Projeto.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b337183",
   "metadata": {},
   "source": [
    "# Covid-19 Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174373d8",
   "metadata": {},
   "source": [
    "## Dataset Information\n",
    "\n",
    "This dataset contains anonymized patient-related information including health pre-conditions. \n",
    "It contains 21 features and 1,048,575 unique patients. In the Boolean features, 1 means \"yes\" and 2 means \"no\".\n",
    "\n",
    "    usmer: Indicates whether the patient treated medical units of the first, second or third level.\n",
    "    medical unit: type of institution of the National Health System that provided the care.\n",
    "    sex: gender of the patient.\n",
    "    patient type: wether the patient returned home or headed for hospitalization.\n",
    "    date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.\n",
    "    intubed: whether the patient was connected to the ventilator.\n",
    "    pneumonia: whether the patient already have air sacs inflammation or not.\n",
    "    age: of the patient.\n",
    "    pregnant: whether the patient is pregnant or not.\n",
    "    diabetes: whether the patient has diabetes or not.\n",
    "    copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.\n",
    "    asthma: whether the patient has asthma or not.\n",
    "    inmsupr: whether the patient is immunosuppressed or not.\n",
    "    hypertension: whether the patient has hypertension or not.\n",
    "    other disease: whether the patient has other disease or not.\n",
    "    cardiovascular: whether the patient has heart or blood vessels related disease.\n",
    "    obesity: whether the patient is obese or not.\n",
    "    renal chronic: whether the patient has chronic renal disease or not.\n",
    "    tobacco: whether the patient is a tobacco user.\n",
    "    classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.\n",
    "    icu: Indicates whether the patient had been admitted to an Intensive Care Unit.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d25428",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "29d66089",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from xgboost import XGBClassifier\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff0251ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b7d11d",
   "metadata": {},
   "source": [
    "### Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "209e10d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./covid19.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cecd2e83",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USMER</th>\n",
       "      <th>MEDICAL_UNIT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>PATIENT_TYPE</th>\n",
       "      <th>DATE_DIED</th>\n",
       "      <th>INTUBED</th>\n",
       "      <th>PNEUMONIA</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PREGNANT</th>\n",
       "      <th>DIABETES</th>\n",
       "      <th>...</th>\n",
       "      <th>ASTHMA</th>\n",
       "      <th>INMSUPR</th>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <th>OTHER_DISEASE</th>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <th>OBESITY</th>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <th>TOBACCO</th>\n",
       "      <th>CLASIFFICATION_FINAL</th>\n",
       "      <th>ICU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>returned home</td>\n",
       "      <td>03/05/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>returned home</td>\n",
       "      <td>03/06/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>hospitalization</td>\n",
       "      <td>09/06/2020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>returned home</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>returned home</td>\n",
       "      <td>21/06/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</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  \\\n",
       "0      2             1  female    returned home  03/05/2020      NaN   \n",
       "1      2             1    male    returned home  03/06/2020      NaN   \n",
       "2      2             1    male  hospitalization  09/06/2020      1.0   \n",
       "3      2             1  female    returned home  12/06/2020      NaN   \n",
       "4      2             1    male    returned home  21/06/2020      NaN   \n",
       "\n",
       "   PNEUMONIA  AGE  PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  \\\n",
       "0        1.0   65       2.0       2.0  ...     2.0      2.0           1.0   \n",
       "1        1.0   72       NaN       2.0  ...     2.0      2.0           1.0   \n",
       "2        2.0   55       NaN       1.0  ...     2.0      2.0           2.0   \n",
       "3        2.0   53       2.0       2.0  ...     2.0      2.0           2.0   \n",
       "4        2.0   68       NaN       1.0  ...     2.0      2.0           1.0   \n",
       "\n",
       "   OTHER_DISEASE  CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  \\\n",
       "0            2.0             2.0      2.0            2.0      2.0   \n",
