Heart Failure Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "629dcf6f",
   "metadata": {},
   "source": [
    "# Heart Failure Prediction\n",
    "\n",
    "Medical Real World Problem Statement :- \n",
    "\n",
    "Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide.\n",
    "Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.\n",
    "\n",
    "If we are able to make a machine learning model, then this will help in early detection and people can be saved.\n",
    "\n",
    "We have to predict a person death event using some features:-\n",
    "    \n",
    "   -  Age ,Gender , blood pressure, smoke, diabetes,ejection fraction, creatinine phosphokinase, serum_creatinine, serum_sodium, time  \n",
    "   \n",
    "Dataset link:- https://www.kaggle.com/andrewmvd/heart-failure-clinical-data\n",
    "\n",
    "## Let's Start        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3920f8f4",
   "metadata": {},
   "source": [
    "# Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "460cdbd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e41e392",
   "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>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
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       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "    <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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"heart_failure_clinical_records_dataset.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b373dc80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de42816f",
   "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": 5,
   "id": "0ab2f2bb",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b208cfd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         0\n",
       "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",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f6525e9",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b99cd78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of living cases:  203\n",
      "Total no. of dead cases:  96\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len_live=len(data[\"DEATH_EVENT\"][data.DEATH_EVENT==0])\n",
    "len_death=len(data[\"DEATH_EVENT\"][data.DEATH_EVENT==1])\n",
    "\n",
    "arr=np.array([len_live, len_death])\n",
    "labels=['LIVING','DEAD']\n",
    "print('Total no. of living cases: ',len_live)\n",
    "print('Total no. of dead cases: ',len_death)\n",
    "\n",
    "plt.pie(arr, labels=labels, explode=[0.2,0.0], shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09997394",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sandeep\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['age'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d2fd02a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of not dead cases above 50:  167\n",
      "Total no. of dead cases above 50:  85\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_above_50_not_died=data['DEATH_EVENT'][data.age>=50][data.DEATH_EVENT==0]\n",
    "age_above_50_died=data['DEATH_EVENT'][data.age>=50][data.DEATH_EVENT==1]\n",
    "\n",
    "len_died=len(age_above_50_died)\n",
    "len_not_died=len(age_above_50_not_died)\n",
    "\n",
    "arr1=[len_died, len_not_died]\n",
    "labels=['DIED','NOT DIED']\n",
    "print('Total no. of not dead cases above 50: ',len_not_died)\n",
    "print('Total no. of dead cases above 50: ',len_died)\n",
    "\n",
    "plt.pie(arr1, labels=labels, explode=[0.2,0.0], shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3dc56c7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of patients died with diabetes:  40\n",
      "Total no. of patients does not died with diabetes:  85\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patient_have_diabetes_alive=data['DEATH_EVENT'][data.diabetes==1][data.DEATH_EVENT==0]\n",
    "patient_have_diabetes_dead=data['DEATH_EVENT'][data.diabetes==1][data.DEATH_EVENT==1]\n",
    "\n",
    "len_d_died=len(patient_have_diabetes_dead)\n",
    "len_d_alive=len(patient_have_diabetes_alive)\n",
    "\n",
    "arr2=[len_d_died, len_d_alive]\n",
    "labels=['DIED WITH DIABETES','DOES NOT DIED WITH DIABETES']\n",
    "print('Total no. of patients died with diabetes: ',len_d_died)\n",
    "print('Total no. of patients does not died with diabetes: ',len_d_alive)\n",
    "\n",
    "plt.pie(arr2, labels=labels, explode=[0.2,0.0], shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d5487e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr=data.corr()\n",
    "plt.subplots(figsize=(20,20))\n",
    "sns.heatmap(corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd925472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c9c2a_row0_col0, #T_c9c2a_row1_col1, #T_c9c2a_row2_col2, #T_c9c2a_row3_col3, #T_c9c2a_row4_col4, #T_c9c2a_row5_col5, #T_c9c2a_row6_col6, #T_c9c2a_row7_col7, #T_c9c2a_row8_col8, #T_c9c2a_row9_col9, #T_c9c2a_row10_col10, #T_c9c2a_row11_col11, #T_c9c2a_row12_col12 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row0_col1, #T_c9c2a_row4_col0, #T_c9c2a_row5_col4, #T_c9c2a_row12_col5 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row0_col2 {\n",
       "  background-color: #5673e0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row0_col3 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row0_col4, #T_c9c2a_row5_col0 {\n",
       "  background-color: #90b2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row0_col5 {\n",
       "  background-color: #89acfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row0_col6 {\n",
       "  background-color: #4e68d8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row0_col7 {\n",
