main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV\n",
    "from sklearn.ensemble import AdaBoostClassifier,GradientBoostingClassifier,RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from xgboost import XGBClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's check the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid=pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>CLASSIFICATION_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>03-05-20</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</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</td>\n",
       "      <td>3</td>\n",
       "      <td>97</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>03-06-20</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</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>97</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>09-06-20</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</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</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>12-06-20</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</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>97</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>21-06-20</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</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</td>\n",
       "      <td>3</td>\n",
       "      <td>97</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  AGE  \\\n",
       "0      2             1    1             1  03-05-20       97          1   65   \n",
       "1      2             1    2             1  03-06-20       97          1   72   \n",
       "2      2             1    2             2  09-06-20        1          2   55   \n",
       "3      2             1    1             1  12-06-20       97          2   53   \n",
       "4      2             1    2             1  21-06-20       97          2   68   \n",
       "\n",
       "   PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
       "0         2         2  ...       2        2             1              2   \n",
       "1        97         2  ...       2        2             1              2   \n",
       "2        97         1  ...       2        2             2              2   \n",
       "3         2         2  ...       2        2             2              2   \n",
       "4        97         1  ...       2        2             1              2   \n",
       "\n",
       "   CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASSIFICATION_FINAL  ICU  \n",
       "0               2        2              2        2                     3   97  \n",
       "1               2        1              1        2                     5   97  \n",
       "2               2        2              2        2                     3    2  \n",
       "3               2        2              2        2                     7   97  \n",
       "4               2        2              2        2                     3   97  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048575, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are 1048575 rows and 21 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1048575 entries, 0 to 1048574\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count    Dtype \n",
      "---  ------                --------------    ----- \n",
      " 0   USMER                 1048575 non-null  int64 \n",
      " 1   MEDICAL_UNIT          1048575 non-null  int64 \n",
      " 2   SEX                   1048575 non-null  int64 \n",
      " 3   PATIENT_TYPE          1048575 non-null  int64 \n",
      " 4   DATE_DIED             1048575 non-null  object\n",
      " 5   INTUBED               1048575 non-null  int64 \n",
      " 6   PNEUMONIA             1048575 non-null  int64 \n",
      " 7   AGE                   1048575 non-null  int64 \n",
      " 8   PREGNANT              1048575 non-null  int64 \n",
      " 9   DIABETES              1048575 non-null  int64 \n",
      " 10  COPD                  1048575 non-null  int64 \n",
      " 11  ASTHMA                1048575 non-null  int64 \n",
      " 12  INMSUPR               1048575 non-null  int64 \n",
      " 13  HIPERTENSION          1048575 non-null  int64 \n",
      " 14  OTHER_DISEASE         1048575 non-null  int64 \n",
      " 15  CARDIOVASCULAR        1048575 non-null  int64 \n",
      " 16  OBESITY               1048575 non-null  int64 \n",
      " 17  RENAL_CHRONIC         1048575 non-null  int64 \n",
      " 18  TOBACCO               1048575 non-null  int64 \n",
      " 19  CLASSIFICATION_FINAL  1048575 non-null  int64 \n",
      " 20  ICU                   1048575 non-null  int64 \n",
      "dtypes: int64(20), object(1)\n",
      "memory usage: 168.0+ MB\n"
     ]
    }
   ],
   "source": [
    "covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USMER                   0\n",
       "MEDICAL_UNIT            0\n",
       "SEX                     0\n",
       "PATIENT_TYPE            0\n",
       "DATE_DIED               0\n",
       "INTUBED                 0\n",
       "PNEUMONIA               0\n",
       "AGE                     0\n",
       "PREGNANT                0\n",
       "DIABETES                0\n",
       "COPD                    0\n",
       "ASTHMA                  0\n",
       "INMSUPR                 0\n",
       "HIPERTENSION            0\n",
       "OTHER_DISEASE           0\n",
       "CARDIOVASCULAR          0\n",
       "OBESITY                 0\n",
       "RENAL_CHRONIC           0\n",
       "TOBACCO                 0\n",
       "CLASSIFICATION_FINAL    0\n",
       "ICU                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.isna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are no null/missing values, but also as specified in dataset, the missing values are replaced by values such as 97,97 and 99"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also have to look into the DATE_DIED column as it contains a different classification of data,so we will change the 9999-99-99 to 2, which means no(basically not dead) and the rest to 1 (which means dead) so that this column is also a type of category thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid['DEATH']=[2 if each ==\"9999-99-99\" else  1 for each in covid['DATE_DIED']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid.drop(columns='DATE_DIED',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>CLASSIFICATION_FINAL</th>\n",
       "      <th>ICU</th>\n",
       "      <th>DEATH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.632194e+00</td>\n",
       "      <td>8.980565e+00</td>\n",
       "      <td>1.499259e+00</td>\n",
       "      <td>1.190765e+00</td>\n",
       "      <td>7.952288e+01</td>\n",
       "      <td>3.346831e+00</td>\n",
       "      <td>4.179410e+01</td>\n",
       "      <td>4.976558e+01</td>\n",
       "      <td>2.186404e+00</td>\n",
       "      <td>2.260569e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.298132e+00</td>\n",
       "      <td>2.128989e+00</td>\n",
       "      <td>2.435143e+00</td>\n",
       "      <td>2.261810e+00</td>\n",
       "      <td>2.125176e+00</td>\n",
       "      <td>2.257180e+00</td>\n",
       "      <td>2.214333e+00</td>\n",
       "      <td>5.305653e+00</td>\n",
       "      <td>7.955397e+01</td>\n",
       "      <td>1.926622e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.822084e-01</td>\n",
       "      <td>3.723278e+00</td>\n",
       "      <td>4.999997e-01</td>\n",
       "      <td>3.929041e-01</td>\n",
       "      <td>3.686889e+01</td>\n",
       "      <td>1.191288e+01</td>\n",
       "      <td>1.690739e+01</td>\n",
       "      <td>4.751073e+01</td>\n",
       "      <td>5.424242e+00</td>\n",
       "      <td>5.132258e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.462843e+00</td>\n",
       "      <td>5.236397e+00</td>\n",
       "      <td>6.646676e+00</td>\n",
       "      <td>5.194850e+00</td>\n",
       "      <td>5.175445e+00</td>\n",
       "      <td>5.135354e+00</td>\n",
       "      <td>5.323097e+00</td>\n",
       "      <td>1.881165e+00</td>\n",
       "      <td>3.682307e+01</td>\n",
       "      <td>2.607556e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>1.210000e+02</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              USMER  MEDICAL_UNIT           SEX  PATIENT_TYPE       INTUBED  \\\n",
       "count  1.048575e+06  1.048575e+06  1.048575e+06  1.048575e+06  1.048575e+06   \n",
       "mean   1.632194e+00  8.980565e+00  1.499259e+00  1.190765e+00  7.952288e+01   \n",
       "std    4.822084e-01  3.723278e+00  4.999997e-01  3.929041e-01  3.686889e+01   \n",
       "min    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "25%    1.000000e+00  4.000000e+00  1.000000e+00  1.000000e+00  9.700000e+01   \n",
