Data Engineering and Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Engineering and Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file contains the data engineering and analysis part. In the next file, we train the models and perform model selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***************************************************************************************************************************"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1 Data Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Declaring all the needed importse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from prettytable import PrettyTable\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from tabulate import tabulate\n",
    "import seaborn as sns\n",
    "# ROC Curves\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_precision_recall_curve, precision_recall_curve, average_precision_score\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, roc_auc_score, precision_recall_fscore_support, roc_curve, f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>encounter_id</th>\n",
       "      <td>2278392</td>\n",
       "      <td>149190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_nbr</th>\n",
       "      <td>8222157</td>\n",
       "      <td>55629189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Caucasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>[0-10)</td>\n",
       "      <td>[10-20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_type_id</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_source_id</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_in_hospital</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payer_code</th>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medical_specialty</th>\n",
       "      <td>Pediatrics-Endocrinology</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <td>41</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procedures</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_medications</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_outpatient</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_emergency</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_inpatient</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_1</th>\n",
       "      <td>250.83</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_2</th>\n",
       "      <td>?</td>\n",
       "      <td>250.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_3</th>\n",
       "      <td>?</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_diagnoses</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_glu_serum</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Cresult</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repaglinide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nateglinide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorpropamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glimepiride</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acetohexamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glipizide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolbutamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pioglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rosiglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acarbose</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miglitol</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>troglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolazamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>examide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citoglipton</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insulin</th>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetesMed</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readmitted</th>\n",
       "      <td>NO</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                                 0          1\n",
       "encounter_id                               2278392     149190\n",
       "patient_nbr                                8222157   55629189\n",
       "race                                     Caucasian  Caucasian\n",
       "gender                                      Female     Female\n",
       "age                                         [0-10)    [10-20)\n",
       "weight                                           ?          ?\n",
       "admission_type_id                                6          1\n",
       "discharge_disposition_id                        25          1\n",
       "admission_source_id                              1          7\n",
       "time_in_hospital                                 1          3\n",
       "payer_code                                       ?          ?\n",
       "medical_specialty         Pediatrics-Endocrinology          ?\n",
       "num_lab_procedures                              41         59\n",
       "num_procedures                                   0          0\n",
       "num_medications                                  1         18\n",
       "number_outpatient                                0          0\n",
       "number_emergency                                 0          0\n",
       "number_inpatient                                 0          0\n",
       "diag_1                                      250.83        276\n",
       "diag_2                                           ?     250.01\n",
       "diag_3                                           ?        255\n",
       "number_diagnoses                                 1          9\n",
       "max_glu_serum                                 None       None\n",
       "A1Cresult                                     None       None\n",
       "metformin                                       No         No\n",
       "repaglinide                                     No         No\n",
       "nateglinide                                     No         No\n",
       "chlorpropamide                                  No         No\n",
       "glimepiride                                     No         No\n",
       "acetohexamide                                   No         No\n",
       "glipizide                                       No         No\n",
       "glyburide                                       No         No\n",
       "tolbutamide                                     No         No\n",
       "pioglitazone                                    No         No\n",
       "rosiglitazone                                   No         No\n",
       "acarbose                                        No         No\n",
       "miglitol                                        No         No\n",
       "troglitazone                                    No         No\n",
       "tolazamide                                      No         No\n",
       "examide                                         No         No\n",
       "citoglipton                                     No         No\n",
       "insulin                                         No         Up\n",
       "glyburide-metformin                             No         No\n",
       "glipizide-metformin                             No         No\n",
       "glimepiride-pioglitazone                        No         No\n",
       "metformin-rosiglitazone                         No         No\n",
       "metformin-pioglitazone                          No         No\n",
       "change                                          No         Ch\n",
       "diabetesMed                                     No        Yes\n",
       "readmitted                                      NO        >30"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the csv file\n",
    "DiabeticDataset = pd.read_csv('diabetic_data.csv')\n",
    "#Data2 Backup\n",
    "data_backup = DiabeticDataset.copy()\n",
    "#Display fields\n",
    "DiabeticDataset.head(2).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 50 columns):\n",
      " #   Column                    Non-Null Count   Dtype \n",
      "---  ------                    --------------   ----- \n",
      " 0   encounter_id              101766 non-null  int64 \n",
      " 1   patient_nbr               101766 non-null  int64 \n",
      " 2   race                      101766 non-null  object\n",
      " 3   gender                    101766 non-null  object\n",
      " 4   age                       101766 non-null  object\n",
      " 5   weight                    101766 non-null  object\n",
      " 6   admission_type_id         101766 non-null  int64 \n",
      " 7   discharge_disposition_id  101766 non-null  int64 \n",
      " 8   admission_source_id       101766 non-null  int64 \n",
      " 9   time_in_hospital          101766 non-null  int64 \n",
      " 10  payer_code                101766 non-null  object\n",
      " 11  medical_specialty         101766 non-null  object\n",
      " 12  num_lab_procedures        101766 non-null  int64 \n",
      " 13  num_procedures            101766 non-null  int64 \n",
      " 14  num_medications           101766 non-null  int64 \n",
      " 15  number_outpatient         101766 non-null  int64 \n",
      " 16  number_emergency          101766 non-null  int64 \n",