       "  background-color: #9bbcff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row0_col8, #T_c9c2a_row4_col2, #T_c9c2a_row6_col1, #T_c9c2a_row6_col7, #T_c9c2a_row11_col9 {\n",
       "  background-color: #6180e9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row0_col9, #T_c9c2a_row3_col6, #T_c9c2a_row5_col1, #T_c9c2a_row5_col8, #T_c9c2a_row8_col5 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row0_col10, #T_c9c2a_row2_col7, #T_c9c2a_row7_col2, #T_c9c2a_row8_col0 {\n",
       "  background-color: #688aef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row0_col11, #T_c9c2a_row1_col5, #T_c9c2a_row1_col8, #T_c9c2a_row8_col1, #T_c9c2a_row10_col0 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row0_col12 {\n",
       "  background-color: #dfdbd9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row1_col0 {\n",
       "  background-color: #8fb1fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row1_col2, #T_c9c2a_row2_col1, #T_c9c2a_row3_col9, #T_c9c2a_row3_col10, #T_c9c2a_row8_col7, #T_c9c2a_row9_col3, #T_c9c2a_row9_col6, #T_c9c2a_row11_col0, #T_c9c2a_row11_col5, #T_c9c2a_row11_col12, #T_c9c2a_row12_col4, #T_c9c2a_row12_col8, #T_c9c2a_row12_col11 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row1_col3, #T_c9c2a_row2_col3, #T_c9c2a_row5_col3 {\n",
       "  background-color: #6282ea;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row1_col4, #T_c9c2a_row9_col0, #T_c9c2a_row11_col8 {\n",
       "  background-color: #88abfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row1_col6 {\n",
       "  background-color: #506bda;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row1_col7, #T_c9c2a_row7_col4 {\n",
       "  background-color: #7b9ff9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row1_col9 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row1_col10, #T_c9c2a_row11_col7 {\n",
       "  background-color: #445acc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row1_col11 {\n",
       "  background-color: #8db0fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row1_col12, #T_c9c2a_row12_col0 {\n",
       "  background-color: #bcd2f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row2_col0 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row2_col4 {\n",
       "  background-color: #7396f5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row2_col5, #T_c9c2a_row7_col10 {\n",
       "  background-color: #5a78e4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row2_col6, #T_c9c2a_row10_col6, #T_c9c2a_row10_col7, #T_c9c2a_row12_col3 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row2_col8, #T_c9c2a_row12_col2 {\n",
       "  background-color: #80a3fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row2_col9, #T_c9c2a_row6_col5, #T_c9c2a_row7_col1 {\n",
       "  background-color: #7da0f9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row2_col10, #T_c9c2a_row4_col3, #T_c9c2a_row8_col10, #T_c9c2a_row12_col9 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row2_col11, #T_c9c2a_row10_col12 {\n",
       "  background-color: #abc8fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row2_col12 {\n",
       "  background-color: #bbd1f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row3_col0, #T_c9c2a_row5_col2 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row3_col1, #T_c9c2a_row3_col2, #T_c9c2a_row4_col7, #T_c9c2a_row11_col2 {\n",
       "  background-color: #6a8bef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row3_col4, #T_c9c2a_row8_col2 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row3_col5, #T_c9c2a_row5_col6, #T_c9c2a_row5_col7, #T_c9c2a_row6_col10 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row3_col7, #T_c9c2a_row10_col5, #T_c9c2a_row11_col6, #T_c9c2a_row12_col10 {\n",
       "  background-color: #5f7fe8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row3_col8 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row3_col11 {\n",
       "  background-color: #b6cefa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row3_col12 {\n",
       "  background-color: #aec9fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row4_col1, #T_c9c2a_row4_col5 {\n",
       "  background-color: #7699f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row4_col6 {\n",
       "  background-color: #7295f4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row4_col8 {\n",
       "  background-color: #a2c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row4_col9, #T_c9c2a_row10_col3 {\n",
       "  background-color: #3d50c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row4_col10, #T_c9c2a_row10_col1, #T_c9c2a_row12_col6 {\n",
       "  background-color: #4f69d9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row4_col11 {\n",
       "  background-color: #b7cff9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row4_col12 {\n",
       "  background-color: #7093f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row5_col9 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row5_col10, #T_c9c2a_row9_col1 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row5_col11, #T_c9c2a_row6_col3, #T_c9c2a_row6_col8, #T_c9c2a_row8_col12, #T_c9c2a_row12_col1 {\n",
       "  background-color: #81a4fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row5_col12 {\n",
       "  background-color: #bfd3f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row6_col0, #T_c9c2a_row9_col8 {\n",
       "  background-color: #6687ed;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row6_col2 {\n",
       "  background-color: #7597f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row6_col4 {\n",