       "1            2.0             2.0      1.0            1.0      2.0   \n",
       "2            2.0             2.0      2.0            2.0      2.0   \n",
       "3            2.0             2.0      2.0            2.0      2.0   \n",
       "4            2.0             2.0      2.0            2.0      2.0   \n",
       "\n",
       "   CLASIFFICATION_FINAL  ICU  \n",
       "0                     3  NaN  \n",
       "1                     5  NaN  \n",
       "2                     3  2.0  \n",
       "3                     7  NaN  \n",
       "4                     3  NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "520a3c33",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USMER</th>\n",
       "      <th>MEDICAL_UNIT</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>ASTHMA</th>\n",
       "      <th>INMSUPR</th>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <th>OTHER_DISEASE</th>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <th>OBESITY</th>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <th>TOBACCO</th>\n",
       "      <th>CLASIFFICATION_FINAL</th>\n",
       "      <th>ICU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>192706.000000</td>\n",
       "      <td>1.032572e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>521310.000000</td>\n",
       "      <td>1.045237e+06</td>\n",
       "      <td>1.045572e+06</td>\n",
       "      <td>1.045596e+06</td>\n",
       "      <td>1.045171e+06</td>\n",
       "      <td>1.045471e+06</td>\n",
       "      <td>1.043530e+06</td>\n",
       "      <td>1.045499e+06</td>\n",
       "      <td>1.045543e+06</td>\n",
       "      <td>1.045569e+06</td>\n",
       "      <td>1.045355e+06</td>\n",
       "      <td>1.048575e+06</td>\n",
       "      <td>192543.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.632194e+00</td>\n",
       "      <td>8.980565e+00</td>\n",
       "      <td>1.825351</td>\n",
       "      <td>1.864379e+00</td>\n",
       "      <td>4.179410e+01</td>\n",
       "      <td>1.984403</td>\n",
       "      <td>1.880420e+00</td>\n",
       "      <td>1.985594e+00</td>\n",
       "      <td>1.969805e+00</td>\n",
       "      <td>1.986442e+00</td>\n",
       "      <td>1.844349e+00</td>\n",
       "      <td>1.973130e+00</td>\n",
       "      <td>1.980135e+00</td>\n",
       "      <td>1.847145e+00</td>\n",
       "      <td>1.981920e+00</td>\n",
       "      <td>1.919285e+00</td>\n",
       "      <td>5.305653e+00</td>\n",
       "      <td>1.912446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.822084e-01</td>\n",
       "      <td>3.723278e+00</td>\n",
       "      <td>0.379668</td>\n",
       "      <td>3.423854e-01</td>\n",
       "      <td>1.690739e+01</td>\n",
       "      <td>0.123911</td>\n",
       "      <td>3.244694e-01</td>\n",
       "      <td>1.191554e-01</td>\n",
       "      <td>1.711242e-01</td>\n",
       "      <td>1.156451e-01</td>\n",
       "      <td>3.625247e-01</td>\n",
       "      <td>1.617045e-01</td>\n",
       "      <td>1.395369e-01</td>\n",
       "      <td>3.598474e-01</td>\n",
       "      <td>1.332413e-01</td>\n",
       "      <td>2.723973e-01</td>\n",
       "      <td>1.881165e+00</td>\n",
       "      <td>0.282647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</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",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>2.000000</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>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>2.000000</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>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>2.000000</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>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.210000e+02</td>\n",
       "      <td>2.000000</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>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              USMER  MEDICAL_UNIT        INTUBED     PNEUMONIA           AGE  \\\n",
       "count  1.048575e+06  1.048575e+06  192706.000000  1.032572e+06  1.048575e+06   \n",
       "mean   1.632194e+00  8.980565e+00       1.825351  1.864379e+00  4.179410e+01   \n",
       "std    4.822084e-01  3.723278e+00       0.379668  3.423854e-01  1.690739e+01   \n",
       "min    1.000000e+00  1.000000e+00       1.000000  1.000000e+00  0.000000e+00   \n",
       "25%    1.000000e+00  4.000000e+00       2.000000  2.000000e+00  3.000000e+01   \n",
       "50%    2.000000e+00  1.200000e+01       2.000000  2.000000e+00  4.000000e+01   \n",
       "75%    2.000000e+00  1.200000e+01       2.000000  2.000000e+00  5.300000e+01   \n",
       "max    2.000000e+00  1.300000e+01       2.000000  2.000000e+00  1.210000e+02   \n",
       "\n",
       "            PREGNANT      DIABETES          COPD        ASTHMA       INMSUPR  \\\n",
       "count  521310.000000  1.045237e+06  1.045572e+06  1.045596e+06  1.045171e+06   \n",
       "mean        1.984403  1.880420e+00  1.985594e+00  1.969805e+00  1.986442e+00   \n",
       "std         0.123911  3.244694e-01  1.191554e-01  1.711242e-01  1.156451e-01   \n",