      " 17  number_inpatient          101766 non-null  int64 \n",
      " 18  diag_1                    101766 non-null  object\n",
      " 19  diag_2                    101766 non-null  object\n",
      " 20  diag_3                    101766 non-null  object\n",
      " 21  number_diagnoses          101766 non-null  int64 \n",
      " 22  max_glu_serum             101766 non-null  object\n",
      " 23  A1Cresult                 101766 non-null  object\n",
      " 24  metformin                 101766 non-null  object\n",
      " 25  repaglinide               101766 non-null  object\n",
      " 26  nateglinide               101766 non-null  object\n",
      " 27  chlorpropamide            101766 non-null  object\n",
      " 28  glimepiride               101766 non-null  object\n",
      " 29  acetohexamide             101766 non-null  object\n",
      " 30  glipizide                 101766 non-null  object\n",
      " 31  glyburide                 101766 non-null  object\n",
      " 32  tolbutamide               101766 non-null  object\n",
      " 33  pioglitazone              101766 non-null  object\n",
      " 34  rosiglitazone             101766 non-null  object\n",
      " 35  acarbose                  101766 non-null  object\n",
      " 36  miglitol                  101766 non-null  object\n",
      " 37  troglitazone              101766 non-null  object\n",
      " 38  tolazamide                101766 non-null  object\n",
      " 39  examide                   101766 non-null  object\n",
      " 40  citoglipton               101766 non-null  object\n",
      " 41  insulin                   101766 non-null  object\n",
      " 42  glyburide-metformin       101766 non-null  object\n",
      " 43  glipizide-metformin       101766 non-null  object\n",
      " 44  glimepiride-pioglitazone  101766 non-null  object\n",
      " 45  metformin-rosiglitazone   101766 non-null  object\n",
      " 46  metformin-pioglitazone    101766 non-null  object\n",
      " 47  change                    101766 non-null  object\n",
      " 48  diabetesMed               101766 non-null  object\n",
      " 49  readmitted                101766 non-null  object\n",
      "dtypes: int64(13), object(37)\n",
      "memory usage: 38.8+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#Print dataset info for each column\n",
    "print(DiabeticDataset.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially, total number of features is 49 and # of examples is 101766"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- # of features = 49\n",
      "- # of examples = 101766\n"
     ]
    }
   ],
   "source": [
    "#Function that calculates number of features and number of examples in the data\n",
    "def printBasicStats(dataset):\n",
    "  print('- # of features = %d' % (len(dataset.keys()) - 1))\n",
    "  print('- # of examples = %d' % len(dataset))\n",
    "#print results from printBasicStats on Diabetes dataset\n",
    "printBasicStats(DiabeticDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will focus on the data cleaning. We will go one feature at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check the percentabloe of null "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101761</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101762</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101763</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101764</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101765</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101766 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        encounter_id  patient_nbr   race  gender    age  weight  \\\n",
       "0              False        False  False   False  False   False   \n",
       "1              False        False  False   False  False   False   \n",
       "2              False        False  False   False  False   False   \n",
       "3              False        False  False   False  False   False   \n",
       "4              False        False  False   False  False   False   \n",
       "...              ...          ...    ...     ...    ...     ...   \n",
       "101761         False        False  False   False  False   False   \n",
       "101762         False        False  False   False  False   False   \n",
       "101763         False        False  False   False  False   False   \n",
       "101764         False        False  False   False  False   False   \n",
       "101765         False        False  False   False  False   False   \n",
       "\n",
       "        admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "0                   False                     False                False   \n",
       "1                   False                     False                False   \n",
       "2                   False                     False                False   \n",
       "3                   False                     False                False   \n",
       "4                   False                     False                False   \n",
       "...                   ...                       ...                  ...   \n",
       "101761              False                     False                False   \n",
       "101762              False                     False                False   \n",
       "101763              False                     False                False   \n",
       "101764              False                     False                False   \n",
       "101765              False                     False                False   \n",
       "\n",
       "        time_in_hospital  ...  citoglipton  insulin  glyburide-metformin  \\\n",
       "0                  False  ...        False    False                False   \n",
       "1                  False  ...        False    False                False   \n",
       "2                  False  ...        False    False                False   \n",
       "3                  False  ...        False    False                False   \n",
       "4                  False  ...        False    False                False   \n",
       "...                  ...  ...          ...      ...                  ...   \n",
       "101761             False  ...        False    False                False   \n",
       "101762             False  ...        False    False                False   \n",
       "101763             False  ...        False    False                False   \n",
       "101764             False  ...        False    False                False   \n",
       "101765             False  ...        False    False                False   \n",
       "\n",
       "        glipizide-metformin  glimepiride-pioglitazone  \\\n",
       "0                     False                     False   \n",
       "1                     False                     False   \n",
       "2                     False                     False   \n",
       "3                     False                     False   \n",
       "4                     False                     False   \n",
       "...                     ...                       ...   \n",
       "101761                False                     False   \n",
       "101762                False                     False   \n",
       "101763                False                     False   \n",
       "101764                False                     False   \n",
       "101765                False                     False   \n",
       "\n",
       "        metformin-rosiglitazone  metformin-pioglitazone  change  diabetesMed  \\\n",
       "0                         False                   False   False        False   \n",
       "1                         False                   False   False        False   \n",
       "2                         False                   False   False        False   \n",
       "3                         False                   False   False        False   \n",
       "4                         False                   False   False        False   \n",
       "...                         ...                     ...     ...          ...   \n",
       "101761                    False                   False   False        False   \n",
       "101762                    False                   False   False        False   \n",
       "101763                    False                   False   False        False   \n",
       "101764                    False                   False   False        False   \n",
       "101765                    False                   False   False        False   \n",
       "\n",
       "        readmitted  \n",
       "0            False  \n",
       "1            False  \n",
       "2            False  \n",
       "3            False  \n",
       "4            False  \n",
       "...            ...  \n",
       "101761       False  \n",
       "101762       False  \n",
       "101763       False  \n",
       "101764       False  \n",
       "101765       False  \n",
       "\n",
       "[101766 rows x 50 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding null values\n",
    "DiabeticDataset.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the results below, we could see that wight, payer code, medical specialty Have high percentage of missing values. We will drop them later.\n",