       "  background-color: #93b5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row6_col9 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row6_col11 {\n",
       "  background-color: #b1cbfc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row6_col12, #T_c9c2a_row7_col0 {\n",
       "  background-color: #a3c2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row7_col3, #T_c9c2a_row9_col4 {\n",
       "  background-color: #5875e1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row7_col5, #T_c9c2a_row10_col4 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row7_col6, #T_c9c2a_row9_col5 {\n",
       "  background-color: #516ddb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row7_col8 {\n",
       "  background-color: #3c4ec2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row7_col9 {\n",
       "  background-color: #6788ee;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row7_col11 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row7_col12 {\n",
       "  background-color: #e6d7cf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row8_col3 {\n",
       "  background-color: #4c66d6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row8_col4 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row8_col6, #T_c9c2a_row9_col7, #T_c9c2a_row10_col8, #T_c9c2a_row11_col3 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row8_col9 {\n",
       "  background-color: #5d7ce6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row8_col11 {\n",
       "  background-color: #c1d4f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row9_col2 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row9_col10 {\n",
       "  background-color: #e1dad6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row9_col11 {\n",
       "  background-color: #aac7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row9_col12 {\n",
       "  background-color: #adc9fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row10_col2 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row10_col9 {\n",
       "  background-color: #e2dad5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row10_col11 {\n",
       "  background-color: #a9c6fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row11_col1 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row11_col4 {\n",
       "  background-color: #8badfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c9c2a_row11_col10 {\n",
       "  background-color: #5b7ae5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c9c2a_row12_col7 {\n",
       "  background-color: #c3d5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c9c2a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c9c2a_level0_col0\" class=\"col_heading level0 col0\" >age</th>\n",
       "      <th id=\"T_c9c2a_level0_col1\" class=\"col_heading level0 col1\" >anaemia</th>\n",
       "      <th id=\"T_c9c2a_level0_col2\" class=\"col_heading level0 col2\" >creatinine_phosphokinase</th>\n",
       "      <th id=\"T_c9c2a_level0_col3\" class=\"col_heading level0 col3\" >diabetes</th>\n",
       "      <th id=\"T_c9c2a_level0_col4\" class=\"col_heading level0 col4\" >ejection_fraction</th>\n",
       "      <th id=\"T_c9c2a_level0_col5\" class=\"col_heading level0 col5\" >high_blood_pressure</th>\n",
       "      <th id=\"T_c9c2a_level0_col6\" class=\"col_heading level0 col6\" >platelets</th>\n",
       "      <th id=\"T_c9c2a_level0_col7\" class=\"col_heading level0 col7\" >serum_creatinine</th>\n",
       "      <th id=\"T_c9c2a_level0_col8\" class=\"col_heading level0 col8\" >serum_sodium</th>\n",
       "      <th id=\"T_c9c2a_level0_col9\" class=\"col_heading level0 col9\" >sex</th>\n",
       "      <th id=\"T_c9c2a_level0_col10\" class=\"col_heading level0 col10\" >smoking</th>\n",
       "      <th id=\"T_c9c2a_level0_col11\" class=\"col_heading level0 col11\" >time</th>\n",
       "      <th id=\"T_c9c2a_level0_col12\" class=\"col_heading level0 col12\" >DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row0\" class=\"row_heading level0 row0\" >age</th>\n",
       "      <td id=\"T_c9c2a_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row0_col1\" class=\"data row0 col1\" >0.088006</td>\n",
       "      <td id=\"T_c9c2a_row0_col2\" class=\"data row0 col2\" >-0.081584</td>\n",
       "      <td id=\"T_c9c2a_row0_col3\" class=\"data row0 col3\" >-0.101012</td>\n",
       "      <td id=\"T_c9c2a_row0_col4\" class=\"data row0 col4\" >0.060098</td>\n",
       "      <td id=\"T_c9c2a_row0_col5\" class=\"data row0 col5\" >0.093289</td>\n",
       "      <td id=\"T_c9c2a_row0_col6\" class=\"data row0 col6\" >-0.052354</td>\n",
       "      <td id=\"T_c9c2a_row0_col7\" class=\"data row0 col7\" >0.159187</td>\n",
       "      <td id=\"T_c9c2a_row0_col8\" class=\"data row0 col8\" >-0.045966</td>\n",
       "      <td id=\"T_c9c2a_row0_col9\" class=\"data row0 col9\" >0.065430</td>\n",
       "      <td id=\"T_c9c2a_row0_col10\" class=\"data row0 col10\" >0.018668</td>\n",
       "      <td id=\"T_c9c2a_row0_col11\" class=\"data row0 col11\" >-0.224068</td>\n",
       "      <td id=\"T_c9c2a_row0_col12\" class=\"data row0 col12\" >0.253729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row1\" class=\"row_heading level0 row1\" >anaemia</th>\n",
       "      <td id=\"T_c9c2a_row1_col0\" class=\"data row1 col0\" >0.088006</td>\n",
       "      <td id=\"T_c9c2a_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row1_col2\" class=\"data row1 col2\" >-0.190741</td>\n",
       "      <td id=\"T_c9c2a_row1_col3\" class=\"data row1 col3\" >-0.012729</td>\n",
       "      <td id=\"T_c9c2a_row1_col4\" class=\"data row1 col4\" >0.031557</td>\n",
       "      <td id=\"T_c9c2a_row1_col5\" class=\"data row1 col5\" >0.038182</td>\n",