       "50%    2.000000e+00  1.200000e+01  1.000000e+00  1.000000e+00  9.700000e+01   \n",
       "75%    2.000000e+00  1.200000e+01  2.000000e+00  1.000000e+00  9.700000e+01   \n",
       "max    2.000000e+00  1.300000e+01  2.000000e+00  2.000000e+00  9.900000e+01   \n",
       "\n",
       "          PNEUMONIA           AGE      PREGNANT      DIABETES          COPD  \\\n",
       "count  1.048575e+06  1.048575e+06  1.048575e+06  1.048575e+06  1.048575e+06   \n",
       "mean   3.346831e+00  4.179410e+01  4.976558e+01  2.186404e+00  2.260569e+00   \n",
       "std    1.191288e+01  1.690739e+01  4.751073e+01  5.424242e+00  5.132258e+00   \n",
       "min    1.000000e+00  0.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "25%    2.000000e+00  3.000000e+01  2.000000e+00  2.000000e+00  2.000000e+00   \n",
       "50%    2.000000e+00  4.000000e+01  9.700000e+01  2.000000e+00  2.000000e+00   \n",
       "75%    2.000000e+00  5.300000e+01  9.700000e+01  2.000000e+00  2.000000e+00   \n",
       "max    9.900000e+01  1.210000e+02  9.800000e+01  9.800000e+01  9.800000e+01   \n",
       "\n",
       "       ...       INMSUPR  HIPERTENSION  OTHER_DISEASE  CARDIOVASCULAR  \\\n",
       "count  ...  1.048575e+06  1.048575e+06   1.048575e+06    1.048575e+06   \n",
       "mean   ...  2.298132e+00  2.128989e+00   2.435143e+00    2.261810e+00   \n",
       "std    ...  5.462843e+00  5.236397e+00   6.646676e+00    5.194850e+00   \n",
       "min    ...  1.000000e+00  1.000000e+00   1.000000e+00    1.000000e+00   \n",
       "25%    ...  2.000000e+00  2.000000e+00   2.000000e+00    2.000000e+00   \n",
       "50%    ...  2.000000e+00  2.000000e+00   2.000000e+00    2.000000e+00   \n",
       "75%    ...  2.000000e+00  2.000000e+00   2.000000e+00    2.000000e+00   \n",
       "max    ...  9.800000e+01  9.800000e+01   9.800000e+01    9.800000e+01   \n",
       "\n",
       "            OBESITY  RENAL_CHRONIC       TOBACCO  CLASSIFICATION_FINAL  \\\n",
       "count  1.048575e+06   1.048575e+06  1.048575e+06          1.048575e+06   \n",
       "mean   2.125176e+00   2.257180e+00  2.214333e+00          5.305653e+00   \n",
       "std    5.175445e+00   5.135354e+00  5.323097e+00          1.881165e+00   \n",
       "min    1.000000e+00   1.000000e+00  1.000000e+00          1.000000e+00   \n",
       "25%    2.000000e+00   2.000000e+00  2.000000e+00          3.000000e+00   \n",
       "50%    2.000000e+00   2.000000e+00  2.000000e+00          6.000000e+00   \n",
       "75%    2.000000e+00   2.000000e+00  2.000000e+00          7.000000e+00   \n",
       "max    9.800000e+01   9.800000e+01  9.800000e+01          7.000000e+00   \n",
       "\n",
       "                ICU         DEATH  \n",
       "count  1.048575e+06  1.048575e+06  \n",
       "mean   7.955397e+01  1.926622e+00  \n",
       "std    3.682307e+01  2.607556e-01  \n",
       "min    1.000000e+00  1.000000e+00  \n",
       "25%    9.700000e+01  2.000000e+00  \n",
       "50%    9.700000e+01  2.000000e+00  \n",
       "75%    9.700000e+01  2.000000e+00  \n",
       "max    9.900000e+01  2.000000e+00  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1048575 entries, 0 to 1048574\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count    Dtype\n",
      "---  ------                --------------    -----\n",
      " 0   USMER                 1048575 non-null  int64\n",
      " 1   MEDICAL_UNIT          1048575 non-null  int64\n",
      " 2   SEX                   1048575 non-null  int64\n",
      " 3   PATIENT_TYPE          1048575 non-null  int64\n",
      " 4   INTUBED               1048575 non-null  int64\n",
      " 5   PNEUMONIA             1048575 non-null  int64\n",
      " 6   AGE                   1048575 non-null  int64\n",
      " 7   PREGNANT              1048575 non-null  int64\n",
      " 8   DIABETES              1048575 non-null  int64\n",
      " 9   COPD                  1048575 non-null  int64\n",
      " 10  ASTHMA                1048575 non-null  int64\n",
      " 11  INMSUPR               1048575 non-null  int64\n",
      " 12  HIPERTENSION          1048575 non-null  int64\n",
      " 13  OTHER_DISEASE         1048575 non-null  int64\n",
      " 14  CARDIOVASCULAR        1048575 non-null  int64\n",
      " 15  OBESITY               1048575 non-null  int64\n",
      " 16  RENAL_CHRONIC         1048575 non-null  int64\n",
      " 17  TOBACCO               1048575 non-null  int64\n",
      " 18  CLASSIFICATION_FINAL  1048575 non-null  int64\n",
      " 19  ICU                   1048575 non-null  int64\n",
      " 20  DEATH                 1048575 non-null  int64\n",
      "dtypes: int64(21)\n",
      "memory usage: 168.0 MB\n"
     ]
    }
   ],
   "source": [
    "covid.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "Let us have a look at how many values are missing and what to do with those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USMER                     2\n",
       "MEDICAL_UNIT             13\n",
       "SEX                       2\n",
       "PATIENT_TYPE              2\n",
       "INTUBED                   4\n",
       "PNEUMONIA                 3\n",
       "AGE                     121\n",
       "PREGNANT                  4\n",
       "DIABETES                  3\n",
       "COPD                      3\n",
       "ASTHMA                    3\n",
       "INMSUPR                   3\n",
       "HIPERTENSION              3\n",
       "OTHER_DISEASE             3\n",
       "CARDIOVASCULAR            3\n",
       "OBESITY                   3\n",
       "RENAL_CHRONIC             3\n",
       "TOBACCO                   3\n",
       "CLASSIFICATION_FINAL      7\n",
       "ICU                       4\n",
       "DEATH                     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.nunique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer from these that when number of unique values is 2, then we are sure that its either 1(yes) or 2(no).\n",
    "Then if the unique values are 3 ,then apart from yes and no there is one missing value parameter too and same goes for 4.  \n",
    "Lets find out how many missing values are there, and if they are significantly large enough, we will remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97    848544\n",
       "2     159050\n",
       "1      33656\n",
       "99      7325\n",
       "Name: INTUBED, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['INTUBED'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is so much (like 90%) of the data which is missing and which makes it an unusable parameter, so we'll remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid.drop(columns='INTUBED',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     892534\n",
       "1     140038\n",
       "99     16003\n",
       "Name: PNEUMONIA, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['PNEUMONIA'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97    523511\n",
       "2     513179\n",
       "1       8131\n",
       "98      3754\n",
       "Name: PREGNANT, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['PREGNANT'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again,we can see like half of the data missing and men cant be pregnate and there is a direct relationship between the SEX and PREGNANT count. So we first try to check the relation.  \n",
    "Before that, lets convert the men count (97) data into non pregnant(2).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid['PREGNANT']=covid['PREGNANT'].replace(97,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1048575 entries, 0 to 1048574\n",
      "Data columns (total 20 columns):\n",
      " #   Column                Non-Null Count    Dtype\n",
      "---  ------                --------------    -----\n",
      " 0   USMER                 1048575 non-null  int64\n",
      " 1   MEDICAL_UNIT          1048575 non-null  int64\n",
      " 2   SEX                   1048575 non-null  int64\n",
      " 3   PATIENT_TYPE          1048575 non-null  int64\n",
      " 4   PNEUMONIA             1048575 non-null  int64\n",
      " 5   AGE                   1048575 non-null  int64\n",
      " 6   PREGNANT              1048575 non-null  int64\n",
      " 7   DIABETES              1048575 non-null  int64\n",
      " 8   COPD                  1048575 non-null  int64\n",
      " 9   ASTHMA                1048575 non-null  int64\n",
      " 10  INMSUPR               1048575 non-null  int64\n",
      " 11  HIPERTENSION          1048575 non-null  int64\n",
      " 12  OTHER_DISEASE         1048575 non-null  int64\n",
      " 13  CARDIOVASCULAR        1048575 non-null  int64\n",
      " 14  OBESITY               1048575 non-null  int64\n",
      " 15  RENAL_CHRONIC         1048575 non-null  int64\n",
      " 16  TOBACCO               1048575 non-null  int64\n",
      " 17  CLASSIFICATION_FINAL  1048575 non-null  int64\n",
      " 18  ICU                   1048575 non-null  int64\n",
