Sepsis_3_RFmodel_xgboost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f08d3332",
   "metadata": {},
   "source": [
    "# Model-3 (Random Forest application for the dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "096209e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48628539",
   "metadata": {},
   "source": [
    "# Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e9e2910",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=pd.read_csv('D:\\Jio Term 2\\ML\\ML_Project\\project_data\\sepsis_training_data.csv')\n",
    "test_df=pd.read_csv('D:\\Jio Term 2\\ML\\ML_Project\\project_data\\sepsis_test_data.csv')\n",
    "val_df=pd.read_csv('D:\\Jio Term 2\\ML\\ML_Project\\project_data\\sepsis_validation_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66eb85d2",
   "metadata": {},
   "source": [
    "# Analyzing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c78c9c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HR</th>\n",
       "      <th>O2Sat</th>\n",
       "      <th>Temp</th>\n",
       "      <th>SBP</th>\n",
       "      <th>MAP</th>\n",
       "      <th>DBP</th>\n",
       "      <th>Resp</th>\n",
       "      <th>EtCO2</th>\n",
       "      <th>BaseExcess</th>\n",
       "      <th>HCO3</th>\n",
       "      <th>FiO2</th>\n",
       "      <th>pH</th>\n",
       "      <th>PaCO2</th>\n",
       "      <th>SaO2</th>\n",
       "      <th>AST</th>\n",
       "      <th>BUN</th>\n",
       "      <th>Alkalinephos</th>\n",
       "      <th>Calcium</th>\n",
       "      <th>Chloride</th>\n",
       "      <th>Creatinine</th>\n",
       "      <th>Bilirubin_direct</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Lactate</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phosphate</th>\n",
       "      <th>Potassium</th>\n",
       "      <th>Bilirubin_total</th>\n",
       "      <th>TroponinI</th>\n",
       "      <th>Hct</th>\n",
       "      <th>Hgb</th>\n",
       "      <th>PTT</th>\n",
       "      <th>WBC</th>\n",
       "      <th>Fibrinogen</th>\n",
       "      <th>Platelets</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Unit1</th>\n",
       "      <th>Unit2</th>\n",
       "      <th>HospAdmTime</th>\n",
       "      <th>ICULOS</th>\n",
       "      <th>SepsisLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.44</td>\n",
       "      <td>124.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.5</td>\n",
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       "      <td>78.0</td>\n",
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       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>75.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-98.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.44</td>\n",
       "      <td>124.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>75.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-98.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>64.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>36.44</td>\n",
       "      <td>125.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>56.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>36.44</td>\n",
       "      <td>123.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.22</td>\n",
       "      <td>120.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>23.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-98.6</td>\n",
       "      <td>5</td>\n",
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      "text/plain": [
       "     HR  O2Sat   Temp    SBP   MAP   DBP  Resp  EtCO2  BaseExcess  HCO3  FiO2  \\\n",
       "0  61.0   99.0  36.44  124.0  65.0  43.0  17.5    0.0         0.0  22.0   0.0   \n",
       "1  61.0   99.0  36.44  124.0  65.0  43.0  17.5    0.0         0.0  22.0   0.0   \n",
       "2  64.0   98.0  36.44  125.0  64.0  41.0  27.0    0.0         0.0  22.0   0.0   \n",
       "3  56.0  100.0  36.44  123.0  65.0  41.0   9.0    0.0         0.0  22.0   0.0   \n",
       "4  66.0   99.0  36.22  120.0  67.0  43.0  23.0    0.0         0.0  22.0   0.0   \n",
       "\n",
       "    pH  PaCO2  SaO2  AST    BUN  Alkalinephos  Calcium  Chloride  Creatinine  \\\n",
       "0  0.0    0.0   0.0  0.0  100.0           0.0      7.9     113.0         2.5   \n",
       "1  0.0    0.0   0.0  0.0  100.0           0.0      7.9     113.0         2.5   \n",
       "2  0.0    0.0   0.0  0.0  100.0           0.0      7.9     113.0         2.5   \n",
       "3  0.0    0.0   0.0  0.0  100.0           0.0      7.9     113.0         2.5   \n",
       "4  0.0    0.0   0.0  0.0  100.0           0.0      7.9     113.0         2.5   \n",
       "\n",
       "   Bilirubin_direct  Glucose  Lactate  Magnesium  Phosphate  Potassium  \\\n",
       "0               0.0     78.0      0.0        2.5        4.4        5.1   \n",
       "1               0.0     78.0      0.0        2.5        4.4        5.1   \n",
       "2               0.0     78.0      0.0        2.5        4.4        5.1   \n",