       "min         1.000000  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "25%         2.000000  2.000000e+00  2.000000e+00  2.000000e+00  2.000000e+00   \n",
       "50%         2.000000  2.000000e+00  2.000000e+00  2.000000e+00  2.000000e+00   \n",
       "75%         2.000000  2.000000e+00  2.000000e+00  2.000000e+00  2.000000e+00   \n",
       "max         2.000000  2.000000e+00  2.000000e+00  2.000000e+00  2.000000e+00   \n",
       "\n",
       "       HIPERTENSION  OTHER_DISEASE  CARDIOVASCULAR       OBESITY  \\\n",
       "count  1.045471e+06   1.043530e+06    1.045499e+06  1.045543e+06   \n",
       "mean   1.844349e+00   1.973130e+00    1.980135e+00  1.847145e+00   \n",
       "std    3.625247e-01   1.617045e-01    1.395369e-01  3.598474e-01   \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    2.000000e+00   2.000000e+00    2.000000e+00  2.000000e+00   \n",
       "\n",
       "       RENAL_CHRONIC       TOBACCO  CLASIFFICATION_FINAL            ICU  \n",
       "count   1.045569e+06  1.045355e+06          1.048575e+06  192543.000000  \n",
       "mean    1.981920e+00  1.919285e+00          5.305653e+00       1.912446  \n",
       "std     1.332413e-01  2.723973e-01          1.881165e+00       0.282647  \n",
       "min     1.000000e+00  1.000000e+00          1.000000e+00       1.000000  \n",
       "25%     2.000000e+00  2.000000e+00          3.000000e+00       2.000000  \n",
       "50%     2.000000e+00  2.000000e+00          6.000000e+00       2.000000  \n",
       "75%     2.000000e+00  2.000000e+00          7.000000e+00       2.000000  \n",
       "max     2.000000e+00  2.000000e+00          7.000000e+00       2.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"CARDIOVASCULAR\"]);df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d81ef6f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['USMER', 'MEDICAL_UNIT', 'SEX', 'PATIENT_TYPE', 'DATE_DIED', 'INTUBED',\n",
       "       'PNEUMONIA', 'AGE', 'PREGNANT', 'DIABETES', 'COPD', 'ASTHMA', 'INMSUPR',\n",
       "       'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'OBESITY',\n",
       "       'RENAL_CHRONIC', 'TOBACCO', 'CLASIFFICATION_FINAL', 'ICU'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = df.columns\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1756d16b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['female', 'male'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_values = df['SEX'].unique()\n",
    "sex_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6faf4e6",
   "metadata": {},
   "source": [
    "### Change 'SEX' to numerical value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ea3a6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex_mapping = {\n",
    "    'female': 0,\n",
    "    'male': 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "19c5ed95",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['SEX'] = df['SEX'].map(sex_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f71a2a55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USMER</th>\n",
       "      <th>MEDICAL_UNIT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>PATIENT_TYPE</th>\n",
       "      <th>DATE_DIED</th>\n",
       "      <th>INTUBED</th>\n",
       "      <th>PNEUMONIA</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PREGNANT</th>\n",
       "      <th>DIABETES</th>\n",
       "      <th>...</th>\n",
       "      <th>ASTHMA</th>\n",
       "      <th>INMSUPR</th>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <th>OTHER_DISEASE</th>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <th>OBESITY</th>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <th>TOBACCO</th>\n",
       "      <th>CLASIFFICATION_FINAL</th>\n",
       "      <th>ICU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>returned home</td>\n",
       "      <td>03/06/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hospitalization</td>\n",
       "      <td>09/06/2020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>returned home</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>returned home</td>\n",
       "      <td>21/06/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   USMER  MEDICAL_UNIT  SEX     PATIENT_TYPE   DATE_DIED  INTUBED  PNEUMONIA  \\\n",
       "0      2             1    0    returned home  03/05/2020      NaN        1.0   \n",
       "1      2             1    1    returned home  03/06/2020      NaN        1.0   \n",
       "2      2             1    1  hospitalization  09/06/2020      1.0        2.0   \n",
       "3      2             1    0    returned home  12/06/2020      NaN        2.0   \n",
       "4      2             1    1    returned home  21/06/2020      NaN        2.0   \n",
       "\n",
       "   AGE  PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
       "0   65       2.0       2.0  ...     2.0      2.0           1.0            2.0   \n",
       "1   72       NaN       2.0  ...     2.0      2.0           1.0            2.0   \n",
       "2   55       NaN       1.0  ...     2.0      2.0           2.0            2.0   \n",
