n4pCy/ljcqHZq9KFWzF21w91XBd3r/O460hKiN4PD3uLquy5Ky2hBW5T+j0qFn41J06S3/pcumLNfWnIM+rOX/deJ6/YHsM3/6L1/UnXp/TF6zQ7MXbQj6+1OwEZLsDi1cmq59pW0XT2ZOWQ+2dUSSVNdg/W+JTUePS5LWphbIsAc5JNkdSlyVG9RtBsqwO5SS1jb+X6DHLnntzqDtI9f5tC2vXNm55UpI3u7X3aucHYbyC6u0cGm6+9+e1rYmpUC1dZ3PSaeiJNl6tO1AGXaH1qQUqLHpeLvjFrzuqOTUb+aJq3L93n/b8spVdTi0dxy7GGjAlGF3KD4pW/FJWe6+KvhO739H47EQtREc/h5X13VZXhFZb8iG3aFlb2/T0ZZWNTWf1O6iKh/W8v86cb3+QPZZsO++158K4Clbitp+QQ3y+1Ow9fmQBAAAYIaQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQhDNOsMcaAgD0TX0yJJmNiLz5q86jpAdq89b9PRrZ2Kh0KGFljiTp09SCgLbhGmlZkqpqmtzb66lNGUX654dX+bXOB2t3SJLeXt02KG58UlZIR35+Ji7V73UCGS2+K+u/2Ot+/NXXB7wsZT6at2uk8jXrd0lqO37+jODtGjH+Dy9u9L1gH2vzxt/959vI4v6Pdu5SXHJ6ENjFyzM0+a4k5eUbPq1bVHI44HY76/41+Hs9hGPU9A/Xfa8Z89b5fc4ln7rWzQV+PM0E0o/87bPvg1qDmY/X71JevuHXNevySYD9fVcam465rzffrjspL9/Q9LnrJEnL3sqSJL20PCMo9dR3GAA+1O8HPdU3Q5LJiMhpWSVB235aZkmPRjY27A4lrmoLFDk7fevYO3KNtCy13Vlxba+ntuWVq6LqiF/r7Cpqe9NJyyyRJCWv3RlxIz8HMlp8V7Jyy9yP93p90zW//Fwjla851WFW1zT5NYK3a8R41/4OjH9dg7/7z7e7fYF3TzV1pzviNSkFys4tV36hLyOsS2VGQ8Dtdtb9a/D3erAfbuxJQT6pqWtWYrLvx9R1zqWkFXWxVHDfbgLpRw7XNne/UA+tTS1QfmGVX9esi2H3r2/1RdPR4+7rzde77PmFVcrdWSFJytnR9h50tPlkUOo50nTc43kkvh+01ydDEgAAQHcISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACb6dEhampSlxcu/DMm2Fy/P0OS73lJevhGS7Xdn+3en2z1QVhvQNoxKh+Li02RUOmRUOjRj3jq98f7XXpZu7XEbZs9d8vINTbnvXeXlG8rLN3TNHX/V1be/of9cvNHPFr3XmbAyp1O7vnLVXWUyyvaWzBK/6vn/PsoLqIbABHbczFTVNJkeu47y8g09/8oXQWvX19fw8fpd3da2KaNIGdkHgtamr/w59zamFwWpVe+vwaZWSU5t/eagZi9K1exFGwK+NqyWl2/okVlrTea0ennsD/P1Sk71uWb9QfB5q71t+oYtxXpjRVu/XVt/NAz1dO1QxZFO0xJW5mhTRpG7j48kfTokrVy7U2tSdodk22tSCpSdW6b8wqqQbL87rc7Tj8sqAnzjtzu0cGm6DLtDht2hxORcGfZGL0sHdiq1b8PsuUt+YZXSsw8ov7BK+YVVyt1ZoR0Fdm3eWuJni97rTFyV26ldX7nqrjbpFE922f92rudI04mAaghM8LqA6pom02PXUX5hlUrK6oPWrq+vYW1qQbe1bcsr1/ETzi6X8adNX/lz7jUcOR6kVr2/BqeiJNm0u7hK8UnZik/KCvjasFp+YZV2Fpj1w1FeHvvDfL3yU32uWX8QfN5qb5vuaDymzJwySVJdQ3MY6vFf4qpcbcsrd/fxkaRPhyQAAABvCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEnwyrB3Hq0ZAIC+ok+GpKqwjMzcprb+qIxKh+Li02RUeh9Fe1NGkWJ/9Kouu3W5Hv3DJ0Gt4Zu8cvfjrNyD3dYiSaXldfq3365WlK1VryVkKjv3YFBryurB9mrrj3azRKskX0Zy7+z+mX9XXr7h1zqbMor0j79+X5L0xLx1AbXbptX9yNbuNSx7K0tS23nb1bFznWd7iqt7UIN5PebzPPfxuo273XV6symjSL+d234feWujq7Z9mW/O7Dx27bdNGUVKWJnjQ3uBtd3duouXZ5geW6PSoaVJWUFpw1+Jydvdj70dV6PSoXh3fcFqu7tzr81t97+vh36/psv+rKSsNgjt+1aP63HiqfMoLj7Na3+Sl2/okVlrfd6mb3V2PT8lba/HvjJ7b9qUUaT/mPdZN9vtqc51O460SPKlfw+vPhmSqsMYkuoammXYHVq4NF2G3fuFvC2vXBVVjSooOqzvdtmDWkPOjtMXaX5hVbe1SG37qPlYq1qdUVr16ff6vrAqqDXl92B7dQ3N3SwRJckW0LaLSmr9rm1bXrnq6tsu8CNNJwJqt83py9HZ7jW4jl91TVOXx851nhWXBvKm0HU95vM897HrHOnq+tqWV66mo+33kbc2uuuaAuu6zM5j137blleuMsPbdRHl5bG/vK+7JqXA9NgadodWrt0ZlDb8dfLk6SDs7bgadoeS3fUFq+3uzr029Y4WrVyzo8v+rLyi677Ot/Z9q8f1+OSpDNBVf5JfWKWdBd76Gl/PN/+uk8ycMo99ZfbetC2vXI6m491st6c61+1oPCbJl/49vPpkSAIAAOgOIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMBERIekuLg42Ww2j58xY8ZYXRYAAOgD+ltdQHeuuOIK/e///q/7eb9+/SysBgAA9BURH5L69+/P3SMAABB2Ef1xmyTt3btXY8eO1YQJE/SrX/1K+/bt63L5lpYWNTQ0ePxYKdLGoQFC5dixk1aX0CN19VyrADxFdEi6/vrrtWLFCm3YsEFJSUmqqKjQDTfcoMOHD3tdZ8mSJRo+fLj7Z9y4cZ2W2VlQEcqyPSxNytJrCZle5xuVDs1elKqUtL1hqeevK9pGp779gRV66Pcfm46ebTY6dVKyt9HRfdF5xOfElTkaPPFF/eHFjZKkhJU5Xkfyzss3FPdamiTpP19M1eL/yuhBLd1b9laWJt/1ltfRu6W24zZj3me67NblevG/MuT76OeBj5L+ny9ukCQ9/+rmLkc9/8tfvZ9vwRwhvqOikraBdb/Lr+g0srjUNrr4y29sDVn7bbp+fR98slNPzPvMcyR0+xFJ0muJWd5W61Gb/iz3hxc3dtpvBcXVAdTkr+5Hmn/lzS81e9GGTvVl5R4McLv+8LadVklO/eHPGzV7UWqn2oxKh7Z+UxqkGgLz5PzPvfa1wdf9/r5/5t/10O/XaPaiDaoyGbS43GjwaTs959nG6k+/lyS98f43Xfa94RbRIWnatGm69957ddVVV+lnP/uZPv/8c0nS+++/73Wd5557TvX19e6fgwc7X8DFB4I1Snr3WlulVacOvhnD7lB8UrYyc8rCUo9rTO+aumatXLPTdPRss9GpT/ToJkHn0+xkq3S0+YTSMkskSYmrcr2O5J1fWKWiU8fMsDepuSW0dyxydhjKzi3zOnp3Wx0OJSZvV0HRYbW0nJTvl1Lgl5xhb5QkpaQVdTnqeb2jJSTt+2p/WW2nkcWlttHFj5wa6Tt0un59dQ0tSkje7lFbz+/2Bu/Yp2WWdNpv+0rD0V91P9L8jt1Vik/K6lRfV9dJ8M43b9uJkmRTWlaJ4pOyO9Vm2B3asdsepBoCU+9o8drXBl/3+7uopFYr1+xQfFKWqk1CUnVtk0/b6TnPNmpP3ck17Ee6OafCK+K/k9Te2Wefrauuukp793q/6xIdHa3o6OgwVgUAAM5EEX0nqaOWlhbt3r1bsbGxVpcCAADOcBEdkv7whz8oPT1d+/fv17Zt2/Rv//Zvamho0MMPP2x1aQAA4AwX0R+3lZWV6de//rWqq6t17rnn6ic/+Ymys7M1fvx4q0sDAABnuIgOSatXr7a6BAAA0EdF9MdtAAAAViEkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAAmCAkAQAQZmaDyyLy9JmQtGN3hS67dbkGT3xR/5tRHPTt29QqydnlMvfP/Ls2ZRQpLj5NRqVDRqVDi5dndLFGa4/a80XCyhwZladHpzYqHXpjxdc93q533l/ThvS9ik/KkiRVtetA8nYZXa7X9bzAapGkt1fnavaiVI/941LlVwcXaH3e3T/z78rLN9zPjUqHElbmeGnb1/aDs48/+ny3pNPnllHp0Ix567TsrWyftxFq5sevtcPj4B837+2d1vGaLCyu7qaWUNfpaqOtv1m8/Et3fXn5hv6+7vsebDO4Fi/P8Lhm9xRXB72NQLnOOaPSoWfjUvRsXKrFFUnzlmyS1FabUenQ7EWpysuv6LBUx+vC27zgSV67w7TftUKfCUm7i6pVUHRYR5tPqM5xLOjbdypKkq3LZYpKarUtr1wLl6bLsDtk2B1ak1LQxRreD48v7fkicVWuDHu7kGR3KDOnrMfb9c77a8r9vkLJa3dK8vwtq+RgXZfrBX4ad71eWmaJ4pOyPfaPi3+/BQb/MisqqVV+YZX7uWF3KHFVrpe2fW0/uPvYdW4ZdocSk3N1uPao39sIFfPjF9Xhcai7R/Ptd7wmD9kd3dQSjm78dH+zJmW3u778wipV1zX3YJvBtSalwOOaLS6tDXobgXKdc4bdoWVvbwvCr7g9V2a07afqmiYZdofik7JVVNJxn3W8LrzNC56UtCLTftcKfSYkAQAA+IOQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAIiH8DPYdXaXmdDpbXW10Gerk+E5K25R70YanWkNfxwl/SJEnxSVnaU1ztMc+mVsnr2NChq23aQyu1KaNIkvT2B9u7WNJbDR2nt3Yxz7uPPt/tfvzqX7+SUemQUenQlswSn7dhXlvP9t3N//auYia9ok0ZRTIqHZoxb51mLdrQTZtmj4Nr6VuZmnzXW8rLN1TQ4VwKra5ek+e8+KQsZXu99kK1b7rf7rwlm5SXb0iSsraXhqiOwC1enqG8fEOzF6Vq+3dGh7mh76e6M/+VzZr30gY9E5cSEfV09PnmQv2fG5YpcVVOu6lW1Hm6zX///RpdduvrXVwP4eS5L55esL5dXd3tp1Dsx87bvO3+9zV9zqchaMs//a0uIFyKDtT4sFToM6PzVAZKXrtTl140ynNel+2Hrjb74SZtyyvX7TdfpG92Hgqgho7To7qY55vvdtll2B2SpOraowFtoyftt9fYdFyNTce1La9c5/xgsBKTc/1oM3THbfuOCklSfmGV9h+sDVk7nfl+niav3alhQ6MD2E5PdL/dMsOh/MIqTboiNswB0zdrUgp0z7TLFZ+UbTLX+t9tU7cU6XBdoyTbqZ/IsmN3pU60SuG6Fr073WarUyooqtb3hVUW1NGR576orW9uV1d3+ykU+7HzNusdLfrbZ7tC0JZ/rL/aAAAAIhAhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCfBRXUOzDPsRq8vopLqmSXX1zVaX0etE8gj2vUFLy0mrSwBCzuZ0Or0NO39GaGho0PDhw6Xz5kk2bwNtetOqwHNkq7ob/NFmOz3gbc/1pFZpwIAonRczVCVl9WFpzxdXXz5aZRUOHa5xDXDbVZvBqKfrbUTZWtXqDOeAnu3r8V6bTU45Tz2yRtf77fKLztGuosPhK8dD1/tw9DmDZD/ckwGUg8U1Cvrp+gYPHqCmpuPWlNOl7vu28Ap9XxSYjnW1Pe/fz6YTJyPhbdezvpEjolVT12JdOWZaW6RD/4/q6+s1bNgwS0rob0mrVnAqgGu6Jxde9+sGN572rJM4frzVj4DU8/Z88d0uux9tBqOerrfR6gx3R+zbCOZOy9+sut4vh6qsvPvW9T6MjIAkmdUWmQFJirxAEmn1uHSsq+15ZAQkqWN9EReQIkSknl0AAACWIiQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACY6IMhqfXUT6DrBnM5f9cNtP1WH+f5s81gCcbrDRVfa7O6zshWV99+dHH2FWDNdRDJfW17PXmPDr7+VhcQfj3Jhb6uG6o2Am0/ysd5/mwzWILxekPF19qsrrM3YV8B1lwHkdzXthdJtURaNQAAABGCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCCkAQAAGCiV4SkN954QxMmTNDAgQN1zTXX6Msvv7S6JAAAcIaL+JD04Ycf6plnntH8+fP17bff6qabbtK0adNUWlpqdWkAAOAMFvEhKT4+Xo899pgef/xxXXbZZVq2bJnGjRunN9980+rSAADAGSyiB7g9duyYtm/frnnz5nlMnzp1qjIzM03XaWlpUUvL6VHH6+vr2x60tpguDwAAItCp922n02lZCREdkqqrq3Xy5EnFxMR4TI+JiVFFRYXpOkuWLNHChQs7z6hYGooSAQBACH3xxRe65557LGk74j9ukySbzebx3Ol0dprm8txzz6m+vt798/rrr4ejRAAAEAKGYVjWdkTfSRo1apT69evX6a6R3W7vdHfJJTo6WtHR0e7nZ599dkhrBAAAoRMVZd39nIi+k3TWWWfpmmuu0aZNmzymb9q0STfccINFVQEAgL4gou8kSdKsWbP00EMP6dprr9XkyZOVmJio0tJSPfHEE1aXBgAAzmARH5Luv/