Final_Hospital_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "os.chdir(\"C:\\\\Users\\\\Raza\\\\Desktop\\\\Data sets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('hospital_patients.csv', sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
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       "      <th>3</th>\n",
       "      <td>500364</td>\n",
       "      <td>82442376</td>\n",
       "      <td>Caucasian</td>\n",
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       "      <td>Caucasian</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[80-90)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101763</th>\n",
       "      <td>443854148</td>\n",
       "      <td>41088789</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[70-80)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Down</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[80-90)</td>\n",
       "      <td>?</td>\n",
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       "      <td>10</td>\n",
       "      <td>...</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101765</th>\n",
       "      <td>443867222</td>\n",
       "      <td>175429310</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[70-80)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</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            2278392      8222157        Caucasian  Female   [0-10)      ?   \n",
       "1             149190     55629189        Caucasian  Female  [10-20)      ?   \n",
       "2              64410     86047875  AfricanAmerican  Female  [20-30)      ?   \n",
       "3             500364     82442376        Caucasian    Male  [30-40)      ?   \n",
       "4              16680     42519267        Caucasian    Male  [40-50)      ?   \n",
       "...              ...          ...              ...     ...      ...    ...   \n",
       "101761     443847548    100162476  AfricanAmerican    Male  [70-80)      ?   \n",
       "101762     443847782     74694222  AfricanAmerican  Female  [80-90)      ?   \n",
       "101763     443854148     41088789        Caucasian    Male  [70-80)      ?   \n",
       "101764     443857166     31693671        Caucasian  Female  [80-90)      ?   \n",
       "101765     443867222    175429310        Caucasian    Male  [70-80)      ?   \n",
       "\n",
       "        admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "0                       6                        25                    1   \n",
       "1                       1                         1                    7   \n",
       "2                       1                         1                    7   \n",
       "3                       1                         1                    7   \n",
       "4                       1                         1                    7   \n",
       "...                   ...                       ...                  ...   \n",
       "101761                  1                         3                    7   \n",
       "101762                  1                         4                    5   \n",
       "101763                  1                         1                    7   \n",
       "101764                  2                         3                    7   \n",
       "101765                  1                         1                    7   \n",
       "\n",
       "        time_in_hospital  ... citoglipton insulin  glyburide-metformin  \\\n",
       "0                      1  ...          No      No                   No   \n",
       "1                      3  ...          No      Up                   No   \n",
       "2                      2  ...          No      No                   No   \n",
       "3                      2  ...          No      Up                   No   \n",
       "4                      1  ...          No  Steady                   No   \n",
       "...                  ...  ...         ...     ...                  ...   \n",
       "101761                 3  ...          No    Down                   No   \n",
       "101762                 5  ...          No  Steady                   No   \n",
       "101763                 1  ...          No    Down                   No   \n",
       "101764                10  ...          No      Up                   No   \n",
       "101765                 6  ...          No      No                   No   \n",
       "\n",
       "        glipizide-metformin  glimepiride-pioglitazone  \\\n",
       "0                        No                        No   \n",
       "1                        No                        No   \n",
       "2                        No                        No   \n",
       "3                        No                        No   \n",
       "4                        No                        No   \n",
       "...                     ...                       ...   \n",
       "101761                   No                        No   \n",
       "101762                   No                        No   \n",
       "101763                   No                        No   \n",
       "101764                   No                        No   \n",