       "3   53       2.0       2.0  ...     2.0      2.0           2.0            2.0   \n",
       "4   68       NaN       1.0  ...     2.0      2.0           1.0            2.0   \n",
       "\n",
       "   CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASIFFICATION_FINAL  ICU  \n",
       "0             2.0      2.0            2.0      2.0                     3  NaN  \n",
       "1             2.0      1.0            1.0      2.0                     5  NaN  \n",
       "2             2.0      2.0            2.0      2.0                     3  2.0  \n",
       "3             2.0      2.0            2.0      2.0                     7  NaN  \n",
       "4             2.0      2.0            2.0      2.0                     3  NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c81861c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['returned home', 'hospitalization'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptype_values = df['PATIENT_TYPE'].unique()\n",
    "ptype_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b744075f",
   "metadata": {},
   "source": [
    "### Change 'PATIENT_TYPE' to numerical value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "57e48086",
   "metadata": {},
   "outputs": [],
   "source": [
    "ptype_mapping = {\n",
    "    'returned home': 0,\n",
    "    'hospitalization': 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "489729ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PATIENT_TYPE'] = df['PATIENT_TYPE'].map(ptype_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e4a397ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <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",
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       "      <th>INMSUPR</th>\n",
       "      <th>HIPERTENSION</th>\n",
       "      <th>OTHER_DISEASE</th>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <th>OBESITY</th>\n",
       "      <th>RENAL_CHRONIC</th>\n",
       "      <th>TOBACCO</th>\n",
       "      <th>CLASIFFICATION_FINAL</th>\n",
       "      <th>ICU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>09/06/2020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21/06/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   USMER  MEDICAL_UNIT  SEX  PATIENT_TYPE   DATE_DIED  INTUBED  PNEUMONIA  \\\n",
       "0      2             1    0             0  03/05/2020      NaN        1.0   \n",
       "1      2             1    1             0  03/06/2020      NaN        1.0   \n",
       "2      2             1    1             1  09/06/2020      1.0        2.0   \n",
       "3      2             1    0             0  12/06/2020      NaN        2.0   \n",
       "4      2             1    1             0  21/06/2020      NaN        2.0   \n",
       "\n",
       "   AGE  PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
       "0   65       2.0       2.0  ...     2.0      2.0           1.0            2.0   \n",
       "1   72       NaN       2.0  ...     2.0      2.0           1.0            2.0   \n",
       "2   55       NaN       1.0  ...     2.0      2.0           2.0            2.0   \n",
       "3   53       2.0       2.0  ...     2.0      2.0           2.0            2.0   \n",
       "4   68       NaN       1.0  ...     2.0      2.0           1.0            2.0   \n",
       "\n",
       "   CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASIFFICATION_FINAL  ICU  \n",
       "0             2.0      2.0            2.0      2.0                     3  NaN  \n",
       "1             2.0      1.0            1.0      2.0                     5  NaN  \n",
       "2             2.0      2.0            2.0      2.0                     3  2.0  \n",
       "3             2.0      2.0            2.0      2.0                     7  NaN  \n",
       "4             2.0      2.0            2.0      2.0                     3  NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee9099be",
   "metadata": {},
   "source": [
    "### Check NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47067a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "855869"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_intubed_count = df['INTUBED'].isna().sum()\n",
    "NaN_intubed_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "672f331f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16003"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_pneumonia_count = df['PNEUMONIA'].isna().sum()\n",
    "NaN_pneumonia_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0cdc07b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "527265"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_pregnant_count = df['PREGNANT'].isna().sum()\n",
    "NaN_pregnant_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d393bb00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3338"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_diabetes_count = df['DIABETES'].isna().sum()\n",
    "NaN_diabetes_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7910e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_sex_count = df['SEX'].isna().sum()\n",