       "      <td id=\"T_c9c2a_row1_col6\" class=\"data row1 col6\" >-0.043786</td>\n",
       "      <td id=\"T_c9c2a_row1_col7\" class=\"data row1 col7\" >0.052174</td>\n",
       "      <td id=\"T_c9c2a_row1_col8\" class=\"data row1 col8\" >0.041882</td>\n",
       "      <td id=\"T_c9c2a_row1_col9\" class=\"data row1 col9\" >-0.094769</td>\n",
       "      <td id=\"T_c9c2a_row1_col10\" class=\"data row1 col10\" >-0.107290</td>\n",
       "      <td id=\"T_c9c2a_row1_col11\" class=\"data row1 col11\" >-0.141414</td>\n",
       "      <td id=\"T_c9c2a_row1_col12\" class=\"data row1 col12\" >0.066270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row2\" class=\"row_heading level0 row2\" >creatinine_phosphokinase</th>\n",
       "      <td id=\"T_c9c2a_row2_col0\" class=\"data row2 col0\" >-0.081584</td>\n",
       "      <td id=\"T_c9c2a_row2_col1\" class=\"data row2 col1\" >-0.190741</td>\n",
       "      <td id=\"T_c9c2a_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row2_col3\" class=\"data row2 col3\" >-0.009639</td>\n",
       "      <td id=\"T_c9c2a_row2_col4\" class=\"data row2 col4\" >-0.044080</td>\n",
       "      <td id=\"T_c9c2a_row2_col5\" class=\"data row2 col5\" >-0.070590</td>\n",
       "      <td id=\"T_c9c2a_row2_col6\" class=\"data row2 col6\" >0.024463</td>\n",
       "      <td id=\"T_c9c2a_row2_col7\" class=\"data row2 col7\" >-0.016408</td>\n",
       "      <td id=\"T_c9c2a_row2_col8\" class=\"data row2 col8\" >0.059550</td>\n",
       "      <td id=\"T_c9c2a_row2_col9\" class=\"data row2 col9\" >0.079791</td>\n",
       "      <td id=\"T_c9c2a_row2_col10\" class=\"data row2 col10\" >0.002421</td>\n",
       "      <td id=\"T_c9c2a_row2_col11\" class=\"data row2 col11\" >-0.009346</td>\n",
       "      <td id=\"T_c9c2a_row2_col12\" class=\"data row2 col12\" >0.062728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row3\" class=\"row_heading level0 row3\" >diabetes</th>\n",
       "      <td id=\"T_c9c2a_row3_col0\" class=\"data row3 col0\" >-0.101012</td>\n",
       "      <td id=\"T_c9c2a_row3_col1\" class=\"data row3 col1\" >-0.012729</td>\n",
       "      <td id=\"T_c9c2a_row3_col2\" class=\"data row3 col2\" >-0.009639</td>\n",
       "      <td id=\"T_c9c2a_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row3_col4\" class=\"data row3 col4\" >-0.004850</td>\n",
       "      <td id=\"T_c9c2a_row3_col5\" class=\"data row3 col5\" >-0.012732</td>\n",
       "      <td id=\"T_c9c2a_row3_col6\" class=\"data row3 col6\" >0.092193</td>\n",
       "      <td id=\"T_c9c2a_row3_col7\" class=\"data row3 col7\" >-0.046975</td>\n",
       "      <td id=\"T_c9c2a_row3_col8\" class=\"data row3 col8\" >-0.089551</td>\n",
       "      <td id=\"T_c9c2a_row3_col9\" class=\"data row3 col9\" >-0.157730</td>\n",
       "      <td id=\"T_c9c2a_row3_col10\" class=\"data row3 col10\" >-0.147173</td>\n",
       "      <td id=\"T_c9c2a_row3_col11\" class=\"data row3 col11\" >0.033726</td>\n",
       "      <td id=\"T_c9c2a_row3_col12\" class=\"data row3 col12\" >-0.001943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row4\" class=\"row_heading level0 row4\" >ejection_fraction</th>\n",
       "      <td id=\"T_c9c2a_row4_col0\" class=\"data row4 col0\" >0.060098</td>\n",
       "      <td id=\"T_c9c2a_row4_col1\" class=\"data row4 col1\" >0.031557</td>\n",
       "      <td id=\"T_c9c2a_row4_col2\" class=\"data row4 col2\" >-0.044080</td>\n",
       "      <td id=\"T_c9c2a_row4_col3\" class=\"data row4 col3\" >-0.004850</td>\n",
       "      <td id=\"T_c9c2a_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row4_col5\" class=\"data row4 col5\" >0.024445</td>\n",
       "      <td id=\"T_c9c2a_row4_col6\" class=\"data row4 col6\" >0.072177</td>\n",
       "      <td id=\"T_c9c2a_row4_col7\" class=\"data row4 col7\" >-0.011302</td>\n",
       "      <td id=\"T_c9c2a_row4_col8\" class=\"data row4 col8\" >0.175902</td>\n",
       "      <td id=\"T_c9c2a_row4_col9\" class=\"data row4 col9\" >-0.148386</td>\n",
       "      <td id=\"T_c9c2a_row4_col10\" class=\"data row4 col10\" >-0.067315</td>\n",
       "      <td id=\"T_c9c2a_row4_col11\" class=\"data row4 col11\" >0.041729</td>\n",
       "      <td id=\"T_c9c2a_row4_col12\" class=\"data row4 col12\" >-0.268603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row5\" class=\"row_heading level0 row5\" >high_blood_pressure</th>\n",
       "      <td id=\"T_c9c2a_row5_col0\" class=\"data row5 col0\" >0.093289</td>\n",
       "      <td id=\"T_c9c2a_row5_col1\" class=\"data row5 col1\" >0.038182</td>\n",
       "      <td id=\"T_c9c2a_row5_col2\" class=\"data row5 col2\" >-0.070590</td>\n",
       "      <td id=\"T_c9c2a_row5_col3\" class=\"data row5 col3\" >-0.012732</td>\n",
       "      <td id=\"T_c9c2a_row5_col4\" class=\"data row5 col4\" >0.024445</td>\n",
       "      <td id=\"T_c9c2a_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row5_col6\" class=\"data row5 col6\" >0.049963</td>\n",
       "      <td id=\"T_c9c2a_row5_col7\" class=\"data row5 col7\" >-0.004935</td>\n",
       "      <td id=\"T_c9c2a_row5_col8\" class=\"data row5 col8\" >0.037109</td>\n",
       "      <td id=\"T_c9c2a_row5_col9\" class=\"data row5 col9\" >-0.104615</td>\n",
       "      <td id=\"T_c9c2a_row5_col10\" class=\"data row5 col10\" >-0.055711</td>\n",
       "      <td id=\"T_c9c2a_row5_col11\" class=\"data row5 col11\" >-0.196439</td>\n",
       "      <td id=\"T_c9c2a_row5_col12\" class=\"data row5 col12\" >0.079351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row6\" class=\"row_heading level0 row6\" >platelets</th>\n",