9+HT58WIsWLZJhGLryyiu1fv16jR8/3qf1L7/88hBXCAAAQuWqq66yrG2b08r/WwcAABChIvo7SQAAAFYhJAEAAJggJAEAAJggJAEAAJiIuP/dtnz5cv3ud7+zugwAAHAGqqys1OjRo31aNuLuJNXW1lpdAgAAOAP179/f54AkRfifAPA2PhsAAEAg6uvrNWzYMJ+Wjbg7SQAAAKGyfft2n5clJAEAgD6joqLC52UJSQAAoM/w56s8hCQAANBnfPTRRz4vS0gCAAB9xtNPP+3zshEXkioqKnTHHXfwP9sAAEBQnX322Zo8ebLPy0dcSFq9erVSUlKsLgMAAJxhGhsbdejQIZ+Xj+i/kwQAAGCViLuTBAAAEAkISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAiTmpqqm688UaNGDFC55xzjv7lX/5FxcXF7vmZmZmaNGmSBg4cqGuvvVaffPKJbDab8vLy3Mvs2rVLd9xxh4YMGaKYmBg99NBDqq6utuDVAOitCEkAIk5jY6NmzZqlb775Rl988YWioqJ09913q7W1VQ6HQ3feeaeuuuoq5ebm6s9//rPmzp3rsb5hGLrllls0adIk5eTkKDU1VZWVlfrlL39p0SsC0BsxwC2AiFdVVaXRo0dr586d+uqrr/T888+rrKxMAwcOlCS99dZbmj59ur799ltNmjRJL7zwgrZt26YNGza4t1FWVqZx48Zpz549uvjii616KQB6Ee4kAYg4xcXFeuCBB3ThhRdq2LBhmjBhgiSptLRUe/bs0Q9/+EN3QJKkH//4xx7rb9++XWlpaRoyZIj759JLL3VvGwB80d/qAgCgozvvvFPjxo1TUlKSxo4dq9bWVl155ZU6duyYnE6nbDabx/Idb4i3trbqzjvv1Msvv9xp27GxsSGtHcCZg5AEIKIcPnxYu3fvVkJCgm666SZJ0ldffeWef+mllyo5OVktLS2Kjo6WJOXk5Hhs40c/+pE+/vhj/cM//IP696ebAxAYPm4DEFF+8IMf6JxzzlFiYqKKioq0efNmzZo1yz3/gQceUGtrq377299q9+7d2rBhg/7yl79IkvsO05NPPqmamhr9+te/1tdff619+/Zp48aNevTRR3Xy5ElLXheA3oeQBCCiREVFafXq1dq+fbuuvPJKPfvss3r11Vfd84cNG6bPPvtMeXl5mjRpkubPn68XXnhBktzfUxo7dqy2bt2qkydP6p/+6Z905ZVX6ve//72GDx+uqCi6PQC+4X+3Aej1kpOT9Zvf/Eb19fUaNGiQ1eUAOEPwYT2AXmfFihW68MILdd555+m7777T3Llz9ctf/pKABCCoCEkAep2Kigq98MILqqioUGxsrO677z4tXrzY6rIAnGH4uA0AAMAE32AEAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAw8f8Dc9eJG9yxVjoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.countplot(x='age',hue='cardio',data=df,palette='colorblind',edgecolor=seaborn.color_palette('dark',n_colors=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "36819c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['yr']=(df['age']/365).round()\n",
    "#df['yr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4c5e17ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#seaborn.countplot(x='yr',hue='cardio',data=df,palette='colorblind',edgecolor=seaborn.color_palette('dark',n_colors=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ffda2fc3",
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19468.865814</td>\n",
       "      <td>1.349571</td>\n",
       "      <td>164.359229</td>\n",
       "      <td>74.205690</td>\n",
       "      <td>128.817286</td>\n",
       "      <td>96.630414</td>\n",
       "      <td>1.366871</td>\n",
       "      <td>1.226457</td>\n",
       "      <td>0.088129</td>\n",
       "      <td>0.053771</td>\n",
       "      <td>0.803729</td>\n",
       "      <td>0.499700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2467.251667</td>\n",
       "      <td>0.476838</td>\n",
       "      <td>8.210126</td>\n",
       "      <td>14.395757</td>\n",
       "      <td>154.011419</td>\n",
       "      <td>188.472530</td>\n",
       "      <td>0.680250</td>\n",
       "      <td>0.572270</td>\n",
       "      <td>0.283484</td>\n",
       "      <td>0.225568</td>\n",
       "      <td>0.397179</td>\n",
       "      <td>0.500003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10798.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>-150.000000</td>\n",
       "      <td>-70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17664.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19703.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21327.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23713.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>16020.000000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        gender        height        weight         ap_hi  \\\n",