    "NaN_sex_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aab97bcd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "856032"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_icu_count = df['ICU'].isna().sum()\n",
    "NaN_icu_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c60266b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3338"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_diabetes_recount = df['DIABETES'].isna().sum()\n",
    "NaN_diabetes_recount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "931c1b78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3003"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_copd_count = df['COPD'].isna().sum()\n",
    "NaN_copd_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "21552957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2979"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_asthma_count = df['ASTHMA'].isna().sum()\n",
    "NaN_asthma_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ddf3b90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3404"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_inmsupr_count = df['INMSUPR'].isna().sum()\n",
    "NaN_inmsupr_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6bc974b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3104"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_hipertension_count = df['HIPERTENSION'].isna().sum()\n",
    "NaN_hipertension_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "64f1c95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5045"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_other_count = df['OTHER_DISEASE'].isna().sum()\n",
    "NaN_other_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b14ad2b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3076"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_cardio_count = df['CARDIOVASCULAR'].isna().sum()\n",
    "NaN_cardio_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ddc56a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3032"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_obs_count = df['OBESITY'].isna().sum()\n",
    "NaN_obs_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aabf397d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3006"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_renal_count = df['RENAL_CHRONIC'].isna().sum()\n",
    "NaN_renal_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c233fe02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3220"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_tobacco_count = df['TOBACCO'].isna().sum()\n",
    "NaN_tobacco_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "908e83b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "856032"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_icu_count = df['ICU'].isna().sum()\n",
    "NaN_icu_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3c25cfc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_patient_count = df['PATIENT_TYPE'].isna().sum()\n",
    "NaN_patient_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f02a9816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "527265"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_pregnant_count = df['PREGNANT'].isna().sum()\n",
    "NaN_pregnant_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "892590da",
   "metadata": {},
   "source": [
    "### Evaluate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6539fee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2., nan,  1.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pregnant_type = df['PREGNANT'].unique()\n",
    "pregnant_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8e764470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  2.,  1.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icu_types = df['ICU'].unique()\n",
    "icu_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f2b245a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  1., nan])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_types = df['DIABETES'].unique()\n",
    "diabetes_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1cd09487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_types = df['PATIENT_TYPE'].unique()\n",
    "patient_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f5663191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  1.,  2.])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intube_types = df['INTUBED'].unique()\n",
    "intube_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a715551c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2., nan,  1.])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pregnant_types = df['PREGNANT'].unique()\n",
    "pregnant_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e8bed94a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_types = df['PATIENT_TYPE'].unique()\n",
    "patient_types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a44172",
   "metadata": {},
   "source": [
    "### Drop NaN Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ccf7fe16",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(df):\n",
    "    # Drop rows with NaN values\n",
