heart_failure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "246b836a",
   "metadata": {},
   "source": [
    "# Heart_failure predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "6cf5230f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d4fb15",
   "metadata": {},
   "source": [
    "import matplotlib\n",
    "print(matplotlib.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "24d96b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  75.0        0                       582         0                 20   \n",
       "1  55.0        0                      7861         0                 38   \n",
       "2  65.0        0                       146         0                 20   \n",
       "3  50.0        1                       111         0                 20   \n",
       "4  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1  265000.00               1.9           130    1   \n",
       "1                    0  263358.03               1.1           136    1   \n",
       "2                    0  162000.00               1.3           129    1   \n",
       "3                    0  210000.00               1.9           137    1   \n",
       "4                    0  327000.00               2.7           116    0   \n",
       "\n",
       "   smoking  time  DEATH_EVENT  \n",
       "0        0     4            1  \n",
       "1        0     6            1  \n",
       "2        1     7            1  \n",
       "3        0     7            1  \n",
       "4        0     8            1  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('heartt.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "932bad6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 13)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "31ad9c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
       "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
       "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
       "       'DEATH_EVENT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e632a52c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       299 non-null    float64\n",
      " 1   anaemia                   299 non-null    int64  \n",
      " 2   creatinine_phosphokinase  299 non-null    int64  \n",
      " 3   diabetes                  299 non-null    int64  \n",
      " 4   ejection_fraction         299 non-null    int64  \n",
      " 5   high_blood_pressure       299 non-null    int64  \n",
      " 6   platelets                 299 non-null    float64\n",
      " 7   serum_creatinine          299 non-null    float64\n",
      " 8   serum_sodium              299 non-null    int64  \n",
      " 9   sex                       299 non-null    int64  \n",
      " 10  smoking                   299 non-null    int64  \n",
      " 11  time                      299 non-null    int64  \n",
      " 12  DEATH_EVENT               299 non-null    int64  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "bc5d0e41",
   "metadata": {},
   "outputs": [
    {
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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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088006</td>\n",
       "      <td>-0.081584</td>\n",
       "      <td>-0.101012</td>\n",
       "      <td>0.060098</td>\n",
       "      <td>0.093289</td>\n",
       "      <td>-0.052354</td>\n",
       "      <td>0.159187</td>\n",
       "      <td>-0.045966</td>\n",
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       "      <td>0.018668</td>\n",
       "      <td>-0.224068</td>\n",
       "      <td>0.253729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anaemia</th>\n",
       "      <td>0.088006</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.190741</td>\n",
       "      <td>-0.012729</td>\n",
       "      <td>0.031557</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>-0.043786</td>\n",
       "      <td>0.052174</td>\n",
       "      <td>0.041882</td>\n",
       "      <td>-0.094769</td>\n",
       "      <td>-0.107290</td>\n",
       "      <td>-0.141414</td>\n",
       "      <td>0.066270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <td>-0.081584</td>\n",
       "      <td>-0.190741</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009639</td>\n",
       "      <td>-0.044080</td>\n",
       "      <td>-0.070590</td>\n",
       "      <td>0.024463</td>\n",
       "      <td>-0.016408</td>\n",
       "      <td>0.059550</td>\n",
       "      <td>0.079791</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>-0.009346</td>\n",
       "      <td>0.062728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>-0.101012</td>\n",
       "      <td>-0.012729</td>\n",
       "      <td>-0.009639</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004850</td>\n",
       "      <td>-0.012732</td>\n",
       "      <td>0.092193</td>\n",
       "      <td>-0.046975</td>\n",
       "      <td>-0.089551</td>\n",
       "      <td>-0.157730</td>\n",
       "      <td>-0.147173</td>\n",
       "      <td>0.033726</td>\n",
       "      <td>-0.001943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ejection_fraction</th>\n",
       "      <td>0.060098</td>\n",
       "      <td>0.031557</td>\n",
       "      <td>-0.044080</td>\n",
       "      <td>-0.004850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024445</td>\n",
       "      <td>0.072177</td>\n",
       "      <td>-0.011302</td>\n",
       "      <td>0.175902</td>\n",
       "      <td>-0.148386</td>\n",
       "      <td>-0.067315</td>\n",
       "      <td>0.041729</td>\n",
       "      <td>-0.268603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <td>0.093289</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>-0.070590</td>\n",
       "      <td>-0.012732</td>\n",
       "      <td>0.024445</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049963</td>\n",
       "      <td>-0.004935</td>\n",
       "      <td>0.037109</td>\n",
       "      <td>-0.104615</td>\n",
       "      <td>-0.055711</td>\n",
       "      <td>-0.196439</td>\n",
       "      <td>0.079351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platelets</th>\n",
       "      <td>-0.052354</td>\n",
       "      <td>-0.043786</td>\n",
       "      <td>0.024463</td>\n",
       "      <td>0.092193</td>\n",
       "      <td>0.072177</td>\n",
       "      <td>0.049963</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.041198</td>\n",
       "      <td>0.062125</td>\n",