       "101765                   No                        No   \n",
       "\n",
       "        metformin-rosiglitazone  metformin-pioglitazone  change diabetesMed  \\\n",
       "0                            No                      No      No          No   \n",
       "1                            No                      No      Ch         Yes   \n",
       "2                            No                      No      No         Yes   \n",
       "3                            No                      No      Ch         Yes   \n",
       "4                            No                      No      Ch         Yes   \n",
       "...                         ...                     ...     ...         ...   \n",
       "101761                       No                      No      Ch         Yes   \n",
       "101762                       No                      No      No         Yes   \n",
       "101763                       No                      No      Ch         Yes   \n",
       "101764                       No                      No      Ch         Yes   \n",
       "101765                       No                      No      No          No   \n",
       "\n",
       "       readmitted  \n",
       "0              NO  \n",
       "1             >30  \n",
       "2              NO  \n",
       "3              NO  \n",
       "4              NO  \n",
       "...           ...  \n",
       "101761        >30  \n",
       "101762         NO  \n",
       "101763         NO  \n",
       "101764         NO  \n",
       "101765         NO  \n",
       "\n",
       "[101766 rows x 50 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#standard plotly imports\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "# Using plotly + cufflinks in offline mode\n",
    "import cufflinks\n",
    "cufflinks.go_offline(connected = True)\n",
    "init_notebook_mode(connected = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "race 2273\n",
      "gender 0\n",
      "age 0\n",
      "weight 98569\n",
      "payer_code 40256\n",
      "medical_specialty 49949\n",
      "diag_1 21\n",
      "diag_2 358\n",
      "diag_3 1423\n",
      "max_glu_serum 0\n",
      "A1Cresult 0\n",
      "metformin 0\n",
      "repaglinide 0\n",
      "nateglinide 0\n",
      "chlorpropamide 0\n",
      "glimepiride 0\n",
      "acetohexamide 0\n",
      "glipizide 0\n",
      "glyburide 0\n",
      "tolbutamide 0\n",
      "pioglitazone 0\n",
      "rosiglitazone 0\n",
      "acarbose 0\n",
      "miglitol 0\n",
      "troglitazone 0\n",
      "tolazamide 0\n",
      "examide 0\n",
      "citoglipton 0\n",
      "insulin 0\n",
      "glyburide-metformin 0\n",
      "glipizide-metformin 0\n",
      "glimepiride-pioglitazone 0\n",
      "metformin-rosiglitazone 0\n",
      "metformin-pioglitazone 0\n",
      "change 0\n",
      "diabetesMed 0\n",
      "readmitted 0\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    if df[col].dtype == object:\n",
    "         print(col,df[col][df[col] == '?'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>...</th>\n",
       "      <th>tolazamide</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149190</td>\n",
       "      <td>55629189</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64410</td>\n",
       "      <td>86047875</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500364</td>\n",
       "      <td>82442376</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[30-40)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16680</td>\n",
       "      <td>42519267</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[40-50)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35754</td>\n",
       "      <td>82637451</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[50-60)</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</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>443847548</td>\n",
       "      <td>100162476</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Male</td>\n",
       "      <td>[70-80)</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Down</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101762</th>\n",
       "      <td>443847782</td>\n",
       "      <td>74694222</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[80-90)</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101763</th>\n",
       "      <td>443854148</td>\n",
       "      <td>41088789</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[70-80)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Down</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101764</th>\n",
       "      <td>443857166</td>\n",
       "      <td>31693671</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[80-90)</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101765</th>\n",
       "      <td>443867222</td>\n",
       "      <td>175429310</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[70-80)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98052 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        encounter_id  patient_nbr             race  gender      age  \\\n",
       "1             149190     55629189        Caucasian  Female  [10-20)   \n",