       "3               0.0     78.0      0.0        2.5        4.4        5.1   \n",
       "4               0.0     78.0      0.0        2.5        4.4        5.1   \n",
       "\n",
       "   Bilirubin_total  TroponinI   Hct  Hgb  PTT   WBC  Fibrinogen  Platelets  \\\n",
       "0              0.0        0.0  27.8  9.7  0.0  11.0         0.0      158.0   \n",
       "1              0.0        0.0  27.8  9.7  0.0  11.0         0.0      158.0   \n",
       "2              0.0        0.0  27.8  9.7  0.0  11.0         0.0      158.0   \n",
       "3              0.0        0.0  27.8  9.7  0.0  11.0         0.0      158.0   \n",
       "4              0.0        0.0  27.8  9.7  0.0  11.0         0.0      158.0   \n",
       "\n",
       "     Age  Gender  Unit1  Unit2  HospAdmTime  ICULOS  SepsisLabel  \n",
       "0  75.91       0    0.0    1.0        -98.6       1            0  \n",
       "1  75.91       0    0.0    1.0        -98.6       2            0  \n",
       "2  75.91       0    0.0    1.0        -98.6       3            0  \n",
       "3  75.91       0    0.0    1.0        -98.6       4            0  \n",
       "4  75.91       0    0.0    1.0        -98.6       5            0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "894e0d22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EtCO2', 'Gender', 'Unit1', 'Unit2', 'SepsisLabel']\n"
     ]
    }
   ],
   "source": [
    "#after imputation of missing values the columns which get the least unique values need to be dropped,hence we will check unique\n",
    "# value count & drop those columns\n",
    "\n",
    "columns_to_drop=[]\n",
    "for column in train_df.columns:\n",
    "    vc=len(train_df[column].value_counts().unique())\n",
    "    if vc<=5:\n",
    "         columns_to_drop.append(column)\n",
    "    else:\n",
    "        pass\n",
    "print(columns_to_drop)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2f5f364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EtCO2', 'Unit1', 'Unit2']\n"
     ]
    }
   ],
   "source": [
    "#here we cannot drop ['Gender','SepsisLabel']\n",
    "items_to_remove=['Gender','SepsisLabel']\n",
    "for column in columns_to_drop:\n",
    "    if column in items_to_remove:\n",
    "        columns_to_drop.remove(column)\n",
    "    else:\n",
    "        pass\n",
    "print(columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92151ccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[train_df,test_df,val_df]\n",
    "\n",
    "def drop_columns(df_list,col_list):\n",
    "    for df in df_list:\n",
    "        df.drop(col_list,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2e2e3ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_columns(data,columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56766df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now that we have dropped the unwanted columns, we will split the data into dependent & independent features\n",
    "#here sepsis lable is the the dependent column\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def dep_ind(df):\n",
    "    \n",
    "    \n",
    "    X=np.array(df[df.columns[:-1]].values)\n",
    "    y=np.array(df[df.columns[-1]].values)\n",
    "    \n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183697df",
   "metadata": {},
   "source": [
    "# Splitting data into dependent & independent datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4fbff03",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HR</th>\n",
       "      <th>O2Sat</th>\n",
       "      <th>Temp</th>\n",
       "      <th>SBP</th>\n",
       "      <th>MAP</th>\n",
       "      <th>DBP</th>\n",
       "      <th>Resp</th>\n",
       "      <th>BaseExcess</th>\n",
       "      <th>HCO3</th>\n",
       "      <th>FiO2</th>\n",
       "      <th>pH</th>\n",
       "      <th>PaCO2</th>\n",
       "      <th>SaO2</th>\n",
       "      <th>AST</th>\n",
       "      <th>BUN</th>\n",
       "      <th>Alkalinephos</th>\n",
       "      <th>Calcium</th>\n",
       "      <th>Chloride</th>\n",
       "      <th>Creatinine</th>\n",
       "      <th>Bilirubin_direct</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Lactate</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phosphate</th>\n",
       "      <th>Potassium</th>\n",
       "      <th>Bilirubin_total</th>\n",
       "      <th>TroponinI</th>\n",
       "      <th>Hct</th>\n",
       "      <th>Hgb</th>\n",
       "      <th>PTT</th>\n",
       "      <th>WBC</th>\n",
       "      <th>Fibrinogen</th>\n",
       "      <th>Platelets</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HospAdmTime</th>\n",
       "      <th>ICULOS</th>\n",
       "      <th>SepsisLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.44</td>\n",
       "      <td>124.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>75.91</td>\n",
       "      <td>0</td>\n",
       "      <td>-98.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.44</td>\n",
       "      <td>124.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>75.91</td>\n",