       "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
       "mean   19468.865814      1.349571    164.359229     74.205690    128.817286   \n",
       "std     2467.251667      0.476838      8.210126     14.395757    154.011419   \n",
       "min    10798.000000      1.000000     55.000000     10.000000   -150.000000   \n",
       "25%    17664.000000      1.000000    159.000000     65.000000    120.000000   \n",
       "50%    19703.000000      1.000000    165.000000     72.000000    120.000000   \n",
       "75%    21327.000000      2.000000    170.000000     82.000000    140.000000   \n",
       "max    23713.000000      2.000000    250.000000    200.000000  16020.000000   \n",
       "\n",
       "              ap_lo   cholesterol          gluc         smoke          alco  \\\n",
       "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
       "mean      96.630414      1.366871      1.226457      0.088129      0.053771   \n",
       "std      188.472530      0.680250      0.572270      0.283484      0.225568   \n",
       "min      -70.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "25%       80.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "50%       80.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "75%       90.000000      2.000000      1.000000      0.000000      0.000000   \n",
       "max    11000.000000      3.000000      3.000000      1.000000      1.000000   \n",
       "\n",
       "             active        cardio  \n",
       "count  70000.000000  70000.000000  \n",
       "mean       0.803729      0.499700  \n",
       "std        0.397179      0.500003  \n",
       "min        0.000000      0.000000  \n",
       "25%        1.000000      0.000000  \n",
       "50%        1.000000      0.000000  \n",
       "75%        1.000000      1.000000  \n",
       "max        1.000000      1.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d6dd30eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022811</td>\n",
       "      <td>-0.081515</td>\n",
       "      <td>0.053684</td>\n",
       "      <td>0.020764</td>\n",
       "      <td>0.017647</td>\n",
       "      <td>0.154424</td>\n",
       "      <td>0.098703</td>\n",
       "      <td>-0.047633</td>\n",
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       "      <td>-0.009927</td>\n",
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       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>-0.022811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499033</td>\n",
       "      <td>0.155406</td>\n",
       "      <td>0.006005</td>\n",
       "      <td>0.015254</td>\n",
       "      <td>-0.035821</td>\n",
       "      <td>-0.020491</td>\n",
       "      <td>0.338135</td>\n",
       "      <td>0.170966</td>\n",
       "      <td>0.005866</td>\n",
       "      <td>0.008109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>-0.081515</td>\n",
       "      <td>0.499033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.290968</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>-0.050226</td>\n",
       "      <td>-0.018595</td>\n",
       "      <td>0.187989</td>\n",
       "      <td>0.094419</td>\n",
       "      <td>-0.006570</td>\n",
       "      <td>-0.010821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>0.053684</td>\n",