       "      <td id=\"T_c9c2a_row6_col0\" class=\"data row6 col0\" >-0.052354</td>\n",
       "      <td id=\"T_c9c2a_row6_col1\" class=\"data row6 col1\" >-0.043786</td>\n",
       "      <td id=\"T_c9c2a_row6_col2\" class=\"data row6 col2\" >0.024463</td>\n",
       "      <td id=\"T_c9c2a_row6_col3\" class=\"data row6 col3\" >0.092193</td>\n",
       "      <td id=\"T_c9c2a_row6_col4\" class=\"data row6 col4\" >0.072177</td>\n",
       "      <td id=\"T_c9c2a_row6_col5\" class=\"data row6 col5\" >0.049963</td>\n",
       "      <td id=\"T_c9c2a_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row6_col7\" class=\"data row6 col7\" >-0.041198</td>\n",
       "      <td id=\"T_c9c2a_row6_col8\" class=\"data row6 col8\" >0.062125</td>\n",
       "      <td id=\"T_c9c2a_row6_col9\" class=\"data row6 col9\" >-0.125120</td>\n",
       "      <td id=\"T_c9c2a_row6_col10\" class=\"data row6 col10\" >0.028234</td>\n",
       "      <td id=\"T_c9c2a_row6_col11\" class=\"data row6 col11\" >0.010514</td>\n",
       "      <td id=\"T_c9c2a_row6_col12\" class=\"data row6 col12\" >-0.049139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row7\" class=\"row_heading level0 row7\" >serum_creatinine</th>\n",
       "      <td id=\"T_c9c2a_row7_col0\" class=\"data row7 col0\" >0.159187</td>\n",
       "      <td id=\"T_c9c2a_row7_col1\" class=\"data row7 col1\" >0.052174</td>\n",
       "      <td id=\"T_c9c2a_row7_col2\" class=\"data row7 col2\" >-0.016408</td>\n",
       "      <td id=\"T_c9c2a_row7_col3\" class=\"data row7 col3\" >-0.046975</td>\n",
       "      <td id=\"T_c9c2a_row7_col4\" class=\"data row7 col4\" >-0.011302</td>\n",
       "      <td id=\"T_c9c2a_row7_col5\" class=\"data row7 col5\" >-0.004935</td>\n",
       "      <td id=\"T_c9c2a_row7_col6\" class=\"data row7 col6\" >-0.041198</td>\n",
       "      <td id=\"T_c9c2a_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row7_col8\" class=\"data row7 col8\" >-0.189095</td>\n",
       "      <td id=\"T_c9c2a_row7_col9\" class=\"data row7 col9\" >0.006970</td>\n",
       "      <td id=\"T_c9c2a_row7_col10\" class=\"data row7 col10\" >-0.027414</td>\n",
       "      <td id=\"T_c9c2a_row7_col11\" class=\"data row7 col11\" >-0.149315</td>\n",
       "      <td id=\"T_c9c2a_row7_col12\" class=\"data row7 col12\" >0.294278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row8\" class=\"row_heading level0 row8\" >serum_sodium</th>\n",
       "      <td id=\"T_c9c2a_row8_col0\" class=\"data row8 col0\" >-0.045966</td>\n",
       "      <td id=\"T_c9c2a_row8_col1\" class=\"data row8 col1\" >0.041882</td>\n",
       "      <td id=\"T_c9c2a_row8_col2\" class=\"data row8 col2\" >0.059550</td>\n",
       "      <td id=\"T_c9c2a_row8_col3\" class=\"data row8 col3\" >-0.089551</td>\n",
       "      <td id=\"T_c9c2a_row8_col4\" class=\"data row8 col4\" >0.175902</td>\n",
       "      <td id=\"T_c9c2a_row8_col5\" class=\"data row8 col5\" >0.037109</td>\n",
       "      <td id=\"T_c9c2a_row8_col6\" class=\"data row8 col6\" >0.062125</td>\n",
       "      <td id=\"T_c9c2a_row8_col7\" class=\"data row8 col7\" >-0.189095</td>\n",
       "      <td id=\"T_c9c2a_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row8_col9\" class=\"data row8 col9\" >-0.027566</td>\n",
       "      <td id=\"T_c9c2a_row8_col10\" class=\"data row8 col10\" >0.004813</td>\n",
       "      <td id=\"T_c9c2a_row8_col11\" class=\"data row8 col11\" >0.087640</td>\n",
       "      <td id=\"T_c9c2a_row8_col12\" class=\"data row8 col12\" >-0.195204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row9\" class=\"row_heading level0 row9\" >sex</th>\n",
       "      <td id=\"T_c9c2a_row9_col0\" class=\"data row9 col0\" >0.065430</td>\n",
       "      <td id=\"T_c9c2a_row9_col1\" class=\"data row9 col1\" >-0.094769</td>\n",
       "      <td id=\"T_c9c2a_row9_col2\" class=\"data row9 col2\" >0.079791</td>\n",
       "      <td id=\"T_c9c2a_row9_col3\" class=\"data row9 col3\" >-0.157730</td>\n",
       "      <td id=\"T_c9c2a_row9_col4\" class=\"data row9 col4\" >-0.148386</td>\n",
       "      <td id=\"T_c9c2a_row9_col5\" class=\"data row9 col5\" >-0.104615</td>\n",
       "      <td id=\"T_c9c2a_row9_col6\" class=\"data row9 col6\" >-0.125120</td>\n",
       "      <td id=\"T_c9c2a_row9_col7\" class=\"data row9 col7\" >0.006970</td>\n",
       "      <td id=\"T_c9c2a_row9_col8\" class=\"data row9 col8\" >-0.027566</td>\n",
       "      <td id=\"T_c9c2a_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row9_col10\" class=\"data row9 col10\" >0.445892</td>\n",
       "      <td id=\"T_c9c2a_row9_col11\" class=\"data row9 col11\" >-0.015608</td>\n",
       "      <td id=\"T_c9c2a_row9_col12\" class=\"data row9 col12\" >-0.004316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row10\" class=\"row_heading level0 row10\" >smoking</th>\n",
       "      <td id=\"T_c9c2a_row10_col0\" class=\"data row10 col0\" >0.018668</td>\n",
       "      <td id=\"T_c9c2a_row10_col1\" class=\"data row10 col1\" >-0.107290</td>\n",
       "      <td id=\"T_c9c2a_row10_col2\" class=\"data row10 col2\" >0.002421</td>\n",
       "      <td id=\"T_c9c2a_row10_col3\" class=\"data row10 col3\" >-0.147173</td>\n",
       "      <td id=\"T_c9c2a_row10_col4\" class=\"data row10 col4\" >-0.067315</td>\n",
       "      <td id=\"T_c9c2a_row10_col5\" class=\"data row10 col5\" >-0.055711</td>\n",
       "      <td id=\"T_c9c2a_row10_col6\" class=\"data row10 col6\" >0.028234</td>\n",
       "      <td id=\"T_c9c2a_row10_col7\" class=\"data row10 col7\" >-0.027414</td>\n",
       "      <td id=\"T_c9c2a_row10_col8\" class=\"data row10 col8\" >0.004813</td>\n",