    "The ones with small percentage missing values, we need to come up with a strategy on how to replace them or just drop those rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------------+----------------+---------------------+\n",
      "|       Column Name        | Missing Values |  Missing Percentage |\n",
      "+--------------------------+----------------+---------------------+\n",
      "|       encounter_id       |       0        |         0.0         |\n",
      "|       patient_nbr        |       0        |         0.0         |\n",
      "|           race           |      2273      |  2.2335554114340743 |\n",
      "|          gender          |       0        |         0.0         |\n",
      "|           age            |       0        |         0.0         |\n",
      "|          weight          |     98569      |  96.85847925633315  |\n",
      "|    admission_type_id     |       0        |         0.0         |\n",
      "| discharge_disposition_id |       0        |         0.0         |\n",
      "|   admission_source_id    |       0        |         0.0         |\n",
      "|     time_in_hospital     |       0        |         0.0         |\n",
      "|        payer_code        |     40256      |   39.5574160328597  |\n",
      "|    medical_specialty     |     49949      |  49.08220820313268  |\n",
      "|    num_lab_procedures    |       0        |         0.0         |\n",
      "|      num_procedures      |       0        |         0.0         |\n",
      "|     num_medications      |       0        |         0.0         |\n",
      "|    number_outpatient     |       0        |         0.0         |\n",
      "|     number_emergency     |       0        |         0.0         |\n",
      "|     number_inpatient     |       0        |         0.0         |\n",
      "|          diag_1          |       21       | 0.02063557573256294 |\n",
      "|          diag_2          |      358       |  0.3517874339170253 |\n",
      "|          diag_3          |      1423      |  1.398305917497003  |\n",
      "|     number_diagnoses     |       0        |         0.0         |\n",
      "|      max_glu_serum       |       0        |         0.0         |\n",
      "|        A1Cresult         |       0        |         0.0         |\n",
      "|        metformin         |       0        |         0.0         |\n",
      "|       repaglinide        |       0        |         0.0         |\n",
      "|       nateglinide        |       0        |         0.0         |\n",
      "|      chlorpropamide      |       0        |         0.0         |\n",
      "|       glimepiride        |       0        |         0.0         |\n",
      "|      acetohexamide       |       0        |         0.0         |\n",
      "|        glipizide         |       0        |         0.0         |\n",
      "|        glyburide         |       0        |         0.0         |\n",
      "|       tolbutamide        |       0        |         0.0         |\n",
      "|       pioglitazone       |       0        |         0.0         |\n",
      "|      rosiglitazone       |       0        |         0.0         |\n",
      "|         acarbose         |       0        |         0.0         |\n",
      "|         miglitol         |       0        |         0.0         |\n",
      "|       troglitazone       |       0        |         0.0         |\n",
      "|        tolazamide        |       0        |         0.0         |\n",
      "|         examide          |       0        |         0.0         |\n",
      "|       citoglipton        |       0        |         0.0         |\n",
      "|         insulin          |       0        |         0.0         |\n",
      "|   glyburide-metformin    |       0        |         0.0         |\n",
      "|   glipizide-metformin    |       0        |         0.0         |\n",
      "| glimepiride-pioglitazone |       0        |         0.0         |\n",
      "| metformin-rosiglitazone  |       0        |         0.0         |\n",
      "|  metformin-pioglitazone  |       0        |         0.0         |\n",
      "|          change          |       0        |         0.0         |\n",
      "|       diabetesMed        |       0        |         0.0         |\n",
      "|        readmitted        |       0        |         0.0         |\n",
      "+--------------------------+----------------+---------------------+\n"
     ]
    }
   ],
   "source": [
    "#creating a table to show percentage of missing values in each column\n",
    "t = PrettyTable(['Column Name', 'Missing Values', 'Missing Percentage'])\n",
    "#Loop through each column get total number of missing values then calculate percentage\n",
    "for col in DiabeticDataset.columns :\n",
    "    lenn = len(DiabeticDataset[DiabeticDataset[col] == '?'])\n",
    "    t.add_row([col,lenn,(lenn / len(DiabeticDataset)*100)])\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Race "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Print count for each race category\n",
    "DiabeticDataset['race'].value_counts()\n",
    "#plot the results\n",
    "sns.countplot(x = 'race', data = DiabeticDataset)\n",
    "plt.title(\"Race\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Race has missing values '?' with less than 2000 rows. We decided we will replace the ? values with the majority category which is caucasian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    76099\n",
       "2    19210\n",
       "5     2037\n",
       "4     1506\n",
       "3      641\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop the missing value\n",
    "DiabeticDataset = DiabeticDataset[DiabeticDataset.race != '?']\n",
    "#Map the categories to numerical values\n",
    "DiabeticDataset['race'] = DiabeticDataset['race'].apply(lambda x : 1 if x == 'Caucasian' \n",
    "                                                            else ( 2 if x == 'AfricanAmerican'                                                          \n",
    "                                                            else ( 3 if x == 'Asian'\n",
    "                                                            else ( 4 if x == 'Other'\n",
    "                                                            else ( 5 if x == 'Hispanic'\n",
    "                                                            else  0)))))\n",
    "#Print new count\n",
    "DiabeticDataset['race'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a very small number of unknowns for Gender. We decided to drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    53575\n",
       "0    45917\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop the 2 missing values\n",
    "DiabeticDataset = DiabeticDataset[DiabeticDataset.gender != 'Unknown/Invalid']\n",
    "#map gender categories to numerical values\n",
    "DiabeticDataset['gender'] = DiabeticDataset['gender'].apply(lambda x : 1 if x == 'Female' \n",
    "                                                            else  0)\n",
    "#New count\n",
    "DiabeticDataset['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEWCAYAAACqitpwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcy0lEQVR4nO3dfbRcdX3v8feXRCPIgwEChiQYVGoFikFyIxarKBWiSwEVbLgqacWbygKfatsFcq/S2twltUp9gnvx8qyIEUTQJQJCwatFIMEISRAJghAJSRQK1F7R4Pf+sX/T7HOYc87MyZ6Zk+T9WmvW2fOb/dv7u/c8fM5+mD2RmUiS1KTtBl2AJGnrY7hIkhpnuEiSGme4SJIaZ7hIkhpnuEiSGme4SJIaZ7hIfRIRN0XEYxExZdC1SL1muEh9EBGzgT8BEjhqsNVIvWe4SP1xAvBD4EJgYasxInaLiG9GxBMRcXtE/ENEfL/2+B9GxPUR8WhE3BMRb+9/6VL3Jg+6AGkbcQLwaeBW4IcRsWdmrgO+APwaeD4wG7gW+DlARDwXuB74KPAG4EDguohYmZkr+74EUhfccpF6LCJeBbwAWJKZy4D7gP8aEZOAtwEfy8z/yMxVwEW1rm8CHsjMCzJzY2beAVwBHNvnRZC6ZrhIvbcQuC4zf1nuX1raplHtPXioNm59+AXAKyLi31o34B1UWznShOZuMamHImJ74O3ApIh4pDRPAZ4H7AlsBGYCPy2Pzap1fwi4OTNf359qpeaEl9yXeicijqc6rjIH+G3toSXA7VTB8jTwHmBv4Drgwcx8VUTsBKwA/jtwWek3B/j3zLy7H/VL4+VuMam3FgIXZOaDmflI6wZ8nmoX1ynALsAjwCXAV4CnADLzSeAIYAHwcBnnTKotH2lCc8tFmkAi4kzg+Zm5cMyRpQnMLRdpgMr3WA6MyjzgRODKQdclbS4P6EuDtRPVrrC9gPXAp4CrBlqR1AB3i0mSGuduMUlS47a53WK77757zp49e9BlSNIWZdmyZb/MzGmdjr/Nhcvs2bNZunTpoMuQpC1KRPy8m/HdLSZJapzhIklqnOEiSWqc4SJJapzhIklqnOEiSWqc4SJJapzhIklqnOEiSWpcz76hHxGzgIupfu/798C5mfmZiDgD+G/AhjLqRzLz26XPaVSXHH8aeH9mXlvaDwYuBLYHvg18IDMzIqaUeRwM/Ar4s8x8oFfLJGkwzjjjjK1yXluzXm65bAQ+nJkvBQ4BTo6I/cpjZ2XmnHJrBct+VL+4tz8wHzg7IiaV8c8BFgH7ltv80n4i8Fhmvhg4i+pX+iRJA9azcMnMtZl5Rxl+ErgbmDFKl6OByzLzqcy8H1gNzIuI6cDOmXlLVr8PcDFwTK3PRWX4cuDwiIjml0aS1I2+HHOJiNnAQcCtpemUiLgzIs6PiKmlbQbwUK3bmtI2owwPbx/SJzM3Ao8Du/ViGSRJnet5uETEjsAVwAcz8wmqXVwvAuYAa6l+eQ+g3RZHjtI+Wp/hNSyKiKURsXTDhg1tukiSmtTTcImIZ1EFy5cz8+sAmbkuM5/OzN8DXwTmldHXALNq3WcCD5f2mW3ah/SJiMnALsCjw+vIzHMzc25mzp02reOfI5AkjVPPwqUc+zgPuDszP11rn14b7S3AijJ8NbAgIqZExD5UB+5vy8y1wJMRcUiZ5gls+o3xq4GFZfhY4Mb0d5slaeB6+WNhhwLvAu6KiOWl7SPA8RExh2r31QPAXwJk5sqIWAKsojrT7OTMfLr0O4lNpyJfU25QhdclEbGaaotlQQ+XR5LUoZ6FS2Z+n/bHRL49Sp/FwOI27UuBA9q0/wY4bjPKlCT1gN/QlyQ1znCRJDXOcJEkNc5wkSQ1znCRJDXOcJEkNc5wkSQ1znCRJDXOcJEkNc5wkSQ1znCRJDXOcJEkNc5wkSQ1znCRJDXOcJEkNc5wkSQ1znCRJDXOcJEkNc5wkSQ1bvKgC5A0cd29+Ma+zeulp7+ub/NS77nlIklqnOEiSWqc4SJJapzhIklqnOEiSWqc4SJJapzhIklqnOEiSWqc4SJJapzhIklqnOEiSWqc4SJJalzPwiUiZkXEv0TE3RGxMiI+UNp3jYjrI+Le8ndqrc9pEbE6Iu6JiCNr7QdHxF3lsc9GRJT2KRHx1dJ+