       "      <td>0</td>\n",
       "      <td>-98.60</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>36.44</td>\n",
       "      <td>125.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>75.91</td>\n",
       "      <td>0</td>\n",
       "      <td>-98.60</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>36.44</td>\n",
       "      <td>123.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>75.91</td>\n",
       "      <td>0</td>\n",
       "      <td>-98.60</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.22</td>\n",
       "      <td>120.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>75.91</td>\n",
       "      <td>0</td>\n",
       "      <td>-98.60</td>\n",
       "      <td>5</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552457</th>\n",
       "      <td>88.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>38.72</td>\n",
       "      <td>135.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.48</td>\n",
       "      <td>34.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>33.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>62.29</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552458</th>\n",
       "      <td>96.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>38.72</td>\n",
       "      <td>174.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.48</td>\n",
       "      <td>34.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>33.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>62.29</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552459</th>\n",
       "      <td>140.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>38.72</td>\n",
       "      <td>133.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>62.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.48</td>\n",
       "      <td>34.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>33.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>62.29</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552460</th>\n",
       "      <td>120.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>38.72</td>\n",
       "      <td>154.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.48</td>\n",
       "      <td>34.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>33.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>62.29</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552461</th>\n",
       "      <td>115.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>38.72</td>\n",
       "      <td>150.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.48</td>\n",
       "      <td>34.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>33.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>62.29</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>552462 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           HR  O2Sat   Temp    SBP    MAP    DBP  Resp  BaseExcess  HCO3  \\\n",
       "0        61.0   99.0  36.44  124.0   65.0   43.0  17.5         0.0  22.0   \n",
       "1        61.0   99.0  36.44  124.0   65.0   43.0  17.5         0.0  22.0   \n",
       "2        64.0   98.0  36.44  125.0   64.0   41.0  27.0         0.0  22.0   \n",
       "3        56.0  100.0  36.44  123.0   65.0   41.0   9.0         0.0  22.0   \n",
       "4        66.0   99.0  36.22  120.0   67.0   43.0  23.0         0.0  22.0   \n",
       "...       ...    ...    ...    ...    ...    ...   ...         ...   ...   \n",
       "552457   88.0   98.0  38.72  135.0   81.0   64.0  16.0         2.0  26.0   \n",
       "552458   96.0   98.0  38.72  174.0   97.0   72.0  16.0         2.0  26.0   \n",
       "552459  140.0   97.0  38.72  133.0   81.5   62.5  16.0         2.0  26.0   \n",
       "552460  120.0   96.0  38.72  154.0  118.0  105.0  16.0         2.0  26.0   \n",
       "552461  115.0   95.0  38.72  150.0  117.0  104.0  16.0         2.0  26.0   \n",
       "\n",
       "        FiO2    pH  PaCO2  SaO2   AST    BUN  Alkalinephos  Calcium  Chloride  \\\n",
       "0        0.0  0.00    0.0   0.0   0.0  100.0           0.0      7.9     113.0   \n",
       "1        0.0  0.00    0.0   0.0   0.0  100.0           0.0      7.9     113.0   \n",
       "2        0.0  0.00    0.0   0.0   0.0  100.0           0.0      7.9     113.0   \n",
       "3        0.0  0.00    0.0   0.0   0.0  100.0           0.0      7.9     113.0   \n",
       "4        0.0  0.00    0.0   0.0   0.0  100.0           0.0      7.9     113.0   \n",
       "...      ...   ...    ...   ...   ...    ...           ...      ...       ...   \n",
       "552457   0.5  7.48   34.0  97.0  90.0   26.0         107.0      8.3     102.0   \n",
       "552458   0.5  7.48   34.0  97.0  90.0   26.0         107.0      8.3     102.0   \n",
       "552459   0.5  7.48   34.0  97.0  90.0   26.0         107.0      8.3     102.0   \n",
       "552460   0.5  7.48   34.0  98.0  90.0   26.0         107.0      8.3     102.0   \n",
       "552461   0.5  7.48   34.0  98.0  90.0   26.0         107.0      8.3     102.0   \n",