       "      <td>-0.125120</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>0.010514</td>\n",
       "      <td>-0.049139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_creatinine</th>\n",
       "      <td>0.159187</td>\n",
       "      <td>0.052174</td>\n",
       "      <td>-0.016408</td>\n",
       "      <td>-0.046975</td>\n",
       "      <td>-0.011302</td>\n",
       "      <td>-0.004935</td>\n",
       "      <td>-0.041198</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.189095</td>\n",
       "      <td>0.006970</td>\n",
       "      <td>-0.027414</td>\n",
       "      <td>-0.149315</td>\n",
       "      <td>0.294278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_sodium</th>\n",
       "      <td>-0.045966</td>\n",
       "      <td>0.041882</td>\n",
       "      <td>0.059550</td>\n",
       "      <td>-0.089551</td>\n",
       "      <td>0.175902</td>\n",
       "      <td>0.037109</td>\n",
       "      <td>0.062125</td>\n",
       "      <td>-0.189095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027566</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.087640</td>\n",
       "      <td>-0.195204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.065430</td>\n",
       "      <td>-0.094769</td>\n",
       "      <td>0.079791</td>\n",
       "      <td>-0.157730</td>\n",
       "      <td>-0.148386</td>\n",
       "      <td>-0.104615</td>\n",
       "      <td>-0.125120</td>\n",
       "      <td>0.006970</td>\n",
       "      <td>-0.027566</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.445892</td>\n",
       "      <td>-0.015608</td>\n",
       "      <td>-0.004316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking</th>\n",
       "      <td>0.018668</td>\n",
       "      <td>-0.107290</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>-0.147173</td>\n",
       "      <td>-0.067315</td>\n",
       "      <td>-0.055711</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>-0.027414</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.445892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022839</td>\n",
       "      <td>-0.012623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>-0.224068</td>\n",
       "      <td>-0.141414</td>\n",
       "      <td>-0.009346</td>\n",
       "      <td>0.033726</td>\n",
       "      <td>0.041729</td>\n",
       "      <td>-0.196439</td>\n",
       "      <td>0.010514</td>\n",
       "      <td>-0.149315</td>\n",
       "      <td>0.087640</td>\n",
       "      <td>-0.015608</td>\n",
       "      <td>-0.022839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.526964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEATH_EVENT</th>\n",
       "      <td>0.253729</td>\n",
       "      <td>0.066270</td>\n",
       "      <td>0.062728</td>\n",
       "      <td>-0.001943</td>\n",
       "      <td>-0.268603</td>\n",
       "      <td>0.079351</td>\n",
       "      <td>-0.049139</td>\n",
       "      <td>0.294278</td>\n",
       "      <td>-0.195204</td>\n",
       "      <td>-0.004316</td>\n",
       "      <td>-0.012623</td>\n",
       "      <td>-0.526964</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               age   anaemia  creatinine_phosphokinase  \\\n",
       "age                       1.000000  0.088006                 -0.081584   \n",
       "anaemia                   0.088006  1.000000                 -0.190741   \n",
       "creatinine_phosphokinase -0.081584 -0.190741                  1.000000   \n",
       "diabetes                 -0.101012 -0.012729                 -0.009639   \n",
       "ejection_fraction         0.060098  0.031557                 -0.044080   \n",
       "high_blood_pressure       0.093289  0.038182                 -0.070590   \n",
       "platelets                -0.052354 -0.043786                  0.024463   \n",
       "serum_creatinine          0.159187  0.052174                 -0.016408   \n",
       "serum_sodium             -0.045966  0.041882                  0.059550   \n",
       "sex                       0.065430 -0.094769                  0.079791   \n",
       "smoking                   0.018668 -0.107290                  0.002421   \n",
       "time                     -0.224068 -0.141414                 -0.009346   \n",
       "DEATH_EVENT               0.253729  0.066270                  0.062728   \n",
       "\n",
       "                          diabetes  ejection_fraction  high_blood_pressure  \\\n",
       "age                      -0.101012           0.060098             0.093289   \n",
       "anaemia                  -0.012729           0.031557             0.038182   \n",
       "creatinine_phosphokinase -0.009639          -0.044080            -0.070590   \n",
       "diabetes                  1.000000          -0.004850            -0.012732   \n",
       "ejection_fraction        -0.004850           1.000000             0.024445   \n",
       "high_blood_pressure      -0.012732           0.024445             1.000000   \n",
       "platelets                 0.092193           0.072177             0.049963   \n",
       "serum_creatinine         -0.046975          -0.011302            -0.004935   \n",
       "serum_sodium             -0.089551           0.175902             0.037109   \n",
       "sex                      -0.157730          -0.148386            -0.104615   \n",
       "smoking                  -0.147173          -0.067315            -0.055711   \n",
       "time                      0.033726           0.041729            -0.196439   \n",
       "DEATH_EVENT              -0.001943          -0.268603             0.079351   \n",
       "\n",
       "                          platelets  serum_creatinine  serum_sodium       sex  \\\n",
       "age                       -0.052354          0.159187     -0.045966  0.065430   \n",
       "anaemia                   -0.043786          0.052174      0.041882 -0.094769   \n",
       "creatinine_phosphokinase   0.024463         -0.016408      0.059550  0.079791   \n",
       "diabetes                   0.092193         -0.046975     -0.089551 -0.157730   \n",
       "ejection_fraction          0.072177         -0.011302      0.175902 -0.148386   \n",
       "high_blood_pressure        0.049963         -0.004935      0.037109 -0.104615   \n",
       "platelets                  1.000000         -0.041198      0.062125 -0.125120   \n",
       "serum_creatinine          -0.041198          1.000000     -0.189095  0.006970   \n",