a0TM7tXySJI618stl43AhzPzpcAhwMkRsR9wKnBDZu4L3FDuUx5bAOwPzAfOjohJZVrnAIuAfcttfmk/EXgsM18MnAWc2cPlkSR1qGfhkplrM/OOMvwkcDcwAzgauKiMdhFwTBk+GrgsM5/KzPuB1cC8iJgO7JyZt2RmAhcP69Oa1uXA4a2tGknS4PTlmEvZXXUQcCuwZ2auhSqAgD3KaDOAh2rd1pS2GWV4ePuQPpm5EXgc2K3N/BdFxNKIWLphw4aGlkqSNJKeh0tE7AhcAXwwM58YbdQ2bTlK+2h9hjZknpuZczNz7rRp08YqWZK0mXoaLhHxLKpg+XJmfr00ryu7uih/15f2NcCsWveZwMOlfWab9iF9ImIysAvwaPNLIknqRi/PFgvgPODuzPx07aGrgYVleCFwVa19QTkDbB+qA/e3lV1nT0bEIWWaJwzr05rWscCN5biMJGmAJvdw2ocC7wLuiojlpe0jwCeAJRFxIvAgcBxAZq6MiCXAKqozzU7OzKdLv5OAC4HtgWvKDarwuiQiVlNtsSzo4fJIkjrUs3DJzO/T/pgIwOEj9FkMLG7TvhQ4oE37byjhJEmaOPyGviSpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcZMHXYCk9ha/89i+zev0L13et3lp22C4SFIHlnxtXt/m9fbjbuvbvHrF3WKSpMYZLpKkxhkukqTGGS6SpMYZLpKkxhkukqTGGS6SpMYZLpKkxhkukqTGGS6SpMYZLpKkxvUsXCLi/IhYHxEram1nRMQvImJ5ub2x9thpEbE6Iu6JiCNr7QdHxF3lsc9GRJT2KRHx1dJ+a0TM7tWySJK608stlwuB+W3az8rMOeX2bYCI2A9YAOxf+pwdEZPK+OcAi4B9y601zROBxzLzxcBZwJm9WhBJUnd6Fi6Z+T3g0Q5HPxq4LDOfysz7gdXAvIiYDuycmbdkZgIXA8fU+lxUhi8HDm9t1UiSBmsQx1xOiYg7y26zqaVtBvBQbZw1pW1GGR7ePqRPZm4EHgd2azfDiFgUEUsjYumGDRuaWxJJUlv9DpdzgBcBc4C1wKdKe7stjhylfbQ+z2zMPDcz52bm3GnTpnVVsCSpex2FS0Tc0EnbWDJzXWY+nZm/B74ItH59Zw0wqzbqTODh0j6zTfuQPhExGdiFznfDSZJ6aNRwiYjnRMSuwO4RMTUidi232cBe3c6sHENpeQvQOpPsamBBOQNsH6oD97dl5lrgyYg4pBxPOQG4qtZnYRk+FrixHJeRJA3YWD9z/JfAB6mCZBmbdkU9AXxhtI4R8RXgMKpgWgN8DDgsIuZQ7b56oEyfzFwZEUuAVcBG4OTMfLpM6iSqM8+2B64pN4DzgEsiYjXVFsuCMZZFktQno4ZLZn4G+ExEvC8zP9fNhDPz+DbN540y/mJgcZv2pcABbdp/AxzXTU2SpP4Ya8sFgMz8XET8MTC73iczL+5RXZKkLVhH4RIRl1Cd5bUcaO2uan3vRJKkIToKF2AusJ8HzCVJnej0ey4rgOf3shBJ0taj0y2X3YFVEXEb8FSrMTOP6klVkqQtWqfhckYvi5AkbV06PVvs5l4XIknaenR6ttiTbLpu17OBZwG/zsyde1WYJGnL1emWy071+xFxDJuuCyZJ0hDjuipyZn4DeF2zpUiSthad7hZ7a+3udlTfe/E7L5Kktjo9W+zNteGNVBedPLrxaiRJW4VOj7n8Ra8LkSRtPTr9sbCZEXFlRKyPiHURcUVEzBy7pyRpW9TpAf0LqH6cay+q367/ZmmTJOkZOg2XaZl5QWZuLLcLAX+MXpLUVqfh8suIeGdETCq3dwK/6mVhkqQtV6fh8m7g7cAjwFqq36z3IL8kqa1OT0X+OLAwMx8DiIhdgX+iCh1JkobodMvlwFawAGTmo8BBvSlJkrSl6zRctouIqa07Zcul060eSdI2ptOA+BTwrxFxOdVlX94OLO5ZVZKkLVqn39C/OCKWUl2sMoC3ZuaqnlYmSdpidbxrq4SJgSJJGtO4LrkvSdJoDBdJUuMMF0lS4wwXSVLjDBdJUuMMF0lS4wwXSVLjehYuEXF++eXKFbW2XSPi+oi4t/ytX1LmtIhYHRH3RMSRtfaDI+Ku8thnIyJK+5SI+GppvzUiZvdqWSRJ3enllsuFwPxhbacCN2TmvsAN5T4RsR+wANi/9Dk7IiaVPucAi4B9y601zROBxzLzxcBZwJk9WxJJUld6Fi6Z+T3g0WHNRwMXleGLgGNq7Zdl5lOZeT+wGpgXEdOBnTPzlsxM4OJhfVrTuhw4vLVVI0karH4fc9kzM9cClL97lPYZwEO18daUthlleHj7kD6ZuRF4HNit3UwjYlFELI2IpRs2bGhoUSRJI5koB/TbbXHkKO2j9XlmY+a5mTk3M+dOmzZtnCVKkjrV73BZV3Z1Uf6uL+1rgFm18WYCD5f2mW3ah/SJiMnALjxzN5wkaQD6HS5XAwvL8ELgqlr7gnIG2D5UB+5vK7vOnoyIQ8rxlBOG9WlN61jgxnJcRpI0YD37NcmI+ApwGLB7RKwBPgZ8AlgSEScCDwLHAWTmyohYQnVJ/43AyZn5dJnUSVRnnm0PXFNuAOcBl0TEaqotlgW9WhZJUnd6Fi6ZefwIDx0+wviLafPrlpm5FDigTftvKOEkNenzH/5m3+Z1yqfe3Ld5Sf00UQ7oS5K2IoaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxhoskqXGGiySpcYaLJKlxAwmXiHggIu6KiOURsbS07RoR10fEveXv1Nr4p0XE6oi4JyKOrLUfXKazOiI+GxExiOWRJA01yC2X12bmnMycW+6fCtyQmfsCN5T7RMR+wAJgf2A+cHZETCp9zgEWAfuW2/w+1i9JGsFE2i12NHBRGb4IOKbWfllmPpWZ9wOrgXkRMR3YOTNvycwELq71kSQN0KDCJYHrImJZRCwqbXtm5lqA8neP0j4DeKjWd01pm1GGh7c/Q0QsioilEbF0w4YNDS6GJKmdyQOa76GZ+XBE7AFcHxE/GWXcdsdRcpT2ZzZmngucCzB37ty240iSmjOQLZfMfLj8XQ9cCcwD1pVdXZS/68voa4BZte4zgYdL+8w27ZKkAet7uETEcyNip9YwcASwArgaWFhGWwhcVYavBhZExJSI2IfqwP1tZdfZkxFxSDlL7IRaH0nSAA1it9iewJXlrOHJwKWZ+Z2IuB1YEhEnAg8CxwFk5sqIWAKsAjYCJ2fm02VaJwEXAtsD15SbJGnA+h4umfkz4GVt2n8FHD5Cn8XA4jbtS4EDmq5RkrR5JtKpyJKkrYThIklqnOEiSWqc4SJJapzhIklq3KC+oS+1dfOrX9O3eb3mezf3bV7StsYtF0lS4wwXSVLjDBdJUuMMF0lS4wwXSVLjDBdJUuMMF0lS4/yeiyRtQV52+bV9m9ePjz1y3H3dcpEkNc5wkSQ1znCRJDXOcJEkNc5wkSQ1znCRJDXOcJEkNc5wkSQ1zi9RCoBDP3do3+b1g/f9oG/zkjQYbrlIkhpnuEiSGme4SJIaZ7hIkhpnuEiSGme4SJIa56nIE8CDf/9HfZvX3h+9q2/zkrTtcstFktQ4w0WS1LgtPlwiYn5E3BMRqyPi1EHXI0nawo+5RMQk4AvA64E1wO0RcXVmruqk/8F/c3Evyxti2SdP6Nu8JGnQtvQtl3nA6sz8WWb+FrgMOHrANUnSNi8yc9A1jFtEHAvMz8z3lPvvAl6RmacMG28RsKjcfQlwz2bOenfgl5s5jc01EWqAiVHHRKgBJkYdE6EGmBh1TIQaYGLU0UQNL8jMaZ2OvEXvFgOiTdsz0jIzzwXObWymEUszc25T09tSa5godUyEGiZKHROhholSx0SoYaLUMYgatvTdYmuAWbX7M4GHB1SLJKnY0sPldmDfiNgnIp4NLACuHnBNkrTN26J3i2Xmxog4BbgWmAScn5kr+zDrxnaxbYaJUANMjDomQg0wMeqYCDXAxKhjItQAE6OOvtewRR/QlyRNTFv6bjFJ0gRkuEiSGme4SJKal5nb5A2YDfw/YHm5P5/qy5WrgVNH6Xc+sB5YMax9V+B64N7yd2pp/yPgwjHm3dU029T0SeAnwJ3AlcDzao+dVpbpHuDIWvv3WzVQnc79L8DdwErgA+Oo4eNl/suB64C9Oqjhu8CBtTqeA9wG/LjU8Xfd1lEb/6+pvvO0ezfrotY2CfgR8K1xrIszgF+UZVoOvLHbdVHaHgDuKtNYOp51AbyvzGsl8I/drgvgecDl5fV1N/DKLtfFV2vr4YFh67jT18VLatNYDjwBfLDLOuYAP2ytS2DeONbFh8p6XAF8BXhOlzW8DLilPKffBHYeoYYTasv+XeDUMs+VreXucr7Hlb6/B+YOe2ykZT+41Lka+Cybjs+fAvzFaO+9/5xGJyNtjTeqD/gVZXgScB/wQuDZVB9u+43Q79XAy3lmEPwjJZTKi+HMYW+WvdvNe7zTHDbeEcDkMnxmazxgv7IsU4B9yjJOKo99GFhXhqcDLy/DOwE/bS1/FzXU3yjvB/5XBzUspArG1vMQwI5l+FnArcAh3dRRHp9FdQbhzynh0um6qE3jr4BLGRouna6LM4C/btPe8boobQ9QC8dx1PHa8tqbUu7v0e26AC4C3lOGn035x6Wb56M2rU8BHx3PuqhNYxLwCNW3xbtZF9cBbyjDbwRu6vI9MgO4H9i+3F8C/HmXNdwOvKYMvxv4+Ag1/JxN74mPAOuAHajO7v0usG+X830pVUDfRC1cxlj224BXUr0nr6mtux2AH431XGcaLq0n8JXAtbXHTgNO66Rvre0eYHoZng7cU3vsA8DfjtG/q2mOUttbgC+3Ww6qD9zWf54HAr8ZYRpXAa/fjBpOA87poIapZfrtPkR2AO6gupxPV3VQ/af9Mmofzt2sC6ov494AvI6h4dJRDYwcLl2tC0YOl07rWAL8aZd1/Oe6AHam+kCN8dZQGz+Ah9j0wTje18URwA/GsS6uBf6sDB8PXNrluphR6t+V6kP+W8ARXdbwBJu2AGYBq0ao4WbgvjL858Cjtcf+B+WzZBzPwU0MDZe2y16m9ZNa+/HA/67dv5Lalt9IN4+5VFovnJY1pa0be2bmWoDyd4/aY0uBPxlHXaNNcyTvpvpPA0ZfrieAiIjd6p0jYjZwENVWQ1c1RMTiiHgIeAfw0bFqyMzHqP4bnlSbxqSIWE61m/D6zOyqjog4CvhFZv542EPdrIt/Bv6WajdCXTfPxykRcWdEnB8RU8eqod26oNqtd11ELCvXx+u2jj8A/iQibo2ImyPiv4xVB0PXxQuBDcAFEfGjiPg/EfHccawLqF7/6zLz3nGui5YFVLukWjqt44PAJ8vr85+oPlhHrYPausjMX5R+DwJrgccz87oua1gBHFWGj2PT1UWG1/AI1ZY7VO/DHSPixRGxA9VWV6vfeD4f6kZa9hlleHh7S0efZ4ZLpaNrlG2G9cBeDU6vrYg4HdgIfLnV1Ga0+nJtrNcVETsCV1Dt132i2/ln5umZOavMv3Xx0LFq+BW1L/Nm5tOZOYdq62FeRBzQ6fzLm+90NgXbkIfHqGMjsFdEvAlYn5nLOp1vG+cAL6Laz7+WandQJzUMWRfAoZn5cuANwMkR8eou65hMtRVwCPA3wJKIiA7qaL0uJlPtrj0nMw8Cfk21+2U8jmdoKHS7LihX4TgK+No45n8S8KHy+vwQcF6HdbReF1Oprri+D9W6eW5EvLPLGt5N9Twuo9r9/NtRaqgKybybajfZ1cB3qHZjbexyviMZadnHWicdfZ4ZLpW21yiLiFkRsbzc3jvGNNZFxHSA8nd97bHnUB2g61bbaUbEBaWmb7dGjIiFwJuAd2TZdh1puWr3t2vVFRHPogqWL2fm18dTQ82lwNs6rGEK7S82+m9Um/Hzu6jjRVRv/h9HxANlXndExPO7WBeHAkeV/pcBr4uIL3WzLjJzXQnJ3wNfpPppiK7XRWY+XP6up+yK6KaOMr+vZ+U2qi2x3btYF2uANbWtx8upwqabGoiIycBbqQ7ut4zndfEG4I7MXFdr67SOhUDrdf01On9OWuviT4H7M3NDZv6uTOuPu6khM3+SmUdk5sFUQXvfCDU8H/hd7f4TwFGZ+WrgUaoD+N0s+0hGWvY1ZXikddLZ59lY+8221htDj7lMBn5G9cHUOqC/fyd9a22fZOjBtfqZOW+jHOAepX9X0xw23nxgFTBtWPv+DD1g9zM2HbCbTfUCnkz1n8rFwD+3mXanNexbG34fcHkHNQTVLoDW8zCNTQeMtwf+L/CmbuoYVtMDbDrm0tG6GNb/MIYec+l0XUyvDX8IuGwc6+K5wE614X+l+nmJbup4L/D3ZfgPqHaBRDfrojwHLynDZwCf7Pb5oHp93tzFa3PIuqj1uYxhZyp1sS7uBg4rw4cDy7p8j7yC6oyrHUp9FwHv67KG1gkV21G93949Qg0PMvQkl7Wlhr2pztqb2s18a/O/iaHHXEZb9tuptnhbB/TrZzx+Dlgw5vtvrBG21hvPPGPrjVRnSd0HnD5Kv6+UJ/t3VAl/Ymnfjeog8L3l7661Pp8H3jzKvLue5rCaVlN9cCwvt3qQnV6W6R7KGR+l/c1U+40BXkX1X+KdDDt9tosarqDap3wn1WmWMzqoYW554bbeSAdSnf57Z5nWR2vjdlTHsJoeYOipyGOui2H9D2NouHS6Li6hOo3zTqrdGdPHsS5eSPXGb52Wffo46ng28KWyLu8AXtftuqDatbe0LMs32PTB1vHzAVwIvLdNe0frorTtQLWrbJdh0+h0XbwKWFbW563AweNYF39H9eG+ojzHU7qs4QNUnzE/BT5B7USJYTUsrL0O5lL9DsuqUvvh41j2t1B9rjxFdebZtR0s+9yynPdRfX7Va72DNieaDL9ts9cWKweuv5WZHe/TH+d8plCd/fGqzNzYz3mPUdf5wGszc58B1vAZqjf6R1wXrotaDdv0uqh/PpR1cXVm3tDPGkYSEQcBf5WZ7xpr3G35mMvTwC7lzKRe2ptq07V+EK5f8x7NT4DJA65hBdVuF9eF66JuW18X9c+HFRMlWIrdqU6HHtM2u+UiSeqdbXnLRZLUI4aLJKlxhoskqXGGiySpcYaL1EcR8Y1yrbCVreuFRcSJEfHTiLgpIr4YEZ8v7dMi4oqIuL3cDh1s9VLnPFtM6qOI2DUzH42I7am+BX0k8AOqy6o8CdwI/DgzT4mIS4GzM/P7EbE31ZffXjqw4qUuTB57FEkNen9EvKUMzwLeRXVplEcBIuJrVJdqgep6VvtV15oEYOeI2Ckzn+xnwdJ4GC5Sn0TEYVSB8crM/I+IuInqshsjbY1sV8Ydz0VPpYHymIvUP7sAj5Vg+UOqCwPuALwmIqaWqwe/rTb+dWz66QIiYk4/i5U2h+Ei9c93qC4ncifwcarfdP8F8D+prqX1XaoLFD5exn8/MDeqHx1bRXWVY2mL4AF9acAiYsfM/Pey5XIlcH5mXjnouqTN4ZaLNHhntC5SSPWb9d8YaDVSA9xykSQ1zi0XSVLjDBdJUuMMF0lS4wwXSVLjDBdJUuP+P0Fqhz5gWTc1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the age columns distribution\n",
    "sns.countplot(x = 'age', data = DiabeticDataset)\n",
    "plt.title(\"Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will remap the age categories into numeric values. We just chose mid age of each range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75    25468\n",
       "65    21988\n",
       "55    16895\n",
       "85    16800\n",
       "45     9465\n",
       "35     3699\n",
       "95     2724\n",
       "25     1611\n",
       "15      682\n",
       "5       160\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for training the models, we will convert each category to mid value\n",
    "DiabeticDataset['age'] = DiabeticDataset['age'].map({'[70-80)': 75,\n",
    "                             '[60-70)': 65, '[50-60)': 55, '[80-90)':85, '[40-50)':45,                                                                  \n",
    "                             '[30-40)': 35, '[90-100)':95, '[20-30)':25, '[10-20)':15,\n",
    "                             '[0-10)':5 })\n",
    "#show count\n",
    "DiabeticDataset['age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Admission Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will group 5: Not available, 6: NULL and 8: Not Mapped on the same category 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    52900\n",
       "3    18507\n",
       "2    17786\n",
       "5    10269\n",
       "7       20\n",
       "4       10\n",
       "Name: admission_type_id, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We will group 5: Not available, 6: NULL and 8: Not Mapped on the same category 5\n",