       "\n",
       "        Creatinine  Bilirubin_direct  Glucose  Lactate  Magnesium  Phosphate  \\\n",
       "0              2.5               0.0     78.0      0.0        2.5        4.4   \n",
       "1              2.5               0.0     78.0      0.0        2.5        4.4   \n",
       "2              2.5               0.0     78.0      0.0        2.5        4.4   \n",
       "3              2.5               0.0     78.0      0.0        2.5        4.4   \n",
       "4              2.5               0.0     78.0      0.0        2.5        4.4   \n",
       "...            ...               ...      ...      ...        ...        ...   \n",
       "552457         2.5               0.0    205.0      1.6        2.2        3.5   \n",
       "552458         2.5               0.0    205.0      1.6        2.2        3.5   \n",
       "552459         2.5               0.0    205.0      1.6        2.2        3.5   \n",
       "552460         2.5               0.0    205.0      1.6        2.2        3.5   \n",
       "552461         2.5               0.0    205.0      1.6        2.2        3.5   \n",
       "\n",
       "        Potassium  Bilirubin_total  TroponinI   Hct   Hgb   PTT   WBC  \\\n",
       "0             5.1              0.0        0.0  27.8   9.7   0.0  11.0   \n",
       "1             5.1              0.0        0.0  27.8   9.7   0.0  11.0   \n",
       "2             5.1              0.0        0.0  27.8   9.7   0.0  11.0   \n",
       "3             5.1              0.0        0.0  27.8   9.7   0.0  11.0   \n",
       "4             5.1              0.0        0.0  27.8   9.7   0.0  11.0   \n",
       "...           ...              ...        ...   ...   ...   ...   ...   \n",
       "552457        3.9              0.9        0.0  27.8  10.6  33.4  11.8   \n",
       "552458        3.9              0.9        0.0  27.8  10.6  33.4  11.8   \n",
       "552459        3.9              0.9        0.0  27.8  10.6  33.4  11.8   \n",
       "552460        3.9              0.9        0.0  27.8  10.6  33.4  11.8   \n",
       "552461        3.9              0.9        0.0  27.8  10.6  33.4  11.8   \n",
       "\n",
       "        Fibrinogen  Platelets    Age  Gender  HospAdmTime  ICULOS  SepsisLabel  \n",
       "0              0.0      158.0  75.91       0       -98.60       1            0  \n",
       "1              0.0      158.0  75.91       0       -98.60       2            0  \n",
       "2              0.0      158.0  75.91       0       -98.60       3            0  \n",
       "3              0.0      158.0  75.91       0       -98.60       4            0  \n",
       "4              0.0      158.0  75.91       0       -98.60       5            0  \n",
       "...            ...        ...    ...     ...          ...     ...          ...  \n",
       "552457         0.0      284.0  62.29       1        -0.03      31            1  \n",
       "552458         0.0      284.0  62.29       1        -0.03      32            1  \n",
       "552459         0.0      284.0  62.29       1        -0.03      33            1  \n",
       "552460         0.0      284.0  62.29       1        -0.03      34            1  \n",
       "552461         0.0      284.0  62.29       1        -0.03      35            1  \n",
       "\n",
       "[552462 rows x 38 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3280b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "#since using randomforest algorithm , we will not scale down the features\n",
    "\n",
    "X_feature = train_df.columns[:-1]\n",
    "y_feature = train_df.columns[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e0e058d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#divinding data into dependent & independent features\n",
    "\n",
    "X_train=train_df[X_feature]\n",
    "y_train=train_df[y_feature]\n",
    "\n",
    "X_test=test_df[X_feature]\n",
    "y_test=test_df[y_feature]\n",
    "\n",
    "X_val=val_df[X_feature]\n",
    "y_val=val_df[y_feature]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f89c06e",
   "metadata": {},
   "source": [
    "# RandomForest Application for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5bab6dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_features=37, n_estimators=9)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "#now we will look at the most important features by running a random forest model\n",
    "\n",
    "rf_classifier=RandomForestClassifier(n_estimators=9, max_features=37, criterion='gini', )\n",
    "rf_classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c44c083a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_features=37, n_estimators=9)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed8cf448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ICULOS', 'Temp', 'Age', 'Glucose', 'Platelets', 'PTT', 'WBC', 'Hct',\n",
       "       'HospAdmTime', 'HR', 'PaCO2', 'Potassium', 'BUN', 'SBP', 'Hgb',\n",
