Regression PySpark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vRBjG71s_Cta"
   },
   "source": [
    "\n",
    "***\n",
    "# Regression - ML Section\n",
    "\n",
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZERtoljn548U"
   },
   "source": [
    "# ML Objectives\n",
    "\n",
    "<br>\n",
    "</br>\n",
    "\n",
    "## Regression - select a column of your choice and state your objective\n",
    "\n",
    "The objective for this task is to demostrate the ability to build ML regression model and evalute its performance.\n",
    "\n",
    "#### Column to be selected as target is Age, The reason behind it being it is the only column with continous values and Regression works best on continous data.\n",
    "\n",
    "<br>\n",
    "\n",
    "### Dataset Details\n",
    "You will use the provided dataset which could be downloaded from [here](https://www.kaggle.com/datasets/meirnizri/covid19-dataset)\n",
    "\n",
    "<br>\n",
    "\n",
    "### ML Expectations\n",
    "\n",
    "1. Data Preprocessing: prepare the dataset as you see fit but make sure to include enough comments and details.\n",
    "1. Complete the EDA and data visualization.\n",
    "    - The minimum number of plots is 10.\n",
    "    - Each plot should have an insight and it needs to be detailed \"At least 2 lines of code\"\n",
    "1. Do the Feature selection after features transformation.\n",
    "1. Split the data into training and test.\n",
    "1. Build at least 3 modules and compare them to your baseline.\n",
    "1. Provide complete model evaluation and show the result as a dataframe\n",
    "1. Perform hyperparamter tuning and use the tuned model, compare its results to the baseline model.\n",
    "1. Use your best model to create a pipeline that goes through all the steps after model split to finding predictions\n",
    "\n",
    "### Important Notes:\n",
    "- Set all random states to be 42 for results reproductivity when applicable\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mHyPnM3G_Ctb"
   },
   "source": [
    "## Importing Packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 1490,
     "status": "ok",
     "timestamp": 1661505346080,
     "user": {
      "displayName": "Sas Na",
      "userId": "17642443539075104066"
     },
     "user_tz": -180
    },
    "id": "y7dQc4oDc0fz"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XbFzxDVL_Ctd"
   },
   "source": [
    "## Loading Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 317,
     "status": "ok",
     "timestamp": 1661443827879,
     "user": {
      "displayName": "SAIF ALZAHRANI",
      "userId": "08619190164956123442"
     },
     "user_tz": -60
    },
    "id": "Uj4YuXAjlH-A",
    "outputId": "ef41e6c4-8204-4c41-9dda-39a3efbcb7b5"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Spark Dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "executionInfo": {
     "elapsed": 4362,
     "status": "ok",
     "timestamp": 1661505352053,
     "user": {
      "displayName": "Sas Na",
      "userId": "17642443539075104066"
     },
     "user_tz": -180
    },
    "id": "xeIq9PWLaGof",
    "outputId": "69704084-f70b-4ee1-ca99-8ea5cf20a80a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USMER</th>\n",
       "      <th>MEDICAL_UNIT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>PATIENT_TYPE</th>\n",
       "      <th>DATE_DIED</th>\n",
       "      <th>INTUBED</th>\n",
       "      <th>PNEUMONIA</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PREGNANT</th>\n",
       "      <th>DIABETES</th>\n",
       "      <th>...</th>\n",
       "      <th>ASTHMA</th>\n",
       "      <th>INMSUPR</th>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <th>OTHER_DISEASE</th>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <th>OBESITY</th>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <th>TOBACCO</th>\n",
       "      <th>CLASIFFICATION_FINAL</th>\n",
       "      <th>ICU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>03/05/2020</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>03/06/2020</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>09/06/2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>21/06/2020</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   USMER  MEDICAL_UNIT  SEX  PATIENT_TYPE   DATE_DIED  INTUBED  PNEUMONIA  \\\n",
       "0      2             1    1             1  03/05/2020       97          1   \n",
       "1      2             1    2             1  03/06/2020       97          1   \n",
       "2      2             1    2             2  09/06/2020        1          2   \n",
       "3      2             1    1             1  12/06/2020       97          2   \n",
       "4      2             1    2             1  21/06/2020       97          2   \n",
       "\n",
       "   AGE  PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
       "0   65         2         2  ...     2.0      2.0           1.0            2.0   \n",
       "1   72        97         2  ...     2.0      2.0           1.0            2.0   \n",
       "2   55        97         1  ...     2.0      2.0           2.0            2.0   \n",
       "3   53         2         2  ...     2.0      2.0           2.0            2.0   \n",
       "4   68        97         1  ...     2.0      2.0           1.0            2.0   \n",
       "\n",
       "   CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASIFFICATION_FINAL   ICU  \n",
       "0             2.0      2.0            2.0      2.0                   3.0  97.0  \n",
       "1             2.0      1.0            1.0      2.0                   5.0  97.0  \n",
       "2             2.0      2.0            2.0      2.0                   3.0   2.0  \n",
       "3             2.0      2.0            2.0      2.0                   7.0  97.0  \n",
       "4             2.0      2.0            2.0      2.0                   3.0  97.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 181657 entries, 0 to 181656\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   USMER                 181657 non-null  int64  \n",
      " 1   MEDICAL_UNIT          181657 non-null  int64  \n",
      " 2   SEX                   181657 non-null  int64  \n",
      " 3   PATIENT_TYPE          181657 non-null  int64  \n",
      " 4   DATE_DIED             181657 non-null  object \n",
      " 5   INTUBED               181657 non-null  int64  \n",
      " 6   PNEUMONIA             181657 non-null  int64  \n",
      " 7   AGE                   181657 non-null  int64  \n",
      " 8   PREGNANT              181657 non-null  int64  \n",
      " 9   DIABETES              181657 non-null  int64  \n",
      " 10  COPD                  181656 non-null  float64\n",
      " 11  ASTHMA                181656 non-null  float64\n",
      " 12  INMSUPR               181656 non-null  float64\n",
      " 13  HIPERTENSION          181656 non-null  float64\n",
      " 14  OTHER_DISEASE         181656 non-null  float64\n",
      " 15  CARDIOVASCULAR        181656 non-null  float64\n",
      " 16  OBESITY               181656 non-null  float64\n",
      " 17  RENAL_CHRONIC         181656 non-null  float64\n",
      " 18  TOBACCO               181656 non-null  float64\n",
      " 19  CLASIFFICATION_FINAL  181656 non-null  float64\n",
      " 20  ICU                   181656 non-null  float64\n",
      "dtypes: float64(11), int64(9), object(1)\n",
      "memory usage: 29.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zVOCiaDviYY0"
   },
   "source": [
    "## Data Preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "flcqL_mR_Ctd"
   },
   "source": [
    "### As mentioned in dataset description, data mapped as 97, 98, 99 are considered as null values.\n",
    "### But if the test of following is not avaible then doctors will consider as the patient is not suffering with following diesease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    98385\n",
      "1    83272\n",
      "Name: USMER, dtype: int64\n",
      "4    162162\n",
      "3     19175\n",
      "2       169\n",
      "1       151\n",
      "Name: MEDICAL_UNIT, dtype: int64\n",
      "2    95737\n",
      "1    85920\n",
      "Name: SEX, dtype: int64\n",
      "1    113348\n",
      "2     68309\n",
      "Name: PATIENT_TYPE, dtype: int64\n",
      "9999-99-99    140250\n",
      "07/07/2020       552\n",
      "16/06/2020       541\n",
      "06/07/2020       537\n",
      "13/07/2020       536\n",
      "               ...  \n",
      "20/10/2020         1\n",
      "18/10/2020         1\n",
      "16/10/2020         1\n",
      "14/10/2020         1\n",
      "12/10/2020         1\n",
      "Name: DATE_DIED, Length: 337, dtype: int64\n",
      "97    113348\n",
      "2      49683\n",
      "1      17429\n",
      "99      1197\n",
      "Name: INTUBED, dtype: int64\n",
      "2     135239\n",
      "1      41662\n",
      "99      4756\n",
      "Name: PNEUMONIA, dtype: int64\n",
      "37     4066\n",
      "40     4043\n",
      "30     3992\n",
      "35     3959\n",
      "36     3958\n",
      "       ... \n",
      "100      12\n",
      "103       1\n",
      "101       1\n",
      "107       1\n",
      "102       1\n",
      "Name: AGE, Length: 105, dtype: int64\n",
      "97    95737\n",
      "2     84691\n",
      "1      1121\n",
      "98      108\n",
      "Name: PREGNANT, dtype: int64\n",
      "2     148571\n",
      "1      32854\n",
      "98       232\n",
      "Name: DIABETES, dtype: int64\n",
      "2.0     177431\n",
      "1.0       3992\n",
      "98.0       233\n",
      "Name: COPD, dtype: int64\n",
      "2.0     175967\n",
      "1.0       5454\n",
      "98.0       235\n",
      "Name: ASTHMA, dtype: int64\n",
      "2.0     178070\n",
      "1.0       3350\n",
      "98.0       236\n",
      "Name: INMSUPR, dtype: int64\n",
      "2.0     139119\n",
      "1.0      42323\n",
      "98.0       214\n",
      "Name: HIPERTENSION, dtype: int64\n",
      "2.0     171978\n",
      "1.0       9428\n",
      "98.0       250\n",
      "Name: OTHER_DISEASE, dtype: int64\n",
      "2.0     176706\n",
      "1.0       4713\n",
      "98.0       237\n",
      "Name: CARDIOVASCULAR, dtype: int64\n",
      "2.0     149029\n",
      "1.0      32483\n",
      "98.0       144\n",
      "Name: OBESITY, dtype: int64\n",
      "2.0     175335\n",
      "1.0       6086\n",
      "98.0       235\n",
      "Name: RENAL_CHRONIC, dtype: int64\n",
      "2.0     167903\n",
      "1.0      13502\n",
      "98.0       251\n",
      "Name: TOBACCO, dtype: int64\n",
      "3.0    133728\n",
      "6.0     23669\n",
      "7.0     16009\n",
      "5.0      6452\n",
      "2.0      1249\n",
      "1.0       355\n",
      "4.0       194\n",
      "Name: CLASIFFICATION_FINAL, dtype: int64\n",
      "97.0    113347\n",
      "2.0      65021\n",
      "1.0       2080\n",
      "99.0      1208\n",
      "Name: ICU, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(df[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     148571\n",
       "1      32854\n",
       "98       232\n",
       "Name: DIABETES, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DIABETES'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df.columns:\n",
    "    df[i][(df[i]==97) | (df[i]==99) | (df[i]==98)]=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USMER</th>\n",
       "      <th>MEDICAL_UNIT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>PATIENT_TYPE</th>\n",
       "      <th>DATE_DIED</th>\n",
       "      <th>INTUBED</th>\n",
       "      <th>PNEUMONIA</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PREGNANT</th>\n",
       "      <th>DIABETES</th>\n",
       "      <th>...</th>\n",
       "      <th>ASTHMA</th>\n",
       "      <th>INMSUPR</th>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <th>OTHER_DISEASE</th>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <th>OBESITY</th>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <th>TOBACCO</th>\n",
       "      <th>CLASIFFICATION_FINAL</th>\n",