    "DiabeticDataset['admission_type_id'] = DiabeticDataset['admission_type_id'].apply(lambda x : 5 if int(x) in [6, 8]\n",
    "                                                            else int(x))\n",
    "DiabeticDataset['admission_type_id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discharge Disposition ID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will drop all rows that contain Hospice and Expired to remove bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     58767\n",
       "3     13614\n",
       "6     12698\n",
       "18     3673\n",
       "2      2061\n",
       "22     1981\n",
       "5      1143\n",
       "25      973\n",
       "4       765\n",
       "7       611\n",
       "23      406\n",
       "28      138\n",
       "8       107\n",
       "15       63\n",
       "24       48\n",
       "9        21\n",
       "17       14\n",
       "16       11\n",
       "10        6\n",
       "27        5\n",
       "12        3\n",
       "Name: discharge_disposition_id, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We will drop all rows that contain Hospice and Expired to remove bias\n",
    "DiabeticDataset = DiabeticDataset[~DiabeticDataset.discharge_disposition_id.isin([11,13,14,19,20,21])]\n",
    "\n",
    "DiabeticDataset['discharge_disposition_id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Admission Source ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     55012\n",
       "1     28461\n",
       "17     6489\n",
       "4      2923\n",
       "6      1964\n",
       "2      1021\n",
       "5       800\n",
       "3       185\n",
       "20      158\n",
       "9        54\n",
       "8        15\n",
       "22       12\n",
       "10        7\n",
       "11        2\n",
       "14        2\n",
       "25        2\n",
       "13        1\n",
       "Name: admission_source_id, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show count of each admissions source id\n",
    "DiabeticDataset.admission_source_id.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will group Null 9: Not available, 15: Not Available, 17:Null, 20: not mapped as they all mean the value is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     55012\n",
       "1     28461\n",
       "9      6701\n",
       "4      2923\n",
       "6      1964\n",
       "2      1021\n",
       "5       800\n",
       "3       185\n",
       "8        15\n",
       "22       12\n",
       "10        7\n",
       "25        2\n",
       "14        2\n",
       "11        2\n",
       "13        1\n",
       "Name: admission_source_id, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group Null 9: Not available, 15: Not Available, 17:Null, 20: not mapped as they all mean the value is missing\n",
    "DiabeticDataset['admission_source_id'] = DiabeticDataset['admission_source_id'].apply(lambda x : 9 if int(x) in [15, 17, 20]\n",
    "                                                            else int(x))\n",
    "DiabeticDataset.admission_source_id.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time in Hospital"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No changes needed for this field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time_in_hospital', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEHCAYAAACEKcAKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAActUlEQVR4nO3dfZQV9Z3n8fcn4AM+oCKNwW6SJoaYQY4TtZdhzMTNhtmBJC4QRzJ4dMAJM2wY8mCymQjr7pjZLOdokh0TM5E5xAfAOBCCJhAnRgnGmFkVpn0KTxJ7BpVWhE5iDJuckKDf/aN+fVI2t7svXXXvtenP65x7bt1v1e9bv9tw+9v1q7r1U0RgZmY2UG9odAfMzGxwcyExM7NCXEjMzKwQFxIzMyvEhcTMzAoZ3ugO1Nvo0aOjtbW10d0wMxtUHn300Z9ERFOldUOukLS2ttLe3t7obpiZDSqSnu1tnYe2zMysEBcSMzMrxIXEzMwKcSExM7NCXEjMzKwQFxIzMyvEhcTMzApxITEzs0JcSMzMrJAh9832o8UPv3pxabne9Vd3l5bLzIYeH5GYmVkhLiRmZlaIC4mZmRXiQmJmZoW4kJiZWSG+aqtGnv/KolLzNS/6Sqn5zMzK4iMSMzMrxIXEzMwKqVkhkXSrpP2StvWIf1TSLknbJX0uF18iqSOtm5aLXyBpa1p3oySl+HGSvp7imyW11uq9mJlZ72p5RLICmJ4PSPpPwEzg3Ig4B/hCik8E5gDnpDY3SRqWmi0DFgAT0qM753zgpYh4K3ADcH0N34uZmfWiZoUkIh4EftYjvBC4LiIOpm32p/hMYE1EHIyI3UAHMFnSWGBkRDwcEQGsAmbl2qxMy+uAqd1HK2ZmVj/1PkfyNuBdaSjqB5L+Q4o3A3ty23WmWHNa7hl/TZuIOAS8DJxeaaeSFkhql9Te1dVV2psxM7P6F5LhwGnAFOBvgLXpKKLSkUT0Eaefda8NRiyPiLaIaGtqajryXpuZWa/qXUg6gbsiswV4FRid4uNy27UAL6R4S4U4+TaShgOncPhQmpmZ1Vi9C8m3gPcASHobcCzwE2ADMCddiTWe7KT6lojYCxyQNCUducwF1qdcG4B5aflS4P50HsXMzOqoZt9sl7QaeDcwWlIncC1wK3BruiT4N8C89Mt/u6S1wA7gELAoIl5JqRaSXQE2ArgnPQBuAW6X1EF2JDKnVu/FzMx6V7NCEhGX9bLqil62XwosrRBvByZViP8amF2kj2ZmVpy/2W5mZoW4kJiZWSEuJGZmVogLiZmZFeJCYmZmhbiQmJlZIS4kZmZWiAuJmZkV4kJiZmaFuJCYmVkhLiRmZlaIC4mZmRXiQmJmZoW4kJiZWSEuJGZmVkjNComkWyXtT5NY9Vz3KUkhaXQutkRSh6Rdkqbl4hdI2prW3ZhmSiTNpvj1FN8sqbVW78XMzHpXyyOSFcD0nkFJ44D/DDyXi00km+HwnNTmJknD0uplwAKy6Xcn5HLOB16KiLcCNwDX1+RdmJlZn2pWSCLiQbIpcHu6Afg0kJ9ffSawJiIORsRuoAOYLGksMDIiHk5T8q4CZuXarEzL64Cp3UcrZmZWP3U9RyJpBvB8RDzZY1UzsCf3ujPFmtNyz/hr2kTEIeBl4PQadNvMzPpQsznbe5J0AnAN8CeVVleIRR/xvtpU2vcCsuEx3vSmN/XbVzMzq149j0jOAsYDT0p6BmgBHpP0RrIjjXG5bVuAF1K8pUKcfBtJw4FTqDyURkQsj4i2iGhramoq7Q2ZmVkdC0lEbI2IMRHRGhGtZIXg/Ih4EdgAzElXYo0nO6m+JSL2AgckTUnnP+YC61PKDcC8tHwpcH86j2JmZnVUy8t/VwMPA2dL6pQ0v7dtI2I7sBbYAXwXWBQRr6TVC4GbyU7A/xtwT4rfApwuqQP4JLC4Jm/EzMz6VLNzJBFxWT/rW3u8XgosrbBdOzCpQvzXwOxivTQzs6L8zXYzMyukbldtvd50LftaqfmaFl5Raj4zs8FiyBYS69vqFdP636hKl115b2m5zOz1x0NbZmZWiAuJmZkV4kJiZmaFuJCYmVkhLiRmZlaIC4mZmRXiQmJmZoW4kJiZWSEuJGZmVogLiZmZFeJCYmZmhbiQmJlZIS4kZmZWSC1nSLxV0n5J23Kxz0t6StKPJH1T0qm5dUskdUjaJWlaLn6BpK1p3Y1pyl3StLxfT/HNklpr9V7MzKx3tTwiWQFM7xHbCEyKiHOBHwNLACRNBOYA56Q2N0kaltosAxaQzeM+IZdzPvBSRLwVuAG4vmbvxMzMelWzQhIRDwI/6xG7LyIOpZePAC1peSawJiIORsRusvnZJ0saC4yMiIcjIoBVwKxcm5VpeR0wtftoxczM6qeR50g+BNyTlpuBPbl1nSnWnJZ7xl/TJhWnl4HTK+1I0gJJ7ZLau7q6SnsDZmbWoEIi6RrgEHBHd6jCZtFHvK82hwcjlkdEW0S0NTU1HWl3zcysD3UvJJLmARcDl6fhKsiONMblNmsBXkjxlgrx17SRNBw4hR5DaWZmVnt1LSSSpgNXAzMi4le5VRuAOelKrPFkJ9W3RMRe4ICkKen8x1xgfa7NvLR8KXB/rjCZmVmdDK9VYkmrgXcDoyV1AteSXaV1HLAxnRd/JCI+HBHbJa0FdpANeS2KiFdSqoVkV4CNIDun0n1e5RbgdkkdZEcic2r1XszMrHc1KyQRcVmF8C19bL8UWFoh3g5MqhD/NTC7SB/NzKw4f7PdzMwKcSExM7NCXEjMzKwQFxIzMyvEhcTMzApxITEzs0JcSMzMrBAXEjMzK8SFxMzMCnEhMTOzQlxIzMysEBcSMzMrxIXEzMwKcSExM7NCXEjMzKyQmhUSSbdK2i9pWy42StJGSU+n59Ny65ZI6pC0S9K0XPwCSVvTuhvTTImk2RS/nuKbJbXW6r2YmVnvanlEsgKY3iO2GNgUEROATek1kiaSzXB4Tmpzk6Rhqc0yYAHZ9LsTcjnnAy9FxFuBG4Dra/ZOzMysVzUrJBHxINkUuHkzgZVpeSUwKxdfExEHI2I30AFMljQWGBkRD6f52Ff1aNOdax0wtftoxczM6qfe50jOiIi9AOl5TIo3A3ty23WmWHNa7hl/TZuIOAS8DJxeaaeSFkhql9Te1dVV0lsxMzN4/Zxsr3QkEX3E+2pzeDBieUS0RURbU1PTALtoZmaVVFVIJG2qJlaFfWm4ivS8P8U7gXG57VqAF1K8pUL8NW0kDQdO4fChNDMzq7E+C4mk4yWNAkZLOi1ddTUqXSF15gD2twGYl5bnAetz8TnpSqzxZCfVt6ThrwOSpqTzH3N7tOnOdSlwfzqPYmZmdTS8n/X/FbiKrGg8yu+Gk34BfKWvhpJWA+8mK0KdwLXAdcBaSfOB54DZABGxXdJaYAdwCFgUEa+kVAvJrgAbAdyTHgC3ALdL6iA7EpnT77s1M7PS9VlIIuJLwJckfTQivnwkiSPisl5WTe1l+6XA0grxdmBShfivSYXIzMwap78jEgAi4suSLgRa820iYlWN+mVHuevXTOt/oypdPefe0nKZ2ZGrqpBIuh04C3gC6B5y6v5eh5mZDWFVFRKgDZjok9lmZtZTtd8j2Qa8sZYdMTOzwanaI5LRwA5JW4CD3cGImFGTXpmZ2aBRbSH5TC07YWZmg1e1V239oNYdMTOzwanaq7YO8Lv7WB0LHAP8MiJG1qpjZmY2OFR7RHJy/rWkWcDkWnTIzMwGlwHd/TcivgW8p9yumJnZYFTt0NYluZdvIPteib9TYmZmVV+19V9yy4eAZ8hmKDQzsyGu2nMkf1HrjpiZ2eBU7cRWLZK+KWm/pH2S7pTU0n9LMzM72lV7sv02somkziSbK/3bKWZmZkNctYWkKSJui4hD6bECGPDk55I+IWm7pG2SVnfPxChpo6Sn0/Npue2XSOqQtEvStFz8Aklb07ob0yyKZmZWR9UWkp9IukLSsPS4AvjpQHYoqRn4GNAWEZOAYWSzGy4GNkXEBGBTeo2kiWn9OcB04CZJw1K6ZcACsql5J6T1ZmZWR9UWkg8BHwReBPaSzZFe5AT8cGCEpOHACcALZFeBrUzrVwKz0vJMYE1EHIyI3UAHMFnSWGBkRDycbm+/KtfGzMzqpNpC8llgXkQ0RcQYssLymYHsMCKeB75ANmf7XuDliLgPOCMi9qZt9gJjUpNmYE8uRWeKNaflnnEzM6ujagvJuRHxUveLiPgZcN5AdpjOfcwExpOdvD8xDZX12qRCLPqIV9rnAkntktq7urqOtMtmZtaHagvJG3qc/B5F9V9m7OmPgd0R0RURvwXuAi4E9qXhKtLz/rR9JzAu176FbCisMy33jB8mIpZHRFtEtDU1DfgaATMzq6DaQvJ/gIckfVbS/wIeAj43wH0+B0yRdEK6ymoqsJPs8uJ5aZt5wPq0vAGYI+k4SePJTqpvScNfByRNSXnm5tqYmVmdVPvN9lWS2slu1CjgkojYMZAdRsRmSeuAx8hut/I4sBw4CVgraT5ZsZmdtt8uaS2wI22/KCJeSekWAiuAEcA96WFmZnVU9fBUKhwDKh4Vcl0LXNsjfJDs6KTS9kuBpRXi7cCkMvpkZmYDM6DbyJuZmXVzITEzs0JcSMzMrBAXEjMzK8SFxMzMChnolwrNXtfeu35e/xtV6Z6ZK/vfyGwI8xGJmZkV4kJiZmaFuJCYmVkhLiRmZlaIC4mZmRXiQmJmZoW4kJiZWSEuJGZmVogLiZmZFeJCYmZmhTSkkEg6VdI6SU9J2inpDyWNkrRR0tPpOT9H/BJJHZJ2SZqWi18gaWtad2OactfMzOqoUUckXwK+GxFvB36fbM72xcCmiJgAbEqvkTQRmAOcA0wHbpI0LOVZBiwgm8d9QlpvZmZ1VPdCImkkcBFwC0BE/CYifg7MBLrvjrcSmJWWZwJrIuJgROwGOoDJksYCIyPi4YgIYFWujZmZ1UkjjkjeAnQBt0l6XNLNkk4EzoiIvQDpeUzavhnYk2vfmWLNabln/DCSFkhql9Te1dVV7rsxMxviGlFIhgPnA8si4jzgl6RhrF5UOu8RfcQPD0Ysj4i2iGhramo60v6amVkfGlFIOoHOiNicXq8jKyz70nAV6Xl/bvtxufYtwAsp3lIhbmZmdVT3QhIRLwJ7JJ2dQlOBHcAGoHs2onnA+rS8AZgj6ThJ48lOqm9Jw18HJE1JV2vNzbUxM7M6adQMiR8F7pB0LPDvwF+QFbW1kuYDzwGzASJiu6S1ZMXmELAoIl5JeRYCK4ARwD3pYWZmddSQQhIRTwBtFVZN7WX7pcDSCvF2YFKpnTOrwvvv+mJpuf75kqtKy2XWCP5mu5mZFeJCYmZmhbiQmJlZIS4kZmZWiAuJmZkV4kJiZmaFuJCYmVkhLiRmZlaIC4mZmRXiQmJmZoW4kJiZWSEuJGZmVogLiZmZFeJCYmZmhbiQmJlZIQ0rJJKGSXpc0t3p9ShJGyU9nZ5Py227RFKHpF2SpuXiF0jamtbdmGZKNDOzOmrUDIkAHwd2AiPT68XApoi4TtLi9PpqSROBOcA5wJnA9yS9Lc2SuAxYADwCfAeYjmdJtKPAxevuKC3X3ZdeXlous0oackQiqQV4P3BzLjwTWJmWVwKzcvE1EXEwInYDHcBkSWOBkRHxcEQEsCrXxszM6qRRQ1tfBD4NvJqLnRERewHS85gUbwb25LbrTLHmtNwzfhhJCyS1S2rv6uoq5Q2YmVmm7oVE0sXA/oh4tNomFWLRR/zwYMTyiGiLiLampqYqd2tmZtVoxDmSdwIzJL0POB4YKelrwD5JYyNibxq22p+27wTG5dq3AC+keEuFuJmZ1VHdj0giYklEtEREK9lJ9Psj4gpgAzAvbTYPWJ+WNwBzJB0naTwwAdiShr8OSJqSrtaam2tjZmZ10sirtnq6DlgraT7wHDAbICK2S1oL7AAOAYvSFVsAC4EVwAiyq7V8xZaZWZ01tJBExAPAA2n5p8DUXrZbCiytEG8HJtWuh2Zm1h9/s93MzApxITEzs0JcSMzMrBAXEjMzK8SFxMzMCnEhMTOzQlxIzMysEBcSMzMrxIXEzMwKeT3dIsXM6mTmuntLy7X+0mn9b2RHNR+RmJlZIS4kZmZWiAuJmZkV4kJiZmaFuJCYmVkhjZizfZyk70vaKWm7pI+n+ChJGyU9nZ5Py7VZIqlD0i5J03LxCyRtTetuTDMlmplZHTXi8t9DwH+LiMcknQw8KmkjcCWwKSKuk7QYWAxcLWki2ZS85wBnAt+T9LY0S+IyYAHwCPAdYDqeJdGs4T5451Ol5Vr7p28vLZfVRiPmbN8bEY+l5QPATqAZmAmsTJutBGal5ZnAmog4GBG7gQ5gsqSxwMiIeDgiAliVa2NmZnXS0HMkklqB84DNwBkRsReyYgOMSZs1A3tyzTpTrDkt94ybmVkdNayQSDoJuBO4KiJ+0demFWLRR7zSvhZIapfU3tXVdeSdNTOzXjWkkEg6hqyI3BERd6XwvjRcRXren+KdwLhc8xbghRRvqRA/TEQsj4i2iGhramoq742YmVlDrtoScAuwMyL+PrdqAzAvLc8D1uficyQdJ2k8MAHYkoa/DkiaknLOzbUxM7M6acRVW+8E/hzYKumJFPvvwHXAWknzgeeA2QARsV3SWmAH2RVfi9IVWwALgRXACLKrtXzFltkQsPKu8oao513iUYqi6l5IIuJfqHx+A2BqL22WAksrxNuBSeX1zszMjpS/2W5mZoW4kJiZWSGe2MrMrIdHVuzvf6MqTblyTP8bDXI+IjEzs0JcSMzMrBAPbZmZ1dGLn3+21Hxv/Js3l5pvIHxEYmZmhfiIxMzsKLLvxgdLy3XGxy6qajsfkZiZWSEuJGZmVogLiZmZFeJCYmZmhbiQmJlZIS4kZmZWiAuJmZkV4kJiZmaFDPpCImm6pF2SOiQtbnR/zMyGmkFdSCQNA74CvBeYCFwmaWJje2VmNrQM6kICTAY6IuLfI+I3wBpgZoP7ZGY2pCgiGt2HAZN0KTA9Iv4yvf5z4A8i4iM9tlsALEgvzwZ2HcFuRgM/KaG7zu/8gym38zt/T2+OiKZKKwb7TRtVIXZYZYyI5cDyAe1Aao+ItoG0dX7nr2X+wdx35z+68g/2oa1OYFzudQvwQoP6YmY2JA32QvKvwARJ4yUdC8wBNjS4T2ZmQ8qgHtqKiEOSPgLcCwwDbo2I7SXvZkBDYs7v/HXIP5j77vxHUf5BfbLdzMwab7APbZmZWYO5kJiZWSEuJL2QdKuk/ZK21Sj/OEnfl7RT0nZJHy8x9/GStkh6MuX+u7Jy99jPMEmPS7q7BrmfkbRV0hOS2muQ/1RJ6yQ9lf4N/rDE3Genfnc/fiHpqrLyp318Iv3bbpO0WtLxJef/eMq9vYy+V/o8SRolaaOkp9PzaSXnn536/6qkQpe59pL/8+n/z48kfVPSqSXn/2zK/YSk+ySdWWb+3LpPSQpJowean4jwo8IDuAg4H9hWo/xjgfPT8snAj4GJJeUWcFJaPgbYDEypwXv4JPBPwN01yP0MMLqG/74rgb9My8cCp9ZoP8OAF8m+zFVWzmZgNzAivV4LXFli/knANuAEsgtyvgdMKJjzsM8T8DlgcVpeDFxfcv7fI/sC8gNAWw36/yfA8LR8fQ36PzK3/DHgH8vMn+LjyC5WerbI581HJL2IiAeBn9Uw/96IeCwtHwB2kv2CKCN3RMT/Sy+PSY9Sr6qQ1AK8H7i5zLz1IGkk2QfrFoCI+E1E/LxGu5sK/FtEPFty3uHACEnDyX7hl/n9qd8DHomIX0XEIeAHwAeKJOzl8zSTrKCTnmeVmT8idkbEkdzF4kjz35d+PgCPkH2Prcz8v8i9PJECn+E+fp/dAHy6SG7w0NbrgqRW4DyyI4eycg6T9ASwH9gYEaXlTr5I9h/w1ZLzdgvgPkmPplvclOktQBdwWxqau1nSiSXvo9scYHWZCSPieeALwHPAXuDliLivxF1sAy6SdLqkE4D38dov/pbljIjYC9kfVsCYGuyjXj4E3FN2UklLJe0BLgf+tuTcM4DnI+LJorlcSBpM0knAncBVPf4CKSQiXomId5D9lTRZ0qSycku6GNgfEY+WlbOCd0bE+WR3dl4k6aIScw8nO8xfFhHnAb8kG1opVfqS7AzgGyXnPY3sr/nxwJnAiZKuKCt/ROwkG6rZCHwXeBI41GejIUzSNWQ/nzvKzh0R10TEuJT7I/1tX630B8I1lFScXEgaSNIxZEXkjoi4qxb7SEM2DwDTS0z7TmCGpGfI7rj8HklfKzE/EfFCet4PfJPsTs9l6QQ6c0dp68gKS9neCzwWEftKzvvHwO6I6IqI3wJ3AReWuYOIuCUizo+Ii8iGRJ4uM3+yT9JYgPS8vwb7qClJ84CLgcsjnXSokX8C/rTEfGeR/SHyZPoctwCPSXrjQJK5kDSIJJGN0e+MiL8vOXdT9xUkkkaQ/eJ5qqz8EbEkIloiopVs6Ob+iCjtL2JJJ0o6uXuZ7KRmaVfPRcSLwB5JZ6fQVGBHWflzLqPkYa3kOWCKpBPS/6OpZOfYSiNpTHp+E3AJtXkfG4B5aXkesL4G+6gZSdOBq4EZEfGrGuSfkHs5g3I/w1sjYkxEtKbPcSfZxT8vDjShH5WvclhNNv782/RDnl9y/j8iOw/wI+CJ9HhfSbnPBR5PubcBf1vDn9O7KfmqLbJzGE+mx3bgmhr0+x1Ae/oZfQs4reT8JwA/BU6p0c/978h+sWwDbgeOKzn/D8mK65PA1BLyHfZ5Ak4HNpEd7WwCRpWc/wNp+SCwD7i35PwdwJ7c57fIVVWV8t+Z/n1/BHwbaC4zf4/1z1Dgqi3fIsXMzArx0JaZmRXiQmJmZoW4kJiZWSEuJGZmVogLiZmZFeJCYmZmhbiQ2JCRbh3/12n5TEnrarivD0uaO4B2V0r6h1r0KeWfIWlxWp4laWIVbT4j6VO16pMNfi4kNpScCvw1ZLdgiYhLa7WjiPjHiFhVq/wDFREbIuK69HIW0G8hMeuPC4kNJdcBZ6WJgr7RPclPOgr4lqRvS9ot6SOSPpnuDPyIpFFpu7MkfTfdkfiHkt7e247yf8VLekDS9comG/uxpHf1088z036elvS5XM7LlE32tU3S9Sk2TNKKFNsq6RO5fX5R0kNp3eTce/0HSReS3Xbj8+nncZakv5L0r8omRLsz3djPrF/DG90BszpaDEyKiHekW/fnZ3acRHYr/+PJbn1xdUScJ+kGYC7ZbfOXAx+OiKcl/QFwE/CeKvc9PCImS3ofcC3Z/c96847Ul4PALklfBl4huyPvBcBLZLfYn0V2i47miJgE2fBdLs+JEXFhunPyrek9AhARD0naQHZ7m3Wp7c8j4qtp+X+T3abjy1W+PxvCXEjMMt+PbIKxA5JeJru3EcBW4Nx0u/8LgW9k90kE4LgjyN99d+dHgdZ+tt0UES8DSNoBvJnsvlQPRERXit9BNjnXZ4G3pGLzz0B+XpLVkE1qJGmk+p8KdlIqIKcCJ5HNnGfWLxcSs8zB3PKrudevkn1O3gD8PLI5Xorkf4X+P3f5vnRvr0obRsRLkn4fmAYsAj5INskSHD7rXX831lsBzIqIJyVdSXZDTrN++RyJDSUHgJMH0jCyScd2S5oN2TQA6Rd4vWwG/qOk0ZKGkd2i/geSRgNviIg7gf/Ja+dV+bPU1z8im0Xx5R45e/48Tgb2pnlyLq/R+7CjkI9IbMiIiJ9K+r/pJPtA5u+4HFgm6X8Ax5BN6lV4mtJqRMReSUuA75MdnXwnItanYnabpO4/Cpfkmr0k6SFgJL87SslbA3xV0seAS8kK0WbgWbIhvQEVXRt6fBt5s6OQpAeAT0VEe6P7Ykc/D22ZmVkhPiIxK0DSNcDsHuFvRMTSftpNI7ucN293RHygzP6Z1YMLiZmZFeKhLTMzK8SFxMzMCnEhMTOzQlxIzMyskP8PBKdgoyC28AEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This field shows how long patient stayed in hospital. \n",
    "#Show count of each period\n",
    "sns.countplot(x = 'time_in_hospital', data = DiabeticDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Payer Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we've seen earlier in our missing value percentages for each column, Payer code has 39% of missing values. Also, this field mainly shows the insurance provider of each patient (how patient paid) this logically has no impact on our prediction. We will later drop this field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='payer_code', ylabel='count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show Payer_code Distribution\n",
    "#Null values has the highest amount. We will drop this field\n",
    "sns.countplot(x = 'payer_code', data = DiabeticDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medical Specialty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Medical specialty has around 71 different categories and 49% missing values. We could see for the results below that the vast majority of rows in this fields are missing values. So we decided to drop this field although it might have some impact on our prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number medical specialty: 73\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "medical_specialty\n",
       "?                                47461\n",
       "InternalMedicine                 13805\n",
       "Emergency/Trauma                  7395\n",
       "Family/GeneralPractice            7085\n",
       "Cardiology                        5175\n",
       "                                 ...  \n",
       "Speech                               1\n",
       "Pediatrics-InfectiousDiseases        1\n",
       "SportsMedicine                       1\n",
       "Proctology                           1\n",
       "Neurophysiology                      1\n",
       "Length: 73, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print distribution of medical specialty and sort values descending order\n",
    "print('Number medical specialty:', DiabeticDataset.medical_specialty.nunique())\n",
    "DiabeticDataset.groupby('medical_specialty').size().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Num Lab Procedures, Medication, outpatient, emergency, inpatient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No changes required for for: Num_lab_procedures, num_procedure, num_medication, num_outpatient, num_emergency, num_inpatient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      3091\n",
       "43     2699\n",
       "44     2399\n",
       "45     2283\n",
       "38     2131\n",
       "       ... \n",
       "114       1\n",
       "118       1\n",
       "129       1\n",
       "120       1\n",
       "121       1\n",
       "Name: num_lab_procedures, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of lab tests performed at the visit\n",
    "DiabeticDataset.num_lab_procedures.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diag1, Diag2, Diag3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These 3 fields contain numerical values. Each range corresponds to a specific type of diagnose. We will use use rule that we found on \"Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records.\" Research paper to map to numerical values to its right category. 1 => circulatory 2 => respiratory 3 => digestive 4 => diabetes 5 => injury 6 => musculoskeletal 7 => genitourinary 8 => neoplasms 9 => pregnecy 10 => other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10] \n",
      "\n",
      "[ 1  2  3  4  5  6  7  8  9 10] \n",
      "\n",
      "[ 1  2  3  4  5  6  7  8  9 10] \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='diag_3', ylabel='count'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets look for the most common value in each column\n",
    "diag_1 = Counter(list(DiabeticDataset['diag_1'])).most_common(1)[0][0]\n",
    "diag_2 = Counter(list(DiabeticDataset['diag_2'])).most_common(1)[0][0]\n",
    "diag_3 = Counter(list(DiabeticDataset['diag_3'])).most_common(1)[0][0]\n",
    "\n",
    "#Replace empty values by the most common one for each column\n",
    "DiabeticDataset['diag_1'] = DiabeticDataset['diag_1'].apply(lambda x : diag_1 if x == '?' else x)\n",
    "DiabeticDataset['diag_2'] = DiabeticDataset['diag_1'].apply(lambda x : diag_2 if x == '?' else x)\n",
    "DiabeticDataset['diag_3'] = DiabeticDataset['diag_3'].apply(lambda x : diag_3 if x == '?' else x)\n",
    "\n",
    "#Map each value to its category for Diag1\n",
    "DiabeticDataset['diag_1'] = DiabeticDataset['diag_1'].apply(lambda x : 10 if (str(x).find('V') != -1 or str(x).find('E') != -1)  \n",
    "                                        else (1 if int(float(x)) in range(390, 460) or int(float(x)) == 785\n",
    "                                        else     (2 if int(float(x)) in range(460, 520) or int(float(x)) == 786\n",
    "                                        else     (3 if int(float(x)) in range(520, 580) or int(float(x)) == 787\n",
    "                                        else     (4 if int(float(x)) == 250\n",