       "       'Creatinine', 'Calcium', 'Phosphate', 'Magnesium', 'pH', 'Chloride',\n",
       "       'Lactate', 'HCO3', 'Resp', 'BaseExcess', 'MAP', 'FiO2', 'DBP', 'AST',\n",
       "       'Alkalinephos', 'O2Sat', 'Bilirubin_total', 'SaO2', 'Fibrinogen',\n",
       "       'Bilirubin_direct', 'Gender', 'TroponinI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#not lets analyze the important features\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(20,10))\n",
    "rf_classifier.feature_importances_\n",
    "\n",
    "features_imp=pd.Series(rf_classifier.feature_importances_, index=X_train.columns)\n",
    "features_imp.nlargest(37).plot(kind='barh')\n",
    "\n",
    "#Below we observe the features in ascending order with their importance\n",
    "\n",
    "features_imp.sort_values(ascending=False).index\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc8c86c",
   "metadata": {},
   "source": [
    "# Performance metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5039c99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[114632,   1051],\n",
       "       [  2363,    272]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, auc, precision_recall_curve, roc_curve\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "y_pred=rf_classifier.predict(X_test)\n",
    "confusion_matrix(y_test,y_pred)\n",
    "\n",
    "#performance of confusion matrix is better on random forest when compared to logistic regression\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ce14b3e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_prob=rf_classifier.predict_proba(X_test)[:,1]\n",
    "precison,recall,threshold=precision_recall_curve(y_test,y_pred_prob)\n",
    "area_under_curve = auc(recall, precison)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "auroc = auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a3aac4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99    115683\n",
      "           1       0.21      0.10      0.14      2635\n",
      "\n",
      "    accuracy                           0.97    118318\n",
      "   macro avg       0.59      0.55      0.56    118318\n",
      "weighted avg       0.96      0.97      0.97    118318\n",
      "\n",
      "f1score: 0.1374431531076301\n",
      "Precision: 0.20559334845049132\n",
      "Recall: 0.1032258064516129\n",
      "accuraccy: 0.9711455568890617\n",
      "auc: 0.7008595595950794\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))\n",
    "print('f1score:', f1_score(y_test,y_pred))\n",
    "print('Precision:', precision_score(y_test,y_pred))\n",
    "print('Recall:', recall_score(y_test,y_pred))\n",
    "print('accuraccy:', accuracy_score(y_test,y_pred))\n",
    "print('auc:',auroc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2000d0c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'precision vs recall_randomforest')"
      ]
     },
     "execution_count": 18,
     "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": [
    "plt.plot(recall,precison)\n",
    "plt.xlabel('recall')\n",
    "plt.ylabel('precison')\n",
    "plt.title('precision vs recall_randomforest')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843a6294",
   "metadata": {},
   "source": [
    "# PLOT METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a07db21b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plots\n",
    "\n",
    "from plot_metric.functions import BinaryClassification\n",
    "bc=BinaryClassification(y_test,y_pred_prob,labels=['nonSepsis','Sepsis'])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot2grid(shape=(2,6), loc=(0,0), colspan=2)\n",
    "bc.plot_roc_curve()\n",
    "plt.subplot2grid(shape=(2,6), loc=(0,3), colspan=2)\n",
    "bc.plot_precision_recall_curve()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ed23adc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   ________________________\n",
      "                  |  Classification Report |\n",
      "                   ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   nonSepsis       0.98      0.99      0.99    115683\n",
      "      Sepsis       0.21      0.10      0.14      2635\n",
      "\n",
      "    accuracy                           0.97    118318\n",
      "   macro avg       0.59      0.55      0.56    118318\n",
      "weighted avg       0.96      0.97      0.97    118318\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bc.print_report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb38416a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9aee28c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "136cf485",
   "metadata": {},
   "source": [