       "      <td>0.155406</td>\n",
       "      <td>0.290968</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030702</td>\n",
       "      <td>0.043710</td>\n",
       "      <td>0.141768</td>\n",
       "      <td>0.106857</td>\n",
       "      <td>0.067780</td>\n",
       "      <td>0.067113</td>\n",
       "      <td>-0.016867</td>\n",
       "      <td>0.181660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ap_hi</th>\n",
       "      <td>0.020764</td>\n",
       "      <td>0.006005</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.030702</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016086</td>\n",
       "      <td>0.023778</td>\n",
       "      <td>0.011841</td>\n",
       "      <td>-0.000922</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>-0.000033</td>\n",
       "      <td>0.054475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ap_lo</th>\n",
       "      <td>0.017647</td>\n",
       "      <td>0.015254</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.043710</td>\n",
       "      <td>0.016086</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024019</td>\n",
       "      <td>0.010806</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.010601</td>\n",
       "      <td>0.004780</td>\n",
       "      <td>0.065719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cholesterol</th>\n",
       "      <td>0.154424</td>\n",
       "      <td>-0.035821</td>\n",
       "      <td>-0.050226</td>\n",
       "      <td>0.141768</td>\n",
       "      <td>0.023778</td>\n",
       "      <td>0.024019</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.451578</td>\n",
       "      <td>0.010354</td>\n",
       "      <td>0.035760</td>\n",
       "      <td>0.009911</td>\n",
       "      <td>0.221147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gluc</th>\n",
       "      <td>0.098703</td>\n",
       "      <td>-0.020491</td>\n",
       "      <td>-0.018595</td>\n",
       "      <td>0.106857</td>\n",
       "      <td>0.011841</td>\n",
       "      <td>0.010806</td>\n",
       "      <td>0.451578</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004756</td>\n",
       "      <td>0.011246</td>\n",
       "      <td>-0.006770</td>\n",
       "      <td>0.089307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoke</th>\n",
       "      <td>-0.047633</td>\n",
       "      <td>0.338135</td>\n",
       "      <td>0.187989</td>\n",
       "      <td>0.067780</td>\n",
       "      <td>-0.000922</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.010354</td>\n",
       "      <td>-0.004756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340094</td>\n",
       "      <td>0.025858</td>\n",
       "      <td>-0.015486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alco</th>\n",
       "      <td>-0.029723</td>\n",
       "      <td>0.170966</td>\n",
       "      <td>0.094419</td>\n",
       "      <td>0.067113</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>0.010601</td>\n",
       "      <td>0.035760</td>\n",
       "      <td>0.011246</td>\n",
       "      <td>0.340094</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025476</td>\n",
       "      <td>-0.007330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>active</th>\n",
       "      <td>-0.009927</td>\n",
       "      <td>0.005866</td>\n",
       "      <td>-0.006570</td>\n",