      " 19  DEATH                 1048575 non-null  int64\n",
      "dtypes: int64(20)\n",
      "memory usage: 160.0 MB\n"
     ]
    }
   ],
   "source": [
    "covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fv3751Kc+le233z6DBg3K//7v/6Zt27b1xhsyZEh+/vOf5/XXX//I2qdPn55HH300Z599dj796U/nd7/7XYP9hgwZkpEjR2bhwoXLtbVo0aJe3VVVVcu9nxYtWqzUsVxVwgEAANiAjRo1arlpydttt12S5K233sqQIUOy7bbbpqqqKt27d893vvOdet+cvvnmmznooIPStWvXVFZWplu3bjn11FNTW1tb7vNhU6FramrKfR566KEceuih6dq1ayoqKnLHHXcsV+vvfve79OvXL5tuumkqKioybdq0tXZcWD9UVVUtNzMgSebOnZubbropSVbpZPm+++7Lm2++mTPPPPND+3zwVotHH310+RKHj3LttddmwIABadu2bb75zW/m6quvbrDf0KFD89577+Wyyy5b6brXBeEAAABs4HbYYYd605IffvjhJMnMmTMzc+bM/PSnP82zzz6bcePGZcKECRk0aFD5tU2aNMmXv/zl3HXXXfnb3/6WcePG5Q9/+ENOPvnk5fazbMr3skfHjh3LbfPnz88uu+ySK6644kPrnD9/fvbZZ59ceOGFa/Ddsz6qq6vLH/7wh0ycODEHHHBAefsWW2yRjTfeOO3atcuNN96YL33pS+Wwa5l77rknG2+8cb3Hj3/84yTJ3/72tyTJtttuW+7/5JNP1ut7zz331BuvoqIiF1xwQX71q1/l5ZdfbrDepUuXZty4cfnmN7+ZJDnqqKPy8MMPZ/r06cv1bdWqVUaOHJnRo0cvdwlDY2rW2AUAAABrV7NmzdK5c+fltu+444657bbbys+32mqrnH/++fnmN7+Z9957L82aNcsmm2ySU045pdynR48e+fa3v13vOvBlOnbsmHbt2jVYw8EHH5yDDz74I+s85phjkpQWgqOYlp3YL168OEuXLs1//Md/ZNSoUXnyySeTJH/84x/TqlWrPPbYY/nxj3+csWPHLjfG/vvvnzFjxtTb1r59+w/d584771yepbLNNtvkvffeW65P//79s88+++QHP/hBbrzxxuXa77///syfPz+HHHJIkmSzzTbLgQcemGuuuSbnnXfecv0HDRqUn/3sZ7nwwgvLwUVjEw4AAMAG7sUXX0zXrl3TsmXLVFdXZ/To0enevXuDfefOnZs2bdqkWbOGTxVmzpyZ3/3ud/n85z+/XFvv3r2zcOHC7Ljjjhk1alQ++9nPrtH3wYZv2Yl9ixYt0rVr1+X+Hvbs2TPt2rXLtttum9dffz1HHnlkHnrooXp9Ntpoo2y99dYNjr/NNtskKc1y2WuvvZKU1ij4sP7vd8EFF6S6ujpnnHHGcm1XX3113nrrrVRVVZW3LV26NH/+85/zwx/+ME2a1J+036xZs5x//vk57rjjcuqpp65w3+uCywoAAGAD1qdPn/LlAmPGjMn06dPzuc99brlbsyXJG2+8kfPOOy/f+ta3lms7+uij06pVq2y++eZp06ZNrrrqqnJbly5dMnbs2Nx222257bbb0q1bt+y333556qmn1up7Y8Oz7MS+e/fuHxpQLTN48OA8++yzuf3221d6/H79+qV9+/ardenKnnvuma9+9as5++yz621/8803c+edd+amm27KtGnTyo+nn346//rXv3Lfffc1ON7Xv/717LDDDvnhD3+4yrWsDWYOAADABuz9U/l33nnn9OnTJz169Mgtt9xSb22B2traDBgwIL169Wrw/u0XX3xxRo4cmb/97W8ZPnx4hg0bliuvvDJJ6frt91/Dvffee+fll1/OxRdfnOuvv37tvTkKrVWrVjnppJMycuTIHHbYYeWFBBcuXFhvMcyk9E39Zpttlo033jhXXXVVjjzyyAwYMCDf+c53ss022+Ttt9/OhAkTkiRNmzb90H2ef/752WGHHeoFF9dff3023XTTHHHEEcstZnjIIYfk6quvzkEHHdTgeBdccEH69++/Wu9/TTNzAAAACqRdu3b59Kc/nZdeeqm8bd68eTnooIPSunXr3H777WnevPlyr+vcuXO22267fOlLX8ovf/nLjBkzJq+99tqH7mfPPfestw9YG0499dT89a9/za233lreNmHChHTp0qXeY5999im3f+UrX8mjjz6aVq1a5dhjj822226bAw44IA888EBuuummfPGLX/zQ/X3605/OCSeckAULFpS3XXPNNfnKV76yXDCQJIcffnjuuuuuvPHGGw2Od8ABB+SAAw5ocJ2Dda2irq6urrGLKIra2tq0bdu2fB0XAACsa2+//Xa6d++eUaNG5Tvf+U5qa2vTv3//VFZW5ve//31atWq1wjEeeuihfP7zn8/06dOz5ZZbNtjnwAMPTOvWrRu813tFRUVuv/32HHbYYQ2+9u9//3t69uyZp59+Or17916Fd8cn2YIFCzJ9+vT07NkzLVu2bOxyNigfdWxX9jzUZQUAALABO/3003PooYemR48emTlzZkaOHJmmTZvm6KOPTm1tbfr165d33nknN9xwQ2pra1NbW5sk6dChQ5o2bZrf//73mTVrVvbYY49svPHG+ctf/pIzzjgjn/3sZ8vBwCWXXJKePXtmhx12yIIFC3LVVVflgQceqHet9dtvv11vJsH06dMzbdq0tG/fvrw44ltvvZUZM2Zk5syZSUqLxiWlWQsN3W0BWHOEAwAAsAF79dVXc/TRR+fNN99Mhw4dss8+++Sxxx5Lhw4d8uCDD+bxxx9PkuVWa182K6Cqqir/9V//ldNOOy0LFy5Mt27dlluUbdGiRfnud7+bf/7zn2nVqlV23nnn/OEPf8j+++9f7vOnP/2p3vNhw4YlSQYOHJhx48YlSe66664cf/zx5T5HHXVUkmTkyJENroMArDkuK1iHXFYAAAAUlcsK1p41cVmBBQmB9cKoUaNSUVFR77HddtuV23/1q19lv/32S5s2bVJRUZE5c+YsN8aXvvSldO/ePS1btkyXLl1yzDHHlKctJqV/VI877rjstNNOadasWYPXQR533HHL1VFRUZEddtih3GfevHkZOnRoevTokaqqquy999558skn1+jxAACANcllBcB6Y4cddsgf/vCH8vP330LmnXfeyUEHHZSDDjoow4cPb/D1+++/f773ve+lS5cu+ec//5nTTz89X/va1/Loo48mSZYsWZKqqqp85zvfyW233dbgGJdeemkuuOCC8vP33nsvu+yyS77+9a+Xt5144ol59tlnc/3116dr16654YYb0rdv3zz33HPZfPPNP9YxAOCTbca5OzV2CaxD3Uc809glwBojHADWG82aNfvQxYiGDh2aJHnwwQc/9PWnnXZa+fc9evTI2WefncMOOyyLFy9O8+bNs9FGG2XMmDFJkkceeaTB2Qdt27ZN27Zty8/vuOOO/Otf/ypfH/nuu+/mtttuy5133pl99903SWnWw913350xY8bkRz/60aq8ZQAAWCdcVgCsN1588cV07do1n/rUp/KNb3wjM2bMWO2x3nrrrfzmN7/J3nvv3eC9nFfW1Vdfnb59+6ZHjx5JSjMJlixZsty1XlVVVXn44YdXez8AALA2CQeA9UKfPn0ybty4TJgwIWPGjMn06dPzuc99LvPmzVulcc4666xstNFG2XTTTTNjxozceeedq13TzJkzc++99+bEE08sb2vdunWqq6tz3nnnZebMmVmyZEluuOGGTJkyJa+99tpq7wsAANYm4QCwXjj44IPz9a9/PTvvvHP69++f3//+95kzZ05uueWWVRrnjDPOyNNPP5377rsvTZs2zbHHHpvVvWnLddddl3bt2i23cOH111+furq6bL755qmsrMwvfvGLHH300WnSxD+5AAB8MllzAFgvtWvXLp/+9Kfz0ksvrdLrNttss2y22Wb59Kc/ne233z7dunXLY489lurq6lUap66uLtdcc02OOeaYtGjRol7bVlttlcmTJ2f+/Pmpra1Nly5dcuSRR+ZTn/rUKu0DAIB/2+2MX6/T/U39ybHrdH+NzddYwHrp7bffzssvv5wuXbqs9hhLly5NkixcuHCVXzt58uS89NJLGTRo0If22WijjdKlS5f861//ysSJE/PlL395tWsFAOCT7aGHHsqhhx6arl27pqKiInfccUdjl7RKzBwA1gunn356Dj300PTo0SMzZ87MyJEj07Rp0xx99NFJkpqamtTU1JRnEjzzzDNp3bp1unfvnvbt2+fxxx/Pk08+mX322SebbLJJXn755fzgBz/IVlttVW/WwHPPPZdFixblrbfeyrx58zJt2rQkSe/evevVc/XVV6dPnz7Zcccdl6t14sSJqaury7bbbpuXXnopZ5xxRrbbbrvyHQ0AANjwzJ8/P7vssktOOOGEfPWrX23sclaZcABYL7z66qs5+uij8+abb6ZDhw7ZZ5998thjj6VDhw5JkrFjx+aHP/xhuf+y2whee+21Oe6449KqVav87ne/y8iRIzN//vx06dIlBx10UM4555xUVlaWX3fIIYfkH//4R/n5Zz7zmSSpty7B3Llzc9ttt+XSSy9tsNa5c+dm+PDhefXVV9O+ffscfvjhOf/88z/WXREAAPhkO/jgg3PwwQc3dhmrTTgArBduuummj2wfNWpURo0a9aHtO+20Ux544IEV7ufvf//7Cvu0bds277zzzoe2H3HEETniiCNWOA4AAHxSWHMAAAAACk44AAAAAAXnsgJgOTPO3amxS2Ad6j7imcYuAQCARmbmAAAAABScmQMAAADwMb399tvl22onyfTp0zNt2rS0b98+3bt3b8TKVo5wAAAAgE+8qT85trFL+Eh/+tOfsv/++5efDxs2LEkycODAjBs3rpGqWnnCAQAAAPiY9ttvv9TV1TV2GavNmgMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIJr1HBg1KhRqaioqPfYbrvtyu0LFizI4MGDs+mmm2bjjTfO4YcfnlmzZtUbY8aMGRkwYEBatWqVjh075owzzsh7771Xr8+DDz6YXXfdNZWVldl6660bvMfkFVdckS233DItW7ZMnz598sQTT9RrX5laAAAAYH3U6DMHdthhh7z22mvlx8MPP1xuO+2003L33Xfn1ltvzeTJkzNz5sx89atfLbcvWbIkAwYMyKJFi/Loo4/muuuuy7hx4zJixIhyn+nTp2fAgAHZf//9M23atAwdOjQnnnhiJk6cWO5z8803Z9iwYRk5cmSeeuqp7LLLLunfv39ef/31la4FAAAA1lfNGr2AZs3SuXPn5bbPnTs3V199dW688cYccMABSZJrr70222+/fR577LHstddeue+++/Lcc8/lD3/4Qzp16pTevXvnvPPOy1lnnZVRo0alRYsWGTt2bHr27Jmf/exnSZLtt98+Dz/8cC6++OL0798/SfLzn/88J510Uo4//vgkydixYzN+/Phcc801Ofvss1eqloYsXLgwCxcuLD+vra1dcwcOAAAA1pBGDwdefPHFdO3aNS1btkx1dXVGjx6d7t27Z+rUqVm8eHH69u1b7rvddtule/fumTJlSvbaa69MmTIlO+20Uzp16lTu079//5xyyin5y1/+ks985jOZMmVKvTGW9Rk6dGiSZNGiRZk6dWqGDx9ebm/SpEn69u2bKVOmJMlK1dKQ0aNH54c//OHHPkYAAABFN+Pcndbp/rqPeGad7q+xNeplBX369Mm4ceMyYcKEjBkzJtOnT8/nPve5zJs3LzU1NWnRokXatWtX7zWdOnVKTU1NkqSmpqZeMLCsfVnbR/Wpra3Nu+++mzfeeCNLlixpsM/7x1hRLQ0ZPnx45s6dW3688sorK3dgAAAAWK+MHj06e+yxR1q3bp2OHTvmsMMOywsvvNDYZa20Rp05cPDBB5d/v/POO6dPnz7p0aNHbrnlllRVVTViZWtGZWVlKisrG7sMAAAA1rLJkydn8ODB2WOPPfLee+/le9/7Xvr165fnnnsuG220UWOXt0KNflnB+7Vr1y6f/vSn89JLL+XAAw/MokWLMmfOnHrf2M+aNau8RkHnzp2Xu6vAsjsIvL/PB+8qMGvWrLRp0yZVVVVp2rRpmjZt2mCf94+xoloAAAAorgkTJtR7Pm7cuHTs2DFTp07Nvvvu20hVrbxGv1vB+7399tt5+eWX06VLl+y2225p3rx5Jk2aVG5/4YUXMmPGjFRXVydJqqur88wzz9S7q8D999+fNm3apFevXuU+7x9jWZ9lY7Ro0SK77bZbvT5Lly7NpEmTyn1WphYAAABYZu7cuUmS9u3bN3IlK6dRZw6cfvrpOfTQQ9OjR4/MnDkzI0eOTNOmTXP00Uenbdu2GTRoUIYNG5b27dunTZs2GTJkSKqrq8sLAPbr1y+9evXKMccck4suuig1NTU555xzMnjw4PJ0/pNPPjmXX355zjzzzJxwwgl54IEHcsstt2T8+PHlOoYNG5aBAwdm9913z5577plLLrkk8+fPL9+9YGVqAQAAgKT0hfPQoUPz2c9+NjvuuGNjl7NSGjUcePXVV3P00UfnzTffTIcOHbLPPvvkscceS4cOHZIkF198cZo0aZLDDz88CxcuTP/+/XPllVeWX9+0adPcc889OeWUU1JdXZ2NNtooAwcOzLnnnlvu07Nnz4wfPz6nnXZaLr300myxxRa56qqryrcxTJIjjzwys2fPzogRI1JTU5PevXtnwoQJ9RYpXFEtAAAAkCSDBw/Os88+m4cffrixS1lpFXV1dXWNXURR1NbWpm3btpk7d27atGnT2OXAh1rXt4mhcRXtNj0Aa5PP0GLxGbpqFixYkOnTp6dnz55p2bLlKr9+fbmV4amnnpo777wzDz30UHr27LmGq2rYRx3blT0P/UQtSAgAAADro7q6ugwZMiS33357HnzwwXUWDKwpwgEAAAD4mAYPHpwbb7wxd955Z1q3bp2ampokpTXsqqqqGrm6FRMOAAAA8In3Sb+MY8yYMUmS/fbbr972a6+9Nscdd9y6L2gVCQcAAADgY1rfl/Nr0tgFAAAAAI1LOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKrlljFwAAAAAr8tnLPrtO9/fIkEfW6f4am5kDAAAAsAbMmzcvQ4cOTY8ePVJVVZW99947Tz75ZLn97bffzqmnnpotttgiVVVV6dWrV8aOHduIFf+bmQMAAACwBpx44ol59tlnc/3116dr16654YYb0rdv3zz33HPZfPPNM2zYsDzwwAO54YYbsuWWW+a+++7Lt7/97XTt2jVf+tKXGrV2MwcAAADgY3r33Xdz22235aKLLsq+++6brbfeOqNGjcrWW2+dMWPGJEkeffTRDBw4MPvtt1+23HLLfOtb38ouu+ySJ554opGrFw4AAADAx/bee+9lyZIladmyZb3tVVVVefjhh5Mke++9d+66667885//TF1dXf7nf/4nf/vb39KvX7/GKLke4QAAAAB8TK1bt051dXXOO++8zJw5M0uWLMkNN9yQKVOm5LXXXkuSXHbZZenVq1e22GKLtGjRIgcddFCuuOKK7Lvvvo1cvXAAAAAA1ojrr78+dXV12XzzzVNZWZlf/OIXOfroo9OkSenU+7LLLstjjz2Wu+66K1OnTs3PfvazDB48OH/4wx8auXILEgIAAMAasdVWW2Xy5MmZP39+amtr06VLlxx55JH51Kc+lXfffTff+973cvvtt2fAgAFJkp133jnTpk3LT3/60/Tt27dRazdzAAAAANagjTbaKF26dMm//vWvTJw4MV/+8pezePHiLF68uDyLYJmmTZtm6dKljVTpv5k5AAAAAGvAxIkTU1dXl2233TYvvfRSzjjjjGy33XY5/vjj07x583z+85/PGWeckaqqqvTo0SOTJ0/Or3/96/z85z9v7NKFAwAAAHzyPTLkkcYuYYXmzp2b4cOH59VXX0379u1z+OGH5/zzz0/z5s2TJDfddFOGDx+eb3zjG3nrrbfSo0ePnH/++Tn55JMbuXLhAAAAAKwRRxxxRI444ogPbe/cuXOuvfbadVjRyrPmAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAALDO1NXVNXYJG5w1cUyFAwAAAKx1TZs2TZIsWrSokSvZ8LzzzjtJUr5l4upwK0MAAADWumbNmqVVq1aZPXt2mjdvniZNfFf9cdXV1eWdd97J66+/nnbt2pUDmNUhHAAAAGCtq6ioSJcuXTJ9+vT84x//aOxyNijt2rVL586dP9YYwgEAAADWiRYtWmSbbbZxacEa1Lx58481Y2AZ4QAAAADrTJMmTdKyZcvGLoMPcJEHAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACu4TEw5ccMEFqaioyNChQ8vbFixYkMGDB2fTTTfNxhtvnMMPPzyzZs2q97oZM2ZkwIABadWqVTp27Jgzzjgj7733Xr0+Dz74YHbddddUVlZm6623zrhx45bb/xVXXJEtt9wyLVu2TJ8+ffLEE0/Ua1+ZWgAAAGB99IkIB5588sn88pe/zM4771xv+2mnnZa77747t956ayZPnpyZM2fmq1/9arl9yZIlGTBgQBYtWpRHH3001113XcaNG5cRI0aU+0yfPj0DBgzI/vvvn2nTpmXo0KE58