    "# MODEL-4 XG Application for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3992b211",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\syed naziaa\\anaconda3\\lib\\site-packages (1.7.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\syed naziaa\\anaconda3\\lib\\site-packages (from xgboost) (1.6.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\syed naziaa\\anaconda3\\lib\\site-packages (from xgboost) (1.22.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46373426",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "XG_model=XGBClassifier(max_depth=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "42a49d15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XG_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "74f085a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[114632,   1051],\n",
       "       [  2363,    272]], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_xg=XG_model.predict(X_test)\n",
    "confusion_matrix(y_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90db8f60",
   "metadata": {},
   "source": [
    "# Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f82f986c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_prob=XG_model.predict_proba(X_test)[:,1]\n",
    "precison,recall,threshold=precision_recall_curve(y_test,y_pred_xg)\n",
    "area_under_curve = auc(recall, precison)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_xg)\n",
    "auroc = auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f87bd67d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99    115683\n",
      "           1       0.22      0.04      0.07      2635\n",
      "\n",
      "    accuracy                           0.98    118318\n",
      "   macro avg       0.60      0.52      0.53    118318\n",
      "weighted avg       0.96      0.98      0.97    118318\n",
      "\n",
      "f1score: 0.07318611987381704\n",
      "Precision: 0.21682242990654205\n",
      "Recall: 0.04402277039848197\n",
      "accuraccy: 0.9751686133977924\n",
      "auc: 0.5202004017358107\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred_xg))\n",
    "print('f1score:', f1_score(y_test,y_pred_xg))\n",
    "print('Precision:', precision_score(y_test,y_pred_xg))\n",
    "print('Recall:', recall_score(y_test,y_pred_xg))\n",
    "print('accuraccy:', accuracy_score(y_test,y_pred_xg))\n",
    "print('auc:',auroc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0afede5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1217e62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0840103b",
   "metadata": {},
   "source": [
    "# TESTING MODEL ON VALIDATION SET TRAININGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "84fd954d",
   "metadata": {},
   "outputs": [],
   "source": [
    "setB_validation=pd.read_csv('D:\\\\Jio Term 2\\\\ML\\ML_Project\\\\project_data\\\\sepsis_testB_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d09a4ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we split the data into dependent & independent features & drop the 3 features as dropped while training the model\n",
    "\n",
    "X_validation_setB = setB_validation[X_feature]\n",
    "y_validation_setB = setB_validation[y_feature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "815a536a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HR</th>\n",
       "      <th>O2Sat</th>\n",
       "      <th>Temp</th>\n",
       "      <th>SBP</th>\n",
       "      <th>MAP</th>\n",
       "      <th>DBP</th>\n",
       "      <th>Resp</th>\n",
       "      <th>BaseExcess</th>\n",
       "      <th>HCO3</th>\n",
       "      <th>FiO2</th>\n",
       "      <th>pH</th>\n",
       "      <th>PaCO2</th>\n",
       "      <th>SaO2</th>\n",
       "      <th>AST</th>\n",
       "      <th>BUN</th>\n",
       "      <th>Alkalinephos</th>\n",
       "      <th>Calcium</th>\n",
       "      <th>Chloride</th>\n",
       "      <th>Creatinine</th>\n",
       "      <th>Bilirubin_direct</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Lactate</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phosphate</th>\n",
       "      <th>Potassium</th>\n",
       "      <th>Bilirubin_total</th>\n",
       "      <th>TroponinI</th>\n",
       "      <th>Hct</th>\n",
       "      <th>Hgb</th>\n",
       "      <th>PTT</th>\n",
       "      <th>WBC</th>\n",
       "      <th>Fibrinogen</th>\n",
       "      <th>Platelets</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HospAdmTime</th>\n",
       "      <th>ICULOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>110.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>-214.64</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>110.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>-214.64</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>108.0</td>\n",
       "      <td>84.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>-214.64</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>123.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>-214.64</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>110.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>7.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>-214.64</td>\n",
       "      <td>5</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <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>762014</th>\n",
       "      <td>80.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>36.4</td>\n",