       "serum_sodium               0.062125         -0.189095      1.000000 -0.027566   \n",
       "sex                       -0.125120          0.006970     -0.027566  1.000000   \n",
       "smoking                    0.028234         -0.027414      0.004813  0.445892   \n",
       "time                       0.010514         -0.149315      0.087640 -0.015608   \n",
       "DEATH_EVENT               -0.049139          0.294278     -0.195204 -0.004316   \n",
       "\n",
       "                           smoking      time  DEATH_EVENT  \n",
       "age                       0.018668 -0.224068     0.253729  \n",
       "anaemia                  -0.107290 -0.141414     0.066270  \n",
       "creatinine_phosphokinase  0.002421 -0.009346     0.062728  \n",
       "diabetes                 -0.147173  0.033726    -0.001943  \n",
       "ejection_fraction        -0.067315  0.041729    -0.268603  \n",
       "high_blood_pressure      -0.055711 -0.196439     0.079351  \n",
       "platelets                 0.028234  0.010514    -0.049139  \n",
       "serum_creatinine         -0.027414 -0.149315     0.294278  \n",
       "serum_sodium              0.004813  0.087640    -0.195204  \n",
       "sex                       0.445892 -0.015608    -0.004316  \n",
       "smoking                   1.000000 -0.022839    -0.012623  \n",
       "time                     -0.022839  1.000000    -0.526964  \n",
       "DEATH_EVENT              -0.012623 -0.526964     1.000000  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "600845c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b851e520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
       "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
       "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
       "       'DEATH_EVENT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "43687cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diabetes\n",
       "True     174\n",
       "False    125\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.value_counts(data['diabetes']==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "768bb338",
   "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.833893</td>\n",
       "      <td>0.431438</td>\n",
       "      <td>581.839465</td>\n",
       "      <td>0.418060</td>\n",
       "      <td>38.083612</td>\n",
       "      <td>0.351171</td>\n",
       "      <td>263358.029264</td>\n",
       "      <td>1.39388</td>\n",
       "      <td>136.625418</td>\n",
       "      <td>0.648829</td>\n",
       "      <td>0.32107</td>\n",
       "      <td>130.260870</td>\n",
       "      <td>0.32107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.894809</td>\n",
       "      <td>0.496107</td>\n",
       "      <td>970.287881</td>\n",
       "      <td>0.494067</td>\n",
       "      <td>11.834841</td>\n",
       "      <td>0.478136</td>\n",
       "      <td>97804.236869</td>\n",
       "      <td>1.03451</td>\n",
       "      <td>4.412477</td>\n",
       "      <td>0.478136</td>\n",
       "      <td>0.46767</td>\n",
       "      <td>77.614208</td>\n",
       "      <td>0.46767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25100.000000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>212500.000000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262000.000000</td>\n",
       "      <td>1.10000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>303500.000000</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7861.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>850000.000000</td>\n",
       "      <td>9.40000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>285.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age     anaemia  creatinine_phosphokinase    diabetes  \\\n",
       "count  299.000000  299.000000                299.000000  299.000000   \n",
       "mean    60.833893    0.431438                581.839465    0.418060   \n",
       "std     11.894809    0.496107                970.287881    0.494067   \n",
       "min     40.000000    0.000000                 23.000000    0.000000   \n",
       "25%     51.000000    0.000000                116.500000    0.000000   \n",
       "50%     60.000000    0.000000                250.000000    0.000000   \n",
       "75%     70.000000    1.000000                582.000000    1.000000   \n",
       "max     95.000000    1.000000               7861.000000    1.000000   \n",
       "\n",
       "       ejection_fraction  high_blood_pressure      platelets  \\\n",
       "count         299.000000           299.000000     299.000000   \n",
       "mean           38.083612             0.351171  263358.029264   \n",
       "std            11.834841             0.478136   97804.236869   \n",
       "min            14.000000             0.000000   25100.000000   \n",
       "25%            30.000000             0.000000  212500.000000   \n",
       "50%            38.000000             0.000000  262000.000000   \n",
       "75%            45.000000             1.000000  303500.000000   \n",
       "max            80.000000             1.000000  850000.000000   \n",
       "\n",
       "       serum_creatinine  serum_sodium         sex    smoking        time  \\\n",
       "count         299.00000    299.000000  299.000000  299.00000  299.000000   \n",
       "mean            1.39388    136.625418    0.648829    0.32107  130.260870   \n",
       "std             1.03451      4.412477    0.478136    0.46767   77.614208   \n",
       "min             0.50000    113.000000    0.000000    0.00000    4.000000   \n",
       "25%             0.90000    134.000000    0.000000    0.00000   73.000000   \n",
       "50%             1.10000    137.000000    1.000000    0.00000  115.000000   \n",
       "75%             1.40000    140.000000    1.000000    1.00000  203.000000   \n",
       "max             9.40000    148.000000    1.000000    1.00000  285.000000   \n",
       "\n",
       "       DEATH_EVENT  \n",
       "count    299.00000  \n",
       "mean       0.32107  \n",
       "std        0.46767  \n",
       "min        0.00000  \n",
       "25%        0.00000  \n",
       "50%        0.00000  \n",
       "75%        1.00000  \n",
       "max        1.00000  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "65bd34bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10, 20, 30, 40, 50])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.arange(0,60,10)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "fed218c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['age'] = pd.cut(data.age, bins = bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d0a34c3d",