cQTM3HixHKfm2++OcOGDcvIkSPz1FNPZZdddkn//v3z+uuvr3QtAAAAsL6qqKurq2vMAt5+++3suuuuufLKK/OjH/0ovXv3ziWXXJK5c+emQ4cOufHGG/O1r30tSfL8889n++23z5QpU7LXXnvl3nvvzRe/+MXMnDkznTp1SpKMHTs2Z511VmbPnp0WLVrkrLPOyvjx4/Pss8+W93nUUUdlzpw5mTBhQpKkT58+2WOPPXL55ZcnSZYuXZpu3bplyJAhOfvss1eqloYsXLgwCxcuLD+vra1Nt27dMnfu3LRp02bNH0xYQ2acu1Njl8A61H3EM41dAsAGw2dosfgMZX1QW1ubtm3brvA8tNFnDgwePDgDBgxI3759622fOnVqFi9eXG/7dtttl+7du2fKlClJkilTpmSnnXYqBwNJ0r9//9TW1uYvf/lLuc8Hx+7fv395jEWLFmXq1Kn1+jRp0iR9+/Yt91mZWhoyevTotG3btvzo1q3bKh0bAAAAWBcaNRy46aab8tRTT2X06NHLtdXU1KRFixZp165dve2dOnVKTU1Nuc/7g4Fl7cvaPqpPbW1t3n333bzxxhtZsmRJg33eP8aKamnI8OHDM3fu3PLjlVde+dC+AAAA0FiaNdaOX3nllfy///f/cv/996dly5aNVcZaVVlZmcrKysYuAwAAAD5So80cmDp1al5//fXsuuuuadasWZo1a5bJkyfnF7/4RZo1a5ZOnTpl0aJFmTNnTr3XzZo1K507d06SdO7cebk7Bix7vqI+bdq0SVVVVTbbbLM0bdq0wT7vH2NFtQAAAMD6qtHCgS984Qt55plnMm3atPJj9913zze+8Y3y75s3b55JkyaVX/PCCy9kxowZqa6uTpJUV1fnmWeeqXdXgfvvvz9t2rRJr169yn3eP8ayPsvGaNGiRXbbbbd6fZYuXZpJkyaV++y2224rrAUAAADWV412WUHr1q2z44471tu20UYbZdNNNy1vHzRoUIYNG5b27dunTZs2GTJkSKqrq8t3B+jXr1969eqVY445JhdddFFqampyzjnnZPDgweXp/CeffHIuv/zynHnmmTnhhBPywAMP5JZbbsn48ePL+x02bFgGDhyY3XffPXvuuWcuueSSzJ8/P8cff3ySpG3btiusBQAAANZXjRYOrIyLL744TZo0yeGHH56FCxemf//+ufLKK8vtTZs2zT333JNTTjkl1dXV2WijjTJw4MCce+655T49e/bM+PHjc9ppp+XSSy/NFltskauuuir9+/cv9znyyCMze/bsjBgxIjU1Nendu3cmTJhQb5HCFdUCAAAA66uKurq6usYuoihW9v6S0Njco7lY3KMZYM3xGVosPkNZH6zseWij3soQAAAAaHzCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAXXqOHAmDFjsvPOO6dNmzZp06ZNqqurc++995bbFyxYkMGDB2fTTTfNxhtvnMMPPzyzZs2qN8aMGTMyYMCAtGrVKh07dswZZ5yR9957r16fBx98MLvuumsqKyuz9dZbZ9y4ccvVcsUVV2TLLbdMy5Yt06dPnzzxxBP12lemFgAAAFgfNWo4sMUWW+SCCy7I1KlT86c//SkHHHBAvvzlL+cvf/lLkuS0007L3XffnVtvvTWTJ0/OzJkz89WvfrX8+iVLlmTAgAFZtGhRHn300Vx33XUZN25cRowYUe4zffr0DBgwIPvvv3+mTZuWoUOH5sQTT8zEiRPLfW6++eYMGzYsI0eOzFNPPZVddtkl/fv3z+uvv17us6JaAAAAYH1VUVdXV9fYRbxf+/bt85Of/CRf+9rX0qFDh9x444352te+liR5/vnns/3222fKlCnZa6+9cu+99+aLX/xiZs6cmU6dOiVJxo4dm7POOiuzZ89OixYtctZZZ2X8+PF59tlny/s46qijMmfOnEyYMCFJ0qdPn+yxxx65/PLLkyRLly5Nt27dMmTIkJx99tmZO3fuCmtZGbW1tWnbtm3mzp2bNm3arLFjBmvajHN3auwSWIe6j3imsUsA2GD4DC0Wn6GsD1b2PPQTs+bAkiVLctNNN2X+/Pmprq7O1KlTs3jx4vTt27fcZ7vttkv37t0zZcqUJMmUKVOy0047lYOBJOnfv39qa2vLsw+mTJlSb4xlfZaNsWjRokydOrVenyZNmqRv377lPitTS0MWLlyY2traeg8AAAD4pGn0cOCZZ57JxhtvnMrKypx88sm5/fbb06tXr9TU1KRFixZp165dvf6dOnVKTU1NkqSmpqZeMLCsfVnbR/Wpra3Nu+++mzfeeCNLlixpsM/7x1hRLQ0ZPXp02rZtW35069Zt5Q4KAAAArEONHg5su+22mTZtWh5//PGccsopGThwYJ577rnGLmuNGD58eObOnVt+vPLKK41dEgAAACynWWMX0KJFi2y99dZJkt122y1PPvlkLr300hx55JFZtGhR5syZU+8b+1mzZqVz585Jks6dOy93V4FldxB4f58P3lVg1qxZadOmTaqqqtK0adM0bdq0wT7vH2NFtTSksrIylZWVq3A0AAAAYN1r9JkDH7R06dIsXLgwu+22W5o3b55JkyaV21544YXMmDEj1dXVSZLq6uo888wz9e4qcP/996dNmzbp1atXuc/7x1jWZ9kYLVq0yG677Vavz9KlSzNp0qRyn5WpBQAAANZXjTpzYPjw4Tn44IPTvXv3zJs3LzfeeGMefPDBTJw4MW3bts2gQYMybNiwtG/fPm3atMmQIUNSXV1dvjtAv3790qtXrxxzzDG56KKLUlNTk3POOSeDBw8uf2N/8skn5/LLL8+ZZ56ZE044IQ888EBuueWWjB8/vlzHsGHDMnDgwOy+++7Zc889c8kll2T+/Pk5/vjjk2SlagEAAID1VaOGA6+//nqOPfbYvPbaa2nbtm123nnnTJw4MQceeGCS5OKLL06TJk1y+OGHZ+HChenfv3+uvPLK8uubNm2ae+65J6ecckqqq6uz0UYbZeDAgTn33HPLfXr27Jnx48fntNNOy6WXXpotttgiV111Vfr371/uc+SRR2b27NkZMWJEampq0rt370yYMKHeIoUrqgUAAADWVxV1dXV1jV1EUazs/SWhsblHc7G4RzPAmuMztFh8hrI+WNnz0E/cmgMAAADAuiUcAAAAgIITDgAAAEDBCQcAAACg4FYrHDjggAMyZ86c5bbX1tbmgAMO+Lg1AQAAAOvQaoUDDz74YBYtWrTc9gULFuSPf/zjxy4KAAAAWHearUrnP//5z+XfP/fcc6mpqSk/X7JkSSZMmJDNN998zVUHAAAArHWrFA707t07FRUVqaioaPDygaqqqlx22WVrrDgAAABg7VulcGD69Ompq6vLpz71qTzxxBPp0KFDua1Fixbp2LFjmjZtusaLBAAAANaeVQoHevTokSRZunTpWikGAAAAWPdWKRx4vxdffDH/8z//k9dff325sGDEiBEfuzAAAABg3VitcOC//uu/csopp2SzzTZL586dU1FRUW6rqKgQDgAAAMB6ZLXCgR/96Ec5//zzc9ZZZ63pegAAAIB1rMnqvOhf//pXvv71r6/pWgAAAIBGsFrhwNe//vXcd999a7oWAAAAoBGs1mUFW2+9dX7wgx/ksccey0477ZTmzZvXa//Od76zRooDAAAA1r7VCgd+9atfZeONN87kyZMzefLkem0VFRXCAQAAAFiPrFY4MH369DVdBwAAANBIVmvNAQAAAGDDsVozB0444YSPbL/mmmtWqxgAAABg3VutcOBf//pXveeLFy/Os88+mzlz5uSAAw5YI4UBAAAA68ZqhQO33377ctuWLl2aU045JVtttdXHLgoAAABYd9bYmgNNmjTJsGHDcvHFF6+pIQEAAIB1YI0uSPjyyy/nvffeW5NDAgAAAGvZal1WMGzYsHrP6+rq8tprr2X8+PEZOHDgGikMAAAAWDdWKxx4+umn6z1v0qRJOnTokJ/97GcrvJMBAAAA8MmyWuHA//zP/6zpOgAAAIBGslrhwDKzZ8/OCy+8kCTZdttt06FDhzVSFAAAALDurNaChPPnz88JJ5yQLl26ZN99982+++6brl27ZtCgQXnnnXfWdI0AAADAWrRa4cCwYcMyefLk3H333ZkzZ07mzJmTO++8M5MnT853v/vdNV0jAAAAsBat1mUFt912W/77v/87++23X3nbIYcckqqqqhxxxBEZM2bMmqoPAAAAWMtWa+bAO++8k06dOi23vWPHji4rAAAAgPXMaoUD1dXVGTlyZBYsWFDe9u677+aHP/xhqqur11hxAAAAwNq3WpcVXHLJJTnooIOyxRZbZJdddkmS/O///m8qKytz3333rdECAQAAgLVrtcKBnXbaKS+++GJ+85vf5Pnnn0+SHH300fnGN76RqqqqNVogAAAAsHatVjgwevTodOrUKSeddFK97ddcc01mz56ds846a40UBwAAAKx9q7XmwC9/+ctst912y23fYYcdMnbs2I9dFAAAALDurFY4UFNTky5duiy3vUOHDnnttdc+dlEAAADAurNa4UC3bt3yyCOPLLf9kUceSdeuXT92UQAAAMC6s1prDpx00kkZOnRoFi9enAMOOCBJMmnSpJx55pn57ne/u0YLBAAAANau1QoHzjjjjLz55pv59re/nUWLFiVJWrZsmbPOOivDhw9fowUCAAAAa9dqhQMVFRW58MIL84Mf/CB//etfU1VVlW222SaVlZVruj4AAABgLVutcGCZjTfeOHvssceaqgUAAABoBKu1ICEAAACw4RAOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBNWo4MHr06Oyxxx5p3bp1OnbsmMMOOywvvPBCvT4LFizI4MGDs+mmm2bjjTfO4YcfnlmzZtXrM2PGjAwYMCCtWrVKx44dc8YZZ+S9996r1+fBBx/MrrvumsrKymy99dYZN27ccvVcccUV2XLLLdOyZcv06dMnTzzxxCrXAgAAAOubRg0HJk+enMGDB+exxx7L/fffn8WLF6dfv36ZP39+uc9pp52Wu+++O7feemsmT56cmTNn5qtf/Wq5fcmSJRkwYEAWLVqURx99NNddd13GjRuXESNGlPtMnz49AwYMyP77759p06Zl6NChOfHEEzNx4sRyn5tvvjnDhg3LyJEj89RTT2WXXXZJ//798/rrr690LQAAALA+qqirq6tr7CKWmT17djp27JjJkydn3333zdy5c9OhQ4fceOON+drXvpYkef7557P99ttnypQp2WuvvXLvvffmi1/8YmbOnJlOnTolScaOHZuzzjors2fPTosWLXLWWWdl/PjxefbZZ8v7OuqoozJnzpxMmDAhSdKnT5/sscceufzyy5MkS5cuTbdu3TJkyJCcffbZK1XLitTW1qZt27aZO3du2rRps0aPHaxJM87dqbFLYB3qPuKZxi4BYIPhM7RYfIayPljZ89BP1JoDc+fOTZK0b98+STJ16tQsXrw4ffv2LffZbrvt0r1790yZMiVJMmXKlOy0007lYCBJ+vfvn9ra2vzlL38p93n/GMv6LBtj0aJFmTp1ar0+TZo0Sd++fct9VqaWD1q4cGFqa2vrPQAAAOCT5hMTDixdujRDhw7NZz/72ey4445JkpqamrRo0SLt2rWr17dTp06pqakp93l/MLCsfVnbR/Wpra3Nu+++mzfeeCNLlixpsM/7x1hRLR80evTotG3btvzo1q3bSh4NAAAAWHc+MeHA4MGD8+yzz+amm25q7FLWmOHDh2fu3LnlxyuvvNLYJQEAAMBymjV2AUly6qmn5p577slDDz2ULbbYory9c+fOWbRoUebMmVPvG/tZs2alc+fO5T4fvKvAsjsIvL/PB+8qMGvWrLRp0yZVVVVp2rRpmjZt2mCf94+xolo+qLKyMpWVlatwJAAAAGDda9SZA3V1dTn11FNz++2354EHHkjPnj3rte+2225p3rx5Jk2aVN72wgsvZMaMGamurk6SVFdX55lnnql3V4H7778/bdq0Sa9evcp93j/Gsj7LxmjRokV22223en2WLl2aSZMmlfusTC0AAACwPmrUmQODBw/OjTfemDvvvDOtW7cuX7vftm3bVFVVpW3bthk0aFCGDRuW9u3bp02bNhkyZEiqq6vLdwfo169fevXqlWOOOSYXXXRRampqcs4552Tw4MHlb+1PPvnkXH755TnzzDNzwgkn5IEHHsgtt9yS8ePHl2sZNmxYBg4cmN133z177rlnLrnkksyfPz/HH398uaYV1QIAAADro0YNB8aMGZMk2W+//eptv/baa3PcccclSS6++OI0adIkhx9+eBYuXJj+/fvnyiuvLPdt2rRp7rnnnpxyyimprq7ORhttlIEDB+bcc88t9+nZs2fGjx+f0047LZdeemm22GKLXHXVVenfv3+5z5FHHpnZs2dnxIgRqampSe/evTNhwoR6ixSuqBYAAABYH1XU1dXVNXYRRbGy95eExuYezcXiHs0Aa47P0GLxGcr6YGXPQz8xdysAAAAAGodwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQME1ajjw0EMP5dBDD03Xrl1TUVGRO+64o157XV1dRowYkS5duqSqqip9+/bNiy++WK/PW2+9lW984xtp06ZN2rVrl0GDBuXtt9+u1+fPf/5zPve5z6Vly5bp1q1bLrroouVqufXWW7PddtulZcuW2WmnnfL73/9+lWsBAACA9VGjhgPz58/PLrvskiuuuKLB9osuuii/+MUvMnbs2Dz++OPZaKON0r9//yxYsKDc5xvf+Eb+8pe/5P77788999yThx56KN/61rfK7bW1tenXr1969OiRqVOn5ic/+UlGjRqVX/3qV+U+jz76aI4++ugMGjQoTz/9dA477LAcdthhefbZZ1epFgAAAFgfVdTV1dU1dhFJUlFRkdtvvz2HHXZYktI39V27ds13v/vdnH766UmSuXPnplOnThk3blyOOuqo/PWvf02vXr3y5JNPZvfdd0+STJgwIYccckheffXVdO3aNWPGjMn3v//91NTUpEWLFkmSs88+O3fccUeef/75JMmRRx6Z+fPn55577inXs9dee6V3794ZO3bsStWyMmpra9O2bdvMnTs3bdq0WSPHDdaGGefu1NglsA51H/FMY5cAsMHwGVosPkNZH6zseegnds2B6dOnp6amJn379i1va9u2bfr06ZMpU6YkSaZMmZJ27dqVg4Ek6du3b5o0aZLHH3+83GffffctBwNJ0r9//7zwwgv517/+Ve7z/v0s67NsPytTS0MWLlyY2traeg8AAAD4pPnEhgM1NTVJkk6dOtXb3qlTp3JbTU1NOnbsWK+9WbNmad++fb0+DY3x/n18WJ/3t6+oloaMHj06bdu2LT+6deu2gncNAAAA694nNhzYEAwfPjxz584tP1555ZXGLgkAAACW84kNBzp37pwkmTVrVr3ts2bNKrd17tw5r7/+er329957L2+99Va9Pg2N8f59fFif97evqJaGVFZWpk2bNvUeAAAA8EnziQ0Hevbsmc6dO2fSpEnlbbW1tXn88cdTXV2dJKmurs6cOXMyderUcp8HHnggS5cuTZ8+fcp9HnrooSxevLjc5/7778+2226bTTbZpNzn/ftZ1mfZflamFgAAAFhfNWo48Pbbb2fatGmZNm1aktLCf9OmTcuMGTNSUVGRoUOH5kc/+lHuuuuuPPPMMzn22GPTtWvX8h0Ntt9++xx00EE56aST8sQTT+SRRx7JqaeemqOOOipdu3ZNkvzHf/xHWrRokUGDBuUvf/lLbr755lx66aUZNmxYuY7/9//+XyZMmJCf/exnef755zNq1Kj86U9/yqmnnpokK1ULAAAArK+aNebO//SnP2X//fcvP192wj5w4MCMGzcuZ555ZubPn59vfetbmTNnTvbZZ59MmDAhLVu2LL/mN7/5TU499dR84QtfSJMmTXL44YfnF7/4Rbm9bdu2ue+++zJ48ODstttu2WyzzTJixIh861vfKvfZe++9c+ONN+acc87J9773vWyzzTa54447suOOO5b7rEwtAAAAsD6qqKurq2vsIopiZe8vCY3NPZqLxT2aAdYcn6HF4jOU9cHKnod+YtccAAAAANYN4QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABSccAAAAAAKTjgAAAAABSccAAAAgIITDgAAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAAAAoOCEAwAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOMAGbcmSJfnBD36Qnj17pqqqKltttVXOO++81NXVlfv87ne/S79+/bLpppumoqIi06ZNW26c//zP/8xWW22VqqqqdOjQIV/+8pfz/PPP1+vzne98J7vttlsqKyvTu3fvtfzOAAAA1hzhABu0Cy+8MGPGjMnll1+ev/71r7nwwgtz0UUX5bLLLiv3mT9/fvbZZ59ceOGFHzrObrvtlmuvvTZ//etfM3HixNTV1aVfv35ZsmRJvX4nnHBCjjzyyLX2fgAAANaGZo1dAKxNjz76aL785S9nwIABSZItt9wyv/3tb/PEE0+U+xxzzDFJkr///e8fOs63vvWt8u+33HLL/OhHP8ouu+ySv//979lqq62SJL/4xS+SJLNnz86f//znNf1WAAAA1hozB9ig7b333pk0aVL+9re/JUn+93//Nw8//HAOPvjg1R5z/vz5ufbaa9OzZ89069ZtTZUKAADQaMwcYIN29tlnp7a2Ntttt12aNm2aJUuW5Pzzz883vvGNVR7ryiuvzJlnnpn58+dn2223zf33358WLVqshaoBAADWLTMH2KDdcsst+c1vfpMbb7wxTz31VK677rr89Kc/zXXXXbfKY33jG9/I008/ncmTJ+fTn/50jjjiiCxYsGAtVA2sjDFjxmTnnXdOmzZt0qZNm1RXV+fee+9NUrpMqKKiosHHrbfeWh6jofabbrqpwf098sgjadasmQVHAYANkpkDbNDOOOOMnH322TnqqKOSJDvttFP+8Y9/ZPTo0Rk4cOAqjdW2bdu0bds222yzTfbaa69ssskmuf3223P00UevjdKBFdhiiy1ywQUXZJtttkldXV2uu+66fPnLX87TTz+d7bbbLq+99lq9/r/61a/yk5/8ZLnLiq699tocdNBB5eft2rVbbl9z5szJsccemy984QuZNWvWWnk/AACNSTjABu2dd95Jkyb1J8g0bdo0S5cu/Vjj1tXVpa6uLgsXLvxY4wCr79BDD633/Pzzz8+YMWPy2GOPZYcddkjnzp3rtd9+++054ogjsvHGG9fb3q5du+X6ftDJJ5+c//iP/0jTpk1zxx13rJH6AQA+SVxWwAbt0EMPzfnnn5/x48fn73//e26//fb8/Oc/z1e+8pVyn7feeivTpk3Lc889lyR54YUXMm3atNTU1CRJ/u///i+jR4/O1KlTM2PGjDz66KP5+te/nqqqqhxyyCHlcV566aXy6959991MmzYt06ZNy6JFi9btm4YCWrJkSW666abMnz8/1dXVy7VPnTo106ZNy6BBg5ZrGzx4cDbbbLPsueeeueaaa1JXV1ev/dprr83//d//ZeTIkWutfgCAxiYcYIN22WWX5Wtf+1q+/e1vZ/vtt8/pp5+e//zP/8x5551X7nPXXXflM5/5TPl2h0cddVQ+85nPZOzYsUmSli1b5o9//GMOOeSQbL311jnyyCPTunXrPProo+nYsWN5nBNPPDGf+cxn8stf/jJ/+9vf8pnPfCaf+cxnMnPmzHX7pqFAnnnmmWy88caprKzMySefnNtvvz29evVart/VV1+d7bffPnvvvXe97eeee25uueWW3H///Tn88MPz7W9/O5dddlm5/cUXX8zZZ5+dG264Ic2amWwHK+uKK67IlltumZYtW6ZPnz71biEMrN/8fG+4Kuo++BUJa01tbW3atm2buXPnpk2bNo1dDnyoGefu1NglsA51H/FMY5ew2hYtWpQZM2Zk7ty5+e///u9cddVVmTx5cr2A4N13302XLl3ygx/8IN/97nc/crwRI0bk2muvzSuvvJIlS5Zkr732yqBBg3LyyScnSUaNGpU77rgj06ZNW5tvC9ZrN998c4499tiMHTs2ffr0ySWXXJJbb701L7zwQr1QfUPlM7RY1ufP0NVR9J/v9dXKnocKB9Yh4QDrC/+xKZYN6T82ffv2zVZbbZVf/vKX5W3XX399Bg0alH/+85/p0KHDR75+/Pjx+eIXv5gFCxbk3XffzSabbJKmTZuW25cuXZq6uro0bdo09913Xw444IC19l5gfdWnT5/sscceufzyy5OUfm66deuWIUOG5Oyzz27k6tY+n6HFsiF9hq6Mov98r69W9jzUHEkANhhLly5dbqHQq6++Ol/60pdWGAwkybRp07LJJpuksrIyzZs3zzPP1P9P35VXXpkHHngg//3f/52ePXuu0dphQ7Bo0aJMnTo1w4cPL29r0qRJ+vbtmylTpjRiZcDH5ed7wyccYKXsdsavG7sE1qHbWzd2BbBiw4cPz8EHH5zu3btn3rx5ufHGG/Pggw9m4sSJ5T4vvfRSHnroofz+979f7vV33313Zs2alb322istW7bM/fffnx//+Mc5/fTTk5T+w7PjjjvWe03Hjh3TsmXL5bYDJW+88UaWLFmSTp061dveqVOnPP/8841UFbAm+Pne8AkHAFgvvf766zn22GPz2muvpW3bttl5550zceLEHHjggeU+11xzTbbYYov069dvudc3b948V1xxRU477bTU1dVl6623zs9//vOcdNJJ6/JtAAB8IggHAFgvXX311Svs8+Mf/zg//vGPG2w76KCDctBBB63SPkeNGpVRo0at0mugSDbbbLM0bdo0s2bNqrd91qxZ6dy5cyNVBawJfr43fG5lCADAGtGiRYvstttumTRpUnnb0qVLM2nSpFRXVzdiZcDH5ed7w2fmAAAAa8ywYcMycODA7L777tlzzz1zySWXZP78+Tn++OMbuzTgY/LzvWETDqyiK664Ij/5yU9SU1OTXXbZJZdddln23HPPxi4LYLV99rLPNnYJrEOPDHmksUtgA3fkkUdm9uzZGTFiRGpqatK7d+9MmDBhuUXMgPWPn+8Nm8sKVsHNN9+cYcOGZeTIkXnqqaeyyy67pH///nn99dcbuzQAgE+MU089Nf/4xz+ycOHCPP744+nTp09jlwSsIX6+N1zCgVWwbBXr448/Pr169crYsWPTqlWrXHPNNY1dGgAAAKw2lxWspEWLFmXq1KkZPnx4eVuTJk3St2/fTJkypcHXLFy4MAsXLiw/nzt3bpKktrZ27Ra7FixZ+G5jl8A6NK/5ksYugXXovXffa+wSWIfWx88gWJ/MW+AztEj8m8r6YNnf07q6uo/sJxxYSW+88UaWLFmy3PU0nTp1yvPPP9/ga0aPHp0f/vCHy23v1q3bWqkR1pQdG7sAYK1pe1bbxi4BYMMx2r+prD/mzZuXtm0//O+scGAtGj58eIYNG1Z+vnTp0rz11lvZdNNNU1FR0YiVAR9UW1ubbt265ZVXXkmbNm0auxwAWG/4DIVPtrq6usybNy9du3b9yH7CgZW02WabpWnTppk1a1a97bNmzUrnzp0bfE1lZWUqKyvrbWvXrt3aKhFYA9q0aeM/NgCwGnyGwifXR80YWMaChCupRYsW2W233TJp0qTytqVLl2bSpEmprq5uxMoAAADg4zFzYBUMGzYsAwcOzO67754999wzl1xySebPn5/jjz++sUsDAACA1SYcWAVHHnlkZs+enREjRqSmpia9e/fOhAkTllukEFj/VFZWZuTIkctdCgQAfDSfobBhqKhb0f0MAAAAgA2aNQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAKLyHHnoohx56aLp27ZqKiorccccdjV0SAHzijR49OnvssUdat26djh075rDDDssLL7zQ2GUBq0k4ABTe/Pnzs8suu+SKK65o7FIAYL0xefLkDB48OI899ljuv//+LF68OP369cv8+fMbuzRgNbiVIcD7VFRU5Pbbb89hhx3W2KUAwHpl9uzZ6dixYyZPnpx99923scsBVpGZAwAAwMc2d+7cJEn79u0buRJgdQgHAACAj2Xp0qUZOnRoPvvZz2bHHXds7HKA1dCssQsAAADWb4MHD86zzz6bhx9+uLFLAVaTcAAAAFhtp556au6555489NBD2WKLLRq7HGA1CQcAAIBVVldXlyFDhuT222/Pgw8+mJ49ezZ2ScDHIBwACu/tt9/OSy+9VH4+ffr0TJs2Le3bt0/37t0bsTIA+OQaPHhwbrzxxtx5551p3bp1ampqkiRt27ZNVVVVI1cHrCq3MgQK78EHH8z++++/3PaBAwdm3Lhx674gAFgPVFRUNLj92muvzXHHHbduiwE+NuEAAAAAFJxbGQIAAEDBCQcAAACg4IQDAAAAUHDCAQAAACg44QAAAAAUnHAAAAAACk44AAAAAAUnHAAAAICCEw4AAABAwQkHAIBGMXv27Jxyyinp3r17Kisr07lz5/Tv3z+PPPJIkmTLLbdMRUXFco8LLrggSfL73/8+LVq0yFNPPVVv3J/97GfZbLPNUlNTs87fEwCsr5o1dgEAQDEdfvjhWbRoUa677rp86lOfyqxZszJp0qS8+eab5T7nnntuTjrppHqva926dZLkkEMOybHHHptjjz02U6dOTWVlZZ577rmcc845GTduXDp37rxO3w8ArM8q6urq6hq7CACgWObMmZNNNtkkDz74YD7/+c832GfLLbfM0KFDM3To0A8dZ968edlpp51y1FFH5Uc/+lGqq6vTs2fP3HLLLWupcgDYMJk5AACscxtvvHE23njj3HHHHdlrr71SWVm5WuO0bt0611xzTfr375/p06fnlVdeyYQJE9ZwtQCw4TNzAABoFLfddltOOumkvPvuu9l1113z+c9/PkcddVR23nnnJKWZA6+99lqaN29e73X33ntvPve5z9XbdvTRR+emm27KzTffnCOOOGKdvQcA2FAIBwCARrNgwYL88Y9/zGOPPZZ77703TzzxRK666qocd9xx2XLLLfPNb34zxx13XL3XbL755qmqqio//+c//5kdd9wxixYtygknnJDLLrtsHb8LAFj/CQcAgE+ME088Mffff3/+8Y9/rNSaA0lpYcJFixbl+9//fg488MBMmjTpQ9cxAAAa5laGAMAnRq9evTJ//vyV7n/VVVfl4YcfztVXX539998/p5xySk444YRVGgMAEA4AAI3gzTffzAEHHJAbbrghf/7znzN9+vTceuutueiii/LlL3+53G/evHmpqamp96itrU2S/OMf/8iwYcPy05/+ND169EiSXHjhhamoqMjZZ5/dKO8LANZXLisAANa5hQsXZtSoUbnvvvvy8ssvZ/HixenWrVu+/vWv53vf+16qqqqy5ZZb5h//+Mdyr/3P//zPjBkzJgceeGCaNm2aiRMn1mt/+OGHs99++7m8AABWgXAAAAAACs5lBQAAAFBwwgEAAAAoOOEAAAAAFJxwAAAAAApOOAAAAAAFJxwAAACAghMOAAAAQMEJBwAAAKDghAMAAABQcMIBAAAAKDjhAAAAABTc/wewVPKuB+GOCwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "ax=sns.countplot(x=\"SEX\",hue=\"PREGNANT\",data=covid)\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)\n",
    "plt.title(\"PREGNANCIES - SEX\",color=\"red\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     892534\n",
       "1     140038\n",
       "99     16003\n",
       "Name: PNEUMONIA, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['PNEUMONIA'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30     27010\n",
       "31     25927\n",
       "28     25313\n",
       "29     25134\n",
       "34     24872\n",
       "       ...  \n",
       "114        2\n",
       "116        2\n",
       "111        1\n",
       "121        1\n",
       "113        1\n",
       "Name: AGE, Length: 121, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['AGE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     920248\n",
       "1     124989\n",
       "98      3338\n",
       "Name: DIABETES, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['DIABETES'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     1030510\n",
       "1       15062\n",
       "98       3003\n",
       "Name: COPD, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['COPD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     1014024\n",
       "1       31572\n",
       "98       2979\n",
       "Name: ASTHMA, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['ASTHMA'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     1031001\n",
       "1       14170\n",
       "98       3404\n",
       "Name: INMSUPR, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['INMSUPR'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     882742\n",
       "1     162729\n",
       "98      3104\n",
       "Name: HIPERTENSION, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['HIPERTENSION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     1024730\n",
       "1       20769\n",
       "98       3076\n",
       "Name: CARDIOVASCULAR, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['CARDIOVASCULAR'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     1015490\n",
       "1       28040\n",
       "98       5045\n",
       "Name: OTHER_DISEASE, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['OTHER_DISEASE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     885727\n",
       "1     159816\n",
       "98      3032\n",
       "Name: OBESITY, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['OBESITY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     1026665\n",
       "1       18904\n",
       "98       3006\n",
       "Name: RENAL_CHRONIC, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['RENAL_CHRONIC'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     960979\n",
       "1      84376\n",
       "98      3220\n",
       "Name: TOBACCO, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['TOBACCO'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    499250\n",
       "3    381527\n",
       "6    128133\n",
       "5     26091\n",
       "1      8601\n",
       "4      3122\n",
       "2      1851\n",
       "Name: CLASSIFICATION_FINAL, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['CLASSIFICATION_FINAL'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97    848544\n",
       "2     175685\n",
       "1      16858\n",
       "99      7488\n",
       "Name: ICU, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['ICU'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see too many missing data, so we will drop this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid.drop(columns='ICU',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1048575 entries, 0 to 1048574\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count    Dtype\n",
      "---  ------                --------------    -----\n",
      " 0   USMER                 1048575 non-null  int64\n",
      " 1   MEDICAL_UNIT          1048575 non-null  int64\n",