       "      <td>115.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.1</td>\n",
       "      <td>239.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>29.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762015</th>\n",
       "      <td>74.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>36.4</td>\n",
       "      <td>114.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.1</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>29.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762016</th>\n",
       "      <td>78.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>110.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.1</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>29.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762017</th>\n",
       "      <td>82.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>124.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.1</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>29.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762018</th>\n",
       "      <td>80.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.1</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>29.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>762019 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          HR  O2Sat  Temp    SBP   MAP   DBP  Resp  BaseExcess  HCO3  FiO2  \\\n",
       "0       93.0   92.5  36.5  110.0  76.0  56.0  22.0         0.0   0.0   0.0   \n",
       "1       93.0   92.5  36.5  110.0  76.0  56.0  22.0         0.0   0.0   0.0   \n",
       "2       91.0   96.0  36.5  108.0  84.5  72.0  23.5         0.0   0.0   0.0   \n",
       "3       93.0   98.0  36.5  123.0  87.0  61.0  21.0         0.0   0.0   0.0   \n",
       "4       93.0   95.0  36.5  110.0  81.0  70.0  20.0         0.0   0.0   0.0   \n",
       "...      ...    ...   ...    ...   ...   ...   ...         ...   ...   ...   \n",
       "762014  80.0   96.0  36.4  115.0  87.0  65.0  15.0         0.0   0.0   0.0   \n",
       "762015  74.0   97.0  36.4  114.0  83.0  67.0  15.0         0.0   0.0   0.0   \n",
       "762016  78.0   98.0  36.6  110.0  83.0  69.0  15.0         0.0   0.0   0.0   \n",
       "762017  82.0   99.0  36.6  124.0  91.0  71.0  16.0         0.0   0.0   0.0   \n",
       "762018  80.0   97.0  36.6  121.0  97.0  73.0  15.0         0.0   0.0   0.0   \n",
       "\n",
       "         pH  PaCO2  SaO2   AST   BUN  Alkalinephos  Calcium  Chloride  \\\n",
       "0       0.0    0.0   0.0   0.0  30.0           0.0      7.8       0.0   \n",
       "1       0.0    0.0   0.0   0.0  30.0           0.0      7.8       0.0   \n",
       "2       0.0    0.0   0.0   0.0  30.0           0.0      7.8       0.0   \n",
       "3       0.0    0.0   0.0   0.0  30.0           0.0      7.8       0.0   \n",
       "4       0.0    0.0   0.0   0.0  30.0           0.0      7.8       0.0   \n",
       "...     ...    ...   ...   ...   ...           ...      ...       ...   \n",
       "762014  0.0    0.0   0.0  18.0  11.0          75.0      8.9       0.0   \n",
       "762015  0.0    0.0   0.0  18.0  11.0          75.0      8.9       0.0   \n",
       "762016  0.0    0.0   0.0  18.0  11.0          75.0      8.9       0.0   \n",
       "762017  0.0    0.0   0.0  18.0  11.0          75.0      8.9       0.0   \n",
       "762018  0.0    0.0   0.0  18.0  11.0          75.0      8.9       0.0   \n",
       "\n",
       "        Creatinine  Bilirubin_direct  Glucose  Lactate  Magnesium  Phosphate  \\\n",
       "0             1.50               0.0    233.0      0.0        2.1        0.0   \n",
       "1             1.50               0.0    233.0      0.0        2.1        0.0   \n",
       "2             1.50               0.0    233.0      0.0        2.1        0.0   \n",
       "3             1.50               0.0    233.0      0.0        2.1        0.0   \n",
       "4             1.50               0.0    233.0      0.0        2.1        0.0   \n",
       "...            ...               ...      ...      ...        ...        ...   \n",
       "762014        0.54               0.1    239.0      0.0        2.2        4.0   \n",
       "762015        0.54               0.1    242.0      0.0        2.2        4.0   \n",
       "762016        0.54               0.1    242.0      0.0        2.2        4.0   \n",
       "762017        0.54               0.1    242.0      0.0        2.2        4.0   \n",
       "762018        0.54               0.1    242.0      0.0        2.2        4.0   \n",
       "\n",
       "        Potassium  Bilirubin_total  TroponinI   Hct   Hgb   PTT   WBC  \\\n",
       "0             3.7              0.0        0.0  35.3  11.3   0.0  10.8   \n",
       "1             3.7              0.0        0.0  35.3  11.3   0.0  10.8   \n",
       "2             3.7              0.0        0.0  35.3  11.3   0.0  10.8   \n",
       "3             3.7              0.0        0.0  35.3  11.3   0.0  10.8   \n",