       "      <td>-0.016867</td>\n",
       "      <td>-0.000033</td>\n",
       "      <td>0.004780</td>\n",
       "      <td>0.009911</td>\n",
       "      <td>-0.006770</td>\n",
       "      <td>0.025858</td>\n",
       "      <td>0.025476</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.035653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardio</th>\n",
       "      <td>0.238159</td>\n",
       "      <td>0.008109</td>\n",
       "      <td>-0.010821</td>\n",
       "      <td>0.181660</td>\n",
       "      <td>0.054475</td>\n",
       "      <td>0.065719</td>\n",
       "      <td>0.221147</td>\n",
       "      <td>0.089307</td>\n",
       "      <td>-0.015486</td>\n",
       "      <td>-0.007330</td>\n",
       "      <td>-0.035653</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  age    gender    height    weight     ap_hi     ap_lo  \\\n",
       "age          1.000000 -0.022811 -0.081515  0.053684  0.020764  0.017647   \n",
       "gender      -0.022811  1.000000  0.499033  0.155406  0.006005  0.015254   \n",
       "height      -0.081515  0.499033  1.000000  0.290968  0.005488  0.006150   \n",
       "weight       0.053684  0.155406  0.290968  1.000000  0.030702  0.043710   \n",
       "ap_hi        0.020764  0.006005  0.005488  0.030702  1.000000  0.016086   \n",
       "ap_lo        0.017647  0.015254  0.006150  0.043710  0.016086  1.000000   \n",
       "cholesterol  0.154424 -0.035821 -0.050226  0.141768  0.023778  0.024019   \n",
       "gluc         0.098703 -0.020491 -0.018595  0.106857  0.011841  0.010806   \n",
       "smoke       -0.047633  0.338135  0.187989  0.067780 -0.000922  0.005186   \n",
       "alco        -0.029723  0.170966  0.094419  0.067113  0.001408  0.010601   \n",
       "active      -0.009927  0.005866 -0.006570 -0.016867 -0.000033  0.004780   \n",
       "cardio       0.238159  0.008109 -0.010821  0.181660  0.054475  0.065719   \n",
       "\n",
       "             cholesterol      gluc     smoke      alco    active    cardio  \n",
       "age             0.154424  0.098703 -0.047633 -0.029723 -0.009927  0.238159  \n",
       "gender         -0.035821 -0.020491  0.338135  0.170966  0.005866  0.008109  \n",
       "height         -0.050226 -0.018595  0.187989  0.094419 -0.006570 -0.010821  \n",
       "weight          0.141768  0.106857  0.067780  0.067113 -0.016867  0.181660  \n",
       "ap_hi           0.023778  0.011841 -0.000922  0.001408 -0.000033  0.054475  \n",
       "ap_lo           0.024019  0.010806  0.005186  0.010601  0.004780  0.065719  \n",
       "cholesterol     1.000000  0.451578  0.010354  0.035760  0.009911  0.221147  \n",
       "gluc            0.451578  1.000000 -0.004756  0.011246 -0.006770  0.089307  \n",
       "smoke           0.010354 -0.004756  1.000000  0.340094  0.025858 -0.015486  \n",
       "alco            0.035760  0.011246  0.340094  1.000000  0.025476 -0.007330  \n",
       "active          0.009911 -0.006770  0.025858  0.025476  1.000000 -0.035653  \n",
       "cardio          0.221147  0.089307 -0.015486 -0.007330 -0.035653  1.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show corelation with every columns\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9775ecb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df=df.drop(['yr'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c2ffb14d",
   "metadata": {},
   "outputs": [
    {