      " 2   SEX                   1048575 non-null  int64\n",
      " 3   PATIENT_TYPE          1048575 non-null  int64\n",
      " 4   PNEUMONIA             1048575 non-null  int64\n",
      " 5   AGE                   1048575 non-null  int64\n",
      " 6   PREGNANT              1048575 non-null  int64\n",
      " 7   DIABETES              1048575 non-null  int64\n",
      " 8   COPD                  1048575 non-null  int64\n",
      " 9   ASTHMA                1048575 non-null  int64\n",
      " 10  INMSUPR               1048575 non-null  int64\n",
      " 11  HIPERTENSION          1048575 non-null  int64\n",
      " 12  OTHER_DISEASE         1048575 non-null  int64\n",
      " 13  CARDIOVASCULAR        1048575 non-null  int64\n",
      " 14  OBESITY               1048575 non-null  int64\n",
      " 15  RENAL_CHRONIC         1048575 non-null  int64\n",
      " 16  TOBACCO               1048575 non-null  int64\n",
      " 17  CLASSIFICATION_FINAL  1048575 non-null  int64\n",
      " 18  DEATH                 1048575 non-null  int64\n",
      "dtypes: int64(19)\n",
      "memory usage: 152.0 MB\n"
     ]
    }
   ],
   "source": [
    "covid.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing the missing rows\n",
    "We will remove the missing rows which have very less number of missing data compared to the original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid = covid[(covid.PNEUMONIA == 1) | (covid.PNEUMONIA == 2)]\n",
    "covid = covid[(covid.DIABETES == 1) | (covid.DIABETES == 2)]\n",
    "covid = covid[(covid.COPD == 1) | (covid.COPD == 2)]\n",
    "covid = covid[(covid.ASTHMA == 1) | (covid.ASTHMA == 2)]\n",
    "covid = covid[(covid.INMSUPR == 1) | (covid.INMSUPR == 2)]\n",
    "covid = covid[(covid.HIPERTENSION == 1) | (covid.HIPERTENSION == 2)]\n",
    "covid = covid[(covid.OTHER_DISEASE == 1) | (covid.OTHER_DISEASE == 2)]\n",
    "covid = covid[(covid.CARDIOVASCULAR == 1) | (covid.CARDIOVASCULAR == 2)]\n",
    "covid = covid[(covid.OBESITY == 1) | (covid.OBESITY == 2)]\n",
    "covid = covid[(covid.RENAL_CHRONIC == 1) | (covid.RENAL_CHRONIC == 2)]\n",
    "covid = covid[(covid.TOBACCO == 1) | (covid.TOBACCO == 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1025152 entries, 0 to 1048574\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count    Dtype\n",
      "---  ------                --------------    -----\n",
      " 0   USMER                 1025152 non-null  int64\n",
      " 1   MEDICAL_UNIT          1025152 non-null  int64\n",
      " 2   SEX                   1025152 non-null  int64\n",
      " 3   PATIENT_TYPE          1025152 non-null  int64\n",
      " 4   PNEUMONIA             1025152 non-null  int64\n",
      " 5   AGE                   1025152 non-null  int64\n",
      " 6   PREGNANT              1025152 non-null  int64\n",
      " 7   DIABETES              1025152 non-null  int64\n",
      " 8   COPD                  1025152 non-null  int64\n",
      " 9   ASTHMA                1025152 non-null  int64\n",
      " 10  INMSUPR               1025152 non-null  int64\n",
      " 11  HIPERTENSION          1025152 non-null  int64\n",
      " 12  OTHER_DISEASE         1025152 non-null  int64\n",
      " 13  CARDIOVASCULAR        1025152 non-null  int64\n",
      " 14  OBESITY               1025152 non-null  int64\n",
      " 15  RENAL_CHRONIC         1025152 non-null  int64\n",
      " 16  TOBACCO               1025152 non-null  int64\n",
      " 17  CLASSIFICATION_FINAL  1025152 non-null  int64\n",
      " 18  DEATH                 1025152 non-null  int64\n",
      "dtypes: int64(19)\n",
      "memory usage: 156.4 MB\n"
     ]
    }
   ],
   "source": [
    "covid.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data\n",
    "Now we will derive some relations between death and other factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEATH                   1.000000\n",
       "PNEUMONIA               0.470800\n",
       "DIABETES                0.217115\n",
       "HIPERTENSION            0.205158\n",
       "CLASSIFICATION_FINAL    0.197313\n",
       "MEDICAL_UNIT            0.154588\n",
       "RENAL_CHRONIC           0.119396\n",
       "USMER                   0.118973\n",
       "COPD                    0.089846\n",
       "CARDIOVASCULAR          0.076582\n",
       "OTHER_DISEASE           0.057247\n",
       "OBESITY                 0.056427\n",
       "INMSUPR                 0.049308\n",
       "PREGNANT                0.011433\n",
       "TOBACCO                 0.005245\n",
       "ASTHMA                 -0.017221\n",
       "SEX                    -0.080882\n",
       "AGE                    -0.322412\n",
       "PATIENT_TYPE           -0.520023\n",
       "Name: DEATH, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.corr()['DEATH'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.histplot(x=covid['AGE'])\n",
    "plt.savefig('age.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "sns.heatmap(covid.corr(),annot=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "1. The highly correlated factors with death are the ones with which are shaded with dark colour(correlation factor less than -0.2) or the ones shaded with light colours(correlation factor greater than 0.2).\n",
    "2. We will keep only these shades as they matter the most with death and drop the remaining columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "unrelevant_columns = [\"SEX\",\"PREGNANT\",\"COPD\",\"ASTHMA\",\"INMSUPR\",\"OTHER_DISEASE\",\"CARDIOVASCULAR\",\n",
    "                      \"OBESITY\",\"TOBACCO\"]\n",
    "\n",
    "covid.drop(columns=unrelevant_columns,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>PATIENT_TYPE</th>\n",
       "      <th>PNEUMONIA</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIABETES</th>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <th>CLASSIFICATION_FINAL</th>\n",
       "      <th>DEATH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.025152e+06</td>\n",
       "      <td>1.025152e+06</td>\n",
       "      <td>1.025152e+06</td>\n",
       "      <td>1.025152e+06</td>\n",
       "      <td>1.025152e+06</td>\n",
       "      <td>1.025152e+06</td>\n",
       "      <td>1.025152e+06</td>\n",
       "      <td>1.025152e+06</td>\n",
       "      <td>1.025152e+06</td>\n",
       "      <td>1.025152e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.642105e+00</td>\n",
       "      <td>8.993643e+00</td>\n",
       "      <td>1.187191e+00</td>\n",
       "      <td>1.865777e+00</td>\n",
       "      <td>4.188778e+01</td>\n",
       "      <td>1.880588e+00</td>\n",
       "      <td>1.844338e+00</td>\n",
       "      <td>1.982099e+00</td>\n",
       "      <td>5.291136e+00</td>\n",
       "      <td>1.927119e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.793813e-01</td>\n",
       "      <td>3.722439e+00</td>\n",
       "      <td>3.900648e-01</td>\n",
       "      <td>3.408919e-01</td>\n",
       "      <td>1.674833e+01</td>\n",
       "      <td>3.242723e-01</td>\n",
       "      <td>3.625346e-01</td>\n",
       "      <td>1.325909e-01</td>\n",
       "      <td>1.889586e+00</td>\n",
       "      <td>2.599410e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.210000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              USMER  MEDICAL_UNIT  PATIENT_TYPE     PNEUMONIA           AGE  \\\n",
       "count  1.025152e+06  1.025152e+06  1.025152e+06  1.025152e+06  1.025152e+06   \n",
       "mean   1.642105e+00  8.993643e+00  1.187191e+00  1.865777e+00  4.188778e+01   \n",
       "std    4.793813e-01  3.722439e+00  3.900648e-01  3.408919e-01  1.674833e+01   \n",
       "min    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  0.000000e+00   \n",
       "25%    1.000000e+00  4.000000e+00  1.000000e+00  2.000000e+00  3.000000e+01   \n",
       "50%    2.000000e+00  1.200000e+01  1.000000e+00  2.000000e+00  4.000000e+01   \n",
       "75%    2.000000e+00  1.200000e+01  1.000000e+00  2.000000e+00  5.300000e+01   \n",
       "max    2.000000e+00  1.300000e+01  2.000000e+00  2.000000e+00  1.210000e+02   \n",
       "\n",
       "           DIABETES  HIPERTENSION  RENAL_CHRONIC  CLASSIFICATION_FINAL  \\\n",
       "count  1.025152e+06  1.025152e+06   1.025152e+06          1.025152e+06   \n",
       "mean   1.880588e+00  1.844338e+00   1.982099e+00          5.291136e+00   \n",
       "std    3.242723e-01  3.625346e-01   1.325909e-01          1.889586e+00   \n",
       "min    1.000000e+00  1.000000e+00   1.000000e+00          1.000000e+00   \n",
       "25%    2.000000e+00  2.000000e+00   2.000000e+00          3.000000e+00   \n",
       "50%    2.000000e+00  2.000000e+00   2.000000e+00          6.000000e+00   \n",
       "75%    2.000000e+00  2.000000e+00   2.000000e+00          7.000000e+00   \n",
       "max    2.000000e+00  2.000000e+00   2.000000e+00          7.000000e+00   \n",