       "4             3.7              0.0        0.0  35.3  11.3   0.0  10.8   \n",
       "...           ...              ...        ...   ...   ...   ...   ...   \n",
       "762014        3.6              0.9        0.0  37.1  11.6  29.1   5.4   \n",
       "762015        3.6              0.9        0.0  37.1  11.6  29.1   5.4   \n",
       "762016        3.6              0.9        0.0  37.1  11.6  29.1   5.4   \n",
       "762017        3.6              0.9        0.0  37.1  11.6  29.1   5.4   \n",
       "762018        3.6              0.9        0.0  37.1  11.6  29.1   5.4   \n",
       "\n",
       "        Fibrinogen  Platelets  Age  Gender  HospAdmTime  ICULOS  \n",
       "0              0.0      170.0   73       1      -214.64       1  \n",
       "1              0.0      170.0   73       1      -214.64       2  \n",
       "2              0.0      170.0   73       1      -214.64       3  \n",
       "3              0.0      170.0   73       1      -214.64       4  \n",
       "4              0.0      170.0   73       1      -214.64       5  \n",
       "...            ...        ...  ...     ...          ...     ...  \n",
       "762014         0.0      216.0   62       0         0.00      31  \n",
       "762015         0.0      216.0   62       0         0.00      32  \n",
       "762016         0.0      216.0   62       0         0.00      33  \n",
       "762017         0.0      216.0   62       0         0.00      34  \n",
       "762018         0.0      216.0   62       0         0.00      35  \n",
       "\n",
       "[762019 rows x 37 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_validation_setB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5fb51839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[719774,  31465],\n",
       "       [  9116,   1664]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Of all the models applied Random Forest has given the highest precision score & f1 score , hence we will proceed with random\n",
    "#forest \n",
    "\n",
    "\n",
    "y_pred_setB=rf_classifier.predict(X_validation_setB)\n",
    "\n",
    "confusion_matrix(y_validation_setB,y_pred_setB)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b36c50",
   "metadata": {},
   "source": [
    "# Performance on SetB validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d6be2b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_prob_setB=rf_classifier.predict_proba(X_validation_setB)[:,1]\n",
    "precison,recall,threshold=precision_recall_curve(y_validation_setB,y_pred_setB)\n",
    "area_under_curve = auc(recall, precison)\n",
    "fpr, tpr, thresholds = roc_curve(y_validation_setB, y_pred_setB)\n",
    "auroc = auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "15b4cb63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.97    751239\n",
      "           1       0.05      0.15      0.08     10780\n",
      "\n",
      "    accuracy                           0.95    762019\n",
      "   macro avg       0.52      0.56      0.52    762019\n",
      "weighted avg       0.97      0.95      0.96    762019\n",
      "\n",
      "f1score: 0.07579311758409439\n",
      "Precision: 0.050227897008663105\n",
      "Recall: 0.15435992578849722\n",
      "accuraccy: 0.9467454223582351\n",
      "auc: 0.5562378925278274\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_validation_setB,y_pred_setB))\n",
    "print('f1score:', f1_score(y_validation_setB,y_pred_setB))\n",
    "print('Precision:', precision_score(y_validation_setB,y_pred_setB))\n",
    "print('Recall:', recall_score(y_validation_setB,y_pred_setB))\n",
    "print('accuraccy:', accuracy_score(y_validation_setB,y_pred_setB))\n",
    "print('auc:',auroc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d04d4c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'precision vs recall_randomforest')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(recall,precison)\n",
    "plt.xlabel('recall')\n",
    "plt.ylabel('precison')\n",
    "plt.title('precision vs recall_randomforest')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153552e7",
   "metadata": {},
   "source": [
    "# PLOT METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "520b10b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plots\n",
    "\n",
    "from plot_metric.functions import BinaryClassification\n",
    "bc=BinaryClassification(y_validation_setB,y_pred_setB,labels=['nonSepsis','Sepsis'])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot2grid(shape=(2,6), loc=(0,0), colspan=2)\n",
    "bc.plot_roc_curve()\n",
    "plt.subplot2grid(shape=(2,6), loc=(0,3), colspan=2)\n",
    "bc.plot_precision_recall_curve()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fe723f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20c0a7a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69429a86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "694023d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
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