       "2              64410     86047875  AfricanAmerican  Female  [20-30)   \n",
       "3             500364     82442376        Caucasian    Male  [30-40)   \n",
       "4              16680     42519267        Caucasian    Male  [40-50)   \n",
       "5              35754     82637451        Caucasian    Male  [50-60)   \n",
       "...              ...          ...              ...     ...      ...   \n",
       "101761     443847548    100162476  AfricanAmerican    Male  [70-80)   \n",
       "101762     443847782     74694222  AfricanAmerican  Female  [80-90)   \n",
       "101763     443854148     41088789        Caucasian    Male  [70-80)   \n",
       "101764     443857166     31693671        Caucasian  Female  [80-90)   \n",
       "101765     443867222    175429310        Caucasian    Male  [70-80)   \n",
       "\n",
       "        admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "1                       1                         1                    7   \n",
       "2                       1                         1                    7   \n",
       "3                       1                         1                    7   \n",
       "4                       1                         1                    7   \n",
       "5                       2                         1                    2   \n",
       "...                   ...                       ...                  ...   \n",
       "101761                  1                         3                    7   \n",
       "101762                  1                         4                    5   \n",
       "101763                  1                         1                    7   \n",
       "101764                  2                         3                    7   \n",
       "101765                  1                         1                    7   \n",
       "\n",
       "        time_in_hospital  num_lab_procedures  ...  tolazamide  insulin  \\\n",
       "1                      3                  59  ...          No       Up   \n",
       "2                      2                  11  ...          No       No   \n",
       "3                      2                  44  ...          No       Up   \n",
       "4                      1                  51  ...          No   Steady   \n",
       "5                      3                  31  ...          No   Steady   \n",
       "...                  ...                 ...  ...         ...      ...   \n",
       "101761                 3                  51  ...          No     Down   \n",
       "101762                 5                  33  ...          No   Steady   \n",
       "101763                 1                  53  ...          No     Down   \n",
       "101764                10                  45  ...          No       Up   \n",
       "101765                 6                  13  ...          No       No   \n",
       "\n",
       "        glyburide-metformin  glipizide-metformin  glimepiride-pioglitazone  \\\n",
       "1                        No                   No                        No   \n",
       "2                        No                   No                        No   \n",
       "3                        No                   No                        No   \n",
       "4                        No                   No                        No   \n",
       "5                        No                   No                        No   \n",
       "...                     ...                  ...                       ...   \n",
       "101761                   No                   No                        No   \n",
       "101762                   No                   No                        No   \n",
       "101763                   No                   No                        No   \n",
       "101764                   No                   No                        No   \n",
       "101765                   No                   No                        No   \n",
       "\n",
       "       metformin-rosiglitazone metformin-pioglitazone change  diabetesMed  \\\n",
       "1                           No                     No     Ch          Yes   \n",
       "2                           No                     No     No          Yes   \n",
       "3                           No                     No     Ch          Yes   \n",
       "4                           No                     No     Ch          Yes   \n",
       "5                           No                     No     No          Yes   \n",
       "...                        ...                    ...    ...          ...   \n",
       "101761                      No                     No     Ch          Yes   \n",
       "101762                      No                     No     No          Yes   \n",
       "101763                      No                     No     Ch          Yes   \n",
       "101764                      No                     No     Ch          Yes   \n",
       "101765                      No                     No     No           No   \n",
       "\n",
       "       readmitted  \n",
       "1             >30  \n",
       "2              NO  \n",
       "3              NO  \n",