       "      <td id=\"T_c9c2a_row10_col9\" class=\"data row10 col9\" >0.445892</td>\n",
       "      <td id=\"T_c9c2a_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row10_col11\" class=\"data row10 col11\" >-0.022839</td>\n",
       "      <td id=\"T_c9c2a_row10_col12\" class=\"data row10 col12\" >-0.012623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row11\" class=\"row_heading level0 row11\" >time</th>\n",
       "      <td id=\"T_c9c2a_row11_col0\" class=\"data row11 col0\" >-0.224068</td>\n",
       "      <td id=\"T_c9c2a_row11_col1\" class=\"data row11 col1\" >-0.141414</td>\n",
       "      <td id=\"T_c9c2a_row11_col2\" class=\"data row11 col2\" >-0.009346</td>\n",
       "      <td id=\"T_c9c2a_row11_col3\" class=\"data row11 col3\" >0.033726</td>\n",
       "      <td id=\"T_c9c2a_row11_col4\" class=\"data row11 col4\" >0.041729</td>\n",
       "      <td id=\"T_c9c2a_row11_col5\" class=\"data row11 col5\" >-0.196439</td>\n",
       "      <td id=\"T_c9c2a_row11_col6\" class=\"data row11 col6\" >0.010514</td>\n",
       "      <td id=\"T_c9c2a_row11_col7\" class=\"data row11 col7\" >-0.149315</td>\n",
       "      <td id=\"T_c9c2a_row11_col8\" class=\"data row11 col8\" >0.087640</td>\n",
       "      <td id=\"T_c9c2a_row11_col9\" class=\"data row11 col9\" >-0.015608</td>\n",
       "      <td id=\"T_c9c2a_row11_col10\" class=\"data row11 col10\" >-0.022839</td>\n",
       "      <td id=\"T_c9c2a_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_c9c2a_row11_col12\" class=\"data row11 col12\" >-0.526964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9c2a_level0_row12\" class=\"row_heading level0 row12\" >DEATH_EVENT</th>\n",
       "      <td id=\"T_c9c2a_row12_col0\" class=\"data row12 col0\" >0.253729</td>\n",
       "      <td id=\"T_c9c2a_row12_col1\" class=\"data row12 col1\" >0.066270</td>\n",
       "      <td id=\"T_c9c2a_row12_col2\" class=\"data row12 col2\" >0.062728</td>\n",
       "      <td id=\"T_c9c2a_row12_col3\" class=\"data row12 col3\" >-0.001943</td>\n",
       "      <td id=\"T_c9c2a_row12_col4\" class=\"data row12 col4\" >-0.268603</td>\n",
       "      <td id=\"T_c9c2a_row12_col5\" class=\"data row12 col5\" >0.079351</td>\n",
       "      <td id=\"T_c9c2a_row12_col6\" class=\"data row12 col6\" >-0.049139</td>\n",
       "      <td id=\"T_c9c2a_row12_col7\" class=\"data row12 col7\" >0.294278</td>\n",
       "      <td id=\"T_c9c2a_row12_col8\" class=\"data row12 col8\" >-0.195204</td>\n",
       "      <td id=\"T_c9c2a_row12_col9\" class=\"data row12 col9\" >-0.004316</td>\n",
       "      <td id=\"T_c9c2a_row12_col10\" class=\"data row12 col10\" >-0.012623</td>\n",
       "      <td id=\"T_c9c2a_row12_col11\" class=\"data row12 col11\" >-0.526964</td>\n",
       "      <td id=\"T_c9c2a_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2423d49f3d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr().style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca3f65a",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c62e1452",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train:  (209, 12)\n",
      "y_train:  (209,)\n",
      "x_test:  (90, 12)\n",
      "y_test:  (90,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x=data.drop('DEATH_EVENT',axis=1)\n",
    "y=data['DEATH_EVENT']\n",
    "\n",
    "x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.3, random_state=0)\n",
    "print(\"x_train: \",x_train.shape)\n",
    "print(\"y_train: \",y_train.shape)\n",
    "print(\"x_test: \",x_test.shape)\n",
    "print(\"y_test: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ad526b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score\n",
    "\n",
    "def evaluating_model(y_test, y_pred):\n",
    "    print('Accuracy score: ',accuracy_score(y_test,y_pred))\n",
    "    print('Precision score: ',precision_score(y_test,y_pred))\n",
    "    print('Recall score: ',recall_score(y_test,y_pred))\n",
    "    print('Confusion matrix: ',confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a5a85234",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr_clf=LogisticRegression(max_iter=1000)\n",
    "lr_clf.fit(x_train, y_train)\n",
    "\n",
    "lr_clf_pred=lr_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e4032c7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.7888888888888889\n",
      "Precision score:  0.7647058823529411\n",
      "Recall score:  0.4642857142857143\n",
      "Confusion matrix:  [[58  4]\n",
      " [15 13]]\n"
     ]
    }
   ],
   "source": [
    "y_pred=lr_clf.predict(x_test)\n",
    "evaluating_model(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9726283f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8111111111111111\n",
      "Precision score:  0.7894736842105263\n",
      "Recall score:  0.5357142857142857\n",
      "Confusion matrix:  [[58  4]\n",
      " [13 15]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "lr_clf_pip=make_pipeline(StandardScaler(), LogisticRegression())\n",
    "lr_clf_pip.fit(x_train, y_train)\n",
    "\n",
    "y_pred1=lr_clf_pip.predict(x_test)\n",
    "evaluating_model(y_test, y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "066c0a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
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      "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.690 total time=   0.0s\n",
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      "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
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      "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.690 total time=   0.0s\n",
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      "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.690 total time=   0.0s\n",