   "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "      <th>age_(0, 10]</th>\n",
       "      <th>age_(10, 20]</th>\n",
       "      <th>age_(20, 30]</th>\n",
       "      <th>age_(30, 40]</th>\n",
       "      <th>age_(40, 50]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>155000.00</td>\n",
       "      <td>1.1</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>0</td>\n",
       "      <td>1820</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>271</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>0</td>\n",
       "      <td>2060</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>742000.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>0</td>\n",
       "      <td>2413</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>140000.00</td>\n",
       "      <td>1.4</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>395000.00</td>\n",
       "      <td>1.6</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0          0                       582         0                 20   \n",
       "1          0                      7861         0                 38   \n",
       "2          0                       146         0                 20   \n",
       "3          1                       111         0                 20   \n",
       "4          1                       160         1                 20   \n",
       "..       ...                       ...       ...                ...   \n",
       "294        0                        61         1                 38   \n",
       "295        0                      1820         0                 38   \n",
       "296        0                      2060         1                 60   \n",
       "297        0                      2413         0                 38   \n",
       "298        0                       196         0                 45   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                      1  265000.00               1.9           130    1   \n",
       "1                      0  263358.03               1.1           136    1   \n",
       "2                      0  162000.00               1.3           129    1   \n",
       "3                      0  210000.00               1.9           137    1   \n",
       "4                      0  327000.00               2.7           116    0   \n",
       "..                   ...        ...               ...           ...  ...   \n",
       "294                    1  155000.00               1.1           143    1   \n",
       "295                    0  270000.00               1.2           139    0   \n",
       "296                    0  742000.00               0.8           138    0   \n",
       "297                    0  140000.00               1.4           140    1   \n",
       "298                    0  395000.00               1.6           136    1   \n",
       "\n",
       "     smoking  time  DEATH_EVENT  age_(0, 10]  age_(10, 20]  age_(20, 30]  \\\n",
       "0          0     4            1            0             0             0   \n",
       "1          0     6            1            0             0             0   \n",
       "2          1     7            1            0             0             0   \n",
       "3          0     7            1            0             0             0   \n",
       "4          0     8            1            0             0             0   \n",
       "..       ...   ...          ...          ...           ...           ...   \n",
       "294        1   270            0            0             0             0   \n",
       "295        0   271            0            0             0             0   \n",
       "296        0   278            0            0             0             0   \n",
       "297        1   280            0            0             0             0   \n",
       "298        1   285            0            0             0             0   \n",
       "\n",
       "     age_(30, 40]  age_(40, 50]  \n",
       "0               0             0  \n",
       "1               0             0  \n",
       "2               0             0  \n",
       "3               0             1  \n",
       "4               0             0  \n",
       "..            ...           ...  \n",
       "294             0             0  \n",
       "295             0             0  \n",
       "296             0             1  \n",
       "297             0             1  \n",
       "298             0             1  \n",
       "\n",
       "[299 rows x 17 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.get_dummies(data,columns=['age'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "13f00f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "anaemia                     0\n",
       "creatinine_phosphokinase    0\n",
       "diabetes                    0\n",
       "ejection_fraction           0\n",
       "high_blood_pressure         0\n",
       "platelets                   0\n",
       "serum_creatinine            0\n",
       "serum_sodium                0\n",
       "sex                         0\n",
       "smoking                     0\n",
       "time                        0\n",
       "DEATH_EVENT                 0\n",
       "age_(0, 10]                 0\n",
       "age_(10, 20]                0\n",
       "age_(20, 30]                0\n",
       "age_(30, 40]                0\n",
       "age_(40, 50]                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "237676e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop('DEATH_EVENT',axis=1)\n",
    "y = data.DEATH_EVENT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "7bef0ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c910c043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>age_(0, 10]</th>\n",
       "      <th>age_(10, 20]</th>\n",
       "      <th>age_(20, 30]</th>\n",
       "      <th>age_(30, 40]</th>\n",
       "      <th>age_(40, 50]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>0</td>\n",
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       "      <td>35</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>309000.0</td>\n",
       "      <td>1.5</td>\n",
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       "      <th>2</th>\n",