    "    df_clean = df.dropna(subset=['USMER', 'MEDICAL_UNIT', 'SEX', 'PATIENT_TYPE', 'INTUBED', 'PNEUMONIA', 'AGE', 'PREGNANT',\n",
    "                                 'DIABETES', 'COPD', 'ASTHMA', 'INMSUPR', 'HIPERTENSION', 'OTHER_DISEASE',\n",
    "                                 'CARDIOVASCULAR', 'OBESITY', 'RENAL_CHRONIC', 'TOBACCO', 'DATE_DIED'])\n",
    "\n",
    "    # Convert 'DATE_DIED' to binary values\n",
    "    df_clean['DATE_DIED'] = df_clean['DATE_DIED'].apply(lambda x: 0 if x != '9999-99-99' else 1)\n",
    "\n",
    "    return df_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e8c5fc",
   "metadata": {},
   "source": [
    "## Imputation by IterativeImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ab263ef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "imput = IterativeImputer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7954a99d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputation on ICU\n",
    "missing_icu = df.columns[df.isnull().any() & df.columns.str.contains('ICU')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2b226e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[missing_icu] = imput.fit_transform(df.loc[:,missing_icu])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6042cb8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputation on Pregnant\n",
    "missing_pregnant = df.columns[df.isnull().any() & df.columns.str.contains('PREGNANT')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8f1ce957",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[missing_pregnant] = imput.fit_transform(df[missing_pregnant])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6d4b93b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputation on Patient Type\n",
    "missing_intubed = df.columns[df.isnull().any() & df.columns.str.contains('INTUBED')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "36db85db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[missing_intubed] = imput.fit_transform(df[missing_intubed])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "23cbc248",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DATE_DIED'] = df['DATE_DIED'].apply(lambda x: 0 if x != '9999-99-99' else 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6718b2e",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "be3c2d7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"AGE\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8dea94fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"SEX\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c8264026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"ASTHMA\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0de4d86e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"CARDIOVASCULAR\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0ae0c727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"PATIENT_TYPE\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1456414b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"ICU\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8e3e1708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"CLASIFFICATION_FINAL\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0e6bddd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGsCAYAAAAPJKchAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfkUlEQVR4nO3df1BVdf7H8dcV8mIqNGIiKCK6WbSUFWSBMX2tpEWHHWfalR13wxRnYjJZZXVHssl0nKVadVwrsSbRcZZYhtKmNrZgtk38UTPBwtQmU62aaEIMOHtBbVHgfP9wpb0LKJeEd/f2fMzcP/h4zr3ve8a8z8659+JyHMcRAACAkWHWAwAAgB82YgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmPKrGKmsrFR6erqioqLkcrn0xhtv+HwfjuNo48aNmjZtmtxut6Kjo/W73/3u6g8LAAD6Jdh6AF+cPXtW06dP16JFi/TQQw8N6D5+/etfq7y8XBs3btQtt9wij8ej5ubmqzwpAADoL5e//qI8l8ulvXv3at68ed1r58+f15NPPqmioiL961//Unx8vJ599ln93//9nySprq5Ot956q/7xj3/oxhtvtBkcAAB48avLNFeyaNEiHTx4UH/605/08ccf6+c//7l+8pOf6IsvvpAkvfXWW5oyZYr+/Oc/KzY2VpMnT9aSJUt0+vRp48kBAPjhCpgYOXLkiIqLi1VaWqqUlBRNnTpVK1eu1D333KOdO3dKko4eParjx4+rtLRUu3fv1q5du1RdXa2f/exnxtMDAPDD5VfvGbmcv//973IcR9OmTfNab29vV3h4uCSpq6tL7e3t2r17d/d2O3bsUEJCgj777DMu3QAAYCBgYqSrq0tBQUGqrq5WUFCQ15+NGjVKkhQZGang4GCvYImLi5Mk1dfXEyMAABgImBi5/fbb1dnZqaamJqWkpPS6zcyZM9XR0aEjR45o6tSpkqTPP/9ckhQTEzNkswIAgG/51adpzpw5o3/+85+SLsbH5s2bNWvWLI0ZM0aTJk3Sr371Kx08eFCbNm3S7bffrubmZr333nu65ZZbNGfOHHV1denOO+/UqFGjtGXLFnV1dWnp0qUKDQ1VeXm58bMDAOCHya9i5P3339esWbN6rC9cuFC7du3ShQsXtGHDBu3evVtfffWVwsPDlZSUpHXr1umWW26RJJ06dUrLli1TeXm5Ro4cqbS0NG3atEljxowZ6qcDAADkZzECAAACT8B8tBcAAPgnYgQAAJjyi0/TdHV16dSpUxo9erRcLpf1OAAAoB8cx1FbW5uioqI0bFjf5z/8IkZOnTql6Oho6zEAAMAAnDhxQhMnTuzzz/0iRkaPHi3p4pMJDQ01ngYAAPRHa2uroqOju1/H++IXMXLp0kxoaCgxAgCAn7nSWyx4AysAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPB1gMAABBIJq9+23oEn335zFzTx+fMCAAAMOVzjFRWVio9PV1RUVFyuVx64403rrjPvn37lJCQoJCQEE2ZMkXbt28fyKwAACAA+RwjZ8+e1fTp0/XCCy/0a/tjx45pzpw5SklJUU1NjZ544gnl5OTo9ddf93lYAAAQeHx+z0haWprS0tL6vf327ds1adIkbdmyRZIUFxenqqoqbdy4UQ899JCvDw8AAALMoL9n5IMPPlBqaqrX2oMPPqiqqipduHCh133a29vV2trqdQMAAIFp0GOksbFRERERXmsRERHq6OhQc3Nzr/vk5+crLCys+xYdHT3YYwIAACND8mkal8vl9bPjOL2uX5KXlyePx9N9O3HixKDPCAAAbAz694yMHz9ejY2NXmtNTU0KDg5WeHh4r/u43W653e7BHg0AAHwPDPqZkaSkJFVUVHitlZeXKzExUddcc81gPzwAAPie8zlGzpw5o9raWtXW1kq6+NHd2tpa1dfXS7p4iSUzM7N7++zsbB0/fly5ubmqq6tTYWGhduzYoZUrV16dZwAAAPyaz5dpqqqqNGvWrO6fc3NzJUkLFy7Url271NDQ0B0mkhQbG6uysjKtWLFCL774oqKiorR161Y+1gsAACRJLufSu0m/x1pbWxUWFiaPx6PQ0FDrcQAA6BO/m+Zb/X395nfTAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADA1oBjZtm2bYmNjFRISooSEBO3fv/+y2xcVFWn69Om69tprFRkZqUWLFqmlpWVAAwMAgMDic4yUlJRo+fLlWrNmjWpqapSSkqK0tDTV19f3uv2BAweUmZmprKwsffrppyotLdVHH32kJUuWfOfhAQCA//M5RjZv3qysrCwtWbJEcXFx2rJli6Kjo1VQUNDr9h9++KEmT56snJwcxcbG6p577tGjjz6qqqqq7zw8AADwfz7FyPnz51VdXa3U1FSv9dTUVB06dKjXfZKTk3Xy5EmVlZXJcRx9/fXXeu211zR37tw+H6e9vV2tra1eNwAAEJh8ipHm5mZ1dnYqIiLCaz0iIkKNjY297pOcnKyioiJlZGRo+PDhGj9+vK677jo9//zzfT5Ofn6+wsLCum/R0dG+jAkAAPzIgN7A6nK5vH52HKfH2iWHDx9WTk6OnnrqKVVXV+udd97RsWPHlJ2d3ef95+XlyePxdN9OnDgxkDEBAIAfCPZl47FjxyooKKjHWZCmpqYeZ0suyc/P18yZM7Vq1SpJ0q233qqRI0cqJSVFGzZsUGRkZI993G633G63L6MBAAA/5dOZkeHDhyshIUEVFRVe6xUVFUpOTu51n3PnzmnYMO+HCQoKknTxjAoAAPhh8/kyTW5url555RUVFhaqrq5OK1asUH19ffdll7y8PGVmZnZvn56erj179qigoEBHjx7VwYMHlZOToxkzZigqKurqPRMAAOCXfLpMI0kZGRlqaWnR+vXr1dDQoPj4eJWVlSkmJkaS1NDQ4PWdI4888oja2tr0wgsv6De/+Y2uu+463XfffXr22Wev3rMAAAB+y+X4wbWS1tZWhYWFyePxKDQ01HocAAD6NHn129Yj+OzLZ/r+uo3vor+v3/xuGgAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmBhQj27ZtU2xsrEJCQpSQkKD9+/dfdvv29natWbNGMTExcrvdmjp1qgoLCwc0MAAACCzBvu5QUlKi5cuXa9u2bZo5c6ZeeuklpaWl6fDhw5o0aVKv+8yfP19ff/21duzYoR/96EdqampSR0fHdx4eAAD4P5fjOI4vO9x111264447VFBQ0L0WFxenefPmKT8/v8f277zzjn7xi1/o6NGjGjNmzICGbG1tVVhYmDwej0JDQwd0HwAADIXJq9+2HsFnXz4zd1Dut7+v3z5dpjl//ryqq6uVmprqtZ6amqpDhw71us+bb76pxMREPffcc5owYYKmTZumlStX6ptvvunzcdrb29Xa2up1AwAAgcmnyzTNzc3q7OxURESE13pERIQaGxt73efo0aM6cOCAQkJCtHfvXjU3N+uxxx7T6dOn+3zfSH5+vtatW+fLaAAAwE8N6A2sLpfL62fHcXqsXdLV1SWXy6WioiLNmDFDc+bM0ebNm7Vr164+z47k5eXJ4/F0306cODGQMQEAgB/w6czI2LFjFRQU1OMsSFNTU4+zJZdERkZqwoQJCgsL616Li4uT4zg6efKkbrjhhh77uN1uud1uX0YDAAB+yqczI8OHD1dCQoIqKiq81isqKpScnNzrPjNnztSpU6d05syZ7rXPP/9cw4YN08SJEwcwMgAACCQ+X6bJzc3VK6+8osLCQtXV1WnFihWqr69Xdna2pIuXWDIzM7u3X7BggcLDw7Vo0SIdPnxYlZWVWrVqlRYvXqwRI0ZcvWcCAAD8ks/fM5KRkaGWlhatX79eDQ0Nio+PV1lZmWJiYiRJDQ0Nqq+v795+1KhRqqio0LJly5SYmKjw8HDNnz9fGzZsuHrPAgAA+C2fv2fEAt8zAgDwF3zPyLcG5XtGAAAArjZiBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAICpAcXItm3bFBsbq5CQECUkJGj//v392u/gwYMKDg7WbbfdNpCHBQAAAcjnGCkpKdHy5cu1Zs0a1dTUKCUlRWlpaaqvr7/sfh6PR5mZmbr//vsHPCwAAAg8PsfI5s2blZWVpSVLliguLk5btmxRdHS0CgoKLrvfo48+qgULFigpKWnAwwIAgMDjU4ycP39e1dXVSk1N9VpPTU3VoUOH+txv586dOnLkiNauXduvx2lvb1dra6vXDQAABCafYqS5uVmdnZ2KiIjwWo+IiFBjY2Ov+3zxxRdavXq1ioqKFBwc3K/Hyc/PV1hYWPctOjralzEBAIAfGdAbWF0ul9fPjuP0WJOkzs5OLViwQOvWrdO0adP6ff95eXnyeDzdtxMnTgxkTAAA4Af6d6riP8aOHaugoKAeZ0Gampp6nC2RpLa2NlVVVammpkaPP/64JKmrq0uO4yg4OFjl5eW67777euzndrvldrt9GQ0AAPgpn86MDB8+XAkJCaqoqPBar6ioUHJyco/tQ0ND9cknn6i2trb7lp2drRtvvFG1tbW66667vtv0AADA7/l0ZkSScnNz9fDDDysxMVFJSUl6+eWXVV9fr+zsbEkXL7F89dVX2r17t4YNG6b4+Hiv/ceNG6eQkJAe6wAA4IfJ5xjJyMhQS0uL1q9fr4aGBsXHx6usrEwxMTGSpIaGhit+5wgAAMAlLsdxHOshrqS1tVVhYWHyeDwKDQ21HgcAgD5NXv229Qg++/KZuYNyv/19/eZ30wAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwNaAY2bZtm2JjYxUSEqKEhATt37+/z2337Nmj2bNn6/rrr1doaKiSkpL07rvvDnhgAAAQWHyOkZKSEi1fvlxr1qxRTU2NUlJSlJaWpvr6+l63r6ys1OzZs1VWVqbq6mrNmjVL6enpqqmp+c7DAwAA/+dyHMfxZYe77rpLd9xxhwoKCrrX4uLiNG/ePOXn5/frPn784x8rIyNDTz31VL+2b21tVVhYmDwej0JDQ30ZFwCAITV59dvWI/jsy2fmDsr99vf126czI+fPn1d1dbVSU1O91lNTU3Xo0KF+3UdXV5fa2to0ZsyYPrdpb29Xa2ur1w0AAAQmn2KkublZnZ2dioiI8FqPiIhQY2Njv+5j06ZNOnv2rObPn9/nNvn5+QoLC+u+RUdH+zImAADwIwN6A6vL5fL62XGcHmu9KS4u1tNPP62SkhKNGzeuz+3y8vLk8Xi6bydOnBjImAAAwA8E+7Lx2LFjFRQU1OMsSFNTU4+zJf+rpKREWVlZKi0t1QMPPHDZbd1ut9xuty+jAQAAP+XTmZHhw4crISFBFRUVXusVFRVKTk7uc7/i4mI98sgjevXVVzV37uC8SQYAAPgnn86MSFJubq4efvhhJSYmKikpSS+//LLq6+uVnZ0t6eIllq+++kq7d++WdDFEMjMz9Yc//EF3331391mVESNGKCws7Co+FQAA4I98jpGMjAy1tLRo/fr1amhoUHx8vMrKyhQTEyNJamho8PrOkZdeekkdHR1aunSpli5d2r2+cOFC7dq167s/AwAA4Nd8/p4RC3zPCADAX/A9I98alO8ZAQAAuNqIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmgq0HsDZ59dvWI/jsy2fmWo8AAMBVw5kRAABgihgBAACmiBEAAGCKGAEAAKYGFCPbtm1TbGysQkJClJCQoP379192+3379ikhIUEhISGaMmWKtm/fPqBhAQBA4PE5RkpKSrR8+XKtWbNGNTU1SklJUVpamurr63vd/tixY5ozZ45SUlJUU1OjJ554Qjk5OXr99de/8/AAAMD/+RwjmzdvVlZWlpYsWaK4uDht2bJF0dHRKigo6HX77du3a9KkSdqyZYvi4uK0ZMkSLV68WBs3bvzOwwMAAP/n0/eMnD9/XtXV1Vq9erXXempqqg4dOtTrPh988IFSU1O91h588EHt2LFDFy5c0DXXXNNjn/b2drW3t3f/7PF4JEmtra2+jNsvXe3nrvp9DrbBOA4AgKuD15We9+s4zmW38ylGmpub1dnZqYiICK/1iIgINTY29rpPY2Njr9t3dHSoublZkZGRPfbJz8/XunXreqxHR0f7Mm7ACttiPQEAIJAM9utKW1ubwsLC+vzzAX0Dq8vl8vrZcZwea1favrf1S/Ly8pSbm9v9c1dXl06fPq3w8PDLPo6vWltbFR0drRMnTig0NPSq3S964lgPDY7z0OA4Dw2O89AYzOPsOI7a2toUFRV12e18ipGxY8cqKCiox1mQpqamHmc/Lhk/fnyv2wcHBys8PLzXfdxut9xut9fadddd58uoPgkNDeUv+hDhWA8NjvPQ4DgPDY7z0Bis43y5MyKX+PQG1uHDhyshIUEVFRVe6xUVFUpOTu51n6SkpB7bl5eXKzExsdf3iwAAgB8Wnz9Nk5ubq1deeUWFhYWqq6vTihUrVF9fr+zsbEkXL7FkZmZ2b5+dna3jx48rNzdXdXV1Kiws1I4dO7Ry5cqr9ywAAIDf8vk9IxkZGWppadH69evV0NCg+Ph4lZWVKSYmRpLU0NDg9Z0jsbGxKisr04oVK/Tiiy8qKipKW7du1UMPPXT1nsUAud1urV27tsclIVx9HOuhwXEeGhznocFxHhrfh+Pscq70eRsAAIBBxO+mAQAApogRAABgihgBAACmiBEAAGAqoGOksrJS6enpioqKksvl0htvvHHFffbt26eEhASFhIRoypQp2r59++AP6ud8Pc579uzR7Nmzdf311ys0NFRJSUl69913h2ZYPzaQv8+XHDx4UMHBwbrtttsGbb5AMZDj3N7erjVr1igmJkZut1tTp05VYWHh4