       "      <th>ICU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>03/05/2020</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>03/06/2020</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>09/06/2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>21/06/2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   USMER  MEDICAL_UNIT  SEX  PATIENT_TYPE   DATE_DIED  INTUBED  PNEUMONIA  \\\n",
       "0      2             1    1             1  03/05/2020        2          1   \n",
       "1      2             1    2             1  03/06/2020        2          1   \n",
       "2      2             1    2             2  09/06/2020        1          2   \n",
       "3      2             1    1             1  12/06/2020        2          2   \n",
       "4      2             1    2             1  21/06/2020        2          2   \n",
       "\n",
       "   AGE  PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
       "0   65         2         2  ...     2.0      2.0           1.0            2.0   \n",
       "1   72         2         2  ...     2.0      2.0           1.0            2.0   \n",
       "2   55         2         1  ...     2.0      2.0           2.0            2.0   \n",
       "3   53         2         2  ...     2.0      2.0           2.0            2.0   \n",
       "4   68         2         1  ...     2.0      2.0           1.0            2.0   \n",
       "\n",
       "   CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASIFFICATION_FINAL  ICU  \n",
       "0             2.0      2.0            2.0      2.0                   3.0  2.0  \n",
       "1             2.0      1.0            1.0      2.0                   5.0  2.0  \n",
       "2             2.0      2.0            2.0      2.0                   3.0  2.0  \n",
       "3             2.0      2.0            2.0      2.0                   7.0  2.0  \n",
       "4             2.0      2.0            2.0      2.0                   3.0  2.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    164228\n",
       "1     17429\n",
       "Name: INTUBED, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"INTUBED\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### if a person is not tested for a particular disease then doctors will consider it as patient is not affected by it\n",
    "### so all the expected null values have been replaced with two"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now Date_Died columns has the dates on which the person has died which basically shows us that the person is alive or dead.\n",
    "### So encoding these values in that feature as follows:\n",
    "#### Alive - 0 [ date with 9999-99-99 ]\n",
    "#### Death - 1 [ date other than 9999-99-99 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9999-99-99    140250\n",
       "07/07/2020       552\n",
       "16/06/2020       541\n",
       "06/07/2020       537\n",
       "13/07/2020       536\n",
       "               ...  \n",
       "20/10/2020         1\n",
       "18/10/2020         1\n",
       "16/10/2020         1\n",
       "14/10/2020         1\n",
       "12/10/2020         1\n",
       "Name: DATE_DIED, Length: 337, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"DATE_DIED\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"PATIENT_STATUS\"]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"PATIENT_STATUS\"][df[\"DATE_DIED\"]!='9999-99-99']=1\n",
    "df[\"PATIENT_STATUS\"][df[\"DATE_DIED\"]=='9999-99-99']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    140250\n",
       "1     41407\n",
       "Name: PATIENT_STATUS, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PATIENT_STATUS\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing the datatype of Date_Died to date and time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DATE_PARSED']=pd.to_datetime(df['DATE_DIED'][df['DATE_DIED']!=\"9999-99-99\"], format=\"%d/%m/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2020-05-03\n",
       "1        2020-06-03\n",
       "2        2020-06-09\n",
       "3        2020-06-12\n",
       "4        2020-06-21\n",
       "            ...    \n",
       "181652          NaT\n",
       "181653          NaT\n",
       "181654          NaT\n",
       "181655          NaT\n",
       "181656          NaT\n",
       "Name: DATE_PARSED, Length: 181657, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DATE_PARSED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={1:'JAN',2:'FEB',3:'MAR',4:'APR',5:'MAY',6:'JUNE',7:'JULY',8:'AUG',9:'SEPT',10:'OCT',11:'NOV',12:'DEC'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MONTH']=df[\"DATE_PARSED\"].dt.month.map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          MAY\n",
       "1         JUNE\n",
       "2         JUNE\n",
       "3         JUNE\n",
       "4         JUNE\n",
       "          ... \n",
       "181652     NaN\n",
       "181653     NaN\n",
       "181654     NaN\n",
       "181655     NaN\n",
       "181656     NaN\n",
       "Name: MONTH, Length: 181657, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MONTH']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "enIiifQUmVD5"
   },
   "source": [
    "***\n",
    "## Exploratory Data  Analysis\n",
    "\n",
    "####  Note: minimum 10 plots with insights \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IQ5bxmJ4_Ctg"
   },
   "source": [
    "### Number of Patients Cured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "sUeD-GOv_Ctg"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"NUMBER OF PATIENTS CURED\", fontsize = 20)\n",
    "sns.countplot(df[\"PATIENT_STATUS\"],hue=df[\"PATIENT_STATUS\"])\n",
    "plt.xlabel('Patient Status')\n",
    "plt.legend(labels = ['Alive','Dead'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. More People are Alive than dead\n",
    "#### 2. Death Ratio is around 0.29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R97QbsLm_Ctg"
   },
   "source": [
    "### Month Wise Death Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "d_UVZNcw_Ctg"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,5))\n",
    "ax = sns.countplot(x =df[\"MONTH\"], hue =df[\"PATIENT_STATUS\"],order=['JAN','FEB','MAR','APR','MAY','JUNE','JULY','AUG','SEPT','OCT','NOV','DEC'])\n",
    "plt.title(\"DEAD NUMBERS OF COVID BY MONTH\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Most No. of Patients Died on May, June and July\n",
    "#### 2. Deaths are increased in the Hot Weather"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5PJk0EQW_Cth"
   },
   "source": [
    "### Diesease Severity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "jSqVbOC7_Cth"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"DIESEASES SEVERITY\",fontsize = 20)\n",
    "sns.countplot(df['PATIENT_TYPE'],hue=df['PATIENT_STATUS'])\n",
    "plt.legend(labels = ['Alive','Dead'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Out of all the people who were sent home, Only few people suffered to death.\n",
    "#### 2. Out of all the people hospitalzed, only Few were able to survive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hLeJMtgm_Cth"
   },
   "source": [
    "### Gender Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "8Crq3V-i_Cth"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"GENDER RATIO\",fontsize = 20)\n",
    "sns.countplot(df['SEX'],hue=df['PATIENT_STATUS'])\n",
    "plt.legend(labels = ['Alive','Dead'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. People who were suffering from covid has equal gender ratio.\n",
    "#### 2. Compared to females, unfortunately males suffer more death."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7dDUT0S1_Cti"
   },
   "source": [
    "### Distribution Of Age vs Patient Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "HrEFDKcW_Cti"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"AGE DISTRIBUTION vs PATIENT STATUS\",fontsize = 20)\n",
    "sns.histplot(x=df['AGE'],bins=10,hue=df['PATIENT_STATUS'])\n",
    "plt.legend(labels = ['Alive','Dead'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Younger People suffered more deaths compared to Older People\n",
    "#### 2. Age group of 20-50 where affected by covid more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of age vs Sex ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"AGE DISTRIBUTION vs SEX RATIO\",fontsize = 20)\n",
    "sns.histplot(x=df['AGE'],bins=10,hue=df['SEX'])\n",
    "plt.legend(labels = ['Female','Male'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Impact of Covid was equally on Male and Female\n",
    "#### 2. Comparitively Females were affected by Covid More than the males as females grew older than 40 yrs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Severity Level w.r.t. Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"DIESEASES SEVERITY vs DIABETES\",fontsize = 20)\n",
    "sns.countplot(df['DIABETES'],hue=df['PATIENT_STATUS'])\n",
    "plt.legend(labels = ['Alive','Dead'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Outof all the people who have Diabetes have unfortunatly werent able to survive\n",
    "#### 2. People who didnt have diabetes had the larger probability to survive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dieseases Severity w.r.t to Pregnancy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAFSCAYAAADl+DgoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAsLUlEQVR4nO3df7xVVZ3/8ddbQNHMXwhOeclLhZX5K7yYpdM4UoKNimaFpoKm8u2bpv2YRvupY9rUV8vSGovSARoH8msp+B3LSCXLwrio+du8GQaMwhUU/AEq8Pn+sdaR4+Gce8/m/jjXe9/Px+M8ztlrr7322ufH/uy91jp7KyIwMzOr11aNroCZmb22OHCYmVkhDhxmZlaIA4eZmRXiwGFmZoU4cJiZWSEOHGZmVkxEdPsDiIrHi0A7cBfwY+AIYFCNZafnZU6pkd7RY3rFMoOAM4DfAKuAl4EVwL25HkdX5D+0jnVEB9s9EtiQ8329k/eoV+oGjAWuAR7Pn8Ma4C/AjcC/AK8r8Lm+DfgR0AasA54H/gr8CvgqsFtF/vl11PmCnPeOPP3BOurxSM77rjx9QR3rmV9RxuKK+RuB1cAC4NPAkI6+2x181zt7nJ2fWztYx5A8Pyq/B33hAZxSZbtezN+F6cBedeZ/PH8396uxnsKfa9myuwNfA/4APEX6fa0G7gb+HTi0k+36PzXKPTTP/89O3qM/53y/7yTf/JxvA7BPjTzTc57315g/DPgK8PuybV0J/Bb4IrAbMBi4M5dzbAf1OTbnuRMYXCvfYHrWv+bnQcBOwDuBk4HTgFZJJ0bEnwuWOQe4p8a8V9IlDQL+HzABeAb4b2ApsHWux8eAtwNzq5TzOOnDKup00llcAKdK+mpErK/M1Ft1k3QSMAMQcCtwPbAW2AM4BDgS+DkpEHRW1mG5nkNJP8ZfkoLQG4H3Ah8gfXGXV1l8BmlHXc38/PyjXM7pwE0d1OMfgD2B1oi4u2L2b8rKq1Rr/d8lfQaDgDcBHwIuA8YBR9WqR5l/rZL2aWDHsrLL3U7a1jPysl+ssvz5wAHAtIio9h3oK/4E3JBf70jaqU4BPirpsIhY0EH+HYCDSd/14ySNi4g7aqyn0OcqaSrpvR8KPEz63q8AXkc6+JkM/G9Jl0bE52uUe7ak70fE4zXm1yTpH4HRpP3AeyTtHRH3d7LYVsAlpH1CkXUdCfwn6f1vY9O27gi8G7iI9B17K3ASKXD+SNKCiHiioqy/A6aRDghPrLbvekUPHZHUPDInRb9rc56/ASNqRNdT6knvoA4n5fz3ADtWmb8d8I81jiaqHsV0sr5BwBLSUc33czkfalTdchmrgfXAuBp53gvsVGd5j+b1T6kxf19gZEXa/LzMoXWUvy1pJ/sSFWcuFfl+kss8oyztAsrOXurcnsV5meaK9LcCz+V5/1Dku91Z2WXzX0c6It0AHFLlM1mf59d9NtibDzYdmU+vSFfZ7/S2zvLneT+ozN/Fz3VyXmYVMLFGnh2AzwNX1Niu0nf9mirLln6HNc84gFk5z7/l58s7yDu/Yp0fqJKn9J6+vyL9H0hnF2tz3VVl2X1IB43NeXpqLuuXlflJB2yv+m3VevR6H0dELAeOJ71hI6l+xNUd3pufp0fE6ir1eCEibuvG9R0BNAE/Ba7MaWc0sG57k34g90fELdUyRMTvI+KZzgqSNIK0Q10dETNqlHVvRCzZ0spGxFrSkdMQ0o+gWj12Aj5M2rHP2tJ1dVKPNtIRLqRmvp5Yx/Okg4eNwE8k7QAgaXs2BcYTc76aJB0kKSRd30GehyS9KGmXPC1JUyT9XlK7pHWSlki6WdKkLm5XkJqBAA6sc7Gr8nOX32tJOwLfyZOTImJOtXwRsSYiLgE+U6Ooa0lH5idIailYh2Gk5p5HSc1HTwInSRrayaJfJH3ul0jqdL+c8/yQ1AR1TkRMz+//q0TEfcD7gWV5ehqpJWM88Kmy8j5J2ofNiYgfdbb+hnSOR8RG0ikUpA9HPbCalfl5zx4ou5qp+Xl6pNPSRcDhkvaokrc36lZaxxslva6LZZXOXLaX9IYultWR0hf2tBrzTyI1P8yKiOd6sB4lL/dUwRHxR1IbfDPwvZz8XeDNwIURsbCOMhaQ+ns+mHdYryLpQFKT540RsSonX0w6gv070g7y28CvSX0CH9nyLdq02lL1Ci7XHe/1h4GdSf0K8zrLHLWbYgL4Z9K2XFqwDlOAbUj7gfWkPpyd6fy9vZt04LRfLqMz/0BqdlvGpuBbVURsjIjy9/d0UpPyNyXtJWlPUjPZcmof7L5KT/dxdOR3pJ3RCNKP5691LneMpOYa82ZHxMP59c+Bc4FPSHo9qe1vUdTXZtks6YIa8x6OiNnlCZJ2Bz4I/Dkifp+Tp5PaqU8nHXmU6426PQYsJB3J3SHpR6Q+iAci4qU61vOKiHhR0hzgOOB3kq4kdbzdFxEv1FHEKZIOrTHvBxHxZF7PnyT9EThQ0qERMb8i7+n5eVqNsg7t4L35ZWze5r4ZSW8jNUdA+o72pItJbdonS1oHfJz0GX29QBkzcv4T2BSASqaU5Sn5X6Sdzd6Vn52kXQusdzP5APCTefLOOhcrHXB19F7X+7kenJ9vrXPdNUXErZL+G/gnSUdH/X1NZ5DOJGfm6enA50jb+ZNOlv0SKcB8TdLsfBZeyyH5eX5EbKizbgBERLukj5P6LK8hBe3tgA9HRHu9hfREG2in7cA535M574FV2vNOqdHO19HjmIplPgo8UZFnJWlHfVSV+hxaxzpuqLLcV/K8L5Sl7UIaObKMKiPIeqNupM7e2yryvET6UZ8L7FDgM90Z+BnpR