       "\n",
       "              DEATH  \n",
       "count  1.025152e+06  \n",
       "mean   1.927119e+00  \n",
       "std    2.599410e-01  \n",
       "min    1.000000e+00  \n",
       "25%    2.000000e+00  \n",
       "50%    2.000000e+00  \n",
       "75%    2.000000e+00  \n",
       "max    2.000000e+00  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid = pd.get_dummies(covid,columns=[\"MEDICAL_UNIT\",\"CLASSIFICATION_FINAL\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler = RobustScaler()\n",
    "# covid.AGE = scaler.fit_transform(covid.AGE.values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1025152 entries, 0 to 1048574\n",
      "Data columns (total 26 columns):\n",
      " #   Column                  Non-Null Count    Dtype\n",
      "---  ------                  --------------    -----\n",
      " 0   USMER                   1025152 non-null  int64\n",
      " 1   PATIENT_TYPE            1025152 non-null  int64\n",
      " 2   PNEUMONIA               1025152 non-null  int64\n",
      " 3   AGE                     1025152 non-null  int64\n",
      " 4   DIABETES                1025152 non-null  int64\n",
      " 5   HIPERTENSION            1025152 non-null  int64\n",
      " 6   RENAL_CHRONIC           1025152 non-null  int64\n",
      " 7   DEATH                   1025152 non-null  int64\n",
      " 8   MEDICAL_UNIT_2          1025152 non-null  uint8\n",
      " 9   MEDICAL_UNIT_3          1025152 non-null  uint8\n",
      " 10  MEDICAL_UNIT_4          1025152 non-null  uint8\n",
      " 11  MEDICAL_UNIT_5          1025152 non-null  uint8\n",
      " 12  MEDICAL_UNIT_6          1025152 non-null  uint8\n",
      " 13  MEDICAL_UNIT_7          1025152 non-null  uint8\n",
      " 14  MEDICAL_UNIT_8          1025152 non-null  uint8\n",
      " 15  MEDICAL_UNIT_9          1025152 non-null  uint8\n",
      " 16  MEDICAL_UNIT_10         1025152 non-null  uint8\n",
      " 17  MEDICAL_UNIT_11         1025152 non-null  uint8\n",
      " 18  MEDICAL_UNIT_12         1025152 non-null  uint8\n",
      " 19  MEDICAL_UNIT_13         1025152 non-null  uint8\n",
      " 20  CLASSIFICATION_FINAL_2  1025152 non-null  uint8\n",
      " 21  CLASSIFICATION_FINAL_3  1025152 non-null  uint8\n",
      " 22  CLASSIFICATION_FINAL_4  1025152 non-null  uint8\n",
      " 23  CLASSIFICATION_FINAL_5  1025152 non-null  uint8\n",
      " 24  CLASSIFICATION_FINAL_6  1025152 non-null  uint8\n",
      " 25  CLASSIFICATION_FINAL_7  1025152 non-null  uint8\n",
      "dtypes: int64(8), uint8(18)\n",
      "memory usage: 88.0 MB\n"
     ]
    }
   ],
   "source": [
    "covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    833253\n",
       "2    191899\n",
       "Name: PATIENT_TYPE, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid[\"PATIENT_TYPE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEATH                     1.000000\n",
       "PNEUMONIA                 0.470800\n",
       "DIABETES                  0.217115\n",
       "HIPERTENSION              0.205158\n",
       "CLASSIFICATION_FINAL_7    0.172424\n",
       "MEDICAL_UNIT_12           0.150388\n",
       "RENAL_CHRONIC             0.119396\n",
       "USMER                     0.118973\n",
       "MEDICAL_UNIT_9            0.027839\n",
       "CLASSIFICATION_FINAL_5    0.017371\n",
       "CLASSIFICATION_FINAL_6    0.014901\n",
       "CLASSIFICATION_FINAL_4    0.005627\n",
       "MEDICAL_UNIT_7            0.003410\n",
       "MEDICAL_UNIT_2            0.002287\n",
       "MEDICAL_UNIT_11          -0.000004\n",
       "MEDICAL_UNIT_13          -0.000100\n",
       "MEDICAL_UNIT_3           -0.002724\n",
       "MEDICAL_UNIT_5           -0.003462\n",
       "MEDICAL_UNIT_8           -0.016079\n",
       "MEDICAL_UNIT_10          -0.034569\n",
       "MEDICAL_UNIT_6           -0.055971\n",
       "MEDICAL_UNIT_4           -0.139503\n",
       "CLASSIFICATION_FINAL_2   -0.149625\n",
       "CLASSIFICATION_FINAL_3   -0.179366\n",
       "AGE                      -0.322412\n",
       "PATIENT_TYPE             -0.520023\n",
       "Name: DEATH, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.corr()['DEATH'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USMER                       2\n",
       "PATIENT_TYPE                2\n",
       "PNEUMONIA                   2\n",
       "AGE                       121\n",
       "DIABETES                    2\n",
       "HIPERTENSION                2\n",
       "RENAL_CHRONIC               2\n",
       "DEATH                       2\n",
       "MEDICAL_UNIT_2              2\n",
       "MEDICAL_UNIT_3              2\n",
       "MEDICAL_UNIT_4              2\n",
       "MEDICAL_UNIT_5              2\n",
       "MEDICAL_UNIT_6              2\n",
       "MEDICAL_UNIT_7              2\n",
       "MEDICAL_UNIT_8              2\n",
       "MEDICAL_UNIT_9              2\n",
       "MEDICAL_UNIT_10             2\n",
       "MEDICAL_UNIT_11             2\n",
       "MEDICAL_UNIT_12             2\n",
       "MEDICAL_UNIT_13             2\n",
       "CLASSIFICATION_FINAL_2      2\n",
       "CLASSIFICATION_FINAL_3      2\n",
       "CLASSIFICATION_FINAL_4      2\n",
       "CLASSIFICATION_FINAL_5      2\n",
       "CLASSIFICATION_FINAL_6      2\n",
       "CLASSIFICATION_FINAL_7      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=covid\n",
    "a = temp.drop(columns=\"PATIENT_TYPE\")\n",
    "b = temp[\"PATIENT_TYPE\"]\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "rus = RandomUnderSampler(random_state=0)\n",
    "a,b = rus.fit_resample(a,b)\n",
    "covid[\"PATIENT_TYPE\"]=b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    186766\n",
       "2.0    186349\n",
       "Name: PATIENT_TYPE, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid[\"PATIENT_TYPE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid[\"PATIENT_TYPE\"]=covid[\"PATIENT_TYPE\"].fillna(0).astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    652037\n",
       "1    186766\n",
       "2    186349\n",
       "Name: PATIENT_TYPE, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid[\"PATIENT_TYPE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid = covid[(covid.PATIENT_TYPE == 1) | (covid.PATIENT_TYPE== 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEATH                     1.000000\n",
       "PNEUMONIA                 0.446630\n",
       "PATIENT_TYPE              0.267344\n",
       "DIABETES                  0.242937\n",
       "HIPERTENSION              0.239950\n",
       "CLASSIFICATION_FINAL_7    0.165679\n",
       "RENAL_CHRONIC             0.134944\n",
       "USMER                     0.085138\n",
       "CLASSIFICATION_FINAL_6    0.067848\n",
       "MEDICAL_UNIT_3            0.035023\n",
       "MEDICAL_UNIT_5            0.018881\n",
       "CLASSIFICATION_FINAL_5    0.016693\n",
       "MEDICAL_UNIT_7            0.012516\n",
       "MEDICAL_UNIT_2            0.006409\n",
       "MEDICAL_UNIT_4            0.006290\n",
       "CLASSIFICATION_FINAL_4   -0.003697\n",
       "MEDICAL_UNIT_6           -0.017901\n",
       "MEDICAL_UNIT_8           -0.144212\n",
       "CLASSIFICATION_FINAL_2   -0.155444\n",
       "CLASSIFICATION_FINAL_3   -0.199272\n",
       "AGE                      -0.419433\n",
       "MEDICAL_UNIT_9                 NaN\n",
       "MEDICAL_UNIT_10                NaN\n",
       "MEDICAL_UNIT_11                NaN\n",
       "MEDICAL_UNIT_12                NaN\n",
       "MEDICAL_UNIT_13                NaN\n",
       "Name: DEATH, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.corr()['DEATH'].sort_values(ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the data into test and training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = covid.drop(columns=\"DEATH\")\n",
    "y = covid[\"DEATH\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "rus = RandomUnderSampler(random_state=0)\n",
    "x,y = rus.fit_resample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train_x : (77188, 25)\n",
      "Test_x : (19298, 25)\n",
      "Train_y : (77188,)\n",
      "Test_y : (19298,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_x, test_x, train_y, test_y = train_test_split(x,y, test_size=0.2, random_state=42)\n",
    "print(\"Train_x :\",train_x.shape)\n",
    "print(\"Test_x :\",test_x.shape)\n",
    "print(\"Train_y :\",train_y.shape)\n",
    "print(\"Test_y :\",test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       USMER  PATIENT_TYPE  PNEUMONIA  AGE  DIABETES  HIPERTENSION  \\\n",
      "14562      1             1          2   71         2             1   \n",
      "72248      1             1          2   38         2             2   \n",
      "30338      2             1          2   77         1             1   \n",
      "67646      1             2          1   26         2             2   \n",
      "66223      1             1          2   32         2             2   \n",