A/r5wZyrIuKijR9+nRde+21ioyM1KJFi9TS0jL4w/qp/Px83XnnnRo9erTGjRunefPm6bPPPrvifkP9WhjQMXL27FlNnz5dL7zwQr+2P3bsmObMmaOUlBTV1NToiSeeUE5Ojl5//fVBntS/+XqcKysrNXv2bJWVlam6ulqzZs1Senq6ampqBnlS/+brcb7E4/EoMzNT999//yBNFlgGcpznz5+vv/71r9qxY4c+++wzFRcX66abbhrEKQODr8f6wIEDyszMVFZWlj799FOVlpbqo48+0pIlSwZ5Uv+1b98+LV26VB9++KEqKirU0dGh1NRUnT17ts99TF4LnR8ISc7evXsvu81vf/tb56abbvJae/TRR5277757ECcLLP05zr25+eabnXXr1l39gQKUL8c5IyPDefLJJ521a9c606dPH9S5Ak1/jvNf/vIXJywszGlpaRmaoQJUf47173//e2fKlClea1u3bnUmTpw4iJMFlqamJkeSs2/fvj63sXgtDOgzI7764IMPlJqa6rX24IMPqqqqShcuXDCaKvB1dXWpra1NY8aMsR4l4OzcuVNHjhzR2rVrrUcJWG+++aYSExP13HPPacKECZo2bZpWrlypb775xnq0gJOcnKyTJ0+qrKxMjuPo66+/1muvvaa5c+daj+Y3PB6PJF3231uL18IB/dbeQNXY2NjjF/5FRESoo6NDzc3NioyMNJossG3atElnz57V/PnzrUcJKF988YVWr16t/fv3KziY/9QHy9GjR3XgwAGFhIRo7969am5u1mOPPabTp0/zvpGrLDk5WUVFRcrIyNC///1vdXR06Kc//amef/5569H8guM4ys3N1T333KP4+Pg+t7N4LeTMyP9wuVxePzv/+YLa/13H1VFcXKynn35aJSUlGjdunPU4AaOzs1MLFizQunXrNG3aNOtxAlpXV5dcLpeKioo0Y8YMzZkzR5s3b9auXbs4O3KVHT58WDk5OXrqqadUXV2td955R8eOHev+3Wi4vMcff1wff/yxiouLr7jtUL8W8r9L/2X8+PFqbGz0WmtqalJwcLDCw8ONpgpcJSUlysrKUmlpqR544AHrcQJKW1ubqqqqVFNTo8cff1zSxRdNx3EUHBys8vJy3XfffcZTBobIyEhNmDDB69ekx8XFyXEcnTx5UjfccIPhdIElPz9fM2fO1KpVqyRJt956q0aOHKmUlBRt2LCBs9eXsWzZMr355puqrKzUxIkTL7utxWshMfJfkpKS9NZbb3mtlZeXKzExUddcc43RVIGpuLhYixcvVnFxMdd7B0FoaKg++eQTr7Vt27bpvffe02uvvabY2FijyQLPzJkzVVpaqjNnzmjUqFGSpM8//1zDhg274j/68M25c+d6XHIMCgqS9O3/ucOb4zhatmyZ9u7dq/fff79f/+1bvBYG9GWaM2fOqLa2VrW1tZIuflyptra2+7cK5+XlKTMzs3v77OxsHT9+XLm5uaqrq1NhYaF27NihlStXWozvN3w9zsXFxcrMzNSmTZt09913q7GxUY2Njd1vrELvfDnOw4YNU3x8vNdt3LhxCgkJUXx8vEaOHGn1NL73fP37vGDBAoWHh2vRokU6fPiwKisrtWrVKi1evFgjRoyweAp+w9djnZ6erj179qigoEBHjx7VwYMHlZOToxkzZigqKsriKXzvLV26VH/84x/16quvavTo0d3/3v73JcTvxWvhoH1O53vgb3/7myOpx23hwoWO4zjOwoULnXvvvddrn/fff9+5/fbbneHDhzuTJ092CgoKhn5wP+Prcb733nsvuz16N5C/z/+Nj/b2z0COc11dnfPAAw84I0aMcCZOnOjk5uY6586dG/rh/cxAjvXWrVudm2++2RkxYoQTGRnp/PKXv3ROnjw59MP7id6OryRn586d3dt8H14LXf8ZFgAAwERAX6YBAADff8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMPX/t0C/FvYLruMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"OTHER_DISEASE\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d7a7868",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# create heatmap\n",
    "f, ax = plt.subplots(figsize=[20,20])\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8030743a",
   "metadata": {},
   "source": [
    "### - Conclusion: There's a relationship between the usmer (level of medical unit treatment) and the admission of patients on icu (intensive care unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3f11d7",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc98f263",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_random_forest(df):\n",
    "    X = df.drop(['DATE_DIED', 'SEX'], axis=1)\n",
    "    y = df['DATE_DIED']\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=50, shuffle=True)\n",
    "    \n",
    "    rf = RandomForestClassifier(n_estimators=100, random_state=50)\n",
    "    rf.fit(X_train, y_train)\n",
    "    \n",
    "    rf_pred = rf.predict(X_test)\n",
    "    rf_score = accuracy_score(y_test, rf_pred)\n",
    "    rf_report = classification_report(y_test, rf_pred)\n",
    "    \n",
    "    cm = confusion_matrix(y_test, rf_pred)\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(cm, annot=True, fmt=\".6g\")\n",
    "    plt.title('Confusion Matrix')\n",
    "    \n",
    "    return rf_score, rf_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cce45a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_xgboost(df):\n",
    "    X = df[['USMER', 'MEDICAL_UNIT', 'SEX', 'PATIENT_TYPE', 'INTUBED', 'PNEUMONIA', 'AGE', 'PREGNANT', 'DIABETES',\n",
    "            'COPD', 'ASTHMA', 'INMSUPR', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'OBESITY', 'RENAL_CHRONIC',\n",
    "            'TOBACCO']]\n",
    "    y = df['DATE_DIED']\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=50, shuffle=True)\n",
    "    \n",
    "    xgb = XGBClassifier()\n",
    "    xgb.fit(X_train, y_train)\n",
    "    \n",
    "    xgb_pred = xgb.predict(X_test)\n",
    "    xgb_score = accuracy_score(y_test, xgb_pred)\n",
    "    xgb_report = classification_report(y_test, xgb_pred)\n",
    "    \n",
    "    return xgb_score, xgb_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "51e33fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = preprocess_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4cf69783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 1.0\n",
      "Random Forest Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    307546\n",
      "\n",
      "    accuracy                           1.00    307546\n",
      "   macro avg       1.00      1.00      1.00    307546\n",
      "weighted avg       1.00      1.00      1.00    307546\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train and evaluate Random Forest model\n",
    "rf_score, rf_report = train_random_forest(df_clean)\n",
    "print(\"Random Forest Accuracy:\", rf_score)\n",
    "print(\"Random Forest Report:\")\n",
    "print(rf_report)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}