1EqawOpw/MiqvRLUN+oqv0rljmNKm3IpAAYwF1V1nNBHev5dMUyi3P6d/LyXyPtYEv9G5ds6XebTvo4KvK+BXg2518DvLngb60pfw4LK9K3zt+n5ZSNjslpfwW26cLv+xQ29dFdkB+XkY6aA3gBOKiT/N8mHdgE6TeyZ1c/Vza10X+iSlk7la37lUeN7booT+9FOrh9qPQe0kEfB/D3ed7NFemlEXLv6OA38tY8XeoX+XJZnulU9HGQmgQD+EYXPsdSGQF8v9CyW7rSTirU6Y8r51ue846t8iadUpG3anod6xgCHA5cSBqC+nTZmzWDsg4itqBznNTct5j04929Yt51ubyqQyp7um5ly76DNAx0JmmUSWkdjwOjCpbVTDpq/RFpR1AKJCvLP8eKH8WhBcp/HWkHuhbYuSx9Wi7rf1dZ5oI874IC61lc9j5UPs7vynebAoEj5/9mzv/Nop9tXv5Xefm9ytKOy2nfrsh7eU7/C2knNYEqAzQ6Wd8pVd6zl/L3aSb1Dcct/w6+qcZ6Cn2udBw4mqutv0Y9LypL+2FO+2SePpTagaPUP3V8RfpZ1T6Lit9IKXDsQBoVtYZ8MEbPBY5ty96LbYss27A/AObOol3yZH2nR1sgIl6OiF9FxFcj4ihgV2ASacjZZGBiF1cxnjS8dV5ELKuYNz0/T6WKXqhbaT0PRcTlETE5It5OCiR/IJ2RXFawrMUR8cOIOCMi9s9l3Ej6LDvtVKuj/OeB/yL1ZZwEkPtojie9L9d0dR0VRkWESD+i95DOoM6XdHI3r6cjayuei5qen6eUpZVeVw5m+Ex+PAecB/wCeErSHElvLbjeGRGh/Ng6IvbI37EHO8pPOtj6O1LTzEjgRknbFVx3NU/m5zdWzsjf21JdRe4srsNXSe/V+blZuSpJO5P6WJ5h05Djkv8iBdbJkrbpaGURsYY0TPv1pMBZS2ko7e4dldfJutZWe12PRv5z/BBSH8vyiFjcWyuNiA0RcS2bdpiHdbHIUlAYn0e4vPIg7VABJkga2YC61VrPw6T/03R5HRGxlLRTfwnYrzR6p4tKfRilPo3jST+kn+YfVreLiHWR2sqPIDUdXSlpsx1QH3U96Qj1JEmD8ii4I4A/RcSfyjPm79h3ImI/0tD44/LyRwO/7GzH1h0iWR4RXwe+RRrKfVEni9Wj9D+Qcd1QFgCRRoFeQuqLPa+DrJNJBzs7AWsr9gMrSU2Hw0jvd2d+SBqOfbqkt9fIU+oTOjT/L6xXNSRw5KFkX8qT/9WIOpB2DrBpFEhh+Q8zR5J+tFfVeNxB+o/Hx3uzbr28jhdJgaNbyouIu0ij0vbNI4M66xTvNpH+FPV1UpNZtT/39Tn5aPFa0pH2+0l/qhvM5mcblcutiIifR8RHSR3KbyEN4+5NF5JaHM6SNKqLZV1HOuJ/r6RuCx6kkVX/QzpTa6qRpzQaaRbV9wPXVeSrKdJorHNJn+ElNbL9hjSirgk4taPyJG0laUhn6y2i1wNHPhqaTWor/BvFRpAUWc8Jkj5QbUx03uGXPsDbu7Caj5M+3Gsi4vRqDza1m55Wqktv1E3SKEln57HtlfPEpsDd6TokvU7SVyTtViPLp4HtgQcjYmWNPEWVmr0uBQ4C7o2IekfqdNUVpP63UySN7qV1dtX0/Dw5P0pDQV8haRtJB1csR96plM4U6xkl120i4llSH88QOm6aqaes1aTvIsC1+V/Vm8nNYnXvSCONPvsKqTnz/CrlvZd0xYcHI+JjNfYDk0j9OYfW852KiBtIIxePZNNosfL5G0l9jeuByyWdVO1vDZL2IvWBbXGTVjU9Ohy3bAjdVmy65MghpNO2P5L+5PRUwWI7Go67OCKm59fvBs4BnpT0OzYN9x0F/BPpSzCHTUcC5Toa8gppJM5qNh0J/7hWxohok/QbUqA8gjQErkfrFulPfTuS/hdwiaQ7gPtJZxkjSM1TbyZ1wn2ug7JKhpCODM/Pw2XvIXXk70L6Uu9D6n/4RI3lOxqOe0/+kVT6L1LQ+Ps8XU//SUfDNp+JiO/UUQYR8YKkb5CaDC8kDXXt0yLiDkltpOGcQ0j/3VhRkW1b0nDqNtIZ3eOk5pUPkPq95kbEQ71Y7ZJ/J/1v4iRJ36hSh7o/14iYkZvbLif1nTxEOutfQTq4aSINSNmeYgdm00lBaZ8q80rN1TX/TxERGyX9Byk4TiX9c70z/0y6dlrVvqeI+I2kD5E65X8CfEXSfNIZ3I5AC2lf8zxb3n9W3Zb2yHfSW185euFF0sW3FpF2ABOArWosO52OR1V19Jhfln8kcCap/fYRUnPSS6ROpZtIHa9bVazj0DrWEaQRGh/IrzcbHlplmz6W887pjbrl/NsAx5B+lHeRjqBLF3pbRGpTHl7n57lV/sy+RRrK+z+5rGdJF2X8DlVGEFHfcNzpHaz3RznPC3RwaRTqG7a5uGKZxeXvV5Uyh5I6UDcC+1Z+tzt5vzosu4P6X9DF392Xy7b3uCrzh5AubPkL0tn+OtJOZgEp6G9d53pO6eyzK5qf9C/mAH7Wlc+1bNkm0v9k7mTTRURXkwY//IDql5Mp1fOiGmWOL1vvf+a0HUk75heBXTt5H0oXQV1Req+pGFVVZZlZZeus5yKHK/O2rsrTX6bisk4Vy3b6fa72UF7YzMysLr4fh5mZFeLAYWZmhThwmJlZIQ4cZmZWiAOHmZkV0sjLqvcpu+66azQ3Nze6GmbWzyxatOipiBje6Hp0JweOrLm5mdbW1kZXw8z6GUmF71ve17mpyszMCnHgMDOzQhw4zMysEPdxmFm3e/nll1m6dCnr1q1rdFV6zdChQ2lqamLIkG69gnmf5MBhZt1u6dKlvP71r6e5uZkqV/vudyKClStXsnTpUkaN6uptRfo+N1WZWbdbt24dw4YNGxBBA0ASw4YNGzBnWD0WOCRdLWmFpPurzPtcvq3irnlaki6X1CbpXkljyvJOkfRofkwpSz9A0n15mctLNzGRtIukeTn/vHwvYDPrZQMlaJQMpO3tyTOO6aR7OLxKvvf24aT7AZQcAYzOj6nAlTnvLqQ7br0bOJB0I6FSILiSdKe80nKldZ0H3BIRo4Fb6Pg+wWbWj91www1I4uGHHwZg8eLF7L13ujtua2srZ599diOr95rVY30cEXF7jTv1XUa6mcycsrSJwMxINwdZIGknSW8g3bxoXkSsApA0D5iQ73K1Q0QsyOkzSTct+kUu69Bc7gzSjVLO7cZNM7OCDvj8zG4tb9Elk+vKN2vWLA455BBmzZrFv/7rq28h39LSQktLS7fWa6Do1T4OSROBZRHxp4pZuwNLyqaX5rSO0pdWSQfYLSKeyK+fBGrdJxtJUyW1Smptb28vujlm1oc999xz/O53v+Oqq65i9uzZm82fP38+Rx55JBs3bqS5uZlnnnnmlXmjR49m+fLltLe3c9xxxzF27FjGjh3LHXfc0Ytb0Hf1WuDIN4j/IvDV3lpnPoOpeYvDiJgWES0R0TJ8eL+6lIzZgDdnzhwmTJjAnnvuybBhw1i0aFHVfFtttRUTJ07k+uuvB+DOO+9kjz32YLfdduOcc87hM5/5DAsXLuRnP/sZp59+em9uQp/Vm8Nx3wKMAv6UO5GagLskHUi6t/PIsrxNOW0Zm5qdSunzc3pTlfwAyyW9ISKeyM1dK7p9S8y2QHc31/Q19TYf9ZZZs2ZxzjnnAHD88ccza9YszjrrrKp5J02axIUXXsipp57K7NmzmTRpEgC//vWvefDBB1/Jt2bNGp577jm23377nt+APqzXAkdE3AeMKE1LWgy0RMRTkuYCZ0maTeoIX513/DcDXy/rED8c+EJErJK0RtJBpJvRTwauyHnmAlOAb+Tn8r4UMxsAVq1axa233sp9992HJDZs2IAkzjzzzKr53/Oe99DW1kZ7ezs33HADX/7ylwHYuHEjCxYsYOjQob1Z/T6vJ4fjzgL+ALxN0lJJp3WQ/SbgMaAN+BHwSYDcKf41YGF+XFjqKM95fpyX+QupYxxSwPiApEeB9+dpMxtArrvuOk4++WQef/xxFi9ezJIlSxg1ahRLliypml8Sxx57LJ/97Gd5xzvewbBhwwA4/PDDueKKK17Jd8899/RG9fu8nhxVdUIn85vLXgdQ9VAgIq4Grq6S3grsXSV9JTCuYHXNrB+ZNWsW55776sGUxx13HP/2b/9Wc5lJkyYxduxYpk+f/kra5Zdfzplnnsm+++7L+vXred/73scPfvCDnqr2a4bSPttaWlrC9+OwnjSQ+jgeeugh3vGOdzSwNo1RbbslLYqIfjXu15ccMTOzQhw4zMysEAcOMzMrxIHDzMwKceAwM7NCHDjMzKwQBw4z65cGDRrE/vvvzzvf+U72228/vvWtb7Fx48ZuKfuCCy7g0ksv7ZayXot861gz63F/u3Cfbi3vTV+9r9M822677Sv/9F6xYgUf+9jHWLNmzWaXV7fifMZhZv3eiBEjmDZtGt/73veICDZs2MDnP/95xo4dy7777ssPf/hDIF2Kfdy4cYwZM4Z99tmHOXM2Xeru4osvZs899+SQQw7hkUceadSm9Ak+4zCzAeHNb34zGzZsYMWKFcyZM4cdd9yRhQsX8uKLL3LwwQdz+OGHM3LkSK6//np22GEHnnrqKQ466CCOPvpo7rrrLmbPns0999zD+vXrGTNmDAcccECjN6lhHDjMbMD51a9+xb333st1110HwOrVq3n00Udpamrii1/8IrfffjtbbbUVy5YtY/ny5fz2t7/l2GOPZbvttgPg6KOPbmT1G86Bw8wGhMcee4xBgwYxYsQIIoIrrriC8ePHvyrP9OnTaW9vZ9GiRQwZMoTm5mbWrVvXoBr3Xe7jMLN+r729nU984hOcddZZSGL8+PFceeWVvPzyywD8+c9/5vnnn2f16tWMGDGCIUOGcNttt/H4448D8L73vY8bbriBtWvX8uyzz3LjjTc2cnMazmccZtYvrV27lv3335+XX36ZwYMHc/LJJ/PZz34WgNNPP53FixczZswYIoLhw4dzww03cOKJJ3LUUUexzz770NLSwtvf/nYAxowZw6RJk9hvv/0YMWIEY8eObeSmNZwvq575surW03xZ9f7Pl1U3MzOrwoHDzMwKceAwM7NCHDjMrEcMtP7TgbS9Dhxm1u2GDh3KypUrB8zONCJYuXIlQ4cObXRVeoWH45pZt2tqamLp0qW0t7c3uiq9ZujQoTQ1NTW6Gr3CgcPMut2QIUMYNWpUo6thPcRNVWZmVogDh5mZFeLAYWZmhThwmJlZIQ4cZmZWSI8FDklXS1oh6f6ytEskPSzpXknXS9qpbN4XJLVJekTS+LL0CTmtTdJ5ZemjJN2Z038qaeucvk2ebsvzm3tqG83MBqKePOOYDkyoSJsH7B0R+wJ/Br4AIGkv4HjgnXmZf5c0SNIg4PvAEcBewAk5L8A3gcsi4q3A08BpOf004OmcflnOZ2Zm3aTHAkdE3A6sqkj7VUSsz5MLgNK/ZSYCsyPixYj4K9AGHJgfbRHxWES8BMwGJkoScBhwXV5+BnBMWVkz8uvrgHE5v5mZdYNG9nF8HPhFfr07sKRs3tKcVit9GPBMWRAqpb+qrDx/dc5vZmbdoCGBQ9KXgPXANY1Yf1k9pkpqldQ6kC6NYGbWFb0eOCSdAhwJnBibroC2DBhZlq0pp9VKXwnsJGlwRfqrysrzd8z5NxMR0yKiJSJahg8f3sUtMzMbGHo1cEiaAPwLcHREvFA2ay5wfB4RNQoYDfwRWAiMziOotiZ1oM/NAec24MN5+SnAnLKypuTXHwZujYFyiU4zs17QYxc5lDQLOBTYVdJS4HzSKKptgHm5v3pBRHwiIh6QdC3wIKkJ68yI2JDLOQu4GRgEXB0RD+RVnAvMlnQRcDdwVU6/CviJpDZS5/zxPbWNZmYDUY8Fjog4oUryVVXSSvkvBi6ukn4TcFOV9MdIo64q09cBHylUWTMzq5v/OW5mZoU4cJiZWSEOHGZmVogDh5mZFeLAYWZmhThwmJlZIQ4cZmZWiAOHmZkV4sBhZmaFOHCYmVkhDhxmZlaIA4eZmRXiwGFmZoU4cJiZWSEOHGZmVogDh5mZFeLAYWZmhThwmJlZIQ4cZmZWiAOHmZkV4sBhZmaFOHCYmVkhDhxmZlaIA4eZmRXiwGFmZoU4cJiZWSEOHGZmVogDh5mZFdJjgUPS1ZJWSLq/LG0XSfMkPZqfd87pknS5pDZJ90oaU7bMlJz/UUlTytIPkHRfXuZySepoHWZm1j168oxjOjChIu084JaIGA3ckqcBjgBG58dU4EpIQQA4H3g3cCBwflkguBI4o2y5CZ2sw8zMukGPBY6IuB1YVZE8EZiRX88AjilLnxnJAmAnSW8AxgPzImJVRDwNzAMm5Hk7RMSCiAhgZkVZ1dZhZmbdoLf7OHaLiCfy6yeB3fLr3YElZfmW5rSO0pdWSe9oHWZm1g0a1jmezxSikeuQNFVSq6TW9vb2nqyKmVm/0duBY3luZiI/r8jpy4CRZfmaclpH6U1V0jtax2YiYlpEtEREy/Dhw7d4o8zMBpLeDhxzgdLIqCnAnLL0yXl01UHA6tzcdDNwuKSdc6f44cDNed4aSQfl0VSTK8qqtg4zM+sGg3uqYEmzgEOBXSUtJY2O+gZwraTTgMeBj+bsNwEfBNqAF4BTASJilaSvAQtzvgsjotTh/knSyK1tgV/kBx2sw8zMukGPBY6IOKHGrHFV8gZwZo1yrgaurpLeCuxdJX1ltXWYmVn38D/HzcysEAcOMzMrxIHDzMwKceAwM7NCHDjMzKwQBw4zMyvEgcPMzApx4DAzs0IcOMzMrBAHDjMzK8SBw8zMCnHgMDOzQhw4zMysEAcOMzMrxIHDzMwKceAwM7NCHDjMzKwQBw4zMyvEgcPMzApx4DAzs0IcOMzMrBAHDjMzK8SBw8zMCnHgMDOzQhw4zMysEAcOMzMrxIHDzMwKceAwM7NCGhI4JH1G0gOS7pc0S9JQSaMk3SmpTdJPJW2d826Tp9vy/Oaycr6Q0x+RNL4sfUJOa5N0XgM20cys3+r1wCFpd+BsoCUi9gYGAccD3wQui4i3Ak8Dp+VFTgOezumX5XxI2isv905gAvDvkgZJGgR8HzgC2As4Iec1M7Nu0KimqsHAtpIGA9sBTwCHAdfl+TOAY/LriXmaPH+cJOX02RHxYkT8FWgDDsyPtoh4LCJeAmbnvGZm1g16PXBExDLgUuBvpICxGlgEPBMR63O2pcDu+fXuwJK87Pqcf1h5esUytdLNzKwbNKKpamfSGcAo4I3A60hNTb1O0lRJrZJa29vbG1EFM7PXnLoCh6Rb6kmr0/uBv0ZEe0S8DPwcOBjYKTddATQBy/LrZcDIvM7BwI7AyvL0imVqpW8mIqZFREtEtAwfPnwLN8fMbGDpMHDk0U67ALtK2lnSLvnRzJY3//wNOEjSdrmvYhzwIHAb8OGcZwowJ7+em6fJ82+NiMjpx+dRV6OA0cAfgYXA6DxKa2tSB/rcLayrmZlVGNzJ/P8FfJrUpLQIUE5fA3xvS1YYEXdKug64C1gP3A1MA/4bmC3popx2VV7kKuAnktqAVaRAQEQ8IOlaUtBZD5wZERsAJJ0F3EwasXV1RDywJXU1M7PNKR28d5JJ+lREXNEL9WmYlpaWaG1tbXQ1rB874PMzG12FHrXoksmNrkKfJGlRRLQ0uh7dqbMzDgAi4gpJ7wWay5eJiP79SzAzs83UFTgk/QR4C3APsCEnB+DAYWY2wNQVOIAWYK+op13LzMz6tXr/x3E/8Hc9WREzM3ttqPeMY1fgQUl/BF4sJUbE0T1SKzMz67PqDRwX9GQlzMzstaPeUVW/6emKmJnZa0O9o6qeJY2iAtgaGAI8HxE79FTFzMysb6r3jOP1pddllzQ/qKcqZWZmfVfhq+NGcgMwvrO8ZmbW/9TbVPWhssmtSP/rWNcjNTIzsz6t3lFVR5W9Xg8sxnfVMzMbkOrt4zi1pytiZmavDfXeyKlJ0vWSVuTHzyQ19XTlzMys76m3c/w/SDdDemN+3JjTzMxsgKk3cAyPiP+IiPX5MR3wvVbNzAagegPHSkknSRqUHyeR7vttZmYDTL2B4+PAR4EngSdI9/4+pYfqZGZmfVi9w3EvBKZExNMAknYBLiUFFDMzG0DqPePYtxQ0ACJiFfCunqmSmZn1ZfUGjq0k7VyayGcc9Z6tmJlZP1Lvzv9bwB8k/d88/RHg4p6pkpmZ9WX1/nN8pqRW4LCc9KGIeLDnqmVmZn1V3c1NOVA4WJiZDXCFL6tuZmYDmwOHmZkV4sBhZmaFOHCYmVkhDQkcknaSdJ2khyU9JOk9knaRNE/So/l555xXki6X1CbpXkljysqZkvM/KmlKWfoBku7Ly1ye75NuZmbdoFFnHN8FfhkRbwf2Ax4CzgNuiYjRwC15GuAIYHR+TAWuhFf+hHg+8G7gQOD8sj8pXgmcUbbchF7YJjOzAaHXA4ekHYH3AVcBRMRLEfEM6Va0M3K2GcAx+fVEYGYkC4CdJL0BGA/Mi4hV+XIo84AJed4OEbEgIgKYWVaWmZl1USPOOEYB7cB/SLpb0o8lvQ7YLSKeyHmeBHbLr3cHlpQtvzSndZS+tEq6mZl1g0YEjsHAGODKiHgX8DybmqUAyGcK0dMVkTRVUquk1vb29p5enZlZv9CIwLEUWBoRd+bp60iBZHluZiI/r8jzlwEjy5ZvymkdpTdVSd9MREyLiJaIaBk+3Dc0NDOrR68Hjoh4Elgi6W05aRzpUiZzgdLIqCnAnPx6LjA5j646CFidm7RuBg6XtHPuFD8cuDnPWyPpoDyaanJZWWZm1kWNujT6p4BrJG0NPAacSgpi10o6DXicdMdBgJuADwJtwAs5LxGxStLXgIU534X5PiEAnwSmA9sCv8gPMzPrBg0JHBFxD9BSZda4KnkDOLNGOVcDV1dJbwX27lotzcysGv9z3MzMCnHgMDOzQhw4zMysEAcOMzMrxIHDzMwKceAwM7NCHDjMzKwQBw4zMyvEgcPMzApx4DAzs0IcOMzMrBAHDjMzK8SBw8zMCnHgMDOzQhw4zMysEAcOMzMrxIHDzMwKceAwM7NCHDjMzKwQBw4zMyvEgcPMzApx4DAzs0IcOMzMrBAHDjMzK8SBw8zMCnHgMDOzQhw4zMysEAcOMzMrxIHDzMwKaVjgkDRI0t2S/l+eHiXpTkltkn4qaeucvk2ebsvzm8vK+EJOf0TS+LL0CTmtTdJ5vb5xZmb92OAGrvsc4CFghzz9TeCyiJgt6QfAacCV+fnpiHirpONzvkmS9gKOB94JvBH4taQ9c1nfBz4ALAUWSpobEQ/21oaZDUR/u3CfRlehR73pq/c1ugp9RkPOOCQ1Af8E/DhPCzgMuC5nmQEck19PzNPk+eNy/onA7Ih4MSL+CrQBB+ZHW0Q8FhEvAbNzXjMz6waNaqr6DvAvwMY8PQx4JiLW5+mlwO759e7AEoA8f3XO/0p6xTK10jcjaaqkVkmt7e3tXdwkM7OBodcDh6QjgRURsai3110pIqZFREtEtAwfPrzR1TEze01oRB/HwcDRkj4IDCX1cXwX2EnS4HxW0QQsy/mXASOBpZIGAzsCK8vSS8qXqZVuZmZd1OtnHBHxhYhoiohmUuf2rRFxInAb8OGcbQowJ7+em6fJ82+NiMjpx+dRV6OA0cAfgYXA6DxKa+u8jrm9sGlmZgNCI0dVVToXmC3pIuBu4KqcfhXwE0ltwCpSICAiHpB0LfAgsB44MyI2AEg6C7gZGARcHREP9OqWmJn1Yw0NHBExH5ifXz9GGhFVmWcd8JEay18MXFwl/Sbgpm6sqpmZZf7nuJmZFeLAYWZmhThwmJlZIQ4cZmZWiAOHmZkV4sBhZmaFOHCYmVkhDhxmZlaIA4eZmRXiwGFmZoU4cJiZWSEOHGZmVogDh5mZFeLAYWZmhThwmJlZIQ4cZmZWiAOHmZkV4sBhZmaFOHCYmVkhDhxmZlaIA4eZmRXiwGFmZoU4cJiZWSEOHGZmVogDh5mZFeLAYWZmhThwmJlZIQ4cZmZWSK8HDkkjJd0m6UFJD0g6J6fvImmepEfz8845XZIul9Qm6V5JY8rKmpLzPyppSln6AZLuy8tcLkm9vZ1mZv1VI8441gOfi4i9gIOAMyXtBZwH3BIRo4Fb8jTAEcDo/JgKXAkp0ADnA+8GDgTOLwWbnOeMsuUm9MJ2mZkNCL0eOCLiiYi4K79+FngI2B2YCMzI2WYAx+TXE4GZkSwAdpL0BmA8MC8iVkXE08A8YEKet0NELIiIAGaWlWVmZl3U0D4OSc3Au4A7gd0i4ok860lgt/x6d2BJ2WJLc1pH6UurpJuZWTdoWOCQtD3wM+DTEbGmfF4+U4heqMNUSa2SWtvb23t6dWZm/UJDAoekIaSgcU1E/DwnL8/NTOTnFTl9GTCybPGmnNZRelOV9M1ExLSIaImIluHDh3dto8zMBohGjKoScBXwUER8u2zWXKA0MmoKMKcsfXIeXXUQsDo3ad0MHC5p59wpfjhwc563RtJBeV2Ty8oyM7MuGtyAdR4MnAzcJ+menPZF4BvAtZJOAx4HPprn3QR8EGgDXgBOBYiIVZK+BizM+S6MiFX59SeB6cC2wC/yw8zMukGvB46I+B1Q638V46rkD+DMGmVdDVxdJb0V2LsL1TQzsxr8z3EzMyvEgcPMzApx4DAzs0IcOMzMrBAHDjMzK8SBw8zMCnHgMDOzQhw4zMysEAcOMzMrxIHDzMwKceAwM7NCHDjMzKwQBw4zMyvEgcPMzApx4DAzs0IcOMzMrBAHDjMzK8SBw8zMCnHgMDOzQhw4zMysEAcOMzMrxIHDzMwKceAwM7NCHDjMzKwQBw4zMyvEgcPMzApx4DAzs0IcOMzMrJB+GzgkTZD0iKQ2Sec1uj5mZv1FvwwckgYB3weOAPYCTpC0V2NrZWbWPwxudAV6yIFAW0Q8BiBpNjAReLC7V3TA52d2d5F9yqJLJje6CmbWx/TLMw5gd2BJ2fTSnGZmZl3UX8846iJpKjA1Tz4n6ZFG1qcv0qVTdgWeanQ9rO/bA/r3d+V8bemSe3RnNfqC/ho4lgEjy6abctqrRMQ0YFpvVeq1SFJrRLQ0uh7W9/m7MnD016aqhcBoSaMkbQ0cD8xtcJ3MzPqFfnnGERHrJZ0F3AwMAq6OiAcaXC0zs36hXwYOgIi4Cbip0fXoB9yUZ/Xyd2WAUEQ0ug5mZvYa0l/7OMzMrIc4cFhVkq6WtELS/Y2ui/VtkkZKuk3Sg5IekHROo+tkPctNVVaVpPcBzwEzI2LvRtfH+i5JbwDeEBF3SXo9sAg4JiK6/UoN1jf4jMOqiojbgVWNrof1fRHxRETclV8/CzyEr9TQrzlwmFm3kdQMvAu4s8FVsR7kwGFm3ULS9sDPgE9HxJpG18d6jgOHmXWZpCGkoHFNRPy80fWxnuXAYWZdIknAVcBDEfHtRtfHep4Dh1UlaRbwB+BtkpZKOq3RdbI+62DgZOAwSffkxwcbXSnrOR6Oa2ZmhfiMw8zMCnHgMDOzQhw4zMysEAcOMzMrxIHDzMwKceCwfkPShjwU9H5J/1fSdlXSb5S0U05vlrS2bAjpPZIm53nbS7pS0l8k3SVpkaQzypYLSZ8qW/f3JJ1SNj1YUrukb1TUcb6k1rLplpw2vqwOz0l6JL+e2ZPvmdmWcOCw/mRtROyfr+b7EvCJKumrgDPLlvlLnld6lHbUPwaeBkZHxBhgArBL2XIrgHPyPe2r+QDwZ+Aj+Q9y5UZIOqI8ISJuLtUBaAVOzNOTi7wBZr3BgcP6q98Cb62S/gc6uXKrpLcABwJfjoiNABHRHhHfLMvWDtwCTKlRzAnAd4G/Ae+pmHcJ8KXONsCsr3LgsH5H0mDgCOC+ivRBwDhgblnyWyqaqv4eeCfwp1LQ6MA3gX/O5ZavZyjwfuBGYBYpiJT7A/CSpH8suGlmfYIDh/Un20q6h9TU8zfS9ZPK058EdgPmlS1T2VT128pCJX0pB5X/KU+PiMdIlw//WMUiRwK3RcRa0oX/jqkMLsBFwJe3ZCPNGs2Bw/qTtWUB4FMR8VJ5OrAHIF7dx1HNg8B+krYCiIiL8/I7VMn7deDcXG7JCcD7JS0m3Q1vGHBY+UIRcSuwLXBQ/Ztn1jc4cNiAEREvAGcDn8vNWbXytZHOWi4qnSnk5qfKTm4i4mFSoDkq59sB+HvgTRHRHBHNpEBV2VwF6azjX7qyTWaN4MBhA0pE3A3cy6YdeWUfx9k5/XTSmUJbHj47j9o7+YuBpvz6WODWiHixbP4c4ChJ21TU5SZSJ7vZa4qvjmtmZoX4jMPMzApx4DAzs0IcOMzMrBAHDjMzK8SBw8zMCnHgMDOzQhw4zMysEAcOMzMr5P8DxRiWCGsCbqoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"DIESEASES SEVERITY vs PREGNANCY\",fontsize = 20)\n",