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      "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
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      "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.690 total time=   0.0s\n",
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      "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.643 total time=   0.0s\n",
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      "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.707 total time=   0.0s\n",
      "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.690 total time=   0.0s\n",
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      "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.714 total time=   0.0s\n",
      "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.707 total time=   0.0s\n",
      "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.714 total time=   0.0s\n",
      "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.707 total time=   0.0s\n",
      "[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.690 total time=   0.0s\n",
      "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.683 total time=   0.0s\n",
      "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
      "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.714 total time=   0.0s\n",
      "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.707 total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         'kernel': ['rbf']},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid={'C':[0.1, 1, 10, 100, 1000],\n",
    "           'gamma':[1, 0.1, 0.01, 0.001, 0.0001],\n",
    "           'kernel':['rbf']}\n",
    "grid=GridSearchCV(SVC(), param_grid, refit=True, verbose=3)\n",
    "grid.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b2dc65fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, gamma=0.0001)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "882707dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.6777777777777778\n",
      "Precision score:  0.4\n",
      "Recall score:  0.07142857142857142\n",
      "Confusion matrix:  [[59  3]\n",
      " [26  2]]\n"
     ]
    }
   ],
   "source": [
    "svc=SVC(C=10, gamma=0.0001)\n",
    "svc.fit(x_train, y_train)\n",
    "y_pred2=svc.predict(x_test)\n",
    "evaluating_model(y_test, y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "80dfdd2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score: 0.847\n",
      "Test score: 0.756\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4, max_features=0.75,\n",
       "                       max_leaf_nodes=25, min_impurity_decrease=0.0005,\n",
       "                       min_samples_split=5, min_weight_fraction_leaf=0.0075,\n",
       "                       random_state=2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "def randomazid_search(params, runs=20, clf=DecisionTreeClassifier(random_state=2)):\n",
    "    rand_clf=RandomizedSearchCV(clf, params, n_iter=runs, cv=5, n_jobs=-1, random_state=2)\n",
    "    rand_clf.fit(x_train, y_train)\n",
    "    best_model=rand_clf.best_estimator_\n",
    "    best_score=rand_clf.best_score_\n",
    "    \n",
    "    print(\"Training score: {:.3f}\".format(best_score))\n",
    "    y_pred=best_model.predict(x_test)\n",
    "    accuracy=accuracy_score(y_test, y_pred)\n",
    "    print(\"Test score: {:.3f}\".format(accuracy))\n",
    "    \n",
    "    return best_model\n",
    "\n",
    "randomazid_search(params={'criterion':['entropy','gini'],\n",
    "                          'splitter':['random','best'],\n",
    "                         'min_weight_fraction_leaf':[0.0,0.0025,0.005,0.0075,0.01],\n",
    "                         'min_samples_split':[2,3,4,5,6,8,10],\n",
    "                         'min_samples_leaf':[1,0.01,0.02,0.03,0.04],\n",
    "                         'min_impurity_decrease':[0.0,0.0005,0.005,0.05,0.10,0.15,0.20],\n",
    "                         'max_leaf_nodes':[10,15,20,25,30,35,40,45,50,None],\n",
    "                         'max_features':['auto',0.95,0.90,0.85,0.80,0.75,0.70],\n",
    "                         'max_depth':[None,2,4,6,8],\n",
    "                         'min_weight_fraction_leaf':[0.0,0.0025,0.0050,0.0075,0.01,0.05]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "009da732",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8111111111111111\n",
      "Precision score:  0.72\n",
      "Recall score:  0.6428571428571429\n",
      "Confusion matrix:  [[55  7]\n",
      " [10 18]]\n"
     ]
    }
   ],
   "source": [
    "ds_clf=DecisionTreeClassifier(max_depth=8, max_features=0.9,\n",
    "                       max_leaf_nodes=30, min_impurity_decrease=0.05,min_samples_leaf=0.02,\n",
    "                       min_samples_split=10, min_weight_fraction_leaf=0.005,\n",
    "                       random_state=2, splitter='random')\n",
    "ds_clf.fit(x_train, y_train)\n",
    "pred4=ds_clf.predict(x_test)\n",
    "evaluating_model(y_test, pred4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d7f26fc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score: 0.847\n",
      "Test score: 0.867\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=2, max_features=0.5,\n",
       "                       min_impurity_decrease=0.01, min_samples_leaf=10,\n",
       "                       random_state=2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "randomazid_search(params={'min_samples_leaf':[1,2,4,6,8,10,20,30],\n",
    "                         'min_impurity_decrease':[0.0,0.01,0.05,0.10,0.15,0.20],\n",