       "4              NO  \n",
       "5             >30  \n",
       "...           ...  \n",
       "101761        >30  \n",
       "101762         NO  \n",
       "101763         NO  \n",
       "101764         NO  \n",
       "101765         NO  \n",
       "\n",
       "[98052 rows x 45 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['weight','payer_code','medical_specialty'], axis = 1)\n",
    "df = df.drop(['citoglipton', 'examide'], axis = 1)\n",
    "df = df.replace('?', np.nan)\n",
    "df = df.replace('Unknown/Invalid', np.nan)\n",
    "df = df.dropna()\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female             54708\n",
       "Male               47055\n",
       "Unknown/Invalid        3\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "race 0\n",
      "gender 0\n",
      "age 0\n",
      "diag_1 0\n",
      "diag_2 0\n",
      "diag_3 0\n",
      "max_glu_serum 0\n",
      "A1Cresult 0\n",
      "metformin 0\n",
      "repaglinide 0\n",
      "nateglinide 0\n",
      "chlorpropamide 0\n",
      "glimepiride 0\n",
      "acetohexamide 0\n",
      "glipizide 0\n",
      "glyburide 0\n",
      "tolbutamide 0\n",
      "pioglitazone 0\n",
      "rosiglitazone 0\n",
      "acarbose 0\n",
      "miglitol 0\n",
      "troglitazone 0\n",
      "tolazamide 0\n",
      "insulin 0\n",
      "glyburide-metformin 0\n",
      "glipizide-metformin 0\n",
      "glimepiride-pioglitazone 0\n",
      "metformin-rosiglitazone 0\n",
      "metformin-pioglitazone 0\n",
      "change 0\n",
      "diabetesMed 0\n",
      "readmitted 0\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    if df[col].dtype == object:\n",
    "         print(col,df[col][df[col] == '?'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Caucasian          75079\n",
       "AfricanAmerican    18881\n",
       "Hispanic            1984\n",
       "Other               1483\n",
       "Asian                625\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     25305\n",
       "7     21809\n",
       "9     16702\n",
       "6     16697\n",
       "5      9265\n",
       "4      3548\n",
       "10     2717\n",
       "3      1478\n",
       "2       466\n",
       "1        65\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code age intervals [0-10) - [90-100) from 1-10\n",
    "for i in range(0,10):\n",
    "    df['age'] = df['age'].replace('['+str(10*i)+'-'+str(10*(i+1))+')', i+1)\n",
    "df['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['readmitted'] = df['readmitted'].replace('>30', 0)\n",
    "df['readmitted'] = df['readmitted'].replace('<30', 1)\n",
    "df['readmitted'] = df['readmitted'].replace('NO', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>...</th>\n",
       "      <th>tolazamide</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149190</td>\n",
       "      <td>55629189</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64410</td>\n",
       "      <td>86047875</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500364</td>\n",
       "      <td>82442376</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16680</td>\n",
       "      <td>42519267</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35754</td>\n",
       "      <td>82637451</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   encounter_id  patient_nbr             race  gender  age  admission_type_id  \\\n",
       "1        149190     55629189        Caucasian  Female    2                  1   \n",
       "2         64410     86047875  AfricanAmerican  Female    3                  1   \n",
       "3        500364     82442376        Caucasian    Male    4                  1   \n",
       "4         16680     42519267        Caucasian    Male    5                  1   \n",
       "5         35754     82637451        Caucasian    Male    6                  2   \n",
       "\n",
       "   discharge_disposition_id  admission_source_id  time_in_hospital  \\\n",
       "1                         1                    7                 3   \n",
       "2                         1                    7                 2   \n",
       "3                         1                    7                 2   \n",
       "4                         1                    7                 1   \n",
       "5                         1                    2                 3   \n",
       "\n",
       "   num_lab_procedures  ...  tolazamide  insulin  glyburide-metformin  \\\n",
       "1                  59  ...          No       Up                   No   \n",
       "2                  11  ...          No       No                   No   \n",
       "3                  44  ...          No       Up                   No   \n",
       "4                  51  ...          No   Steady                   No   \n",
       "5                  31  ...          No   Steady                   No   \n",
       "\n",
       "   glipizide-metformin  glimepiride-pioglitazone metformin-rosiglitazone  \\\n",
       "1                   No                        No                      No   \n",
       "2                   No                        No                      No   \n",
       "3                   No                        No                      No   \n",