    "sns.countplot(df['PREGNANT'],hue=df['PATIENT_STATUS'])\n",
    "plt.legend(labels = ['Alive','Dead'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Females who are Pregnant and affected by covid, all got cured\n",
    "#### 2. Pregnant female seem to have a strong immunity system which help them to recover."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Patient Recover from ICU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"PATIENT RECOVERED FROM ICU\",fontsize = 20)\n",
    "sns.countplot(df['ICU'],hue=df['PATIENT_STATUS'])\n",
    "plt.legend(labels = ['Alive','Dead'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Patients who went into ICU had the probability of 50% of Surviving\n",
    "#### 2. Patients who were supposed to sent to ICU werent admitted hence they suffered Death"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Patients in Critical State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFSCAYAAADM0wXqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAlFElEQVR4nO3df5xWZZ3/8ddbRNH8jWDmkEOFKf7GwWj1ayYtoquia6VmioayfcO0bCu1Nsxiq83W9UdZbCCgLuQXE7B0jVTSLA1Q1t/mRCjDqoygoOYPwM/3j3ON3oz3DPf8uOcaZ97Px+N+3Pd9neucc5177nnPmetc5xxFBGZm1vU2y90AM7PeygFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAt0JSNHtskPS8pDskfaaV+ean+ssl9Skpry2zzE09Dk/zLih9X7LMBRUs4+IW2nDfJra9oaPtbmX5Z6R601ooD0n/1sK8h6fp17W2jhbm3VPSlZIelrRG0huS/lfSryWNk7Rlmc+h+Xdgdfrcz5CkMuto+ryWNXvfaT/3knX1l/Qvkv6QvpvrJK2SdLekiyTt0spncUjJ+sa3Um+j7ekIScMlXS/pKUmvS1or6S+Sbpb0NUnvSfWWtfHzurjZer5RMu3Dzaa1a9mSLq6g7oK2fB6bd+jT7D2+nZ77AnsCY4CPS6qLiPNLK0r6ADASCKAGOAr4VZr8YsmySk1stp5Syyps4/RW6i5oofxgSSdHxKxNLPtFqtfu1pwr6ccR8VQnLAtJ36Jo82bAHyk+s5eBXYDDgZ8D/xeoKzN76XfgQ8AJwMdS3XM2seoXqcLnJ+kY4Dpge6AeuAlYmd5/BPgucJGkD0XEs2UW0RS6kV5P3tQ6O0LSZyk+cwF3pPa+CuwOHAocA/ySYlv+A9ih2SLOSHXLfdcXlKxHwFkU2yXgbOCfS+q2e9nJ78qUNWk+b+siwo8WHhQ/wChTPhJ4Mz1qm037Xpqv6Xlee9fTrM6CVO/wSspbWU5tqv8U8AawFNiihTY1dLTdrcx7Rpp/WgvlT6bn68vMe3iadl0b1ndRmudp4CMt1DkGuLPC78AhwIb0HRjcwme8rIo/948B6ygC7AxAZebdlyLoastM2wH4G/BnYHZax4Gb+M60uj2b2I6tgTXAemBkC3X+DtihrZ9FmXpHpnrXAM8AjeW+421dNnBxqnNxez+H5g93QbRDRNwOPE7x13V4U7mkzSl+GdYClwCLgaMl7ZahmZuyHPgJMBj4Yua2lHMD8ABwiqRye6QVk1RL8cuzDjg6Isp2vUTEr4DRlSwzIu7h7e/AQR1pX1tJ2gz4GcV/sOdFxLRICdGsjQ8BnwBWlFnMZ4GtgGnpAW/vEVfDPsB2wMPp9+cdIuIPEfFiJ6zr7PT8n8D1wM4U/7F0Ow7g9mvq+yv94h8HvBf4RUS8SvHF7gN8rmubVrFLKP49/oaknTK3pbmg+LdRwKUdXNaZFF0HN0bEw62uNOL1dix/Xbta1X4fAz5MEaxTWqsYEW9GRLn2nU2x9z4D+G/gWeAzTX2wVbAqPb+viusg9XkfB/w5Iv5A1/xxaTf3AbeDpE9Q/AIEsLBkUtMP+Zr0/F/Aj4BxkiZFxJtVbNYZLR2oAX4aZfoAI2K1pEnAD4FvAue/Y86MIuIOSb8G/kHScRExr52LOjQ9l93zag9Jh1EcD3gD+FNnLbdCTduzICI2tHVmSSOA/YDfRETTgdbrga8AJ7OJUG+npRS/K8OBeyT9J/AH4JGIeKMT19P0x3YaQEQ8LGkxxTGbD0VEfSes4/DmB/1K/HdE3FvpghzAFSj5sPtSBO/xFHtml0U6QCRpd+DvgSci4o/wVsDdDJxI0S91axWbObaVaXMo9nDKuRKYAEyQdFVELO3shnXQ1yi6BX4g6ZaIWN+OZeyanhva24hm34Gmg3AC/jkinmnvctupo9vT9C/6tJKyaRQBPJ4qBHBEhKRPUhzkOhy4Kk1aJ+kBioNvV0fE2vauo+TgW9OefZNpFN1EZwNfb+/yS3wsPcp5Eag4gN0FUZmJ6XEhcARwN3BabDwC4iyKz3Nas3mb3p9NdX08ItTCY0lLM6V/uS8CtgC+X+U2tllEPEoRCHuS99/Ipu/ARcCnKXZexkXElRnb1GaStgNOogiKm5rKU9fMYoqRMftVY90R8XREfBwYCpwHXEuxZ3wwxXfvIUmDO7CKI4APAvMjorTf+78o/lM5Q1LfDiy/ybdb+V37j7YsyAFcgZIPd7OI2CkiPh4Rb41BVTHW93MUf3mvbTZ7U//asZLe23WtbpNZFP8efir9e9rdfItiuNhESdu2Y/6mPdR2Hwxt+g4A21D8p7Mc+KmkI9q7zA7oyPacCryH4jjFa82mTUvPVf1DFxGPRcQVEXF6ROwJ7EUxLPD9wGUdWHRTu6c1W99q4GZgIMUQ0m7DAdw5jgHeR/F5NpQOzKY4QPNeij2mbnkwLh1Bbxon2dEDXp0uIp6j6KceCFzQjkX8Pj2P7IS2vBIRvwWOpTjAOl3S1h1dbhs1bc/hKjnRp0JN/4n9U/OTCCi6owBOlbRVp7S0AhHxOHBaetuuP2iSBlB0DQLMLLNtJ6Zp3epgnPuAO0fTl/pXwHNlpvehGJ42TtL3yg0Zyi0i7pI0Fxgj6cRNztD1LgX+Cfgy8Fgb572GovvoRElDU7dGWZK2rGQkREQ8mA4kfT61aVIb29QRvwOeoDgecSbFCSRlpSFrfSJiXRrOdyDwv7R8PGI4xQG6T1P013aVl9LzO84srNBYim60xcCSFuocB3xC0uCI+Gs719O5OmtAcU98UNlA+UEUg8tXA/1aqXd3Wt7ft3M9C+jcEzF+X2bahyn22JtOgsh5IsZ3y8zzOTY+SaM9J2L8Fahroc5o4I5Kt5GiC+A14AVgxzKf8bJO+H619HNvOhHjbxRjesudiDEU+C3pRAyKM90C+For62s6i/Oetm7PJrZjMHAusH2ZaQIuT+v4dVs/izTtiTTt4Fbm/06qM6ktyy6pczGdfCKG94A7bhzFHu518c4+tVI/pxg+NB6YX4V2tDYMbUlEzNnUAiLiCUmTgS90Yrs60zTgSxRneLVJRPxrOlFmIrBQ0h+ARbx9KvJhwJBUVukyV0j6KcUBpa9R7GV3iYj4naR/pDjmcC3wL+k6BI0UpyLXUZyO/ArwqqRtgFMoQru1Pds7KA6M/Z2kvSPikZJpO6vZtTtK/C0iWvvebE8Rsj+UdA/wMMVe70CKbocPUJxG/ZVWllFW+t7vATwUEa0NCZwCfAM4U9LEaN+IGmh9GNqL0ZYDcZ2V5D3xwSb2UCj6fJ9O9fbbxLK2pjjy/AYwsC3riVb+QpeUt/aYVlK/lhb2gNP0ARSnjHa7PeA0/ciS7ap4D7hk/r0o+jofpjhj8Q2Kg1q3Uvwx3bKN34FdKELuFWCXZp/xso5+fi393Eum9wf+hWJM7SqKgF2d3n+z6btG0U0WwC8r+Iya/lu4vNn2tPZ4cRPL3JKij/YnwP0UXXXr0ndtMcV1Kwa083fg+lR+bgXb9ptU94S2fM6pzsUVfA6t/sybP5QWbGZmXcyjIMzMMnEAm5ll4gA2M8vEAWxmlokD2MwsE48DTnbeeeeora3N3Qwz62EWL178fEQMKDfNAZzU1tayaFHFY/DNzCoiqcV7GroLwswsEwewmVkmDmAzs0zcB2xmnW7dunU0NDTw2mutXZ+qZ+nXrx81NTX07Vv5TTeqFsCSplJcqHxlROzTbNpXKK7vOiAink/3crocOJri8npnRMT9qe5YiouKQHGBlump/CCKq2NtBdxCcXvuSHf3/QXFBUSWAZ+OiBeqtZ1m9k4NDQ1su+221NbWUvx692wRwapVq2hoaGDw4MrvqlTNLohpFNdX3YikQcAoiquINTmK4lKAQygu13h1qrsTxeUDP0Jx36iJknZM81xNcYWnpvma1nUBcHtEDKG4C2577qBgZh3w2muv0b9//14RvgCS6N+/f5v3+KsWwBFxF8Vl8Zq7jOLaqaWXYRsDzIjCvcAOknaluPTg/IhYnfZi5wOj07TtIuLeKC7nNoO3b0cyhrevdzq9pNzMulBvCd8m7dneLj0IJ2kMsCIi/qfZpN0obnLYpCGVtVbeUKYcimuyNt208FmK67WaWS80Z84cJPH4448DsGzZMvbZp+gRXbRoEeeee27O5nXdQbh048KLKLofukTqE27xgseSxpNu0vf+97+/q5pl1usc9NUZnbq8xT88vaJ6M2fO5NBDD2XmzJl8+9vf3mhaXV0ddXV1ndquturKPeAPUtwX6n8kLQNqgPvTrdpXUNxbrUlNKmutvKZMOcBzqYuC9LyypQZFxOSIqIuIugEDyp4paGbvUi+//DK///3vmTJlCrNmzXrH9AULFnDMMcfw5ptvUltby4svvvjWtCFDhvDcc8/R2NjIiSeeyPDhwxk+fDj33HNPp7axywI4Ih6KiIERURsRtRTdBsMi4llgHnC6CiOANakb4TZglKQd08G3UcBtadpaSSPSCIrTgblpVfMo7pBKep6LmfU6c+fOZfTo0eyxxx7079+fxYsXl6232WabMWbMGG666SYA7rvvPnbffXd22WUXzjvvPL785S+zcOFCbrzxRs4666xObWM1h6HNBA6nuJFfAzAxIqa0UP0WiiFo9RTD0M4EiIjVkr4DLEz1LomIpgN7X+DtYWi38vZttr8P3CBpHPAUxe21zbLr7H/Du5tKuwW6ysyZMznvvPMAOPnkk5k5cybnnHNO2bonnXQSl1xyCWeeeSazZs3ipJNOAuC3v/0tjz766Fv11q5dy8svv8w222zTKW2sWgBHxCmbmF5b8jqACS3UmwpMLVO+CNinTPkqiltrm1kvtXr1au644w4eeughJLFhwwYkMWFC2Zjhox/9KPX19TQ2NjJnzhy++c3i1IM333yTe++9l379+lWlnT4V2cx6nNmzZ3Paaafx1FNPsWzZMpYvX87gwYNZvnx52fqSOOGEEzj//PPZa6+96N+/PwCjRo3iyiuvfKvekiVLOrWdDmAz63FmzpzJCSecsFHZiSeeyPe+970W5znppJO47rrr3up+ALjiiitYtGgR++23H0OHDuWnP/1pp7bTt6VP6urqwtcDtmrqTX3Ajz32GHvttVfG1uRRbrslLY6IsuPdvAdsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm1mP1KdPHw444AD23ntv9t9/f370ox/x5ptvdsqyL774Yi699NIOL8f3hDOzqnv6kn07dXnv/9ZDm6yz1VZbvXXm2sqVK/nMZz7D2rVr33FZypy8B2xmPd7AgQOZPHkyV111FRHBhg0b+OpXv8rw4cPZb7/9+NnPfgYUl7AcOXIkw4YNY99992Xu3Lcvpjhp0iT22GMPDj30UJ544olOaZf3gM2sV/jABz7Ahg0bWLlyJXPnzmX77bdn4cKFvP766xxyyCGMGjWKQYMGcdNNN7Hddtvx/PPPM2LECI477jjuv/9+Zs2axZIlS1i/fj3Dhg3joIMO6nCbHMBm1uv85je/4cEHH2T27NkArFmzhieffJKamhouuugi7rrrLjbbbDNWrFjBc889x913380JJ5zA1ltvDcBxxx3XKe1wAJtZr7B06VL69OnDwIEDiQiuvPJKjjzyyI3qTJs2jcbGRhYvXkzfvn2pra1t852O28J9wGbW4zU2NvL5z3+ec845B0kceeSRXH311axbtw6AP//5z7zyyiusWbOGgQMH0rdvX+68806eeuopAA477DDmzJnDq6++yksvvcTNN9/cKe3yHrCZ9UivvvoqBxxwAOvWrWPzzTfntNNO4/zzzwfgrLPOYtmyZQwbNoyIYMCAAcyZM4dTTz2VY489ln333Ze6ujr23HNPAIYNG8ZJJ53E/vvvz8CBAxk+fHintNGXo0x8OUqrNl+Osufz5SjNzN4lHMBmZpk4gM3MMnEAm1lV9LbjS+3ZXgewmXW6fv36sWrVql4TwhHBqlWr2nz7eg9DM7NOV1NTQ0NDA42Njbmb0mX69etHTU1Nm+ZxAJtZp+vbty+DBw/O3Yxuz10QZmaZOIDNzDJxAJuZZVK1AJY0VdJKSQ+XlP1Q0uOSHpR0k6QdSqZdKKle0hOSjiwpH53K6iVdUFI+WNJ9qfwXkrZI5Vum9/Vpem21ttHMrCOquQc8DRjdrGw+sE9E7Af8GbgQQNJQ4GRg7zTPTyT1kdQH+DFwFDAUOCXVBfgBcFlEfAh4ARiXyscBL6Tyy1I9M7Nup2oBHBF3Aaublf0mItant/cCTWM2xgCzIuL1iPgrUA8cnB71EbE0It4AZgFjJAk4Apid5p8OHF+yrOnp9WxgZKpvZtat5OwD/hxwa3q9G7C8ZFpDKmupvD/wYkmYN5VvtKw0fU2qb2bWrWQJYEnfANYD1+dYf0k7xktaJGlRbxowbmbdQ5cHsKQzgGOAU+Pt8xRXAINKqtWkspbKVwE7SNq8WflGy0rTt0/13yEiJkdEXUTUDRgwoINbZmbWNl0awJJGA18DjouIv5VMmgecnEYwDAaGAH8CFgJD0oiHLSgO1M1LwX0n8Mk0/1hgbsmyxqbXnwTuiN5yQrqZvatU7VRkSTOBw4GdJTUAEylGPWwJzE/Hxe6NiM9HxCOSbgAepeiamBARG9JyzgFuA/oAUyPikbSKrwOzJH0XeACYksqnANdKqqc4CHhytbbRzKwjqhbAEXFKmeIpZcqa6k8CJpUpvwW4pUz5UopREs3LXwM+1abGmpll4DPhzMwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWiQPYzCyTqgWwpKmSVkp6uKRsJ0nzJT2ZnndM5ZJ0haR6SQ9KGlYyz9hU/0lJY0vKD5L0UJrnCklqbR1mZt1NNfeApwGjm5VdANweEUOA29N7gKOAIekxHrgaijAFJgIfAQ4GJpYE6tXA2SXzjd7EOszMupWqBXBE3AWsblY8BpieXk8Hji8pnxGFe4EdJO0KHAnMj4jVEfECMB8YnaZtFxH3RkQAM5otq9w6zMy6la7uA94lIp5Jr58FdkmvdwOWl9RrSGWtlTeUKW9tHWZm3Uq2g3BpzzVyrkPSeEmLJC1qbGysZlPMzN6hqwP4udR9QHpemcpXAINK6tWkstbKa8qUt7aOd4iIyRFRFxF1AwYMaPdGmZm1R1cH8DygaSTDWGBuSfnpaTTECGBN6ka4DRglacd08G0UcFuatlbSiDT64fRmyyq3DjOzbmXzai1Y0kzgcGBnSQ0Uoxm+D9wgaRzwFPDpVP0W4GigHvgbcCZARKyW9B1gYap3SUQ0Hdj7AsVIi62AW9ODVtZhZtatVC2AI+KUFiaNLFM3gAktLGcqMLVM+SJgnzLlq8qtw8ysu/GZcGZmmTiAzcwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzyyRLAEv6sqRHJD0saaakfpIGS7pPUr2kX0jaItXdMr2vT9NrS5ZzYSp/QtKRJeWjU1m9pAsybKKZ2SZ1eQBL2g04F6iLiH2APsDJwA+AyyLiQ8ALwLg0yzjghVR+WaqHpKFpvr2B0cBPJPWR1Af4MXAUMBQ4JdU1M+tWcnVBbA5sJWlzYGvgGeAIYHaaPh04Pr0ek96Tpo+UpFQ+KyJej4i/AvXAwelRHxFLI+INYFaqa2bWrXR5AEfECuBS4GmK4F0DLAZejIj1qVoDsFt6vRuwPM27PtXvX1rebJ6Wys3MupUcXRA7UuyRDgbeB7yHoguhy0kaL2mRpEWNjY05mmBmvViOLohPAH+NiMaIWAf8EjgE2CF1SQDUACvS6xXAIIA0fXtgVWl5s3laKn+HiJgcEXURUTdgwIDO2DYzs4pVFMCSbq+krEJPAyMkbZ36ckcCjwJ3Ap9MdcYCc9Preek9afodERGp/OQ0SmIwMAT4E7AQGJJGVWxBcaBuXjvbamZWNZu3NlFSP4qDZDunrgOlSdvRzn7ViLhP0mzgfmA98AAwGfg1MEvSd1PZlDTLFOBaSfXAaopAJSIekXQDRXivByZExIbU7nOA2yhGWEyNiEfa01Yzs2pqNYCBfwK+RNFXu5i3A3gtcFV7VxoRE4GJzYqXUoxgaF73NeBTLSxnEjCpTPktwC3tbZ+ZWVdoNYAj4nLgcklfjIgru6hNZma9wqb2gAGIiCsl/R1QWzpPRMyoUrvMzHq8igJY0rXAB4ElwIZUHIAD2MysnSoKYKAOGJpGH5iZWSeodBzww8B7q9kQM7PeptI94J2BRyX9CXi9qTAijqtKq8zMeoFKA/jiajbCzKw3qnQUxO+q3RAzs96m0lEQL1GMegDYAugLvBIR21WrYWZmPV2le8DbNr0uuRbviGo1ysysN2jz1dCiMAc4clN1zcysZZV2QfxjydvNKMYFv1aVFpmZ9RKVjoI4tuT1emAZvs2PmVmHVNoHfGa1G2Jm1ttUekH2Gkk3SVqZHjdKqql248zMerJKD8JdQ3FXifelx82pzMzM2qnSAB4QEddExPr0mAb4JmpmZh1QaQCvkvRZSX3S47MUN8Y0M7N2qjSAPwd8GngWeIbi5phnVKlNZma9QqXD0C4BxkbECwCSdgIupQhmMzNrh0r3gPdrCl+AiFgNHFidJpmZ9Q6VBvBm6bb0wFt7wJXuPZuZWRmVhuiPgD9K+n/p/acoczt4MzOrXKVnws2QtAg4IhX9Y0Q8Wr1mmZn1fBV3I6TAdeiamXWSNl+O0szMOocD2MwsEwewmVkmDmAzs0yyBLCkHSTNlvS4pMckfVTSTpLmS3oyPe+Y6krSFZLqJT0oaVjJcsam+k9KGltSfpCkh9I8V6T72JmZdSu59oAvB/47IvYE9gceAy4Abo+IIcDt6T3AUcCQ9BgPXA1vnQwyEfgIcDAwseRkkauBs0vmG90F22Rm1iZdHsCStgcOA6YARMQbEfEixS2Opqdq04Hj0+sxwIx0M9B7gR0k7UpxU9D5EbE6nSY9Hxidpm0XEfdGRAAzSpZlZtZt5NgDHgw0AtdIekDSzyW9B9glIp5JdZ4FdkmvdwOWl8zfkMpaK28oU/4OksZLWiRpUWNjYwc3y8ysbXIE8ObAMODqiDgQeIW3uxsASHuuUe2GRMTkiKiLiLoBA3x9eTPrWjkCuAFoiIj70vvZFIH8XOo+ID2vTNNXAINK5q9JZa2V15QpNzPrVro8gCPiWWC5pA+nopEUpzjPA5pGMowF5qbX84DT02iIEcCa1FVxGzBK0o7p4Nso4LY0ba2kEWn0w+klyzIz6zZyXVLyi8D1krYAlgJnUvwxuEHSOOApijtwANwCHA3UA39LdYmI1ZK+AyxM9S5J1ykG+AIwDdgKuDU9zMy6lSwBHBFLgLoyk0aWqRvAhBaWMxWYWqZ8EbBPx1ppZlZdPhPOzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZZAtgSX0kPSDpV+n9YEn3SaqX9AtJW6TyLdP7+jS9tmQZF6byJyQdWVI+OpXVS7qgyzfOzKwCOfeAzwMeK3n/A+CyiPgQ8AIwLpWPA15I5ZelekgaCpwM7A2MBn6SQr0P8GPgKGAocEqqa2bWrWQJYEk1wD8AP0/vBRwBzE5VpgPHp9dj0nvS9JGp/hhgVkS8HhF/BeqBg9OjPiKWRsQbwKxU18ysW8m1B/wfwNeAN9P7/sCLEbE+vW8AdkuvdwOWA6Tpa1L9t8qbzdNSuZlZt9LlASzpGGBlRCzu6nWXact4SYskLWpsbMzdHDPrZXLsAR8CHCdpGUX3wBHA5cAOkjZPdWqAFen1CmAQQJq+PbCqtLzZPC2Vv0NETI6IuoioGzBgQMe3zMysDbo8gCPiwoioiYhaioNod0TEqcCdwCdTtbHA3PR6XnpPmn5HREQqPzmNkhgMDAH+BCwEhqRRFVukdczrgk0zM2uTzTddpct8HZgl6bvAA8CUVD4FuFZSPbCaIlCJiEck3QA8CqwHJkTEBgBJ5wC3AX2AqRHxSJduiZlZBbIGcEQsABak10spRjA0r/Ma8KkW5p8ETCpTfgtwSyc21cys0/lMODOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzy6Q73ZbezN7Fnr5k39xNqKr3f+uhTl+m94DNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMujyAJQ2SdKekRyU9Ium8VL6TpPmSnkzPO6ZySbpCUr2kByUNK1nW2FT/SUljS8oPkvRQmucKSerq7TQz25Qce8Drga9ExFBgBDBB0lDgAuD2iBgC3J7eAxwFDEmP8cDVUAQ2MBH4CHAwMLEptFOds0vmG90F22Vm1iZdHsAR8UxE3J9evwQ8BuwGjAGmp2rTgePT6zHAjCjcC+wgaVfgSGB+RKyOiBeA+cDoNG27iLg3IgKYUbIsM7NuI2sfsKRa4EDgPmCXiHgmTXoW2CW93g1YXjJbQyprrbyhTHm59Y+XtEjSosbGxo5tjJlZG2ULYEnbADcCX4qItaXT0p5rVLsNETE5Iuoiom7AgAHVXp2Z2UayBLCkvhThe31E/DIVP5e6D0jPK1P5CmBQyew1qay18poy5WZm3UqOURACpgCPRcS/l0yaBzSNZBgLzC0pPz2NhhgBrEldFbcBoyTtmA6+jQJuS9PWShqR1nV6ybLMzLqNHJejPAQ4DXhI0pJUdhHwfeAGSeOAp4BPp2m3AEcD9cDfgDMBImK1pO8AC1O9SyJidXr9BWAasBVwa3qYmXUrXR7AEfF7oKVxuSPL1A9gQgvLmgpMLVO+CNinA800M6s6nwlnZpaJA9jMLBMHsJlZJr4nnLXK9/kyqx7vAZuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDLZPHcDqkXSaOByoA/w84j4fjXWc9BXZ1Rjsd3GTdvmboFZz9Uj94Al9QF+DBwFDAVOkTQ0b6vMzDbWIwMYOBioj4ilEfEGMAsYk7lNZmYb6akBvBuwvOR9QyozM+s2emwfcCUkjQfGp7cvS3oiZ3u6o91hZ+D53O2omonK3YIew9+VFu3e0oSeGsArgEEl72tS2UYiYjIwuasa9W4kaVFE1OVuh3V//q60XU/tglgIDJE0WNIWwMnAvMxtMjPbSI/cA46I9ZLOAW6jGIY2NSIeydwsM7ON9MgABoiIW4BbcrejB3AXjVXK35U2UkTkboOZWa/UU/uAzcy6PQewlSVpqqSVkh7O3Rbr3iQNknSnpEclPSLpvNxterdwF4SVJekw4GVgRkTsk7s91n1J2hXYNSLul7QtsBg4PiIezdy0bs97wFZWRNwFrM7dDuv+IuKZiLg/vX4JeAyfeVoRB7CZdRpJtcCBwH2Zm/Ku4AA2s04haRvgRuBLEbE2d3veDRzAZtZhkvpShO/1EfHL3O15t3AAm1mHSBIwBXgsIv49d3veTRzAVpakmcAfgQ9LapA0LnebrNs6BDgNOELSkvQ4Onej3g08DM3MLBPvAZuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5g69EkvZyeayWFpC+WTLtK0hmSfpyGTj0q6dWSoVSflLRAUl3JPLVNV4iTdLikNanug5J+K2lgmnaGpMaSZS2RNDTN/6qkByQ9JulPks7o4o/FugkHsPUmK4Hz0n0C3xIREyLiAOBo4C8RcUB6zK5gmXenuvtR3ItwQsm0X5Qs64CSq4P9JSIOjIi9KO5X+CVJZ3Z46+xdxwFsvUkjcDswtrMXnM4G2xZ4oS3zRcRS4Hzg3M5uk3V/PfaecGYt+AFwq6SpnbS8/yNpCdAfeAW4qGTaSZIOLXn/0RaWcT+wZye1x95FvAdsvUra47wP+Eyls2yirKkLYhBwDfBvJdOad0G82sI6VGFbrIdxAFtv9K/A16ks+FYBO5a83wl4voW684DD2tGeAykuYm69jAPYep2IeBx4FDi2guoLgM+mPl4o+o/vbKHuocBf2tKWdAHzS4Er2zKf9QzuA7beahLwQAX1JlP0z/6PpAAWAReWTG/qAxawBjirZFrzPuAvAP8LfFDSA0A/4CXgioiY1s7tsHcxXw3NzCwTd0GYmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDL5/ydX1KweUoKBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"PATIENT IN CRITICAL STATE\",fontsize = 20)\n",