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       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.0</td>\n",
       "      <td>1.3</td>\n",
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       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>228000.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>0.8</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "291        0                       320         0                 35   \n",
       "191        1                        62         0                 60   \n",
       "2          0                       146         0                 20   \n",
       "149        0                      2261         0                 35   \n",
       "127        1                       200         1                 60   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "291                    0   133000.0               1.4           139    1   \n",
       "191                    0   309000.0               1.5           135    0   \n",
       "2                      0   162000.0               1.3           129    1   \n",
       "149                    1   228000.0               0.9           136    1   \n",
       "127                    0   300000.0               0.8           137    0   \n",
       "\n",
       "     smoking  time  age_(0, 10]  age_(10, 20]  age_(20, 30]  age_(30, 40]  \\\n",
       "291        0   258            0             0             0             0   \n",
       "191        0   174            0             0             0             0   \n",
       "2          1     7            0             0             0             0   \n",
       "149        0   115            0             0             0             0   \n",
       "127        0   104            0             0             0             0   \n",
       "\n",
       "     age_(40, 50]  \n",
       "291             0  \n",
       "191             0  \n",
       "2               0  \n",
       "149             0  \n",
       "127             0  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x,y,test_size = 0.3, random_state =32)\n",
    "xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f6ca4eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import plot_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c9139a",
   "metadata": {},
   "source": [
    "# RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "eaf96fff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf = RandomForestClassifier( n_estimators=100,criterion='gini')\n",
    "model_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f8934d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'base_estimator': DecisionTreeClassifier(),\n",
       " 'n_estimators': 100,\n",
       " 'estimator_params': ('criterion',\n",
       "  'max_depth',\n",
       "  'min_samples_split',\n",
       "  'min_samples_leaf',\n",
       "  'min_weight_fraction_leaf',\n",
       "  'max_features',\n",
       "  'max_leaf_nodes',\n",
       "  'min_impurity_decrease',\n",
       "  'random_state',\n",
       "  'ccp_alpha'),\n",
       " 'bootstrap': True,\n",
       " 'oob_score': False,\n",
       " 'n_jobs': None,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False,\n",
       " 'class_weight': None,\n",
       " 'max_samples': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'ccp_alpha': 0.0}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4c30783b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,roc_curve,precision_recall_curve,auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "198069a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
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       "       0, 0], dtype=int64)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.fit(xtrain, ytrain)\n",
    "yhat = model_rf.predict(xtest)\n",
    "yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "4a017935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(ytest, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0c8cdf5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[55,  5],\n",
       "       [ 6, 24]], dtype=int64)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(yhat, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "67397f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(yhat, ytest), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fe2231",
   "metadata": {},
   "source": [
    "# LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8f513e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[55, 13],\n",
       "       [ 6, 16]], dtype=int64)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr = LogisticRegression(max_iter = 10000)\n",
    "model_lr.fit(xtrain, ytrain)\n",
    "yhat= model_lr.predict(xtest)\n",
    "confusion_matrix(yhat, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "57edb849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(yhat, ytest), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "d7f29e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[52,  5],\n",
       "       [ 9, 24]], dtype=int64)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dt = DecisionTreeClassifier()\n",
    "model_dt.fit(xtrain, ytrain)\n",
    "yhat = model_dt.predict(xtest)\n",
    "confusion_matrix(yhat, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "7854f3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(yhat, ytest), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d93b0c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15,12))\n",
    "plt=plot_tree(model_dt)\n",
    "fig.savefig('tree.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534d3f08",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "12fc3e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[56,  5],\n",
       "       [10, 19]], dtype=int64)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model_svm=SVC(kernel = \"linear\")\n",
    "model_svm.fit(xtrain,ytrain)\n",
    "yhat=model_svm.predict(xtest)\n",
    "confusion_matrix(ytest,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f624a5f",
   "metadata": {},
   "source": [