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       "<p>70000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0      18393       2     168    62.0    110     80            1     1      0   \n",
       "1      20228       1     156    85.0    140     90            3     1      0   \n",
       "2      18857       1     165    64.0    130     70            3     1      0   \n",
       "3      17623       2     169    82.0    150    100            1     1      0   \n",
       "4      17474       1     156    56.0    100     60            1     1      0   \n",
       "...      ...     ...     ...     ...    ...    ...          ...   ...    ...   \n",
       "69995  19240       2     168    76.0    120     80            1     1      1   \n",
       "69996  22601       1     158   126.0    140     90            2     2      0   \n",
       "69997  19066       2     183   105.0    180     90            3     1      0   \n",
       "69998  22431       1     163    72.0    135     80            1     2      0   \n",
       "69999  20540       1     170    72.0    120     80            2     1      0   \n",
       "\n",
       "       alco  active  \n",
       "0         0       1  \n",
       "1         0       1  \n",
       "2         0       0  \n",
       "3         0       1  \n",
       "4         0       0  \n",
       "...     ...     ...  \n",
       "69995     0       1  \n",
       "69996     0       1  \n",
       "69997     1       0  \n",
       "69998     0       0  \n",
       "69999     0       1  \n",
       "\n",
       "[70000 rows x 11 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,:-1]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "607c647c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        0\n",
       "        ..\n",
       "69995    0\n",
       "69996    1\n",
       "69997    1\n",
       "69998    1\n",
       "69999    0\n",
       "Name: cardio, Length: 70000, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,11]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "73eee368",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cfe52772",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=.30,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "974327ac",
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23561</th>\n",
       "      <td>16136</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>71.0</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>34858</th>\n",
       "      <td>14615</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>69.0</td>\n",
       "      <td>140</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54953</th>\n",
       "      <td>20507</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>65.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59230</th>\n",
       "      <td>16720</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>53.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>21050</td>\n",
       "      <td>1</td>\n",
       "      <td>159</td>\n",
       "      <td>71.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49100</th>\n",
       "      <td>21289</td>\n",
       "      <td>2</td>\n",
       "      <td>175</td>\n",
       "      <td>78.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20609</th>\n",
       "      <td>19116</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>68.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21440</th>\n",
       "      <td>18049</td>\n",
       "      <td>2</td>\n",