    "                         'max_features':['auto',0.8,0.7,0.6,0.5,0.4],\n",
    "                         'max_depth':[None,2,4,6,8,10,20],}, clf=RandomForestClassifier(random_state=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5051462d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8666666666666667\n",
      "Precision score:  0.9\n",
      "Recall score:  0.6428571428571429\n",
      "Confusion matrix:  [[60  2]\n",
      " [10 18]]\n"
     ]
    }
   ],
   "source": [
    "rf_clf=RandomForestClassifier(max_depth=2, max_features=0.5,\n",
    "                       min_impurity_decrease=0.01, min_samples_leaf=10,\n",
    "                       random_state=2)\n",
    "rf_clf.fit(x_train, y_train)\n",
    "pred5=rf_clf.predict(x_test)\n",
    "evaluating_model(y_test, pred5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "effaae33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.63929\n",
      "[1]\tvalidation_0-logloss:0.59762\n",
      "[2]\tvalidation_0-logloss:0.56422\n",
      "[3]\tvalidation_0-logloss:0.53184\n",
      "[4]\tvalidation_0-logloss:0.50465\n",
      "[5]\tvalidation_0-logloss:0.48130\n",
      "[6]\tvalidation_0-logloss:0.46135\n",
      "[7]\tvalidation_0-logloss:0.44722\n",
      "[8]\tvalidation_0-logloss:0.43508\n",
      "[9]\tvalidation_0-logloss:0.42311\n",
      "[10]\tvalidation_0-logloss:0.40869\n",
      "[11]\tvalidation_0-logloss:0.39828\n",
      "[12]\tvalidation_0-logloss:0.39094\n",
      "[13]\tvalidation_0-logloss:0.38523\n",
      "[14]\tvalidation_0-logloss:0.37836\n",
      "[15]\tvalidation_0-logloss:0.37250\n",
      "[16]\tvalidation_0-logloss:0.36963\n",
      "[17]\tvalidation_0-logloss:0.36676\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sandeep\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:793: UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sandeep\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:793: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18]\tvalidation_0-logloss:0.36467\n",
      "[19]\tvalidation_0-logloss:0.36335\n",
      "[20]\tvalidation_0-logloss:0.36152\n",
      "[21]\tvalidation_0-logloss:0.36137\n",
      "[22]\tvalidation_0-logloss:0.35676\n",
      "[23]\tvalidation_0-logloss:0.35485\n",
      "[24]\tvalidation_0-logloss:0.35313\n",
      "[25]\tvalidation_0-logloss:0.34993\n",
      "[26]\tvalidation_0-logloss:0.34873\n",
      "[27]\tvalidation_0-logloss:0.34697\n",
      "[28]\tvalidation_0-logloss:0.34448\n",
      "[29]\tvalidation_0-logloss:0.34190\n",
      "[30]\tvalidation_0-logloss:0.34213\n",
      "[31]\tvalidation_0-logloss:0.34117\n",
      "[32]\tvalidation_0-logloss:0.33854\n",
      "[33]\tvalidation_0-logloss:0.33701\n",
      "[34]\tvalidation_0-logloss:0.33778\n",
      "[35]\tvalidation_0-logloss:0.33757\n",
      "[36]\tvalidation_0-logloss:0.33578\n",
      "[37]\tvalidation_0-logloss:0.33511\n",
      "[38]\tvalidation_0-logloss:0.33418\n",
      "[39]\tvalidation_0-logloss:0.33277\n",
      "[40]\tvalidation_0-logloss:0.33211\n",
      "[41]\tvalidation_0-logloss:0.32965\n",
      "[42]\tvalidation_0-logloss:0.32868\n",
      "[43]\tvalidation_0-logloss:0.32739\n",
      "[44]\tvalidation_0-logloss:0.32850\n",
      "[45]\tvalidation_0-logloss:0.32888\n",
      "[46]\tvalidation_0-logloss:0.33055\n",
      "[47]\tvalidation_0-logloss:0.32993\n",
      "[48]\tvalidation_0-logloss:0.33082\n",
      "[49]\tvalidation_0-logloss:0.32964\n",
      "[50]\tvalidation_0-logloss:0.32906\n",
      "[51]\tvalidation_0-logloss:0.32838\n",
      "[52]\tvalidation_0-logloss:0.32767\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1.0,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.1, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=4, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=400,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb1=XGBClassifier(colsample_bytree=1.0,\n",
    "                  learning_rate=0.1,\n",
    "                  max_depth=4,\n",
    "                  n_estimators=400,\n",
    "                  subsample=1.0)\n",
    "eval_set=[(x_test, y_test)]\n",
    "xgb1.fit(x_train, y_train, early_stopping_rounds=10,eval_metric=\"logloss\", eval_set=eval_set, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8e2bfac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8555555555555555\n",
      "Precision score:  0.8\n",
      "Recall score:  0.7142857142857143\n",
      "Confusion matrix:  [[57  5]\n",
      " [ 8 20]]\n"
     ]
    }
   ],
   "source": [
    "pred6=xgb1.predict(x_test)\n",
    "evaluating_model(y_test, pred6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "59a138c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "\n",
    "plot_importance(xgb1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e43401c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8555555555555555\n",
      "Precision score:  0.8571428571428571\n",
      "Recall score:  0.6428571428571429\n",
      "Confusion matrix:  [[59  3]\n",
      " [10 18]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "gbdt=GradientBoostingClassifier(n_estimators=200, learning_rate=0.1, max_depth=1, random_state=0)\n",
    "gbdt.fit(x_train, y_train)\n",
    "\n",
    "pred_gdbt=gbdt.predict(x_test)\n",
    "evaluating_model(y_test, pred_gdbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31483701",
   "metadata": {},
   "source": [
    "# Choosing best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b227d3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(xgb1, 'model.pkl')\n",
    "model=joblib.load('model.pkl')\n",
    "model.predict(x_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}