    "sns.countplot(df['INTUBED'],hue=df['PATIENT_STATUS'])\n",
    "plt.legend(labels = ['Alive','Dead'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Patients who were on Ventilator had a very slim chance of survival\n",
    "#### 2. Very Less People were required to sent on Ventilator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GP0G2Q-i_Cti"
   },
   "source": [
    "***\n",
    "# ML Regression Section \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "imJBENMvsYFH"
   },
   "source": [
    "## Baseline Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1FqQ5P3L_Cti"
   },
   "source": [
    "### For Baseline model I have selected Linear regression as it works best on continous value and gives an overall idea about the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jKG4whcF_Cti"
   },
   "source": [
    "## Feature Engineering and Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M9kdQlj2_Cti"
   },
   "source": [
    "#### To check the correlation between The target and independent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['USMER', 'MEDICAL_UNIT', 'SEX', 'PATIENT_TYPE', 'DATE_DIED', 'INTUBED',\n",
       "       'PNEUMONIA', 'AGE', 'PREGNANT', 'DIABETES', 'COPD', 'ASTHMA', 'INMSUPR',\n",
       "       'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'OBESITY',\n",
       "       'RENAL_CHRONIC', 'TOBACCO', 'CLASIFFICATION_FINAL', 'ICU',\n",
       "       'PATIENT_STATUS', 'DATE_PARSED', 'MONTH'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df.columns:\n",
    "    if i not in ['DATE_DIED','DATE_PARSED','MONTH']:\n",
    "        df1[i]=df[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USMER</th>\n",
       "      <th>MEDICAL_UNIT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>PATIENT_TYPE</th>\n",
       "      <th>INTUBED</th>\n",
       "      <th>PNEUMONIA</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PREGNANT</th>\n",
       "      <th>DIABETES</th>\n",
       "      <th>COPD</th>\n",
       "      <th>...</th>\n",
       "      <th>INMSUPR</th>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <th>OTHER_DISEASE</th>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <th>OBESITY</th>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <th>TOBACCO</th>\n",
       "      <th>CLASIFFICATION_FINAL</th>\n",
       "      <th>ICU</th>\n",
       "      <th>PATIENT_STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USMER</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055954</td>\n",
       "      <td>-0.017607</td>\n",
       "      <td>-0.162605</td>\n",
       "      <td>0.073208</td>\n",
       "      <td>0.109852</td>\n",
       "      <td>-0.061984</td>\n",
       "      <td>-0.010703</td>\n",
       "      <td>0.041412</td>\n",
       "      <td>0.019373</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008652</td>\n",
       "      <td>0.036776</td>\n",
       "      <td>0.000558</td>\n",
       "      <td>0.010047</td>\n",
       "      <td>-0.040917</td>\n",
       "      <td>0.039981</td>\n",
       "      <td>-0.013323</td>\n",
       "      <td>-0.072154</td>\n",
       "      <td>0.010232</td>\n",
       "      <td>-0.105030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDICAL_UNIT</th>\n",
       "      <td>0.055954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034162</td>\n",
       "      <td>0.108747</td>\n",
       "      <td>-0.068149</td>\n",
       "      <td>-0.003202</td>\n",
       "      <td>0.054370</td>\n",
       "      <td>-0.000697</td>\n",
       "      <td>-0.037840</td>\n",
       "      <td>-0.025970</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006855</td>\n",
       "      <td>-0.045955</td>\n",
       "      <td>-0.050949</td>\n",
       "      <td>-0.005032</td>\n",
       "      <td>-0.009823</td>\n",
       "      <td>-0.017958</td>\n",
       "      <td>-0.007720</td>\n",
       "      <td>-0.295539</td>\n",
       "      <td>0.037964</td>\n",
       "      <td>0.123059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <td>-0.017607</td>\n",
       "      <td>0.034162</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123713</td>\n",
       "      <td>-0.070929</td>\n",
       "      <td>-0.084328</td>\n",
       "      <td>0.055846</td>\n",
       "      <td>0.083179</td>\n",
       "      <td>-0.003763</td>\n",
       "      <td>-0.002643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005942</td>\n",
       "      <td>-0.003339</td>\n",
       "      <td>0.035228</td>\n",
       "      <td>-0.016796</td>\n",
       "      <td>0.030360</td>\n",
       "      <td>-0.015108</td>\n",
       "      <td>-0.103079</td>\n",
       "      <td>-0.026511</td>\n",
       "      <td>-0.017908</td>\n",
       "      <td>0.113064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATIENT_TYPE</th>\n",
       "      <td>-0.162605</td>\n",
       "      <td>0.108747</td>\n",
       "      <td>0.123713</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.419643</td>\n",
       "      <td>-0.551095</td>\n",
       "      <td>0.496226</td>\n",
       "      <td>0.009510</td>\n",
       "      <td>-0.289792</td>\n",
       "      <td>-0.117664</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.089224</td>\n",
       "      <td>-0.286314</td>\n",
       "      <td>-0.065206</td>\n",
       "      <td>-0.108288</td>\n",
       "      <td>-0.049980</td>\n",
       "      <td>-0.159561</td>\n",
       "      <td>-0.013725</td>\n",
       "      <td>-0.046093</td>\n",
       "      <td>-0.138635</td>\n",
       "      <td>0.609309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTUBED</th>\n",
       "      <td>0.073208</td>\n",
       "      <td>-0.068149</td>\n",
       "      <td>-0.070929</td>\n",
       "      <td>-0.419643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.317030</td>\n",
       "      <td>-0.257075</td>\n",
       "      <td>-0.016601</td>\n",
       "      <td>0.139946</td>\n",
       "      <td>0.051507</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039120</td>\n",
       "      <td>0.146943</td>\n",
       "      <td>0.028854</td>\n",
       "      <td>0.050066</td>\n",
       "      <td>0.035774</td>\n",
       "      <td>0.063370</td>\n",
       "      <td>0.014433</td>\n",
       "      <td>0.021634</td>\n",
       "      <td>0.198601</td>\n",
       "      <td>-0.512624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PNEUMONIA</th>\n",
       "      <td>0.109852</td>\n",
       "      <td>-0.003202</td>\n",
       "      <td>-0.084328</td>\n",
       "      <td>-0.551095</td>\n",
       "      <td>0.317030</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344409</td>\n",
       "      <td>-0.025264</td>\n",
       "      <td>0.200037</td>\n",
       "      <td>0.076942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060506</td>\n",
       "      <td>0.195929</td>\n",
       "      <td>0.034575</td>\n",
       "      <td>0.070844</td>\n",
       "      <td>0.047059</td>\n",
       "      <td>0.097377</td>\n",
       "      <td>0.018040</td>\n",
       "      <td>0.065657</td>\n",
       "      <td>0.104732</td>\n",
       "      <td>-0.456165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>-0.061984</td>\n",
       "      <td>0.054370</td>\n",
       "      <td>0.055846</td>\n",
       "      <td>0.496226</td>\n",
       "      <td>-0.257075</td>\n",
       "      <td>-0.344409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.078053</td>\n",
       "      <td>-0.345489</td>\n",
       "      <td>-0.181872</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042590</td>\n",
       "      <td>-0.412659</td>\n",
       "      <td>-0.052972</td>\n",
       "      <td>-0.144002</td>\n",
       "      <td>-0.055520</td>\n",
       "      <td>-0.114998</td>\n",
       "      <td>-0.024600</td>\n",
       "      <td>-0.069365</td>\n",
       "      <td>-0.029288</td>\n",
       "      <td>0.489493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREGNANT</th>\n",
       "      <td>-0.010703</td>\n",
       "      <td>-0.000697</td>\n",
       "      <td>0.083179</td>\n",
       "      <td>0.009510</td>\n",
       "      <td>-0.016601</td>\n",
       "      <td>-0.025264</td>\n",
       "      <td>0.078053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028443</td>\n",
       "      <td>-0.009894</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003486</td>\n",
       "      <td>-0.037443</td>\n",
       "      <td>0.006598</td>\n",
       "      <td>-0.009323</td>\n",
       "      <td>-0.017693</td>\n",
       "      <td>-0.013109</td>\n",
       "      <td>-0.016969</td>\n",
       "      <td>0.009965</td>\n",
       "      <td>0.026537</td>\n",
       "      <td>0.037789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIABETES</th>\n",
       "      <td>0.041412</td>\n",
       "      <td>-0.037840</td>\n",
       "      <td>-0.003763</td>\n",
       "      <td>-0.289792</td>\n",
       "      <td>0.139946</td>\n",
       "      <td>0.200037</td>\n",
       "      <td>-0.345489</td>\n",
       "      <td>-0.028443</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.071343</td>\n",
       "      <td>0.403303</td>\n",
       "      <td>0.022816</td>\n",
       "      <td>0.112965</td>\n",
       "      <td>0.104250</td>\n",
       "      <td>0.192048</td>\n",
       "      <td>0.022249</td>\n",
       "      <td>0.019428</td>\n",
       "      <td>0.029415</td>\n",
       "      <td>-0.265011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COPD</th>\n",
       "      <td>0.019373</td>\n",
       "      <td>-0.025970</td>\n",
       "      <td>-0.002643</td>\n",
       "      <td>-0.117664</td>\n",
       "      <td>0.051507</td>\n",
       "      <td>0.076942</td>\n",
       "      <td>-0.181872</td>\n",
       "      <td>-0.009894</td>\n",
       "      <td>0.103411</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060949</td>\n",
       "      <td>0.125149</td>\n",
       "      <td>0.028237</td>\n",
       "      <td>0.115606</td>\n",
       "      <td>0.040094</td>\n",
       "      <td>0.065785</td>\n",
       "      <td>0.084645</td>\n",
       "      <td>-0.027138</td>\n",
       "      <td>0.007867</td>\n",
       "      <td>-0.127824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASTHMA</th>\n",
       "      <td>-0.004798</td>\n",
       "      <td>0.009925</td>\n",
       "      <td>0.053456</td>\n",
       "      <td>0.037286</td>\n",
       "      <td>-0.017775</td>\n",
       "      <td>-0.026383</td>\n",
       "      <td>0.034215</td>\n",
       "      <td>-0.001506</td>\n",
       "      <td>-0.006487</td>\n",
       "      <td>0.034797</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015206</td>\n",
       "      <td>0.007807</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>0.010044</td>\n",
       "      <td>0.044257</td>\n",
       "      <td>-0.005329</td>\n",
       "      <td>0.000691</td>\n",
       "      <td>-0.019244</td>\n",
       "      <td>-0.002865</td>\n",
       "      <td>0.036079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INMSUPR</th>\n",
       "      <td>0.008652</td>\n",
       "      <td>-0.006855</td>\n",
       "      <td>0.005942</td>\n",
       "      <td>-0.089224</td>\n",
       "      <td>0.039120</td>\n",
       "      <td>0.060506</td>\n",
       "      <td>-0.042590</td>\n",
       "      <td>-0.003486</td>\n",
       "      <td>0.071343</td>\n",
       "      <td>0.060949</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049992</td>\n",
       "      <td>0.109773</td>\n",
       "      <td>0.071574</td>\n",
       "      <td>0.015906</td>\n",
       "      <td>0.131145</td>\n",
       "      <td>0.016067</td>\n",
       "      <td>-0.042202</td>\n",
       "      <td>0.017168</td>\n",
       "      <td>-0.082065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <td>0.036776</td>\n",
       "      <td>-0.045955</td>\n",
       "      <td>-0.003339</td>\n",
       "      <td>-0.286314</td>\n",
       "      <td>0.146943</td>\n",
       "      <td>0.195929</td>\n",
       "      <td>-0.412659</td>\n",
       "      <td>-0.037443</td>\n",
       "      <td>0.403303</td>\n",
       "      <td>0.125149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041177</td>\n",
       "      <td>0.162681</td>\n",
       "      <td>0.156355</td>\n",
       "      <td>0.222751</td>\n",
       "      <td>0.022551</td>\n",
       "      <td>0.020875</td>\n",
       "      <td>0.024283</td>\n",
       "      <td>-0.270866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER_DISEASE</th>\n",
       "      <td>0.000558</td>\n",
       "      <td>-0.050949</td>\n",
       "      <td>0.035228</td>\n",
       "      <td>-0.065206</td>\n",
       "      <td>0.028854</td>\n",
       "      <td>0.034575</td>\n",
       "      <td>-0.052972</td>\n",
       "      <td>0.006598</td>\n",
       "      <td>0.022816</td>\n",
       "      <td>0.028237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109773</td>\n",
       "      <td>0.041177</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052513</td>\n",
       "      <td>0.011600</td>\n",
       "      <td>0.034358</td>\n",
       "      <td>0.016769</td>\n",
       "      <td>-0.004548</td>\n",
       "      <td>0.018205</td>\n",
       "      <td>-0.066016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <td>0.010047</td>\n",
       "      <td>-0.005032</td>\n",
       "      <td>-0.016796</td>\n",
       "      <td>-0.108288</td>\n",
       "      <td>0.050066</td>\n",
       "      <td>0.070844</td>\n",
       "      <td>-0.144002</td>\n",
       "      <td>-0.009323</td>\n",
       "      <td>0.112965</td>\n",
       "      <td>0.115606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.071574</td>\n",
       "      <td>0.162681</td>\n",
       "      <td>0.052513</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058309</td>\n",
       "      <td>0.111442</td>\n",
       "      <td>0.039301</td>\n",
       "      <td>-0.026530</td>\n",
       "      <td>0.020517</td>\n",
       "      <td>-0.111166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBESITY</th>\n",
       "      <td>-0.040917</td>\n",
       "      <td>-0.009823</td>\n",
       "      <td>0.030360</td>\n",
       "      <td>-0.049980</td>\n",
       "      <td>0.035774</td>\n",
       "      <td>0.047059</td>\n",
       "      <td>-0.055520</td>\n",
       "      <td>-0.017693</td>\n",
       "      <td>0.104250</td>\n",
       "      <td>0.040094</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015906</td>\n",
       "      <td>0.156355</td>\n",
       "      <td>0.011600</td>\n",
       "      <td>0.058309</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005247</td>\n",
       "      <td>0.079995</td>\n",
       "      <td>0.047958</td>\n",
       "      <td>0.017832</td>\n",
       "      <td>-0.040058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <td>0.039981</td>\n",
       "      <td>-0.017958</td>\n",
       "      <td>-0.015108</td>\n",
       "      <td>-0.159561</td>\n",
       "      <td>0.063370</td>\n",
       "      <td>0.097377</td>\n",
       "      <td>-0.114998</td>\n",
       "      <td>-0.013109</td>\n",
       "      <td>0.192048</td>\n",
       "      <td>0.065785</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131145</td>\n",
       "      <td>0.222751</td>\n",
       "      <td>0.034358</td>\n",
       "      <td>0.111442</td>\n",
       "      <td>0.005247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017919</td>\n",
       "      <td>-0.037100</td>\n",
       "      <td>0.013030</td>\n",
       "      <td>-0.170405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOBACCO</th>\n",
       "      <td>-0.013323</td>\n",
       "      <td>-0.007720</td>\n",
       "      <td>-0.103079</td>\n",
       "      <td>-0.013725</td>\n",
       "      <td>0.014433</td>\n",
       "      <td>0.018040</td>\n",
       "      <td>-0.024600</td>\n",
       "      <td>-0.016969</td>\n",
       "      <td>0.022249</td>\n",
       "      <td>0.084645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016067</td>\n",
       "      <td>0.022551</td>\n",
       "      <td>0.016769</td>\n",
       "      <td>0.039301</td>\n",
       "      <td>0.079995</td>\n",
       "      <td>0.017919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019289</td>\n",
       "      <td>0.006194</td>\n",
       "      <td>-0.022229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLASIFFICATION_FINAL</th>\n",
       "      <td>-0.072154</td>\n",
       "      <td>-0.295539</td>\n",
       "      <td>-0.026511</td>\n",
       "      <td>-0.046093</td>\n",
       "      <td>0.021634</td>\n",
       "      <td>0.065657</td>\n",
       "      <td>-0.069365</td>\n",
       "      <td>0.009965</td>\n",
       "      <td>0.019428</td>\n",
       "      <td>-0.027138</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042202</td>\n",
       "      <td>0.020875</td>\n",
       "      <td>-0.004548</td>\n",
       "      <td>-0.026530</td>\n",
       "      <td>0.047958</td>\n",
       "      <td>-0.037100</td>\n",
       "      <td>-0.019289</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012989</td>\n",
       "      <td>0.093934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICU</th>\n",
       "      <td>0.010232</td>\n",
       "      <td>0.037964</td>\n",
       "      <td>-0.017908</td>\n",
       "      <td>-0.138635</td>\n",
       "      <td>0.198601</td>\n",
       "      <td>0.104732</td>\n",
       "      <td>-0.029288</td>\n",
       "      <td>0.026537</td>\n",
       "      <td>0.029415</td>\n",
       "      <td>0.007867</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017168</td>\n",
       "      <td>0.024283</td>\n",
       "      <td>0.018205</td>\n",
       "      <td>0.020517</td>\n",
       "      <td>0.017832</td>\n",
       "      <td>0.013030</td>\n",
       "      <td>0.006194</td>\n",
       "      <td>-0.012989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.088544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATIENT_STATUS</th>\n",
       "      <td>-0.105030</td>\n",
       "      <td>0.123059</td>\n",
       "      <td>0.113064</td>\n",
       "      <td>0.609309</td>\n",
       "      <td>-0.512624</td>\n",
       "      <td>-0.456165</td>\n",
       "      <td>0.489493</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>-0.265011</td>\n",
       "      <td>-0.127824</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.082065</td>\n",
       "      <td>-0.270866</td>\n",
       "      <td>-0.066016</td>\n",
       "      <td>-0.111166</td>\n",
       "      <td>-0.040058</td>\n",
       "      <td>-0.170405</td>\n",
       "      <td>-0.022229</td>\n",
       "      <td>0.093934</td>\n",
       "      <td>-0.088544</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         USMER  MEDICAL_UNIT       SEX  PATIENT_TYPE  \\\n",
       "USMER                 1.000000      0.055954 -0.017607     -0.162605   \n",
       "MEDICAL_UNIT          0.055954      1.000000  0.034162      0.108747   \n",
       "SEX                  -0.017607      0.034162  1.000000      0.123713   \n",
       "PATIENT_TYPE         -0.162605      0.108747  0.123713      1.000000   \n",
       "INTUBED               0.073208     -0.068149 -0.070929     -0.419643   \n",
       "PNEUMONIA             0.109852     -0.003202 -0.084328     -0.551095   \n",
       "AGE                  -0.061984      0.054370  0.055846      0.496226   \n",
       "PREGNANT             -0.010703     -0.000697  0.083179      0.009510   \n",
       "DIABETES              0.041412     -0.037840 -0.003763     -0.289792   \n",
       "COPD                  0.019373     -0.025970 -0.002643     -0.117664   \n",
       "ASTHMA               -0.004798      0.009925  0.053456      0.037286   \n",
       "INMSUPR               0.008652     -0.006855  0.005942     -0.089224   \n",
       "HIPERTENSION          0.036776     -0.045955 -0.003339     -0.286314   \n",
       "OTHER_DISEASE         0.000558     -0.050949  0.035228     -0.065206   \n",
       "CARDIOVASCULAR        0.010047     -0.005032 -0.016796     -0.108288   \n",
       "OBESITY              -0.040917     -0.009823  0.030360     -0.049980   \n",
       "RENAL_CHRONIC         0.039981     -0.017958 -0.015108     -0.159561   \n",
       "TOBACCO              -0.013323     -0.007720 -0.103079     -0.013725   \n",
       "CLASIFFICATION_FINAL -0.072154     -0.295539 -0.026511     -0.046093   \n",
       "ICU                   0.010232      0.037964 -0.017908     -0.138635   \n",
       "PATIENT_STATUS       -0.105030      0.123059  0.113064      0.609309   \n",
       "\n",
       "                       INTUBED  PNEUMONIA       AGE  PREGNANT  DIABETES  \\\n",
       "USMER                 0.073208   0.109852 -0.061984 -0.010703  0.041412   \n",
       "MEDICAL_UNIT         -0.068149  -0.003202  0.054370 -0.000697 -0.037840   \n",
       "SEX                  -0.070929  -0.084328  0.055846  0.083179 -0.003763   \n",
       "PATIENT_TYPE         -0.419643  -0.551095  0.496226  0.009510 -0.289792   \n",
       "INTUBED               1.000000   0.317030 -0.257075 -0.016601  0.139946   \n",
       "PNEUMONIA             0.317030   1.000000 -0.344409 -0.025264  0.200037   \n",
       "AGE                  -0.257075  -0.344409  1.000000  0.078053 -0.345489   \n",
       "PREGNANT             -0.016601  -0.025264  0.078053  1.000000 -0.028443   \n",
       "DIABETES              0.139946   0.200037 -0.345489 -0.028443  1.000000   \n",
       "COPD                  0.051507   0.076942 -0.181872 -0.009894  0.103411   \n",
       "ASTHMA               -0.017775  -0.026383  0.034215 -0.001506 -0.006487   \n",
       "INMSUPR               0.039120   0.060506 -0.042590 -0.003486  0.071343   \n",
       "HIPERTENSION          0.146943   0.195929 -0.412659 -0.037443  0.403303   \n",
       "OTHER_DISEASE         0.028854   0.034575 -0.052972  0.006598  0.022816   \n",
       "CARDIOVASCULAR        0.050066   0.070844 -0.144002 -0.009323  0.112965   \n",
       "OBESITY               0.035774   0.047059 -0.055520 -0.017693  0.104250   \n",
       "RENAL_CHRONIC         0.063370   0.097377 -0.114998 -0.013109  0.192048   \n",
       "TOBACCO               0.014433   0.018040 -0.024600 -0.016969  0.022249   \n",