       "      <td>178</td>\n",
       "      <td>82.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50057</th>\n",
       "      <td>21957</td>\n",
       "      <td>1</td>\n",
       "      <td>169</td>\n",
       "      <td>77.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5192</th>\n",
       "      <td>20671</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>65.0</td>\n",
       "      <td>160</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "23561  16136       2     169    71.0    100     80            1     1      1   \n",
       "34858  14615       1     158    69.0    140     80            2     1      0   \n",
       "54953  20507       1     164    65.0    120     80            1     1      0   \n",
       "59230  16720       1     153    53.0    100     60            1     1      0   \n",
       "1730   21050       1     159    71.0    140     90            1     1      0   \n",
       "...      ...     ...     ...     ...    ...    ...          ...   ...    ...   \n",
       "49100  21289       2     175    78.0    120     80            1     1      0   \n",
       "20609  19116       1     164    68.0    120     80            1     1      0   \n",
       "21440  18049       2     178    82.0    120     80            1     1      0   \n",
       "50057  21957       1     169    77.0    120     80            1     1      0   \n",
       "5192   20671       1     174    65.0    160     90            2     2      0   \n",
       "\n",
       "       alco  active  \n",
       "23561     0       1  \n",
       "34858     0       1  \n",
       "54953     0       1  \n",
       "59230     0       1  \n",
       "1730      0       1  \n",
       "...     ...     ...  \n",
       "49100     0       1  \n",
       "20609     0       0  \n",
       "21440     0       1  \n",
       "50057     0       0  \n",
       "5192      0       1  \n",
       "\n",
       "[49000 rows x 11 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9296e4f1",
   "metadata": {},
   "source": [
    "# Used Random forest algorithm for finding accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fd520913",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f8f88cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "RFC=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e52a8e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC.fit(xtrain,ytrain)  #fit means trin to model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8b1b159a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7184761904761905"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC.score(xtest,ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7a2982",
   "metadata": {},
   "source": [
    "# All so find out using decision tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "77ed5313",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4066e473",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc =DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d31d2049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c48e7ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6360476190476191"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.score(xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64045711",
   "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",
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   "version": "3.9.13"
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