    "                                        else     (5 if int(float(x)) in range(800, 1000)\n",
    "                                        else (6 if int(float(x)) in range(710, 740)\n",
    "                                        else (7 if int(float(x)) in range(580, 630) or int(float(x)) == 788\n",
    "                                        else (8 if int(float(x)) in range(140, 240)\n",
    "                                        else (9 if int(float(x)) in range(630, 680)\n",
    "                                        else 10))))))))))\n",
    "#Map each value to its category for Diag2\n",
    "DiabeticDataset['diag_2'] = DiabeticDataset['diag_2'].apply(lambda x : 10 if (str(x).find('V') != -1 or str(x).find('E') != -1)  \n",
    "                                        else (1 if int(float(x)) in range(390, 460) or int(float(x)) == 785\n",
    "                                        else     (2 if int(float(x)) in range(460, 520) or int(float(x)) == 786\n",
    "                                        else     (3 if int(float(x)) in range(520, 580) or int(float(x)) == 787\n",
    "                                        else     (4 if int(float(x)) == 250\n",
    "                                        else     (5 if int(float(x)) in range(800, 1000)\n",
    "                                        else (6 if int(float(x)) in range(710, 740)\n",
    "                                        else (7 if int(float(x)) in range(580, 630) or int(float(x)) == 788\n",
    "                                        else (8 if int(float(x)) in range(140, 240)\n",
    "                                        else (9 if int(float(x)) in range(630, 680)\n",
    "                                        else 10))))))))))\n",
    "#Map each value to its category for Diag3\n",
    "DiabeticDataset['diag_3'] = DiabeticDataset['diag_3'].apply(lambda x : 10 if (str(x).find('V') != -1 or str(x).find('E') != -1)  \n",
    "                                        else (1 if int(float(x)) in range(390, 460) or int(float(x)) == 785\n",
    "                                        else     (2 if int(float(x)) in range(460, 520) or int(float(x)) == 786\n",
    "                                        else     (3 if int(float(x)) in range(520, 580) or int(float(x)) == 787\n",
    "                                        else     (4 if int(float(x)) == 250\n",
    "                                        else     (5 if int(float(x)) in range(800, 1000)\n",
    "                                        else (6 if int(float(x)) in range(710, 740)\n",
    "                                        else (7 if int(float(x)) in range(580, 630) or int(float(x)) == 788\n",
    "                                        else (8 if int(float(x)) in range(140, 240)\n",
    "                                        else (9 if int(float(x)) in range(630, 680)\n",
    "                                        else 10))))))))))  \n",
    "#Print the distributions for diag1, diag2, diag3\n",
    "print(np.unique(DiabeticDataset['diag_1']), '\\n')           \n",
    "print(np.unique(DiabeticDataset['diag_2']), '\\n')\n",
    "print(np.unique(DiabeticDataset['diag_3']), '\\n')\n",
    "sns.countplot(x = 'diag_1', data = DiabeticDataset)\n",
    "sns.countplot(x = 'diag_2', data = DiabeticDataset)\n",
    "sns.countplot(x = 'diag_3', data = DiabeticDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max Glucose Serum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Glucose test serum is a test result. We will map the possible results to numerical values. 200 for '>200' 300 for '>300' 100 for 'Norm' else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='max_glu_serum', ylabel='count'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#map ranges to numerical values\n",
    "DiabeticDataset['max_glu_serum'] = DiabeticDataset['max_glu_serum'].apply(lambda x : 200 if x == '>200' \n",
    "                                                            else ( 300 if x == '>300'                                                          \n",
    "                                                            else ( 100 if x == 'Norm'\n",
    "                                                            else  0)))\n",
    "# print distribution of column\n",
    "sns.countplot(x = 'max_glu_serum', data = DiabeticDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A1Cresult"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A1C is a medical result we will map the possible ranges to a numerical value. 7 => '>7' 8 => '>8' 5 => 'Norm' else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='A1Cresult', ylabel='count'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Map ranges into numerical values\n",
    "DiabeticDataset['A1Cresult'] = DiabeticDataset['A1Cresult'].apply(lambda x : 7 if x == '>7' \n",
    "                                                         else (8 if  x == '>8'                                                        \n",
    "                                                         else ( 5 if x == 'Norm'\n",
    "                                                         else  0)))\n",
    "#Print distribution of column\n",
    "sns.countplot(x = 'A1Cresult', data = DiabeticDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medicaments Columns: 23 in total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following 23 fields are all medicaments with same possible outcome (down, no, steady, up). We will map each category into a numeric value as follows: 10 => 'up' -10 => 'down' 0 => 'steady' Else -20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='metformin', ylabel='count'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For all medicaments columns, change the categorical outcomes to numerical values\n",
    "for col in [\"metformin\", \"repaglinide\", \"nateglinide\", \"chlorpropamide\", \"glimepiride\", \"acetohexamide\", \"glipizide\", \"glyburide\", \"tolbutamide\", \"pioglitazone\", \"rosiglitazone\", \"acarbose\", \"miglitol\", \"troglitazone\", \"tolazamide\", \"examide\", \"citoglipton\", \"insulin\", \"glyburide-metformin\", \"glipizide-metformin\", \"glimepiride-pioglitazone\", \"metformin-rosiglitazone\", \"metformin-pioglitazone\"]:\n",
    "    DiabeticDataset[col] = DiabeticDataset[col].apply(lambda x : 10 if x == 'Up' \n",
    "                                              else ( -10 if x == 'Down'                                                          \n",
    "                                              else ( 0 if x == 'Steady'\n",
    "                                              else  -20)))\n",
    "    #show one example\n",
    "sns.countplot(x = 'metformin', data = DiabeticDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This column basically shows if there was a change in medics, we will map 1 for 'ch' and -1 for no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='change', ylabel='count'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DiabeticDataset['change'] = DiabeticDataset['change'].apply(lambda x : 1 if x == 'Ch'\n",
    "                                                 else -1)\n",
    "#show results\n",
    "sns.countplot(x = 'change', data = DiabeticDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DiabetesMed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This field basically shows if patient had any diabetes medications. We will map 1 for yes and -1 for no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='diabetesMed', ylabel='count'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#map\n",
    "DiabeticDataset['diabetesMed'] = DiabeticDataset['diabetesMed'].apply(lambda x : -1 if x == 'No'\n",
    "                                                else 1)\n",
    "#Show results\n",
    "sns.countplot(x = 'diabetesMed', data = DiabeticDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicted Class: Readmitted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our predicted class is the readmitted column. We chose our lables as follows: Readmitted in <30 => 1 else 0. The reason for this is that the government performs audits on hostpitals and they check they're readmission rates of less than 30 days. If it's high, the cut the government support budget. So this model will help hostpitals be more prepared. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='readmitted', ylabel='count'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JZuC/AB8B1idZAzwMnAVQVfclWQ/cD2wHzq+q59qqzmNwBdiBwE3tBXAlcE2STQz2RFaNa1skSbs2tiCpql/ZxaIVu2h/EXDRiPoEcMKI+jO0IJIkzZ4fl5PtkqQ5yiCRJHUxSCRJXQwSSVIXg0SS1MUgkSR1MUgkSV0MEklSF4NEktTFIJEkdTFIJEldDBJJUheDRJLUxSCRJHUxSCRJXQwSSVIXg0SS1MUgkSR1MUgkSV0MEklSF4NEktTFIJEkdTFIJEldDBJJUheDRJLUxSCRJHUxSCRJXQwSSVIXg0SS1MUgkSR1MUgkSV0MEklSlzkfJElWJnkwyaYkF8z2eCRpvpnTQZJkAfBJ4OeB44BfSXLc7I5KkuaXOR0kwMnApqr6XlX9P+Ba4PRZHpMkzSupqtkewz5Lciawsqre2+Z/FfjnVfUbU9qtBda22Z8CHpzRgc6sw4EfzvYgtE/87Oa2F/rn9xNVtWjUgoUzPZL9LCNqOyVjVV0BXDH+4cy+JBNVtXy2x6G952c3t83nz2+uH9raDBw9NL8EeHSWxiJJ89JcD5JvAsuSHJPkJcAqYMMsj0mS5pU5fWirqrYn+Q3gS8AC4NNVdd8sD2u2zYtDeC9QfnZz27z9/Ob0yXZJ0uyb64e2JEmzzCCRJHUxSOagJJXkY0Pzv5PkQ0Pza5N8t73uSPKmWRmopi3JlUm+neSeJNcneXmrJ8ml7RZA9yT5mdkeq3Y23z8/g2RuehZ4V5LDpy5I8g7gfcCbquq1wK8Bn03yqhkeo4YkOWQPTT5QVW+oqtcDDwOTX6r9eWBZe60FLh/fKLU3krwkycva7Lz+/AySuWk7gytEPjBi2X8CfreqfghQVXcB64DzZ254GmEiyWeTvDXJTl+kraofweA3WOBAnv9i7enA1TXwDeCVSY6asVFrJ0le144IPAi8Bvz8DJK565PAv09y8JT68cCdU2oTra7Z8xrgswx+U70/yQeTvHq4QZLPAH8DvBb4RCsvBh4Zara51TSDkrwsyblJ/gL4FPAA8Pqq+tZQm3n7+Rkkc1T7Dehq4Den0TyMuHWMZk5VPVdVX6yqdwFvBv4p8HCSk4fanAu8msF/Ur/cytO6DZDGbguwBnhvVZ1aVZ+qqqeHG8znz88gmdv+kMFf7pcN1e4HTprS7mdaXbMoycHtBqIbGOyhrAHuGW5TVc8B1wH/tpW8DdCPhzOB/wvckOT3kvzEqEbz9fMzSOawqnoCWM/gP6RJ/x24OMlhAElOBN4NXDbT49PzkvwxcBeDPZFzqurNVbWuqp5pV/Yc29oF+EXgu63rBuCc1uYU4Kmq2jIb2zCfVdWXq+qXgTcBTwE3Jvk/SZb6+c3xW6QIgI/x/BUiVNWGJIuBv0xSwNPA2S/Ev7xzzHrg3VW1fcSyAOuSHNSmvw2c15b9GfB2YBPwD8C5MzBW7UJVPQ58HPh4Oyz5HH5+3iJFktTHQ1uSpC4GiSSpi0EiSepikEiSuhgkkqQuBok0g5K8O8kf7WWfP0vyyvb69aH60iT/bh/GcFWSM/e2n7QrBom0F9oXy2b0301Vvb2q/hZ4JfDrQ4uWAnsdJNL+ZpBIe9B+838gyWUMvp3+n5N8sz1f4sND7T6f5M4k97VboUzWz03yV0m+Cpw6VL8qyeVJvpLke0n+dZJPt5911VC7H7RHBnwE+Mkkdyf5aJv/V23+A0kWJPno0Nje1/onyR8luT/J/waOGPMfmeYZv9kuTc9PMfhW8ucZ3HfpZAbfYt6Q5M1V9TXgPVX1RJIDgW8m+RzwEuDDDO5/9hTwFeBbQ+s9BHgr8E7gCwyC5r2t/4lVdfdQ2wuAE6rqRIAkbwF+p6re0ebXMrgFxz9LcgDw9SRfBt7Yxv/TwJEM7rv26f32J6N5zyCRpuevq+obSf4AeBvPh8HLGTy06GvAbyb5N61+dKu/Cri1qrYBJLmO9gyL5gtVVUnuBR6rqntbu/sYHLq6ey/G+Dbg9UPnPw5uY3gz8KfthoKPJrllL9Yp7ZFBIk3P37f3AL9fVf9zeGHbO/g54F9U1T8kuRV4aVu8u/sQPdve/3FoenJ+b/99Bnh/VX1pytjevocxSF08RyLtnS8B78nzz+RenOQIBr/9P9lC5LXAKa397cBbkhyW5MXAWR0/+2ngFbuZ/xJwXvs5JHlNBo+C/Rqwqp1DOQr42Y4xSDtxj0TaC1X15SSvA24b3DGcvwPOBv4c+LUk9zB4BOs3WvstST4E3Mbg4Uh3AQv28Wc/nuTrSb4D3AR8ENie5NvAVQzuSrsUuKvdznwbcAZwA4PzMPcCfwV8dV9+vrQr3v1XktTFQ1uSpC4GiSSpi0EiSepikEiSuhgkkqQuBokkqYtBIknq8v8BBQf2u0qCPt4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets explore this field\n",
    "sns.countplot(x = 'readmitted', data = DiabeticDataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='readmitted'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#map to 0 and 1\n",
    "DiabeticDataset['readmitted'] = DiabeticDataset.readmitted.apply(lambda x: 1 if x == '<30' else 0)\n",
    "#print results\n",
    "DiabeticDataset.readmitted.value_counts().plot.pie(autopct=\"%1.1f%%\", startangle=120,\n",
    "                                        textprops={'fontsize': 12, 'color':'#0a0a00'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could see that our dataset is highly imbalanced with more non re-admitted case in less than 30."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### drop Duplicates Records"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have many duplicate records in our dataset as patients have duplicated recored when they are readmitted. We will drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length after removing Duplicates: 68166\n"
     ]
    }
   ],
   "source": [
    "DiabeticDataset.drop_duplicates(['patient_nbr'], keep = 'first', inplace = True)\n",
    "print('Length after removing Duplicates:', len(DiabeticDataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop Columns with Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed in previous sections, we decided to drop the following columns because of large number of missing values: Weight, Payer_code and medical specialty. We also need to drop encounter_id and patient_nbr as they are unique identifiers for patients thus not needed in our predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    .dataframe thead th {\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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_type_id</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_source_id</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_in_hospital</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <td>41</td>\n",
       "      <td>59</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <th>number_inpatient</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_1</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>diag_3</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_diagnoses</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_glu_serum</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Cresult</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repaglinide</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nateglinide</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorpropamide</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glimepiride</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acetohexamide</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glipizide</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolbutamide</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pioglitazone</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rosiglitazone</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acarbose</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miglitol</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>troglitazone</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolazamide</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>examide</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citoglipton</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insulin</th>\n",
       "      <td>-20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetesMed</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readmitted</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0   1\n",
       "race                       1   1\n",
       "gender                     1   1\n",
       "age                        5  15\n",
       "admission_type_id          5   1\n",
       "discharge_disposition_id  25   1\n",
       "admission_source_id        1   7\n",
       "time_in_hospital           1   3\n",
       "num_lab_procedures        41  59\n",
       "num_procedures             0   0\n",
       "num_medications            1  18\n",
       "number_outpatient          0   0\n",
       "number_emergency           0   0\n",
       "number_inpatient           0   0\n",
       "diag_1                     4  10\n",
       "diag_2                     4  10\n",
       "diag_3                     4  10\n",
       "number_diagnoses           1   9\n",
       "max_glu_serum              0   0\n",
       "A1Cresult                  0   0\n",
       "metformin                -20 -20\n",
       "repaglinide              -20 -20\n",
       "nateglinide              -20 -20\n",
       "chlorpropamide           -20 -20\n",
       "glimepiride              -20 -20\n",
       "acetohexamide            -20 -20\n",
       "glipizide                -20 -20\n",
       "glyburide                -20 -20\n",
       "tolbutamide              -20 -20\n",
       "pioglitazone             -20 -20\n",
       "rosiglitazone            -20 -20\n",
       "acarbose                 -20 -20\n",
       "miglitol                 -20 -20\n",
       "troglitazone             -20 -20\n",
       "tolazamide               -20 -20\n",
       "examide                  -20 -20\n",
       "citoglipton              -20 -20\n",
       "insulin                  -20  10\n",
       "glyburide-metformin      -20 -20\n",
       "glipizide-metformin      -20 -20\n",
       "glimepiride-pioglitazone -20 -20\n",
       "metformin-rosiglitazone  -20 -20\n",
       "metformin-pioglitazone   -20 -20\n",
       "change                    -1   1\n",
       "diabetesMed               -1   1\n",
       "readmitted                 0   0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop mentioned columns\n",
    "DiabeticDataset = DiabeticDataset.drop(['weight','payer_code','medical_specialty', 'encounter_id', 'patient_nbr'], axis = 1)\n",
    "#print columns of data set\n",
    "DiabeticDataset.head(2).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the data to CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "DiabeticDataset.to_csv('DiabeticDataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we prepared the data and set our y-label, we could perform some analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender, age and Race insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_list = ['gender','age','race']\n",
    "fig, ax =plt.subplots(nrows=1,ncols=3,figsize=(24,8))\n",
    "count =0\n",
    "for i in visual_list:\n",
    "    sns.countplot(DiabeticDataset[i], hue=DiabeticDataset.readmitted, palette='YlOrBr', ax=ax[count]);\n",
    "    count = count+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could see from the plots above that females have more chances of being readmitted in less than 30 days in general. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "insulin\n",
       "-20    0.083373\n",
       "-10    0.106398\n",
       " 0     0.093335\n",
       " 10    0.098921\n",
       "Name: readmitted, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DiabeticDataset.groupby(by = \"insulin\").readmitted.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"insulin\", hue=\"readmitted\", data=DiabeticDataset, palette=\"YlOrBr\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diag_list = ['diag_1','diag_2','diag_3']\n",
    "fig, ax =plt.subplots(nrows=3,ncols=1,figsize=(15,12))\n",
    "count =0\n",
    "for i in diag_list:\n",
    "    sns.countplot(DiabeticDataset[i], hue=DiabeticDataset.readmitted, palette='hot', ax=ax[count]);\n",
    "    count = count+1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}