       "CLASIFFICATION_FINAL  0.021634   0.065657 -0.069365  0.009965  0.019428   \n",
       "ICU                   0.198601   0.104732 -0.029288  0.026537  0.029415   \n",
       "PATIENT_STATUS       -0.512624  -0.456165  0.489493  0.037789 -0.265011   \n",
       "\n",
       "                          COPD  ...   INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
       "USMER                 0.019373  ...  0.008652      0.036776       0.000558   \n",
       "MEDICAL_UNIT         -0.025970  ... -0.006855     -0.045955      -0.050949   \n",
       "SEX                  -0.002643  ...  0.005942     -0.003339       0.035228   \n",
       "PATIENT_TYPE         -0.117664  ... -0.089224     -0.286314      -0.065206   \n",
       "INTUBED               0.051507  ...  0.039120      0.146943       0.028854   \n",
       "PNEUMONIA             0.076942  ...  0.060506      0.195929       0.034575   \n",
       "AGE                  -0.181872  ... -0.042590     -0.412659      -0.052972   \n",
       "PREGNANT             -0.009894  ... -0.003486     -0.037443       0.006598   \n",
       "DIABETES              0.103411  ...  0.071343      0.403303       0.022816   \n",
       "COPD                  1.000000  ...  0.060949      0.125149       0.028237   \n",
       "ASTHMA                0.034797  ...  0.015206      0.007807       0.006098   \n",
       "INMSUPR               0.060949  ...  1.000000      0.049992       0.109773   \n",
       "HIPERTENSION          0.125149  ...  0.049992      1.000000       0.041177   \n",
       "OTHER_DISEASE         0.028237  ...  0.109773      0.041177       1.000000   \n",
       "CARDIOVASCULAR        0.115606  ...  0.071574      0.162681       0.052513   \n",
       "OBESITY               0.040094  ...  0.015906      0.156355       0.011600   \n",
       "RENAL_CHRONIC         0.065785  ...  0.131145      0.222751       0.034358   \n",
       "TOBACCO               0.084645  ...  0.016067      0.022551       0.016769   \n",
       "CLASIFFICATION_FINAL -0.027138  ... -0.042202      0.020875      -0.004548   \n",
       "ICU                   0.007867  ...  0.017168      0.024283       0.018205   \n",
       "PATIENT_STATUS       -0.127824  ... -0.082065     -0.270866      -0.066016   \n",
       "\n",
       "                      CARDIOVASCULAR   OBESITY  RENAL_CHRONIC   TOBACCO  \\\n",
       "USMER                       0.010047 -0.040917       0.039981 -0.013323   \n",
       "MEDICAL_UNIT               -0.005032 -0.009823      -0.017958 -0.007720   \n",
       "SEX                        -0.016796  0.030360      -0.015108 -0.103079   \n",
       "PATIENT_TYPE               -0.108288 -0.049980      -0.159561 -0.013725   \n",
       "INTUBED                     0.050066  0.035774       0.063370  0.014433   \n",
       "PNEUMONIA                   0.070844  0.047059       0.097377  0.018040   \n",
       "AGE                        -0.144002 -0.055520      -0.114998 -0.024600   \n",
       "PREGNANT                   -0.009323 -0.017693      -0.013109 -0.016969   \n",
       "DIABETES                    0.112965  0.104250       0.192048  0.022249   \n",
       "COPD                        0.115606  0.040094       0.065785  0.084645   \n",
       "ASTHMA                      0.010044  0.044257      -0.005329  0.000691   \n",
       "INMSUPR                     0.071574  0.015906       0.131145  0.016067   \n",
       "HIPERTENSION                0.162681  0.156355       0.222751  0.022551   \n",
       "OTHER_DISEASE               0.052513  0.011600       0.034358  0.016769   \n",
       "CARDIOVASCULAR              1.000000  0.058309       0.111442  0.039301   \n",
       "OBESITY                     0.058309  1.000000       0.005247  0.079995   \n",
       "RENAL_CHRONIC               0.111442  0.005247       1.000000  0.017919   \n",
       "TOBACCO                     0.039301  0.079995       0.017919  1.000000   \n",
       "CLASIFFICATION_FINAL       -0.026530  0.047958      -0.037100 -0.019289   \n",
       "ICU                         0.020517  0.017832       0.013030  0.006194   \n",
       "PATIENT_STATUS             -0.111166 -0.040058      -0.170405 -0.022229   \n",
       "\n",
       "                      CLASIFFICATION_FINAL       ICU  PATIENT_STATUS  \n",
       "USMER                            -0.072154  0.010232       -0.105030  \n",
       "MEDICAL_UNIT                     -0.295539  0.037964        0.123059  \n",
       "SEX                              -0.026511 -0.017908        0.113064  \n",
       "PATIENT_TYPE                     -0.046093 -0.138635        0.609309  \n",
       "INTUBED                           0.021634  0.198601       -0.512624  \n",
       "PNEUMONIA                         0.065657  0.104732       -0.456165  \n",
       "AGE                              -0.069365 -0.029288        0.489493  \n",
       "PREGNANT                          0.009965  0.026537        0.037789  \n",
       "DIABETES                          0.019428  0.029415       -0.265011  \n",
       "COPD                             -0.027138  0.007867       -0.127824  \n",
       "ASTHMA                           -0.019244 -0.002865        0.036079  \n",
       "INMSUPR                          -0.042202  0.017168       -0.082065  \n",
       "HIPERTENSION                      0.020875  0.024283       -0.270866  \n",
       "OTHER_DISEASE                    -0.004548  0.018205       -0.066016  \n",
       "CARDIOVASCULAR                   -0.026530  0.020517       -0.111166  \n",
       "OBESITY                           0.047958  0.017832       -0.040058  \n",
       "RENAL_CHRONIC                    -0.037100  0.013030       -0.170405  \n",
       "TOBACCO                          -0.019289  0.006194       -0.022229  \n",
       "CLASIFFICATION_FINAL              1.000000 -0.012989        0.093934  \n",
       "ICU                              -0.012989  1.000000       -0.088544  \n",
       "PATIENT_STATUS                    0.093934 -0.088544        1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pointbiserialr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PointbiserialrResult(correlation=-0.06198398463454277, pvalue=4.332425562350414e-154)\n",
      "PointbiserialrResult(correlation=0.05437018430253222, pvalue=5.72089983996672e-119)\n",
      "PointbiserialrResult(correlation=0.05584602538471476, pvalue=2.0418825708418844e-125)\n",
      "PointbiserialrResult(correlation=0.4962259568377431, pvalue=0.0)\n",
      "PointbiserialrResult(correlation=-0.25707481787083747, pvalue=0.0)\n",
      "PointbiserialrResult(correlation=-0.3444087553941954, pvalue=0.0)\n",
      "PointbiserialrResult(correlation=0.07805265773529568, pvalue=2.152647701143638e-243)\n",
      "PointbiserialrResult(correlation=-0.3454891934641307, pvalue=0.0)\n",
      "PointbiserialrResult(correlation=-0.18187187685383402, pvalue=0.0)\n",
      "PointbiserialrResult(correlation=0.034214822454811084, pvalue=3.402577117433649e-48)\n",
      "PointbiserialrResult(correlation=-0.042590479195606115, pvalue=1.0575543412034764e-73)\n",
      "PointbiserialrResult(correlation=-0.4126592562817075, pvalue=0.0)\n",
      "PointbiserialrResult(correlation=-0.0529717262731439, pvalue=5.095190278384049e-113)\n",
      "PointbiserialrResult(correlation=-0.14400238884627767, pvalue=0.0)\n",
      "PointbiserialrResult(correlation=-0.055520388307399124, pvalue=5.591253971755422e-124)\n",
      "PointbiserialrResult(correlation=-0.11499763753885163, pvalue=0.0)\n",
      "PointbiserialrResult(correlation=-0.02459953097528473, pvalue=1.0006315020083379e-25)\n",
      "PointbiserialrResult(correlation=-0.0693648938823479, pvalue=1.5144647462246485e-192)\n",
      "PointbiserialrResult(correlation=-0.029288062170957366, pvalue=8.958688876828772e-36)\n",
      "PointbiserialrResult(correlation=0.48949289011289804, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "for i in df1.columns:\n",
    "    if i != 'AGE':\n",
    "        print(pointbiserialr(df1['AGE'], df1[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seems Like All the independent features are correlated to the target column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ab_SFd3Lse5F"
   },
   "source": [
    "## Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = shuffle(df1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "MUzsM5hE_Ctj"
   },
   "outputs": [],
   "source": [
    "y=df1['AGE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df1.iloc[:,df1.columns!='AGE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USMER</th>\n",
       "      <th>MEDICAL_UNIT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>PATIENT_TYPE</th>\n",
       "      <th>INTUBED</th>\n",
       "      <th>PNEUMONIA</th>\n",
       "      <th>PREGNANT</th>\n",
       "      <th>DIABETES</th>\n",
       "      <th>COPD</th>\n",
       "      <th>ASTHMA</th>\n",
       "      <th>INMSUPR</th>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <th>OTHER_DISEASE</th>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <th>OBESITY</th>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <th>TOBACCO</th>\n",
       "      <th>CLASIFFICATION_FINAL</th>\n",
       "      <th>ICU</th>\n",
       "      <th>PATIENT_STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181651</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181652</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181653</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181654</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181655</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181656 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        USMER  MEDICAL_UNIT  SEX  PATIENT_TYPE  INTUBED  PNEUMONIA  PREGNANT  \\\n",
       "0           1             4    1             2        2          2         2   \n",
       "1           1             4    2             1        2          2         2   \n",
       "2           1             4    1             2        1          2         2   \n",
       "3           1             4    1             1        2          2         2   \n",
       "4           2             4    2             1        2          2         2   \n",
       "...       ...           ...  ...           ...      ...        ...       ...   \n",
       "181651      2             4    1             1        2          2         2   \n",
       "181652      2             4    2             1        2          2         2   \n",
       "181653      2             4    2             1        2          2         2   \n",
       "181654      2             4    1             1        2          2         2   \n",
       "181655      2             4    2             1        2          1         2   \n",
       "\n",
       "        DIABETES  COPD  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
       "0              2   2.0     2.0      2.0           2.0            2.0   \n",
       "1              2   2.0     2.0      2.0           2.0            2.0   \n",
       "2              1   2.0     2.0      2.0           2.0            2.0   \n",
       "3              1   2.0     2.0      2.0           2.0            2.0   \n",
       "4              2   2.0     2.0      2.0           2.0            2.0   \n",
       "...          ...   ...     ...      ...           ...            ...   \n",
       "181651         2   2.0     2.0      2.0           2.0            2.0   \n",
       "181652         2   2.0     2.0      2.0           2.0            2.0   \n",
       "181653         1   2.0     2.0      2.0           1.0            2.0   \n",
       "181654         1   2.0     2.0      2.0           2.0            2.0   \n",
       "181655         1   2.0     2.0      2.0           2.0            2.0   \n",
       "\n",
       "        CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASIFFICATION_FINAL  \\\n",
       "0                  2.0      2.0            2.0      2.0                   3.0   \n",