    "# AdaboostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "a0ee1bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model = AdaBoostClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c5d68b08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier()"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "46a4e666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p06dTv379xNe+vr7k6uqqtO3p06eTQqEQX+O5ke6mTZuURohERPn5+WRoaEinTp2iP//8kwDQ4cOH1dtxIsrKyqKMjAyV040bN9Te1uXLl8nc3Jw2bNggzhswYAC99957Su3S0tIIAP3+++9ERDRq1Cjq2bMnZWZmUmlpKcXGxpKBgQF17dpV7feuCnikq1cTH9NtRtzc3ODn54eePXsiKCgIAQEBGDFihFgtKzc3F5999hl+/fVX5Obm4unTpyguLq5UN9bV1VX8d8WTF1xcXJTmHTlyRGkdb29vpdcKhQJbtmypMs5z584hPT290kMqy8rKkJmZCS8vL4wbNw6BgYEYMGAA/P39MXLkSNjY2KjcdwcHB5XLNHHr1i0EBgZi9OjRSg+qVMfy5csxadIkODk5wcDAAO7u7hgzZgySkpLqJTamH/jwQjNiaGiIAwcOYPfu3XBycsKSJUvQvXt3sZB3SEgIUlJSEBkZiZMnTyI5ORkymQzFxcVK26mqEPmL8zQpTP6igoICeHt7Izk5WWnKyMjAoEGDAACbNm3CsWPH4OXlhZiYGHTt2hVpaWkqt1kfhxdu3bqFPn36QKFQVKoyVl2B9IovJhsbG+zatQuPHj3CH3/8gcTERJSWlsLR0VGjz4fpNx7pNjMGBgbw8fGBj48PwsLCYGNjg3379mHChAk4fvw4oqKixKI16enpKCwsrJf3PX36tNLrkydPolu3blW2dXd3x7Zt29CmTZtqH3Xu4eEBDw8PfPbZZ3BxccHPP/+sNOJ+3p49e6p94q9UKq02/ps3b6JPnz7w8PBAdHQ0DAyUxysKhQJz585FSUmJ+AW0f/9+uLi4VKq7K5VKIZVKkZeXh3379mHhwoXVvjdrWjjpNiOnTp3CgQMH4O/vD2traxw5cgQFBQVwdnYGAHTp0gU//PAD3N3dce/ePYSGhsLY2Lhe3jszMxOffPIJJk2ahISEBKxbtw4bN26ssu3YsWOxePFiDBs2DPPmzYOdnR2uXr2K+Ph4LFy4EPfv30dUVBSGDBkCOzs7nD9/Hn/88Ue1T3+oy+GFmzdvws/PDw4ODvjmm29w584dcVnFY37efPNNhIeHY/LkyZg9ezZSU1Px3XffYfny5WLbhIQE3L59Gz179kRWVhY++ugjODk5afxEZKbndH1QmSfNJtThRNqFCxcoICCArKysSCKRULdu3ZROBp05c4Y8PT3JxMSEnJ2d6T//+Q/J5XKKjo4mor9OpKWkpIjr7Ny5k16M6auvvlI6EVZxydg777xDZmZmZGVlRfPnz1daBy9cMnbz5k0aN24cyeVyMjExIScnJ5o2bRo9fvyYcnJyKDg4mGxtbcnY2Jg6depEX3/9da0/l5pER0cTgCqn5507d4569+5NJiYmZGdnR4sWLVJafuDAAXJ2diZjY2Oytramd999l+7du1fn+MAn0vRq4ipjekYfq4z5+fnB09MT33zzja5DaZK4yph+4RNpjDGmRZx0GWNMi/jwgp7Rx8MLrGHx4QX9wiNdxhjTIk66TJSVlQVBEJCamqrrUKrVsWNHCIIAQRBQUFCg63AaTP/+/cX9bOx/E6Y+TrpML0VERCA7O1vpQZg11bOtSkVSe36Ki4vTOJ4VK1agY8eOkEgkeOWVV5CYmFht+40bN1Z63xeL4cTHx1e6qYTpP745guklMzMz8cYEQL16tqps2rQJ/fv3F19bWFhoFMuPP/6IDz/8EKtXr4a3tzeWLVuGgIAA/P7777CyslK5nqWlpdKtyxW3VFdo3bo1rK2tNYqFNX480m0iVqxYAXt7e7x4ks3X1xcffvghgPI70vr37w+5XA4LCwv079+/2noFGzdurJQ0vv/+e3Ts2FFp3tq1a+Hs7AyJRILu3bsjOjq6fnZKA1u2bEFJSQmio6Ph4uKC0aNHY8aMGfj2229rXNfCwgK2trbi9OKIsybffvstpkyZgokTJ6J79+5YvXo1pFKpyjvuKgiCoPS+FTUaWNPGSbeJGDlyJLKzs3Hs2DFx3o0bN3D06FG8+eabANSvl6uJLVu2IDw8HIsWLcLFixcxb948fPDBB9ixY4fKdSIiIqotPmNqalqpsllN1Klnq8rUqVNhbW0Nb29vxMTEaPS+xcXFSEpKgr+/vzjPwMAA/fv3R0JCQrXrPnjwAPb29ujQoQOGDh2KCxcuaPTeTD/x4YUmwtraGgMGDEBsbCx8fHwAALGxsejSpQs8PT0BQOknNACsX78e5ubmSExMRO/evWv1vmFhYVi6dCmGDh0KAHB0dMSZM2ewZs0aBAcHV7nO1KlTMXLkyGq3265dO43iyMnJgZOTk9K8ipFjbm5upaIzFb788kv07dsXMpkM+/btw7vvvotHjx5h2rRpar3v3bt3UVZWVmmU2qZNG1y+fFnles7Ozli/fj1cXV3x4MEDfPPNN3j11VeRlpam8b4z/cJJtwkZO3YsZs6cieXLl8PIyAhbt24VR7mA+vVy1fXo0SNkZmYiJCREqbZsSUlJpUMQz7O0tFR66oIuff755+K/3d3dUVhYiCVLlqiddGtLoVBAoVCIr//+97+jW7duWLt2LcLCwhr0vZlucdJtQoYOHYopU6Zg//79cHR0RHJystIjcUJCQnD//n1ERkbC3t4exsbGcHNzq1Qvt4KBgUGlY8TPl0esuFwrOjoaHh4eSu2e/5n/ooiICERERFS7L5o+rFGderbq8Pb2xvz581FaWgojo5r/e1hZWcHQ0LDK937+RF9NWrRoAXd392pHx6xp4KTbhLRs2RLBwcGIjY2Fg4MDevXqpfSTW9N6udbW1sjLy0NRUZF4cuncuXPi8jZt2qBt27a4cuUKRo0apXacDXF4QZN6ttVJTk6GlZWVWgkXAIyNjeHh4YH9+/dj8ODBAICnT5/iwIEDmDlzptrvW1ZWhpSUFAwZMkTtdZie0nWZM540m1BDacedO3eSmZkZOTo60rJly5SWubu7U2BgIF28eJGOHz9OCoWCjI2NVZZuvHv3LslkMgoNDaWMjAyKiooiuVxODg4O4jZXrVpFLVu2pMjISLp06RKdO3eO1qxZQytXrqw2zrpwcHCgyMhIpXl5eXnUpk0bGj9+PKWmplJcXBzJZDJat26d2Gb79u3k7Owsvv7Pf/5Da9eupZSUFMrIyKCVK1eSTCajL7/8UqN44uLiyMTEhDZu3EgXLlygKVOmUOvWrenOnTtim/Hjxys99Tc8PJz27dtHmZmZlJSURKNHjyapVErp6elK266qnOaLwKUd9WrSeQA8afgHqyHpFhcXk1wuJ0NDQ8rOzlZaVpt6ufHx8eTo6EgymYzGjBlDCxcuVEq6REQ//PADubq6krGxMcnlcurbty/t3bu32jjroqqkS1RzPduKurgVfvnlF/rb3/5Gpqam1LJlS3Jzc6OoqCh6+vSp2KbiMzl06FC1MUVGRpK9vT0ZGxuTl5cXnTp1Smm5r68vhYSEiK9nzpwptm/Tpg0NHDiQkpOTK22Xk27Tm7jgjZ7hgjfltwGHhobi/fffb/D3Onz4MIYNG4YrV66gdevWDf5+L8rKyoKjoyNSUlLQo0ePKttwwRv9wtfpMr00a9YsmJqa4tGjRw36Pnv37sWnn36qk4Q7ePBglc98Y/qLR7p6hke6wLVr18SrKDp37lzp9tmm4tatW+KJzoqrTarCI139wklXz3DSZS/ipKtf+PACY4xpESddxhjTIk66jDGmRZx0GWNMi/g2YD0jkUhyBUHgwqtMJJFIcmtuxRoLvnqB1ZogCGYATgNYTETar1yuZYIgeAHYBeBVIsrQdTxMP3HSZbUilF8cGwcgn4je1nU82iIIwnsA3gPwChGprhbEmAqcdFmtCIIwA8BbAP5OREU6Dkdrnn3ZbAJQCmAiXzTNNMVJl2lMEAQFgB0oH+1d0XU82iYIQksApwB8R0RrdR0P0y+cdJlGBEGwBpAEYDoR7dR1PLoiCIIzgGMAAojojK7jYfqDLxljahMEwRDAVgBbmnPCBQAiugRgGoBtgiBovxoO01s80mVqEwThSwC9AfgTUamu42kMBEFYBqAzgGAieqrjcJge4JEuU4sgCEEAJgEYwwlXyccA5ABm6zoQph94pMtqJAiCA8qvxx1BREd1HU9jIwhCewCJAN4kokO6joc1bjzSZdUSBMEEwDaU3wDBCbcKRHQDwHgAWwRBsNN1PKxx45Euq5YgCKsA2KB8lMudpRqCIMwFEAigDxGV1NSeNU880mUqCYIwDkA/AJM44aolAsADAIt0HQhrvHiky6okCEIPAIcA9COi87qOR18IgmCJ8uuYPyKibbqOhzU+PNJllQiCYA7gZwCzOOFqhojuAXgDwCpBELrqOh7W+PBIlyl5VlvgJwD3iOhdXcejrwRBeBfA+yi/VbphH1nM9AonXaZEEISZKD8T/2pzKmRT3559ecU8exnCx8RZBU66TCQIwqsAtqN8dHZV1/HoO0EQZCgvjPM9Ea3RdTysceCkywAAgiDYoPwE0FQi2q3reJqKZ8d1jwMIIqLfdB0P0z0+kcYqCtnEAviBE279IqLfUV70fJsgCHJdx8N0j0e6DIIgLADwCsoL2ZTpOp6mSBCEbwG8BGAQF8Zp3nik28wJgjAIwASUF7LhhNtwZgMwB/CprgNhusUj3WZMEARHACcBvE5Ex3UdT1P3rC7DbwDGE9H/dB0P0w0e6TZTgiBIUF7IZiEnXO0gopsAxgLY9KwyGWuGeKTbTAmCsAZAawCj+BpS7RIEYQ6AwQD8iKhY1/Ew7eKRbjMkCMIEAH4A3uaEqxOLAPwJYLGuA2HaxyPdZkYQBFcAB1BefjBV1/E0V8+eq5YE4BMi+knX8TDt4ZFuMyIIQiuUF7L5Jydc3SKi+wBGAFghCMJLuo6HaQ+PdJuJZ7UAtgG4TUTv6ToeVk4QhHcAzATgTUQFOg6HaQEn3WZCEIRZAEYD6E1ET3QdDyv37MswGkALAOP4GHvTx0m3GRAEwQflo1xvIsrScTjsBc8K4yQAWENEK3UdD2tYnHSbOEEQbFF+Qf4UItqj63hY1QRB6ALgBICBRHRa1/GwhsMn0powQRCMUF7IJpoTbuNGRBkA3gXwExfGado46TYhgiAYC4Kw7rlZXwEoBTBPNxExTRDRdgDxADYLgmAAAIIgzHt2uzZrIjjpNi09UF4tDIIgDEH5LadvciEbvTIHQEsAc5+9dkT5E5lZE2Gk6wBYvXoZwBlBEDoBWAcgmIju6DgmpgEiKhUEYRSA3wRBOAngDMr/rqyJ4JFu0/IygBSU3wAxH8ApQRCC+BihfhAEwVAQhOEA8lD+K+UHADfASbdJ4aTbtLwMwBdABsrv7U8FEA7AVJdBMbUZARgHIBOAB4AVKK/D2/PZSVHWBPAlY03Es/+UhQDuAygAcBPlJ9L+xxfc6xdBENxQXuy8D8q/PDug/GGhfOt2E8Aj3abDHeV3NV0GMJGIXiOi/Zxw9Q8RnSOiUQBeA5CM8hNrw3UaFKs3PNJtIp5dYvR3Ijqm61hY/RIEoSeAm0R0T9exsLrjpMsYY1rEhxcYY0yLqj0jKpVKc4qKitpoKxjGNCGRSJ4WFRXxwIE1OhKJJPfx48e2VS2r9vCCIAh8HoY1WoIggPsna4ye9U2hqmU8SmCMMS3ipMsYY1rESZcxxrSIky5jjGlRs0q6giBg165darefN28ePD09GzAixspx32xGiEjlVL646cjOzqaioiK12+fn59Pdu3cbMCKia9eu0T/+8Q+SSqVkbW1NH330EZWWlqq17p9//kl2dnYEgPLz88X5ISEhBKDS1L17d7HNgwcPaPr06dS+fXuSSqXUp08fSktLq/f9a0hNqX82l7759OlTWrZsGXXt2pUkEgm5uLjQ//3f/ymtGxYWVqnvOjs71+u+NbRnfbPqvKpqATXBpNvYlJaWUo8ePah///509uxZ2rNnD1lZWdHnn3+u1vrDhw+nwMDASh07Ly+PsrOzxemPP/4gS0tLCgsLE9u88cYb1KNHDzp69ChdunSJpk2bRu3ataOHDx/W9242GO6fDaeh+uaKFSvI3Nyc4uPjKTMzk9asWUMmJiZ06tQpsU1YWBi5ubkp9eE7d+7U+z42pGaRdB8+fEhjxowhmUxG7dq1o1WrVpGHh4dSogFAO3fuJCKiq1evEgDavn079e7dm6RSKXl5eSmN9sLCwsjDw6PBYt6zZw8ZGhpSTk6OOG/VqlVkYWFBxcXF1a67fv166t27Nx04cKBSx37Rv//9bxIEgbKysoiIqLCwkAwNDWnv3r1im7KyMrKxsaHVq1fXca+0R1/6J/fNv/qmQqGgTz75RKn98OHDafTo0eLrht43bagu6TaZY7offvghTp06hd27d2Pv3r3YuXMnLl26VON6n3/+OT7//HOcOXMGMpkMkydP1uh9TU1Nq52mTp2qct2EhAS4ubmhTZu/bvoLCAhAXl4e0tPTVa6XmZmJzz77DDExMTAwqPlPuH79evTv3x8ODg4AgNLSUpSVlUEikYhtDAwMYGxsjBMnTqiz20wD3Df/8uTJE6V+BwBSqbRSv7t48SLatm2LTp06Yfz48bhx44a6u93oNYnCyPn5+YiJicFPP/0EPz8/AEB0dDTat29f47off/wx/P39AQBz5sxBQEAAioqKKnUMVZKTk6tdbm5urnJZTk6OUqcGIL7OyclBz549K61TWlqKsWPH4quvvkKnTp1w/fr1at//1q1b+OWXX7B161ZxnpmZGby9vREeHo6tW7dCLpdj+fLluHHjBnJycqrdHtMM901lAwYMQFRUFIKDg+Hm5oZDhw5h+/btKC0tFdt4e3tj48aNcHZ2RnZ2NsLDw/Haa68hJSUFLVu2rHaf9EGTSLpXrlxBSUkJvLy8xHk2Njbo2LFjjes+33natm0LALh9+zbs7e3Vem8nJyfNgq2jBQsWwMbGBm+//bZa7WNiYmBhYYGhQ4cqzd+8eTNCQkLQtm1bGBoaom/fvggKCuLbausZ901lX3zxBW7evCleedGpUye89dZb2LBhg9gmKChI/Lerqyu8vb3h4OCAbdu2ISQkpOF2QEuazOGF2mrRooX4b0Eov1X66dOnaq9fl59wtra2yM3NVZpX8drWtspaGTh06BB2794NIyMjGBkZoV+/8gfFWlhYICIiQqktEWHDhg0YP348jI2NlZY5OTnh+PHjePjwIW7duoX//ve/uHfvHhwd+WnfjUVT7JsymQybNm1CYWEhrl27hkuXLsHMzKzafmdhYYGuXbvi8uXLau97Y9YkRrqdOnVCixYtkJiYiODgYADAnTt3kJWV1eDvXZefcAqFAhEREbhz5w6sra0BAPv374eFhQVeeumlKteJjo7Go0ePxNeJiYmYNGkSEhISKo2efv31V1y+fLnaY4FmZmYwMzPDlStX8Ntvv2HevHnV7g/TDPfNqvumsbEx7OzsUFpaiu3bt+P1119XGUtBQQEyMzPF0b6+axJJ18zMDCEhIZg1axYsLCxgaWmJOXPmwMTERBwhNJS6/ITz9/dHt27dMG7cOCxevBg5OTmYO3cu3n//fXGUc/r0aUyYMAEHDhyAnZ1dpRHB3bt3AQDdunWDqany8yfXr18Pb29v9OjRo9J77927FwYGBnBycsKFCxfwwQcfYODAgQgMDKz1/rDKuG8q98309HScOXMGXl5eyM3NxZdffoni4mLMnj1bXC80NBSDBw+Gg4MDbt26hbCwMBgZGWHUqFG13p/GpMkcXvj222/Rq1cvBAUFITAwEIMGDYK9vb3aJx10wdDQELt27YKhoSEUCgXGjRuHkJAQpdFmYWEhLl26hJKSEo22/eDBA/z8888qR7n379/Hu+++i5deeglTp07FG2+8gR9//LEuu8NU4L75l7KyMixatAg9e/bEoEGDYGlpiWPHjqF169Zimxs3bmDMmDFwdnbGyJEjYWVlhZMnT0Iul9fnLupMk62nm5eXh3bt2mHTpk0YPpyf6dcU6Ws9Xe6bTV919XSbxOEFAEhKSkJGRgZ69eqFe/fu4YsvvoC5uTn/XGY6x32TPa/JJF0iwuLFi/H777/DxMQEXl5eOHLkSJO4ro/pN+6b7HlN9vACa/r09fACa/r4cT2MMdZIcNKtJ5rWQ2WsoXBfbNw46TYjO3fuRK9evSCRSCCXyzF+/Hil5Zs2bULPnj0hk8nQrl07TJ06Ffn5+TqKljVVCxYswN///nfIZDJYWVlV2/bEiRMwMjKqVLD9yJEjGDx4MNq1a6d3XzKcdJuJn3/+GSEhIXjnnXdw/vx5HDlyBMOGDROXHz16FG+99RamTp2KtLQ0xMXF4eDBg/jggw90GDVrioqLi/HGG2/gvffeq7Zdfn4+QkJC0Ldv30rLHj16BDc3N6xYsaKhwmw4qmo+UiOtpxsfH08uLi5kYmJCcrmc/P39qaysjIiITp48Sf369SNLS0tq1aoV9evXj1JTU8V1K+qUxsfHk0KhIIlEQq+88gpdu3aNDh48SD169CBTU1MaOXIkFRQUiOv5+vrSjBkz6L333iNzc3OytramiIgIpbjwXD1UIqLr16/TiBEjyNzcnORyOQ0fPpxu3rwpLj948CB5enqSVColCwsL8vHxodu3bzfIZ1ZSUkJ2dna0bt06lW2WLFlCXbt2VZoXERFBLi4uDRJTfdB1/+S+WDfR0dEkl8tVLh8/fjzNmTOnxvq6L+5vY4CmUk83OzsbY8aMweTJk5Geno6DBw8qVSTKz8/HxIkTceLECRw7dgy2trYYPHgwnjx5orSdL774AuHh4UhMTERRURFGjx6NBQsWIDo6Gvv27cOhQ4ewbNkypXU2bNgAU1NTnD59GosWLUJ4eDji4+OrjLOkpAQBAQGwtLTE8ePH8euvv4KIMGTIEDx9+hSlpaUYNmwY+vTpg9TUVBw7dgwTJkyodt9dXFyqLV7y/OfwojNnzuDmzZsQBAFubm5o164dhgwZolRARKFQICsrC/v27QMR4datW9ixY0e1223OuC/Wri+q68cff0RKSkrTrAWiKhtTIxzpJiUlEQDxCQg1KSoqImNjYzp69CgR/TW62Lhxo9hm7dq1BIDOnj0rzps+fTr169dPfO3r60uurq5K254+fTopFArxNZ77tt20aVOlEWJ+fj4ZGhrSqVOn6M8//yQAdPjwYfV2nIiysrIoIyND5XTjxg2V68bGxhIAcnBwoO3bt9Pp06fp9ddfp/bt2yuNouLi4qhly5ZkZGREAGjUqFHiyK0x0mX/5L5Yu774PFUj3evXr5Otra34y4BHujrk5uYGPz8/9OzZE6NGjcKGDRvw8OFDcXlubi7efvttdOnSBebm5pDL5SguLq5UTNnV1VX8d0VhZhcXF6V5t2/fVlrH29tb6bVCocDFixerjPPcuXNIT09X+va3tbVFWVkZMjMzYWlpiXHjxiEwMBBDhgzB999/X+n9XuTg4AAnJyeVk52dncp1K8oBzp07F8OGDUOvXr3www8/IC8vTzwBceHCBXz44YeYP38+kpKSsGvXLiQnJyM0NLTauJor7ou164vqmDBhAj766COlz6Ep0auka2hoiAMHDmD37t1wcnLCkiVL0L17d7HOZ0hICFJSUhAZGYmTJ08iOTkZMpkMxcXFStupqk7pi/M0qVv6ooKCAnh7eyM5OVlpysjIwKBBgwCUXylw7NgxeHl5ISYmBl27dkVaWprKbdblJ11F/dPnS/K1bNkS9vb2YhJYuHAhXnvtNcycOROurq4YOHAgli1bhu+++06pXB8rx32x4Q4vHD58GB9//LFYl/fLL79EUlISjIyMcPz48TptuzHQu9uADQwM4OPjAx8fH4SFhcHGxgb79u3DhAkTcPz4cURFRYn3tKenp6OwsLBe3vf06dNKr0+ePIlu3bpV2dbd3R3btm1DmzZtYGZmpnKbHh4e8PDwwGeffQYXFxf8/PPPKr/d9+zZU201J6lUqnKZp6cnjI2NkZGRgd69ewMAHj9+jBs3bohPISgsLKy0DUNDw+cPNbEXcF+sWnV9UR0pKSlKr1euXIkjR44gLi4OnTp1qtO2GwO9SrqnTp3CgQMH4O/vD2traxw5cgQFBQVwdnYGAHTp0gU//PAD3N3dce/ePYSGhlZ6YkJtZWZm4pNPPhGLMq9btw4bN26ssu3YsWOxePFiDBs2DPPmzYOdnR2uXr2K+Ph4LFy4EPfv30dUVBSGDBkCOzs7nD9/Hn/88YfK4tAAxIdK1oa5uTmmTJmCsLAwdOjQAe3bt8f8+fPRqlUrcbQzePBgTJkyBX5+fujXrx/++OMPzJo1C/369atUp5dxX6yL69ev4969e7h+/TrKysrEYuvdu3eHsbFxpfrPNjY2kEgkSvMLCgqUTgRfvXoVycnJsLW1Vflki0ZD1cFeaoQn0i5cuEABAQFkZWVFEomEunXrRhs2bBCXnzlzhjw9PcnExIScnZ3pP//5D8nlcoqOjiaiv05epKSkiOvs3Lmz0gmZr776SunkQ8VlOu+88w6ZmZmRlZUVzZ8/X2kdvHAw/+bNmzRu3DiSy+VkYmJCTk5ONG3aNHr8+DHl5ORQcHAw2drakrGxMXXq1Im+/vrr+vyoKnny5AnNnDmTrKysyNzcnAIDA+nSpUtKbZYvX07dunUjqVRK7dq1o4kTJ1Jubm6DxlUXuuyf3BdrLyQkhABUmq5evVpl+6pOpB06dKjKbTz/WHtdQjUn0rjgjRr8/Pzg6emJb775RtehsOc0x4I33Bf1Axe8YYyxRoKTLmOMaREfXmB6qzkeXmD6gQ8vMMZYI8FJlzHGtEhnSTcrKwuCICA1NVVXIailY8eOEAQBgiCgoKBA1+E0C/379xc/c130D+6bTJX66Js80lVDREQEsrOzlR4keP78efj4+EAikaBDhw5YsmRJjduZMWMGPDw8YGJiUqkoMwBs374dAwYMgLW1NczNzfHqq69i//79Sm0ePnyI999/Hx06dIBMJkPfvn1x4cIFjffp+vXrGDhwIGQyGWxsbPDxxx+jrKys2nXUKT69detWuLq6QiqVonPnzli7dq3S8o0bN4qdtmKSSCRKbeLj4yvddcWqVh99My8vD0FBQbCzs4OJiQk6dOiA999/X6mWxFtvvVXp7yYIgtJda/VVWLw2fXPYsGFwcHCARCJB27ZtMX78eNy6dUupTU2fi7b6JiddNZiZmcHW1la8N/7hw4fw9/eHg4MDkpKSsGTJEoSFhWHDhg01bmvSpEkYNWpUlcuOHDmCAQMGYM+ePUhKSoKvry8GDRqE8+fPi23efvtt/Prrr4iNjUVycjK6deuGAQMGaPSEh7KyMgwcOBDFxcU4ceIEYmJiEB0djfDw8GrXq6n49O7duxESEoIZM2YgLS0NX3/9NWbNmoWff/5ZqZ2lpSWys7PF6dq1a0rLW7duDWtra7X3pzmrj74pCAIGDx6MHTt24Pfff0dMTAwOHDiAadOmiW2+++47pb/ZH3/8AUtLS7zxxhtim/ooLF7bvunn54e4uDhcunQJ27dvx5UrVzBy5Ehxubqfi1b6pqq7JqiaO9K+//576tChAz19+lRp/muvvUb//Oc/iUj9Is4Vd+RUVeYtMjKSHBwclOZFRUVR165dycTEpNJdQA3BwcGBIiMjleatXLmSLC0tqbi4WJw3e/ZstQt+11Sq7nndu3enBQsWEBFRYWEhGRoa0t69e8XlZWVlZGNjQ6tXr1Zre0REe/bsIUNDQ8rJyRHnrVq1iiwsLJT2SRVVJfnGjBlDo0ePVpo3a9YseuWVV2pc90VV3bH1oqr6J/fNuvXNCt999x117NhR5fJ///vfJAiCytKWqGW5xbr2zQo7duwgQ0NDsTSpOp9LA/TN+ivtOHLkSGRnZ+PYsWPivBs3buDo0aN48803AahfxFkTW7ZsQXh4OBYtWoSLFy9i3rx5+OCDD7Bjxw6V60RERFRbEcnU1LRSub2aJCQkwM/PT6kaVEBAANLS0pR+ktXV06dPkZ+fD0tLSwBAaWkpysrKlH7yGBgYwNjYGCdOnNAofjc3N7GUYEX8eXl5SE9Pr3W8T548qfRzTCqV4rffflMqkPLgwQPY29ujQ4cOGDp0aK0Oj6jCfbPuffPWrVvYvn07fH19VbZZv349+vfvX+c6DC+qj7557949bNmyBb1794aBgYG4XXU+l4bsmxVqVfDG2toaAwYMQGxsLHx8fAAAsbGx6NKli3issn///krrrF+/Hubm5khMTBQrXWkqLCwMS5cuxdChQwEAjo6OOHPmDNasWYPg4OAq15k6darSz4yqtGvXTqM4cnJy4OTkpDSvopPk5ubC3Nxco+2p8q9//QuFhYUYMWIEgPKfkt7e3ggPD8fWrVshl8uxfPly3LhxAzk5ORrF/3ynfj7+nJwc9OzZs1bxDhgwAKGhoQgJCYGvry+Sk5Oxbt06lJaW4u7du2jbti2cnZ2xfv16uLq64sGDB/jmm2/w6quvIi0tTeO/Q1W4b9a+b44ZMwY7duzA48ePMWTIEKxZs6bKdrdu3cIvv/yCrVu3ahSbOurSN2fPno3vv/8ehYWFUCgUSseU1flcGrpvVqj1Md2xY8ciPj4epaWlAMpPoFSMJAD1izir69GjR8jMzERISIjSSGDp0qXIzMxUuZ6lpWW1BZednJxgZNT4iq3FxsYiPDwcP/30k9JJq82bN+PJkydo27YtpFIp9u3bh6CgIPEbXZemTJmCKVOmICAgAC1atMDAgQPFR79UxKdQKDBhwgT87W9/g6+vL7Zv3w65XF7phFtdcN+snaVLl+LMmTPYsWMHLl++jI8++qjKdjExMbCwsBC/YBqLjz76CGfPnsV///tfGBgYYOLEiRqtr42+CdShtOPQoUMxZcoU7N+/H46OjkhOTlZ6TlNISAju37+PyMhI2Nvbw9jYGG5ubpWKOFcwMDCodHfR8z9JKy6JiY6OhoeHh1K7538yvCgiIgIRERHV7suFCxfEurLqsLW1FYtVV6h4/eK3dG38+OOPmDx5MrZt21bpSahOTk44fvw48vPz8fjxY9jY2OCVV17Byy+/rFH8SUlJVcZfl7J4BgYG+Pbbb7F48WLk5uaiTZs2WLduHUxNTVWefGjRogXc3d2VyvTVFffN2vXNirKIL730EiwtLeHj44O5c+fCxsZGbENE2LBhA8aPH19vpSpfjKG2fdPKygpWVlbo2rUrunXrhg4dOiAxMRG9evWq1efSEH0TqEPSbdmyJYKDgxEbGwsHBwf06tVLafiuaRFna2tr5OXloaioSDwueO7cOXF5mzZt0LZtW1y5ckXl2f+qNMRPOIVCgblz56KkpET8T7V//364uLjU+dBCbGwsJk2ahLi4OPzjH/9Q2c7MzAxmZma4cuUKfvvtN40e4KdQKBAREYE7d+6IyXD//v2wsLCoto6quoyMjMRHtvz4448YOHCgypF4WVkZUlJSMGTIkDq/bwXum3XvmxVfMi9+Ef3666+4fPkyJk+erFFc6qqvvlkRf8Vx+tp8Lg3RN8XgVE2ooV7pzp07yczMjBwdHWnZsmVKy9zd3SkwMJAuXrxIx48fJ4VCQcbGxirrid69e5dkMhmFhoZSRkYGRUVFkVwuVzpDvGrVKmrZsiVFRkbSpUuX6Ny5c7RmzRpauXJljWcca6uqM8R5eXnUpk0bGj9+PKWmplJcXBzJZDKlR5xv376dnJ2dldbLyMigs2fP0rvvvkvdu3ens2fPKj2EcMuWLWRkZEQrVqyg7OxsccrLyxPb/PLLL7Rv3z7KzMyknTt3UqdOnWjIkCEa7VNpaSn16NGD/P39KTk5mfbu3UvW1tY0d+5csc2pU6fI2dlZ6SGD165do7Nnz1J4eDhZWFiI8T958oSIiHJzcykqKorS09MpMTGRxo0bR61bt6bMzExxG+Hh4WL8SUlJNHr0aJJKpZSenq4UY22vXqjAfVP9vrl3715av349nT9/nq5evUq7d+8mFxcXeu211yq957hx48jb27vKePLz88U+AYCWL19OZ8+epezsbLX3qTZ9MzExkb777js6e/YsZWVl0cGDB6l3797k5OQk9k11PpcG6JtV51VVC0iNpFtcXExyuZwMDQ0rfbC1KeIcHx9Pjo6OJJPJaMyYMbRw4cJKl+X88MMP5OrqSsbGxiSXy6lv375Kl1DVt6o6NhHRuXPnqHfv3mRiYkJ2dna0aNEipeXR0dGVkoKvr2+VhZdrWh4SEiK22bp1K3Xs2JFatGhBdnZ2NHv2bHr8+LHS+4SEhJCvr2+1+5WVlUVBQUEklUrJysqKQkNDqbS0VFxeUST6+cLSNRWfzs3NJS8vL5LJZGRqakoDBw6ktLQ0pfedOXMm2dvbk7GxMbVp04YGDhxIycnJleKra9Llvql+3zx8+DApFApq1aoVSSQS6tKlC82ePZvu37+vtF5eXh5JpVKKioqqMh51Cos3RN9MTU2lvn37kqWlJZmYmFDHjh1p6tSpdPPmTY0+lwbom/WfdJsDVR27MfP19W00FfRrq65JtzngvqkbOrlOt7mZNWsWTE1N9eKpuPn5+cjMzNTrR6cPHjy4yT5+u75x39Su+uibXE+3BteuXRPPVHfu3Fm83ZI1nFu3bokntiquLqhKc6+ny31T+zTsm1X+QTjpMr3V3JMua7y4iDljjDUSnHQZY0yLOOkyxpgWVXtHmkQiyRUEoe73tTLWACQSyVNBEHjgwBodiUSSq2pZtSfSGGOM1S8eJTDGmBZx0mWMMS3ipMsYY1rESZcxxrSIky5jjGkRJ13GGNMiTrqMMaZFnHQZY0yLOOkyxpgWcdJljDEt4qTLGGNaxEmXMca0iJMuY4xpESddxhjTIk66jDGmRZx0GWNMizjpMsaYFnHSZYwxLeKkyxhjWsRJlzHGtIiTLmOMaREnXcYY0yJOuowxpkWcdBljTIs46TLGmBZx0mWMMS3ipMsYY1rESZcxxrSIky5jjGkRJ13GGNMiTrqMMaZF/x88xJTSbv2F/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "for index, stump in enumerate (model.estimators_):\n",
    "    plot_tree(stump)\n",
    "ax.figure.savefig('file.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "15664dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 1),\n",
       " (1, 1),\n",
       " (1, 1),\n",
       " (0, 1),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (1, 1),\n",
       " (1, 1),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (0, 1),\n",
       " (0, 0),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (1, 0),\n",
       " (0, 1),\n",
       " (0, 0),\n",
       " (0, 1),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (1, 1),\n",
       " (1, 1),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 1),\n",
       " (1, 0),\n",
       " (1, 1),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (1, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (1, 1),\n",
       " (1, 1),\n",
       " (1, 0),\n",
       " (0, 1),\n",
       " (0, 0),\n",
       " (1, 0),\n",
       " (1, 1),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (1, 0),\n",
       " (1, 1),\n",
       " (0, 1),\n",
       " (0, 0),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (0, 1),\n",
       " (1, 1),\n",
       " (1, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (1, 1),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0),\n",
       " (0, 0)]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = model.predict(xtest)\n",
    "list(zip(ytest, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "5929d483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[52,  7],\n",
       "       [ 9, 22]], dtype=int64)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf = confusion_matrix(yhat, ytest)\n",
    "cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "7c356518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'True'), Text(0, 1.5, 'false')]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.heatmap(cf,annot=True,cmap='Greens')\n",
    "ax.set(xlabel='predicated Values',ylabel='Actual Values',title='Confusion Matrix')\n",
    "ax.xaxis.set_ticklabels(['True','false'])\n",
    "ax.yaxis.set_ticklabels(['True','false'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "01e82978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[52,  9],\n",
       "       [ 7, 22]], dtype=int64)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model_nb=GaussianNB()\n",
    "model_nb.fit(xtrain,ytrain)\n",
    "y_hat=model.predict(xtest)\n",
    "nb=confusion_matrix(ytest,y_hat)\n",
    "nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3b6c2c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'True'), Text(0, 1.5, 'false')]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.heatmap(nb,annot=True,cmap='YlGnBu')\n",
    "ax.set(xlabel='predicated Values',ylabel='Actual Values',title='Confusion Matrix')\n",
    "ax.xaxis.set_ticklabels(['True','false'])\n",
    "ax.yaxis.set_ticklabels(['True','false'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "2836b100",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "243e1cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19eb4cdd9d0>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "y_prob=model.predict_proba(xtest)[:,1]\n",
    "tpr,fpr,t=roc_curve(ytest,y_prob)\n",
    "plt.plot(tpr,fpr,label=f'Adaboost(AUC={round(auc(tpr,fpr),4)})')\n",
    "         \n",
    "y_prob=model_rf.predict_proba(xtest)[:,1]\n",
    "tpr,fpr,t=roc_curve(ytest,y_prob)\n",
    "plt.plot(tpr,fpr,label=f'Random Forest(AUC={round(auc(tpr,fpr),4)})')\n",
    "         \n",
    "y_prob=model_dt.predict_proba(xtest)[:,1]\n",
    "tpr,fpr,t=roc_curve(ytest,y_prob)\n",
    "plt.plot(tpr,fpr,label=f'Decision Tree(AUC={round(auc(tpr,fpr),4)})')\n",
    "         \n",
    "y_prob=model_lr.predict_proba(xtest)[:,1]\n",
    "tpr,fpr,t=roc_curve(ytest,y_prob)\n",
    "plt.plot(tpr,fpr,label=f'Logistic Regression(AUC={round(auc(tpr,fpr),4)})')\n",
    "\n",
    "y_prob=model_nb.predict_proba(xtest)[:,1]\n",
    "tpr,fpr,t=roc_curve(ytest,y_prob)\n",
    "plt.plot(tpr,fpr,label=f'Naive (AUC={round(auc(tpr,fpr),4)})')\n",
    "\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11174ac6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56d961f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf3c6c3a",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}