      "...      ...           ...        ...  ...       ...           ...   \n",
      "39546      1             1          1   50         1             2   \n",
      "64880      2             1          2   46         2             2   \n",
      "42813      1             2          1   63         2             1   \n",
      "43681      2             2          1   59         2             1   \n",
      "58850      1             2          2   46         2             2   \n",
      "\n",
      "       RENAL_CHRONIC  MEDICAL_UNIT_2  MEDICAL_UNIT_3  MEDICAL_UNIT_4  ...  \\\n",
      "14562              2               0               0               1  ...   \n",
      "72248              2               0               0               1  ...   \n",
      "30338              2               0               0               1  ...   \n",
      "67646              2               0               0               1  ...   \n",
      "66223              2               0               0               1  ...   \n",
      "...              ...             ...             ...             ...  ...   \n",
      "39546              2               0               0               1  ...   \n",
      "64880              2               0               0               1  ...   \n",
      "42813              2               0               0               0  ...   \n",
      "43681              2               0               0               0  ...   \n",
      "58850              2               0               0               1  ...   \n",
      "\n",
      "       MEDICAL_UNIT_10  MEDICAL_UNIT_11  MEDICAL_UNIT_12  MEDICAL_UNIT_13  \\\n",
      "14562                0                0                0                0   \n",
      "72248                0                0                0                0   \n",
      "30338                0                0                0                0   \n",
      "67646                0                0                0                0   \n",
      "66223                0                0                0                0   \n",
      "...                ...              ...              ...              ...   \n",
      "39546                0                0                0                0   \n",
      "64880                0                0                0                0   \n",
      "42813                0                0                0                0   \n",
      "43681                0                0                0                0   \n",
      "58850                0                0                0                0   \n",
      "\n",
      "       CLASSIFICATION_FINAL_2  CLASSIFICATION_FINAL_3  CLASSIFICATION_FINAL_4  \\\n",
      "14562                       0                       0                       0   \n",
      "72248                       0                       1                       0   \n",
      "30338                       0                       0                       0   \n",
      "67646                       0                       0                       0   \n",
      "66223                       0                       1                       0   \n",
      "...                       ...                     ...                     ...   \n",
      "39546                       0                       1                       0   \n",
      "64880                       0                       0                       0   \n",
      "42813                       0                       1                       0   \n",
      "43681                       0                       1                       0   \n",
      "58850                       0                       0                       0   \n",
      "\n",
      "       CLASSIFICATION_FINAL_5  CLASSIFICATION_FINAL_6  CLASSIFICATION_FINAL_7  \n",
      "14562                       0                       0                       1  \n",
      "72248                       0                       0                       0  \n",
      "30338                       0                       0                       1  \n",
      "67646                       0                       0                       1  \n",
      "66223                       0                       0                       0  \n",
      "...                       ...                     ...                     ...  \n",
      "39546                       0                       0                       0  \n",
      "64880                       0                       1                       0  \n",
      "42813                       0                       0                       0  \n",
      "43681                       0                       0                       0  \n",
      "58850                       0                       0                       1  \n",
      "\n",
      "[19298 rows x 25 columns]\n"
     ]
    }
   ],
   "source": [
    "print(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USMER                      2\n",
      "PATIENT_TYPE               1\n",
      "PNEUMONIA                  1\n",
      "AGE                       65\n",
      "DIABETES                   2\n",
      "HIPERTENSION               1\n",
      "RENAL_CHRONIC              2\n",
      "DEATH                      1\n",
      "MEDICAL_UNIT_2             0\n",
      "MEDICAL_UNIT_3             0\n",
      "MEDICAL_UNIT_4             0\n",
      "MEDICAL_UNIT_5             0\n",
      "MEDICAL_UNIT_6             0\n",
      "MEDICAL_UNIT_7             0\n",
      "MEDICAL_UNIT_8             0\n",
      "MEDICAL_UNIT_9             0\n",
      "MEDICAL_UNIT_10            0\n",
      "MEDICAL_UNIT_11            0\n",
      "MEDICAL_UNIT_12            0\n",
      "MEDICAL_UNIT_13            0\n",
      "CLASSIFICATION_FINAL_2     0\n",
      "CLASSIFICATION_FINAL_3     1\n",
      "CLASSIFICATION_FINAL_4     0\n",
      "CLASSIFICATION_FINAL_5     0\n",
      "CLASSIFICATION_FINAL_6     0\n",
      "CLASSIFICATION_FINAL_7     0\n",
      "Name: 0, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(covid.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    371781\n",
       "1      1334\n",
       "Name: CLASSIFICATION_FINAL_2, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['CLASSIFICATION_FINAL_2'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the data using various algorithms"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression model is: 0.8814903098766712\n",
      "Precision:  0.8746978243352136\n",
      "Logistic Regression F1 Score : [0.88364284 0.87925664]\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(train_x,train_y)\n",
    "y_pred_logreg=logreg.predict(test_x)\n",
    "acc_logreg=metrics.accuracy_score(test_y,y_pred_logreg)\n",
    "print(\"Accuracy of Logistic Regression model is:\",acc_logreg)\n",
    "print(\"Precision: \",metrics.precision_score(test_y,y_pred_logreg))\n",
    "print(\"Logistic Regression F1 Score :\",metrics.f1_score(test_y,logreg.predict(test_x),average=None))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Random Forest model is: 0.8836667012125609\n",
      "Precision:  0.869299111549852\n"
     ]
    }
   ],
   "source": [
    "rf=RandomForestClassifier()\n",
    "rf.fit(train_x,train_y)\n",
    "y_pred_rf=rf.predict(test_x)\n",
    "acc_rf=metrics.accuracy_score(test_y,y_pred_rf)\n",
    "print(\"Accuracy of Random Forest model is:\",acc_rf)\n",
    "print(\"Precision: \",metrics.precision_score(test_y,y_pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random F1 Score : [0.88694163 0.88019638]\n"
     ]
    }
   ],
   "source": [
    "print(\"Random F1 Score :\",metrics.f1_score(test_y,rf.predict(test_x),average=None))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Gradient Boosting model is: 0.891802259301482\n",
      "Precision:  0.8717274939172749\n"
     ]
    }
   ],
   "source": [
    "gb=GradientBoostingClassifier()\n",
    "gb.fit(train_x,train_y)\n",
    "y_pred_gb=gb.predict(test_x)\n",
    "acc_gb=metrics.accuracy_score(test_y,y_pred_gb)\n",
    "print(\"Accuracy of Gradient Boosting model is:\",acc_gb)\n",
    "print(\"Precision: \",metrics.precision_score(test_y,y_pred_gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB F1 Score : [0.89561044 0.88770571]\n"
     ]
    }
   ],
   "source": [
    "print(\"GB F1 Score :\",metrics.f1_score(test_y,gb.predict(test_x),average=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename=\"model_final.pkl\"\n",
    "pickle.dump(logreg,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "input=[[1 , 2 ,  2, 150 ,  0  , 2  , 2  , 0  , 0  , 0  , 0  , 1  , 0  , 0  , 0  , 0 ,  0 ,  0 , 0  , 0 ,  1 ,  0 ,  0 ,  0,   0]]\n",
    "input=np.array(input)\n",
    "pred=logreg.predict(input)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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