       "1                  2.0      2.0            2.0      2.0                   3.0   \n",
       "2                  2.0      2.0            2.0      2.0                   2.0   \n",
       "3                  2.0      2.0            2.0      2.0                   3.0   \n",
       "4                  2.0      2.0            2.0      2.0                   3.0   \n",
       "...                ...      ...            ...      ...                   ...   \n",
       "181651             2.0      2.0            2.0      2.0                   3.0   \n",
       "181652             2.0      2.0            2.0      2.0                   3.0   \n",
       "181653             2.0      1.0            2.0      2.0                   3.0   \n",
       "181654             2.0      2.0            2.0      2.0                   3.0   \n",
       "181655             2.0      2.0            2.0      2.0                   3.0   \n",
       "\n",
       "        ICU  PATIENT_STATUS  \n",
       "0       2.0               0  \n",
       "1       2.0               0  \n",
       "2       2.0               1  \n",
       "3       2.0               0  \n",
       "4       2.0               0  \n",
       "...     ...             ...  \n",
       "181651  2.0               0  \n",
       "181652  2.0               0  \n",
       "181653  2.0               1  \n",
       "181654  2.0               0  \n",
       "181655  2.0               1  \n",
       "\n",
       "[181656 rows x 20 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         35\n",
       "1         43\n",
       "2         78\n",
       "3         49\n",
       "4         42\n",
       "          ..\n",
       "181651    37\n",
       "181652    32\n",
       "181653    36\n",
       "181654    54\n",
       "181655    58\n",
       "Name: AGE, Length: 181656, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qTwkfOrWswkW"
   },
   "source": [
    "## Train Models\n",
    "\n",
    "####  Note: minimum 3 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jFK296zBug7u"
   },
   "source": [
    "### 1st Regression Model (Linear Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "ALW0DPDF_Ctj"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred1=lr.predict(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xvFI9DEtuXvw"
   },
   "source": [
    "### 2nd Regression Model  (Decision Tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "GAoyDH5-u1PZ"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred2=dt.predict(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ke8DFgiHu_WK"
   },
   "source": [
    "### 3rd Regression Model  (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "cicu5WyGvWGy"
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg=XGBRegressor(tree_method='gpu_hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=0, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=0, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=0, grow_policy='depthwise',\n",
       "             importance_type=None, interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred3=xg.predict(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xdLl8k9_wG6v"
   },
   "source": [
    "## Model Evaluation\n",
    "\n",
    "#### Note: This should include confusion matrix and classification report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EUP0fJJdvmQj"
   },
   "source": [
    "### 1st Model  Evaluation ( Linear Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "ITE4LHiQvuIh"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "zj_ecisd_Ctk"
   },
   "outputs": [],
   "source": [
    "lr1=r2_score(ytest,ypred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dadYirzrzGip"
   },
   "source": [
    "### 2nd Model  Evaluation ( Discision Tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "W_xtasIr_Ctl"
   },
   "outputs": [],
   "source": [
    "dt1=r2_score(ytest,ypred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JhqUCJrNznO6"
   },
   "source": [
    "### 3rd Model  Evaluation ( Xg Boost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "nn0OzB2G_Ctl"
   },
   "outputs": [],
   "source": [
    "xg1=r2_score(ytest,ypred3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show ML Evaluation as Dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans=pd.DataFrame({'linear Regression':lr1,'Descision Tree':dt1,'Xg Boost':xg1},index=['Accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>linear Regression</th>\n",
       "      <th>Descision Tree</th>\n",
       "      <th>Xg Boost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.399505</td>\n",
       "      <td>0.389254</td>\n",
       "      <td>0.441365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          linear Regression  Descision Tree  Xg Boost\n",
       "Accuracy           0.399505        0.389254  0.441365"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KWbeIeUV_Ctl"
   },
   "source": [
    "## Model Selection \n",
    "\n",
    "#### Note: make sure to select one of your models and state the reason behind your choice "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. considering xg boost as it gives more accuracy than the base model i.e. Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fEUrdHRYpLCA"
   },
   "source": [
    "## Model Optimization - Hyperparameter Tuning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from xgboost import XGBRFRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hyperParameterTuning(X_train, y_train):\n",
    "    param_tuning = {\n",
    "        'learning_rate': [0.01, 0.1],\n",
    "        'max_depth': [3, 5, 7, 10],\n",
    "        'min_child_weight': [1, 3, 5],\n",
    "        'subsample': [0.5, 0.7],\n",
    "        'colsample_bytree': [0.5, 0.7],\n",
    "        'n_estimators' : [100, 200, 500],\n",
    "        'objective': ['reg:squarederror']\n",
    "    }\n",
    "\n",
    "    xgb_model = XGBRegressor(tree_method='gpu_hist')\n",
    "\n",
    "    gsearch = GridSearchCV(estimator = xgb_model,\n",
    "                           param_grid = param_tuning,                        \n",
    "                           #scoring = 'neg_mean_absolute_error', #MAE\n",
    "                           #scoring = 'neg_mean_squared_error',  #MSE\n",
    "                           cv = 5,\n",
    "                           n_jobs = -1,\n",
    "                           verbose = 1)\n",
    "\n",
    "    gsearch.fit(X_train,y_train)\n",
    "\n",
    "    return gsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 288 candidates, totalling 1440 fits\n"
     ]
    }
   ],
   "source": [
    "hyperParameterTuning(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "xghy=XGBRegressor(tree_method='gpu_hist',booster='dart',eval_metric='rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;dart&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=&#x27;rmse&#x27;, gamma=0, gpu_id=0, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;dart&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=&#x27;rmse&#x27;, gamma=0, gpu_id=0, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='dart', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric='rmse', gamma=0, gpu_id=0, grow_policy='depthwise',\n",
       "             importance_type=None, interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xghy.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred4=xghy.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37.05893 , 49.19814 , 46.91867 , ..., 55.758392, 48.047375,\n",
       "       53.578655], dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44530492850712455"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(ytest,ypred4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vTXVWTI6pM1Q"
   },
   "source": [
    "# ML Pipeline for Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "DvFjydhN_Ctn"
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "w2LyCJz2_Cto"
   },
   "outputs": [],
   "source": [
    "pipe=Pipeline(steps=[(\"step1\", XGBRegressor(tree_method='gpu_hist',booster='dart',eval_metric='rmse'))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "G_uJ78I2_Cto"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 XGBRegressor(base_score=0.5, booster=&#x27;dart&#x27;, callbacks=None,\n",
       "                              colsample_bylevel=1, colsample_bynode=1,\n",
       "                              colsample_bytree=1, early_stopping_rounds=None,\n",
       "                              enable_categorical=False, eval_metric=&#x27;rmse&#x27;,\n",
       "                              gamma=0, gpu_id=0, grow_policy=&#x27;depthwise&#x27;,\n",
       "                              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "                              learning_rate=0.300000012, max_bin=256,\n",
       "                              max_cat_to_onehot=4, max_delta_step=0,\n",
       "                              max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "                              missing=nan, monotone_constraints=&#x27;()&#x27;,\n",
       "                              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "                              predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "                              reg_lambda=1, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 XGBRegressor(base_score=0.5, booster=&#x27;dart&#x27;, callbacks=None,\n",
       "                              colsample_bylevel=1, colsample_bynode=1,\n",
       "                              colsample_bytree=1, early_stopping_rounds=None,\n",
       "                              enable_categorical=False, eval_metric=&#x27;rmse&#x27;,\n",
       "                              gamma=0, gpu_id=0, grow_policy=&#x27;depthwise&#x27;,\n",
       "                              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "                              learning_rate=0.300000012, max_bin=256,\n",
       "                              max_cat_to_onehot=4, max_delta_step=0,\n",
       "                              max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "                              missing=nan, monotone_constraints=&#x27;()&#x27;,\n",
       "                              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "                              predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "                              reg_lambda=1, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;dart&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=&#x27;rmse&#x27;, gamma=0, gpu_id=0, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 XGBRegressor(base_score=0.5, booster='dart', callbacks=None,\n",
       "                              colsample_bylevel=1, colsample_bynode=1,\n",
       "                              colsample_bytree=1, early_stopping_rounds=None,\n",
       "                              enable_categorical=False, eval_metric='rmse',\n",
       "                              gamma=0, gpu_id=0, grow_policy='depthwise',\n",
       "                              importance_type=None, interaction_constraints='',\n",
       "                              learning_rate=0.300000012, max_bin=256,\n",
       "                              max_cat_to_onehot=4, max_delta_step=0,\n",
       "                              max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "                              missing=nan, monotone_constraints='()',\n",
       "                              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "                              predictor='auto', random_state=0, reg_alpha=0,\n",
       "                              reg_lambda=1, ...))])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=pipe.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44530492850712455"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "zEj3MfTe_CtX"
   ],
   "name": "ML Belt Exam.ipynb",
   "provenance": []
  },
  "gpuClass": "standard",
  "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}