       "4                   No                        No                      No   \n",
       "5                   No                        No                      No   \n",
       "\n",
       "  metformin-pioglitazone change  diabetesMed readmitted  \n",
       "1                     No     Ch          Yes          0  \n",
       "2                     No     No          Yes          2  \n",
       "3                     No     Ch          Yes          2  \n",
       "4                     No     Ch          Yes          2  \n",
       "5                     No     No          Yes          0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df['insulin'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SERG1pYhERERtPSsikvaVdIOkeyXdLem0Et9L0hJJy8v7+BKXpAskDUq6U9K0jmPNLe2XS5rbET9U0l1lnwskqVefJyIiNtbLM5G1wP+2fSAwAzhV0kHAGcBS21OBpWUd4HhgannNAy6EqugAZwFHUM3NftZw4Slt5nXsN7OHnyciIjbQsyJi+xHb3y3La4B7gUnALGBhabYQmF2WZwEXu3ITsKekfYDjgCW2V9leDSwBZpZt42zfaNvAxR3HioiIBjTSJyJpCvAm4GZgou1HoCo0wN6l2STgoY7dhkpsc/GhLvGIiGhIz4uIpN2ArwCfsP3k5pp2iblGvFsO8yQNSBpYuXLlllKOiIgR6mkRkbQjVQG5xPYVJfxouRRFeV9R4kPAvh27TwYe3kJ8cpf4RmzPtz3d9vQJEyZs3YeKiIgX9XJ0loCLgHttf7pj0yJgeITVXOCqjvicMkprBvBEudy1GDhW0vjSoX4ssLhsWyNpRvm35nQcKyIiGrBDD499JPBB4C5Jt5fYHwDnAZdJOgV4EDixbLsGOAEYBJ4CTgawvUrSOcCtpd3ZtleV5Y8Cnwd2Ab5WXtGQKWdc3XYKAHz/vHe0nUJE3+pZEbH9Hbr3WwAc06W9gVM3cawFwIIu8QHg4K1IMyIitkLuWI+IiNpSRCIiorYUkYiIqC1FJCIiaksRiYiI2lJEIiKithSRiIioLUUkIiJqSxGJiIjaUkQiIqK2FJGIiKgtRSQiImpLEYmIiNpSRCIiorYUkYiIqC1FJCIiaksRiYiI2lJEIiKithSRiIiorWdFRNICSSsk/WdHbC9JSyQtL+/jS1ySLpA0KOlOSdM69plb2i+XNLcjfqiku8o+F0ja1HzuERHRI708E/k8MHOD2BnAUttTgaVlHeB4YGp5zQMuhKroAGcBRwCHA2cNF57SZl7Hfhv+WxER0WM9KyK2vwWs2iA8C1hYlhcCszviF7tyE7CnpH2A44AltlfZXg0sAWaWbeNs32jbwMUdx4qIiIY03Scy0fYjAOV97xKfBDzU0W6oxDYXH+oS70rSPEkDkgZWrly51R8iIiIqo6VjvVt/hmvEu7I93/Z029MnTJhQM8WIiNhQ00Xk0XIpivK+osSHgH072k0GHt5CfHKXeERENKjpIrIIGB5hNRe4qiM+p4zSmgE8US53LQaOlTS+dKgfCywu29ZImlFGZc3pOFZERDRkh14dWNK/AL8MvFrSENUoq/OAyySdAjwInFiaXwOcAAwCTwEnA9heJekc4NbS7mzbw531H6UaAbYL8LXyioiIBvWsiNh+/yY2HdOlrYFTN3GcBcCCLvEB4OCtyTEiIrbOaOlYj4iIMShFJCIiaksRiYiI2lJEIiKithSRiIioLUUkIiJqSxGJiIjaUkQiIqK2FJGIiKgtRSQiImpLEYmIiNpSRCIiorYUkYiIqC1FJCIiaksRiYiI2lJEIiKitp5NShXRT6accXXbKQDw/fPe0XYK0WdyJhIREbXlTCQitqmclfWXMX8mImmmpO9JGpR0Rtv5RET0kzF9JiJpe+BvgV8BhoBbJS2yfU+7mUVE9MdZ2Vg/EzkcGLR9v+1ngUuBWS3nFBHRN2S77Rxqk/QeYKbtD5X1DwJH2P7YBu3mAfPK6uuB7zWa6MZeDfy45RxGi3wX6+S7WCffxTqj4bvY3/aEbhvG9OUsQF1iG1VF2/OB+b1PZ2QkDdie3nYeo0G+i3XyXayT72Kd0f5djPXLWUPAvh3rk4GHW8olIqLvjPUiciswVdIBknYCTgIWtZxTRETfGNOXs2yvlfQxYDGwPbDA9t0tpzUSo+bS2iiQ72KdfBfr5LtYZ1R/F2O6Yz0iIto11i9nRUREi1JEIiKithSRiIioLUUkokWSdpW0Xcf6dpJe2WZOTSuf+S1t5xH1pGO9IZJeB3wS2J+OUXG2j24tqZZImsTG38O32suoPZJuAt5u+ydlfTfgOtt99aMq6Ubbb247j9FA0gTgw8AU1v9/5LfaymlzxvQQ3zHmcuDvgX8Enm85l9ZIOh94H3AP674HA31ZRICdhwsIgO2f9NuZSHGdpHcDVzh/2V4FfBv4OmPgtyJFpDlrbV/YdhKjwGzg9bZ/1nYio8RPJU2z/V0ASYcCT7ecUxtOB3YFnpf0NNUjjWx7XLtpteKVtn+/7SRGKkWkOf8m6beBK4EXf0Btr2ovpVbcD+xIx3fQ5z4BXC5p+HE9+1CdqfUV27u3ncMo8lVJJ9i+pu1ERiJ9Ig2R9ECXsG2/pvFkWiTpK8AbgaWsX0w/3lpSLZO0I9XTpQXcZ/u5llNqnCQBHwAOsH2OpH2BfWzf0nJqjZO0huqs7GfAc4zys7IUkWiUpLnd4rYXNp1LmyQdbft6Se/qtt32FU3n1CZJFwIvAEfbPlDSeKoBBoe1nFpsQS5nNUjSwcBBwM7DMdsXt5dR8/qtWGzG24DrgV/rss1AXxURqnmApkm6DcD26vJQ1b5UiuhU1v+tGJWDT1JEGiLpLOCXqYrINcDxwHeAvioikqYCf8bGxbSvLuvZPqu8n9x2LqPEc2W6a8OLw1xfaDeldkj6EHAa1dQWtwMzgBuBUXk7QIpIc95D1Rdwm+2TJU0E/qnlnNrwz8BZwGeAo4CT6T65WF+Q9Arg3Wx8T8DZbeXUkguoBp3sLelcqv9f/rDdlFpzGnAYcJPtoyT9AvCplnPapBSR5jxt+wVJayWNA1YAffXXd7GL7aWSZPsHwB9L+jZVYelHVwFPAMvo4xFrti+RtAw4huqPitm27205rbY8Y/sZSUh6he37JL2+7aQ2JUWkOQOS9qS62XAZ8BOg70aeAM+Ux3wsL3PB/BDYu+Wc2jTZ9sy2kxgllgNPUn6XJO1n+8F2U2rFUPmt+FdgiaTVjOIZWzM6qwWSpgDjbN/ZciqNk3QYcC+wJ3AOMA74C9s3tZpYSyTNBz5n+662c2mTpN+hOht9lOou7eFhrb/YamItk/Q2YA/gWtvPtp1PNykiPSbpF8rp6LRu24fvVI7+JOke4LXAA1SXs/ryx1PSINUIrcfazqUtksbZflLSXt22j9Ybk3M5q/dOB+YBf9VlmxmlIy56RdIS4ETbj5f18cClto9rN7PWHN92AqPEQ1R9Q/3si8CvUl3uNusPODGjtA81ZyLRKEm32X7TlmL9RtLerD/kuS/6AiSdXhbfQHXX/tWs/ySDT7eRV4xc5hNpiKQTJe1elv9Q0hWS+vGH8wVJ+w2vSNqfcm9AP5L0TknLqS5nfRP4PvC1VpNq1u7l9SCwBNipI7Zbi3m1RtKRknYty78p6dOd/8+MNjkTaYikO23/oqRforrZ7i+BP7B9RMupNUrSTGA+1Q8mwFuBebYXt5dVeyTdQXVJ8+u23yTpKOD9tue1nFqjJJ1o+/ItxfqBpDup7in7ReALwEXAu2y/rdXENiFnIs0ZnhfgHcCFtq+i+qurr9i+FpgGfAm4DDi0XwtI8VzpTN5O0na2bwAOaTupFpw5wlg/WFvmVJkF/LXtv6Y6MxuV0rHenB9K+gfg7cD55U7lviniXUapDY9736/cD9Cvo9QeL7MZfhu4RNIKYG3LOTVG0vHACcAkSRd0bBpHH30PG1gj6Uzgg8D/KI+DGbW/1bmc1ZAyW91M4C7byyXtA/x329e1nFojJM23PU/SDV02ux+nCYZqjnXgGaqROB+guifgkn4Z6irpjVRnXucDf0LVP/Y81f0i37C9usX0WiHp54DfAG6x/R1JbwX+2fbPt5xaVykiDevXUTgA5U71N9v+97ZzGU3Kj8bhVD+gt9r+UcspNabMpXIu8CGqQQUC9qV6xtof9OPcKgCSDqEqJO+lGnRxhe3PtZtVd31zOaVtXUbhPEB/jcLB9gtUAwqiKE9svQV4F9VDB2+S9FvtZtWoPwfGA/vbnlaGer+G6oysr/5bkfQ6SX8k6V7gb6junZHto0ZrAYGciTQmo3Aqkj4F3En1l1Xf/8cn6XvAW4YvX0l6FfAftkftA/e2pfKH1es2/G+h9APcZ3tqO5k1T9ILVH1jp9geLLH7R/s0CTkTaU5G4VROBy4HnpX0pKQ1kp5sO6kWDQFrOtbXUP0F2i/c7Y8J28/Tf/cPvRv4EXCDpH+UNPxE41Ft1Pb4vwwNj8L5Fn04CmeY7VE7VLElPwRulnQV1Y/mLOCW4Tu5++CO7Xskzdlwhk9Jvwnc11JOrbB9JXBlGWwxG/hdYGKZOvjK0ToIJ5ezGlL+w3ia6uyv70bhDJM0PArpANvnSNoX2Md2Pz4Wf3jGy02yPWonI9oWJE2imgr4adY9M+owYBfg123/sMX0Wlcexngi8L7ROoIxRaQhkg4AHrH9TFnfBZho+/utJtaw8lfVC8DRtg8sD2C8zvZhLafWujJ6bTfbfXd5T9LRVM/PEnC37aUtpxQjlCLSEEkDVB2oz5b1nYB/77cfT0nftT2t86GLku6w/ca2c2uDpC8CH6G6N2IZ1Rnqp23/RauJRYxQOtabs0PnpDJlue8eewI8V0beGEDSBKozk351UDnzmA1cA+xHdadyxJiQItKclZLeObwiaRbw4xbzacsFwJXA3pLOBb4D/Gm7KbVqx3LD3WzgqnJzXS4PxJiR0VnN+QjVqKy/obru+xAwp92Ummf7EknLgOHhi7Nt39tyWm36B6o7te8AvlUejd93fSIxdqVPpGFlmK9sr9li45ep0pm+Lx1/xPTxAxg3ImkH2303/DvGppyJNETSH22wDoDts1tJqCWSzgH+J/BfrLts04/TBP+m7f/XMbPfhl7u94fEy0SKSHN+2rG8M9Vcyv14Gee9wM93DjLoU7uW99x8GWNaLme1pMwnssj2cW3n0iRJXwE+antF27lExNbLmUh7Xkn1tNJ+82fAbZL+E/jZcND2Oze9y8vPBhMwbcT2x5vKJWJrpIg0RNJdrOsD2B6YAPRVf0ixkGoCorvo7/tDlnUsfwrY7ONPIkarXM5qSBm6OWwt8Gg/jsCR9E3bb2s7j9Gk8+79iLEmRaRh/TyzIYCkT1NdxlrE+pez+naI7/CjYNrOI6KOXM5qSLlb/a+A/wasAPanGp31hjbzasHwX9wzOmJ9N8Q34uUiZyINycyGmyZpou1H286jSZLWsK6P7JXAU8ObqCZqGtdKYhEvUc5EmvOc7cckvTizoaTz206qLZL2oJrJ7TeAA4FJ7WbUrEzOFS8XKSLN6fuZDcscKu+kKhzTqG60m031nUTEGJTLWT0m6bXAROB21p/ZcH/gatvLNrP7y4akS4C3AtcBlwLXA4O2D2g1sYjYKnkUfO99Flhj+6e2X7C91vZCqrkj/rjd1Bp1MLCaajDBfbafJ488jxjzUkR6b4rtOzcM2h4ApjSfTjvKzIXvBcYBX5f0bWB3ST/XbmYRsTVyOavHJA3afu1L3fZyJ2k6Vd/Ie4Ah229pOaWIqCFnIr13q6QPbxiUdArrP/qir9gesH06Vd/QmW3nExH15EykxyRNpJoO9lnWFY3pVPOr/7rtH7WVWxvKnOofprqU1zkp1W+1lVNE1Jci0pByc+HBZfVu29e3mU9bJP0H8G2qgvr8cNz2V1pLKiJqSxGJRkm63fYhbecREdtG+kSiaV+VdELbSUTEtpEzkWhUeWbUrlRP8H2OPCsqYkxLEYmIiNry7KxonKTxwFTWn1clz8+KGINSRKJRkj4EnAZMpnqe2AzgRjKfSMSYlI71aNppwGHAD2wfRTVJ1cp2U4qIulJEomnP2H4GQNIrbN8HvL7lnCKiplzOiqYNSdoT+FdgiaTVwMMt5xQRNWV0VrRG0tuAPYBrbT/bdj4R8dKliESjJM2geuzLmrK+O3CQ7ZvbzSwi6kgRiUZJug2Y5vIfnqTtgAHb09rNLCLqSMd6NE3u+MvF9gukby5izEoRiabdL+njknYsr9OA+9tOKiLqSRGJpn0EeAvwQ2AIOAKY12pGEVFb+kQiIqK2XIuORkj6Pdt/LulzwEZ/udj+eAtpRcRWShGJptxb3gdazSIitqlczorGSNoeOM/2J9vOJSK2jXSsR2NsPw8c2nYeEbHt5HJWNO02SYuAy4GfDgdtX9FeShFRV4pING0v4DHWnz/EQIpIxM49FUUAAAKISURBVBiUIhKNkHS+7d8HrrF9edv5RMS2kT6RaMoJknYEzmw7kYjYdnImEk25FvgxsKukJwF1bHvB9h7tpBURWyNnItEI258sheJq2+Ns7257d+B44JKW04uImnImEo2yPUvSIcD7gfcBDwBfaTeriKgrRSQaIel1wElUxeMx4EtUN7se1WpiEbFVcsd6NELSC8C3gVNsD5bY/bZf025mEbE10icSTXk38CPgBkn/KOkY1u9cj4gxKGci0ShJuwKzqS5rHQ0sBK60fV2riUVELSki0RpJewEnAu+zffSW2kfE6JMiEhERtaVPJCIiaksRiYiI2nKfSEQPSXoeuIvq/7UHgA/afrzdrCK2nZyJRPTW07YPsX0wsAo4te2EIralFJGI5twITAKQtJukpZK+K+kuSbOGG0maI+lOSXdI+kKJTZD0FUm3lteRLX2GiPVkdFZED0n6ie3dyvzylwIX2b5W0g7AK20/KenVwE3AVOAgqgm6jrT9Y0l72V4l6YvA39n+jqT9gMW2D2zrc0UMS59IRG/tIul2YAqwDFhS4gL+VNJbgReozlAmUt2A+WXbPwawvaq0fztwkPTiTf7jJO1ue00jnyJiE3I5K6K3nrZ9CLA/sBPr+kQ+AEwADi3bHwV2piou3S4PbAe8ufSvHGJ7UgpIjAYpIhENsP0E8HHg/5QZHvcAVth+TtJRVEUGYCnwXkmvghfv6ge4DvjY8PHK4/QjWpciEtEQ27cBd1A9Ev8SYLqkAaqzkvtKm7uBc4FvSroD+HTZ/eOl/Z2S7gE+0nT+Ed2kYz0iImrLmUhERNSWIhIREbWliERERG0pIhERUVuKSERE1JYiEhERtaWIREREbf8f7HNhz7Mqu70AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df['race'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('Race')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df['readmitted'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('Readmitted')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df['age'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df['gender'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('gender')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age of Patient VS. Readmission')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wffr02XpZALIJh2effTa77747xx133E5tf8KECRx//PF0796defPmMWHCBAYOHMiQIUOYPn06V155Jcceeywff/wxu+66KzfeeCPDhg3j8ssvZ/jw4XTv3p0hQ4ZsnQy5M1Q5q7OxpPkKsyPioAJp1wBrI+IqSROBzhHxgzztfQs4GxgZEZty0v8FOA/4KnA4MCkiDkt5hwL/GRH5Jltu9anen4p3X3l3B/fUzMxK3ZIlSzjggAOK3Y2iybf/khZGxNCqZYt+SULS7cBjwP6SKiSNT1lXAcdIWgYck9bz+RXZiMNjkhZJuiylPwgsB8qB3wDn5tQ5GvhD/e6JmZlZ81X0SxIRcXo16WuBkbWon3cf0t0R362m2iiyuyjMzMysFooxwrAF6FD54KbGJmlP4NqIWF+M7ZuZmTVFjT7CEBEr2X4yYmNv/w2y5zCYmZlZLRV9DoOZmZmVPgcMZmZmVpADhhr079a/2F0wMzPbavz48QwaNIiBAwcyevTorT+E9cEHH3DqqafSt29fDj/8cFasWFHv2y76XRJmZmal6NBLphUuVAcLrxlTsMz69evp1CnvjzMDcN1117HHHnsAcNFFF3HDDTcwceJEJk+eTKdOnSgvL2fGjBlceumlzJw5s976Dh5hMDMzKxlDhw7lG9/4Bn/5y1/I92DFymAhIti0adPW34y47777GDt2LACjR49m7ty5eevvDAcMZmZmJWLp0qV84xvf4IYbbmDAgAH85Cc/YdWqVduVGTduHJ/+9Kd54YUXOP/88wF49dVX6dUruwGxVatWdOjQgbVr19Zr3xwwmJmZlYiysjJOOOEE7r77bv7617+yfPlyevfuzZNPPrm1zK233sqqVas44IADtl52yDeaUN+/WOmAwczMrIRs2LCBm2++mVGjRrF06VImT57MwIEDtytTVlbGqaeeyl133QVAz549WblyJQCbN29mw4YNdO7cuV775UmPNVhSsXaHJ73UZnKLmZlZrm9+85s89thjnHzyyUybNo1+/fptzYsIXnrpJfr27UtE8MADD9C/f3Y336hRo5g6dSrDhw9n1qxZjBgxot5HGBwwmJmZlYhTTjmFKVOm0KrVJ0/PEcHYsWN5++23iQgGDRrETTfdBGS3W5555pn07duXzp07M2PGjHrvmwMGMzOzPIoxUjxq1Khq83bZZRfmz5+fN69NmzbceeedDdWtbPsN2rqZmZk1Cw4YzMzMrCAHDGZmZlaQAwYzMzMryJMea/DZste4p/01O1T3lStqX6/3Zc/u0DbMzMwaS6OPMEjqI2mTpEWSekmaJ2mJpOckXZBTrrOkOZKWpfe8v8YhabKkf0h6RtIsSZ9K6a0lzZRULukJSX1S+sGSpjTCrpqZmTUbxbok8VJEDAY2AxdHxAHAMOC7kgakMhOBuRHRD5ib1vP5fkQMioiBwCvAeSl9PLA+IvoC1wFXA0TEs0BPSb0bYsfMzMx2lCQuvvjires///nPufzyy7eu33zzzfTv35/+/ftz2GGH8eijjzZa34p6SSIiVgOr0/I7kpYAPYDngROBo1LRqcAjwKV52ngbQNkjrdoClQ/UPhG4PC3PAm6QpMgeuP0AcBrws/reJzMzax5eueLgem2vNpefW7duzd13380Pf/hDunbtul3e7Nmz+fWvf82jjz5K165deeqppzjppJN48skn+fSnP12vfc2nZCY9pksGhwBPpKS9UkBRGVh0q6HurcBrQH/g/6XkHsDKVH8zsAHokvIWAEdW09YESQskLVj33pad2CMzM7O6adWqFRMmTOC66677RN7VV1/NNddcszWQGDJkCGPHjuXGG29slL6VRMCQ5h3cBVxYOWJQFxExDtgbWAKcWtlsvqLpfU0qn6+tmyNiaEQM7dyurK5dMTMz2ynf/e53mT59Ohs2bNgu/bnnnuPQQw/dLm3o0KE899xzjdKvogcMknYlCxamR8TdOVmvS+qeynQnO8kj6aE0YfKW3HYiYgswE/jXlFQB9Ep1WgEdgHUprw2wqWH2yMzMbMftsccejBkzhkmTJhUsGxH1/iNT1SnqHIY072AysCQirq2SfT8wFrgqvd8HEBHHVan/2YgoT8tfA16oUv8xYDTwl9j2g+H7AYsL9W95WRmnd9ojb9788/M/z9vMzGxnXXjhhQwZMoRx48ZtTRswYAALFy5kxIgRW9OeeuopBgwYkK+JelfsEYYjgDOBEWnUYJGkr6a8q4BjJC0DjknrVQmYKulZ4FmgO3BFypsMdJFUDlzE9ndZHA38od73xszMrB507tyZU045hcmTJ29N+8EPfsCll17K2rVrAVi0aBFTpkzh3HPPbZQ+FfsuiUfJP9eAiFgLjCxQ/2OyoCNf3vvAyVXTJbUGhgIX1rW/ZmZmjeXiiy/mhhtu2Lo+atQoXn31VT7/+c8jifbt2/O73/2O7t27N0p/ihEwbAE6SFqUnsXQ2HoDE9OdE2ZmZnkV4ym877777tblvfbai40bN26X/53vfIfvfOc7jd0toAgBQ0SsJE1GLIaIWAYsK9b2zczMmqJiz2EwMzOzJsABg5mZmRXkgMHMzMwK8s9b16B/t/5+3oKZmRkeYTAzM7NacMBgZmbWRIwfP55BgwYxcOBARo8evfU2zA8++IBTTz2Vvn37cvjhh7NixYp637YvSZiZmeVxxP/L+1zAHbajl7g//PBDPvroI9q1a8d1113HHntkP1lw0UUXccMNNzBx4kQmT55Mp06dKC8vZ8aMGVx66aXMnDmzPrvvEQYzM7NStGTJEi6++GL2339/li5dCrA1WIgINm3atPWHp+677z7Gjh0LwOjRo5k7dy7bfj6pfniEoQZLKtZy6CXTit2N7Sy8Zkyxu2BmZg3kvffe44477mDy5MlEBOPGjeOZZ56hffv2W8uMGzeOBx98kAEDBvCLX/wCgFdffZVevbJnIrZq1YoOHTqwdu1aunbtWm99c8BgZmZWIrp3787AgQO55ZZb6N+/f94yt956K1u2bOH8889n5syZjBs3Lu9oQn3/7LUvSZiZmZWIWbNm0aNHD77+9a9zxRVX8PLLL+ctV1ZWxqmnnspdd90FQM+ePVm5ciUAmzdvZsOGDXTu3Lle++aAwczMrEQce+yxzJw5k0cffZQOHTpw4okn8uUvf5kVK1YQEZSXlwPZHIYHHnhg6yjEqFGjmDp1KpAFHSNGjKj3EQZfkjAzMysxXbp04YILLuCCCy7gySefpKysjIhg7NixvP3220QEgwYN4qabbgKy2y3PPPNM+vbtS+fOnZkxY0a998kBg5mZWR6l8qTfww47bOvy/Pn5+9SmTRvuvPPOBu2HL0mYmZlZQR5hqMFny17jnvbXFLsb23nlisbtT+/Lnm3U7ZmZWWkqygiDpD6SNklaJKmNpCcl/UPSc5L+I6fcvpKekLRM0kxJuxVo935Ji3PWO0uak+rPkdQppZ+Qux0zMzOrWTEvSbwUEYOBD4ARETEIGAx8RdKwVOZq4LqI6AesB8ZX15ik/wW8WyV5IjA31Z+b1gH+AIyStHu97Y2ZmTVJ9f1ExKairvtd9DkMkak80e+aXqHsfpARwKyUNxU4KV8bkj4FXARcWSXrxFRvu/qRHaVHgBPqZy/MzKwpatOmDWvXrm1xQUNEsHbtWtq0aVPrOiUxh0FSGbAQ6AvcGBFPSOoKvBURm1OxCqBHNU38X+AXwMYq6XtFxGqAiFgtqVtO3gLgSOCOKn2ZAEwA6NFh1x3fKTMzK3k9e/akoqKCN954o9hdaXRt2rShZ8+etS5fEgFDRGwBBkvqCNwj6SDg9XxFqyZIGgz0jYjvS+pTh82uAfbO05ebgZsBBvZo27JCTjOzFmbXXXdl3333LXY3moSSCBgqRcRbkh4BvkI2YtBRUqs0ytATWJUzGgFwP7AaOFTSCrL96SbpkYg4CnhdUvc0utCdLEio1AbY1Bj7ZWZm1tQVfQ6DpD3TyAKS2gJfBl5I8wzmAaNT0bHAfRGxJSIGp9dlEXFTROwdEX2ALwBLU7AAWUAxNrd+zqb3AxZjZmZmBZXCCEN3YGoaOdgFuCMiZqe8S4EZkq4EngYm17Htq4A7JI0HXgFOzsk7GvhhTZWXl5Vxeqc96rjJ0nk6mJmZWX0pesAQEc8Ah1STtxw4LF9eNeVXAAflrK8FRlYtJ2kvoG1E+KlEZmZmtVCsSxJbgA6SFhVp+72Bi4u0bTMzsyanKCMMEbES6FWMbaft/71Y2zYzM2uKij7p0czMzEqfAwYzMzMryAGDmZmZFVT0uyRKWf9u/X2LpJmZGR5hMDMzs1pwwGBmZmYFOWAwMzOzghwwmJmZWUGe9FiDJRVrOfSSacXuhlVj4TVjit0FM7MWwyMMZmZmVpADBjMzMyvIAYOZmZkV5IDBzMzMCnLAYGZmZgU5YDAzM7OCHDCYmZlZQYqIxt2g1AdYArwYEYNTWhmwAHg1Ik5IafsCM4DOwFPAmRHxYZ72HgG6A5tS0rERsUZSa2AacCiwFjg1IlZIOhi4OCLOKtTXgT3axuxz+u74zpo1gN6XPVvsLphZMyZpYUQMrZperBGGlyqDheQCsiAi19XAdRHRD1gPjK+hvTMiYnB6rUlp44H1EdEXuC61R0Q8C/SU1Ls+dsTMzKwlKPolCUk9gX8BbslJEzACmJWSpgIn1bHpE1M9UjsjU7sADwCn7WifzczMWpqiBwzA9cAPgI9z0roAb0XE5rReAfSooY1bJS2S9H9ygoIewEqA1M6G1C5klz+OzNeQpAmSFkhasO69LTu0Q2ZmZs1NUQMGSScAayJiYdWsPMWrm2xxRkQcTBYAHAmcWYs21gB752ssIm6OiKERMbRzu7Ia+29mZtZSFHuE4QhglKQVZBMcR0j6HfAm0FFS5Y9j9QRWSSpLIwmLJF0BEBGvpvd3gN8Dh6U6FUAvgNROB2BdymvDtkmSZmZmVkBRA4aI+GFE9IyIPmRzCv4SEd+M7NaNecDoVHQscF9EbMmZ3HiZpFaSugJI2hU4AVic6tyf6pHa+UtsuyVkv5xyZmZmVkAp/7z1pcAMSVcCTwOT85RpDTyUgoUy4GHgNylvMnCbpHKykYXcSY5HA38o1IHlZWWc3mmPHd+DejD//PlF3b6ZmRmUUMAQEY8Aj+SsL2fb5YXq6rxH9pyFfHnvAydXTU/PZxgKXLjjvTUzM2tZinFJYgvQQdKiImwboDcwMecODDMzMyug0UcYImIlaTJiMUTEMmBZsbZvZmbWFBX7LgkzMzNrAhwwmJmZWUEOGMzMzKygkrlLohT179bftzWamZnhEQYzMzOrBQcMZmZmVpADBjMzMyvIAYOZmZkV5EmPNVhSsZZDL5lW7G6YNZqF14wpdhfMrER5hMHMzMwKcsBgZmZmBTlgMDMzs4IcMJiZmVlBDhjMzMysIAcMZmZmVpADBjMzMytIEdG4G5T6AEuAFyNisKQVwDvAFmBzRAxN5ToDM4E+wArglIhYn6c9AVcCJ6c2boqISSn9l8BXgY3AWRHxlKQ9gdsi4iuF+jqwR9uYfU7fndpfM7PG1PuyZ4vdBWviJC2sPBfnKtYIw0sRMThn/eiIGFylgxOBuRHRD5ib1vM5C+gF9I+IA4AZKf14oF96TQBuAoiIN4DVko6or50xMzNr7kr5ksSJwNS0PBU4qZpy3wGuiIiPASJiTU79aZF5HOgoqXvKuxc4o2G6bWZm1vyUQsAQwJ8lLZQ0ISd9r4hYDZDeu1VT/7PAqZIWSPqjpH4pvQewMqdcRUoDWAAcma8xSRNSWwvWvbdlB3fJzMyseSmFgOGIiBhCdgnhu5K+WMf6rYH30+WM3wC/TenKU7ZywsYaYO98jUXEzRExNCKGdm5XVseumJmZNU9FDxgiYlV6XwPcAxyWsl6vvISQ3tek5YckLZJ0SypXAdyVlu8BBuak98rZVE9gVVpuA2yq/70xMzNrnooaMEhqJ6l95TJwLLA4Zd8PjE3LY4H7ACLiuDRB8lsp715gRFr+ErA0p/4YZYYBGyovcQD75WzHzMzMCij2z1vvBdyT3QFJK+D3EfGnlHcVcIek8cArZLdN5nMVMF3S94F3gcpA4kGyWyrLyW6rHJdT55HG/poAABdUSURBVGjgD4U6t7ysjNM77VGnHWoK5p8/v9hdMDOzJqaoAUNELAcGVZO3FhhZizbeAv4lT3oA362m2iiyuyjMzMysFup0SSJdNthZW4AOkhbVQ1t1lh7cdG2+h0CZmZlZfrUKGCR9XtLzZE9oRNIgSf+1IxuMiJUR0avKg5saTUS8ERH3FmPbZmZmTVVtRxiuA44D1gJExD+Aut7+aGZmZk1UrS9JRMTKKkl+qpGZmVkLUdtJjyslfR4ISbsB3yNdnjAzM7Pmr7YBw/8m++XHHmQPRPoz1d+B0Gz079bftyCamZlRy4AhIt7EP9ZkZmbWYtUqYJA0KU/yBmBBRNxXv10yMzOzUlPbSY9tgMHAsvQaCHQGxku6voH6ZmZmZiWitnMY+gIjImIzgKSbyOYxHAM820B9MzMzsxJR2xGGHkDuUx7bAXtHxBbgg3rvlZmZmZWU2o4w/AxYJOkRQGQPbfpJelT0ww3Ut6JbUrGWQy+ZVuxumFkjWHjNmGJ3wayk1fYuicmS/gicCbxAdjmiIiLeAy5pwP6ZmZlZCajtXRLfAi4AegKLgGHAY8CIhuuamZmZlYrazmG4APgc8HJEHA0cArzRYL0yMzOzklLbgOH9iHgfQFLriHgB2L/humVmZmalpLaTHiskdQTuBeZIWg+sarhumZmZWSmp7aTHr6fFyyXNAzoAf2qwXpmZmVlJUUQUuw8la2CPtjH7nL7F7oaZmTWg3pf5+YO5JC2MiKFV02s7h6E+O9JH0iZJi9J6R0mzJL0gaYmk4Sm9s6Q5kpal907VtPc3SYvSa5Wke1O6JE2SVC7pGUlDUvqekjw6YmZmVgeNHjAkL0XE4LT8S+BPEdEfGAQsSekTgbkR0Q+Ym9Y/ISKOjIjBqb3HgLtT1vFAv/SaANyUyr8BrJZ0RP3vlpmZWfNUrIABAEl7kD01cjJARHwYEW+l7BOBqWl5KnBSgbbakz0X4t6c+tMi8zjQUVL3lHcv1fxct6QJkhZIWrDuvS07uGdmZmbNS1EDBuAzZM9zuFXS05JuSY+bBtgrIlYDpPduBdr6OtmIxNtpvQewMie/IqUBLACOzNdIRNwcEUMjYmjndmV13yMzM7NmqNgBQytgCHBTRBwCvEc1lx5q4XTg9px15SlTOcNzDbD3Dm7HzMysxSl2wFBB9psUT6T1WWQBBMDrlZcQ0vuatPxQmuB4S2UjkroAhwF/qNJ2r5z1nmx7dkQbYFM974uZmVmzVdsHNzWIiHhN0kpJ+0fEi8BI4PmUfT8wFrgqvd+X6hyXp6mTgdmVT6PMqX+epBnA4cCGykscwH7A4kL9W15Wxumd9tiBPTOA+efPL3YXzMysnhQ1YEjOB6ZL2g1YDoxL6VcBd0gaD7xCFhRU57RUPteDwFeBcmBjTrsAR7P9aISZmZnVoOgBQ0QsAj7xgIiIWEs24lCbNo7KkxbAd6upMorsLgozMzOrhWLMYdgCdKh8cFNjk7QncG1ErC/G9s3MzJqiRh9hiIiVbD8ZsbG3/wbbntVgZmZmtVDsuyTMzMysCXDAYGZmZgUVfdJjKevfrb9vDTQzM8MjDGZmZlYLDhjMzMysIAcMZmZmVpADBjMzMyvIAYOZmZkV5LskarCkYi2HXjKt2N0wM6u1hdeMKXYXrJnyCIOZmZkV5IDBzMzMCnLAYGZmZgU5YDAzM7OCHDCYmZlZQQ4YzMzMrCBFRLH7ULIG9mgbs8/pW+xumJmZfULvy55tkHYlLYyIoVXTG32EQVIfSZskLZK0f3qvfL0t6cJUrrOkOZKWpfdO1bQ3UtJTqf6jkvqm9NaSZkoql/SEpD4p/WBJUxppd83MzJqFYl2SeCkiBkfEi+l9MHAosBG4J5WZCMyNiH7A3LSez03AGamN3wP/ntLHA+sjoi9wHXA1QEQ8C/SU1LshdszMzKw5KqU5DCPJAomX0/qJwNS0PBU4qZp6AeyRljsAq/LUnwWMlKS0/gBwWj3128zMrNkrpYDhNOD2nPW9ImI1QHrvVk29bwEPSqoAzgSuSuk9gJWp/mZgA9Al5S0AjszXmKQJkhZIWrDuvS07sTtmZmbNR0kEDJJ2A0YBd+5A9e8DX42InsCtwLWVzeYpWznDcw2wd77GIuLmiBgaEUM7tyvbge6YmZk1PyURMADHA09FxOs5aa9L6g6Q3tek5YfSBMdbJO0JDIqIJ1KdmcDn03IF0CvVaUV2uWJdymsDbGrIHTIzM2tOSuXXKk9n+8sRAPcDY8kuMYwF7gOIiOMqC1QGApL2i4ilwDHAkir1HwNGA3+JbfeQ7gcsLtSp5WVlnN5pj0LFrIHNP39+sbtgZtbiFT1gkLQ72Yn+nCpZVwF3SBoPvAKcXLVuRGyW9G3gLkkfA+uBs1P2ZOA2SeVkIwu5kxyPBv5QrztiZmbWjBU9YIiIjWybjJibvpbszolC9e9h262YuenvkyfIkNQaGApcuCP9NTMza4mKMYdhC9llhEVF2DZAb2BiunPCzMzMaqHRRxgiYiVpMmIxRMQyYFmxtm9mZtYUlcpdEmZmZlbCHDCYmZlZQQ4YzMzMrKCi3yVRyvp36+9nAJiZmeERBjMzM6sFBwxmZmZWkAMGMzMzK8gBg5mZmRXkSY81WFKxlkMvmVbsbpiVnIXXjCl2F8yskXmEwczMzApywGBmZmYFOWAwMzOzghwwmJmZWUEOGMzMzKwgBwxmZmZWkCKi2H0oWQN7tI3Z5/QtdjfMrJnqfdmzxe6C2SdIWhgRQ6umN/oIg6Q+kjZJWpTWvy/pOUmLJd0uqU1K31fSE5KWSZopabdq2jtV0jOpjZ/lpLdO9cpTO31S+sGSpjT4jpqZmTUjxbok8VJEDJbUA/geMDQiDgLKgNNSmauB6yKiH7AeGF+1EUldgGuAkRFxILCXpJEpezywPiL6Atel9oiIZ4Gekno33O6ZmZk1L6Uwh6EV0FZSK2B3YJUkASOAWanMVOCkPHU/AyyNiDfS+sPAv6blE1M9UjsjU7sAD7AtMDEzM7MCihowRMSrwM+BV4DVwIaI+DPQBXgrIjanohVAjzxNlAP902WOVmRBRa+U1wNYmbazGdiQ2gVYAByZr0+SJkhaIGnBuve27OwumpmZNQtFDRgkdSIbCdgX2BtoJ+mbgPIU/8TszIhYD3wHmAn8DVgBVAYZNbWxJm3vkwUibo6IoRExtHO7strvjJmZWTNW7EsSXwb+GRFvRMRHwN3A54E3gY5p1ACgJ9mlijJJi9LrCoCIeCAiDo+I4cCLwLJUp4I02pDa6QCsS3ltgE2NsH9mZmbNQrF/rfIVYJik3clO4COBBRERkuYBo4EZwFjgvojYAgzObUBSt4hYk0YrzgVOSVn3p3qPpXb+EtvuId0PWFyoc8vLyji90x47u49N3vzz5xe7C2ZmVmRFDRgi4glJs4CnyC4lPA3cnLIvBWZIujKlT66mmV9KGpSWr4iIpWl5MnCbpHKykYXcSY5HA3+ovz0xMzNr3oo9wkBE/Bj4cZ705cBhtah/ejXp7wMnV02X1BoYClxY586amZm1UMWYw7AF6FD54KYi6A1MzLkDw8zMzApo9BGGiFjJtlsfG11ELGPbxEgzMzOrhWLfJWFmZmZNgAMGMzMzK8gBg5mZmRVU9LskSln/bv39DAIzMzM8wmBmZma14IDBzMzMCnLAYGZmZgU5YDAzM7OCPOmxBksq1nLoJdMafDsLrxnT4NswMzPbGR5hMDMzs4IcMJiZmVlBDhjMzMysIAcMZmZmVpADBjMzMyvIAYOZmZkVpIgodh9K1sAebWP2OX2L3Y161fuyZ4vdBTMzK2GSFkbE0KrpDTbCIKmPpE2SFqX1CyQtlvScpAtzynWWNEfSsvTeqZr2zpNULikkdc1Jl6RJKe8ZSUNy8samdpdJGpuT/nB12zEzM7NPauhLEi9FxGBJBwHfBg4DBgEnSOqXykwE5kZEP2BuWs9nPvBl4OUq6ccD/dJrAnATZIEI8GPg8LTdH+cECbcB5+787pmZmbUMjTWH4QDg8YjYGBGbgf8Gvp7yTgSmpuWpwEn5GoiIpyNiRZ6sE4FpkXkc6CipO3AcMCci1kXEemAO8JVU537g9HrYLzMzsxahsQKGxcAXJXWRtDvwVaBXytsrIlYDpPdudWy7B7AyZ70ipVWXTgogWkvqUrUxSRMkLZC0YN17W+rYFTMzs+apUQKGiFgCXE32V/6fgH8Am+upeeXbZA3pldYAe3+iQMTNETE0IoZ2bldWT100MzNr2hrttsqImBwRQyLii8A6YFnKej1dQiC9r0nLD0laJOmWAk1XsG20AqAnsKqG9EptgE07uj9mZmYtSaMFDJK6pffewP8Cbk9Z9wOVdzCMBe4DiIjjImJwRHyrQNP3A2PS3RLDgA3p0sZDwLGSOqXJjsemNCQJ+DSwor72z8zMrDlrzJ+3vivNGfgI+G6aRwBwFXCHpPHAK8DJ+SpL+h7wA7IT/TOSHkzBxINkcyLKgY3AOICIWCfp/wJ/T01cERHr0vKhZJMwa7wssrysjNM77VFwx+afP79gGTMzs6as0QKGiDiymvS1wMha1J8ETMqTHsB3q6nzW+C3ebLOBP6r0DbNzMws05CXJLYAHSof3FRiFkfE3GJ3wszMrKlosBGGiFjJ9pMOS0ZE/KbYfTAzM2tK/ONTZmZmVpADBjMzMyvIAYOZmZkV1Ji3VTY5/bv19y2TZmZmeITBzMzMasEBg5mZmRXkgMHMzMwKcsBgZmZmBTlgqMGSirXF7oKZmVlJcMBgZmZmBTlgMDMzs4IcMJiZmVlBDhjMzMysIAcMZmZmVpADBjMzMyvIAYOZmZkVVFIBg6Q+kjZJWpTWvyLpRUnlkibWUO9Pkt6SNLtK+r6SnpC0TNJMSbul9PMkjSvUn8+WvcYrVxy8s7tlZmbW5JVUwJC8FBGDJZUBNwLHAwOA0yUNqKbONcCZedKvBq6LiH7AemB8Sv8t8L367baZmVnzVYoBQ6XDgPKIWB4RHwIzgBPzFYyIucA7uWmSBIwAZqWkqcBJqfxGYIWkwxqo72ZmZs1KKQcMPYCVOesVKa22ugBvRcTmauovAI6sWknSBEkLJC1Y996WOnbZzMyseSrlgEF50qIe668B9v5EgYibI2JoRAzt3K6sDpszMzNrvko5YKgAeuWs9wRWSTpc0qL0GlVD/TeBjpJa5dbPyW8DbKrXHpuZmTVTrQoXKZq/A/0k7Qu8CpwGfCMingMGF6ocESFpHjCabP7DWOC+nCL7AfPrvddmZmbNUMmOMKS5B+cBDwFLgDtSsPAJkv4G3AmMlFQh6biUdSlwkaRysjkNk3OqHQE8XFMfdut+IL0ve3bndsTMzKwZKOURBiLiQeDBWpT7xOTFlL6c7G6L7Ug6BHguIt7c6U6amZm1AKU2wrAF6FD54KYG1BX4Pw28DTMzs2ajpEYYImIl2090bKjtzGnobZiZmTUnpTbCYGZmZiXIAYOZmZkV5IDBzMzMCnLAYGZmZgU5YDAzM7OCHDCYmZlZQYqoy+85tSyS3gFeLHY/iqwr2e9ytHQ+Dj4G4GMAPgbQ/I/BPhGxZ9XEknoOQwl6MSKGFrsTxSRpQUs/BuDjAD4G4GMAPgbQco+BL0mYmZlZQQ4YzMzMrCAHDDW7udgdKAE+BhkfBx8D8DEAHwNoocfAkx7NzMysII8wmJmZWUEOGKoh6SuSXpRULmlisftTnyT1kjRP0hJJz0m6IKV3ljRH0rL03imlS9KkdCyekTQkp62xqfwySWOLtU87SlKZpKclzU7r+0p6Iu3PTEm7pfTWab085ffJaeOHKf1FSccVZ092jKSOkmZJeiF9H4a3tO+BpO+nfweLJd0uqU1L+B5I+q2kNZIW56TV22cv6VBJz6Y6kySpcfewsGqOwTXp38Mzku6R1DEnL+9nXN35orrvUZMVEX5VeQFlwEvAZ4DdgH8AA4rdr3rcv+7AkLTcHlgKDAB+BkxM6ROBq9PyV4E/AgKGAU+k9M7A8vTeKS13Kvb+1fFYXAT8Hpid1u8ATkvLvwK+k5bPBX6Vlk8DZqblAen70RrYN31vyoq9X3XY/6nAt9LybkDHlvQ9AHoA/wTa5nz+Z7WE7wHwRWAIsDgnrd4+e+BJYHiq80fg+GLvcy2PwbFAq7R8dc4xyPsZU8P5orrvUVN9eYQhv8OA8ohYHhEfAjOAE4vcp3oTEasj4qm0/A6whOw/zhPJTiCk95PS8onAtMg8DnSU1B04DpgTEesiYj0wB/hKI+7KTpHUE/gX4Ja0LmAEMCsVqXoMKo/NLGBkKn8iMCMiPoiIfwLlZN+fkidpD7L/MCcDRMSHEfEWLex7QPY8mraSWgG7A6tpAd+DiPgrsK5Kcr189ilvj4h4LLKz5bSctkpGvmMQEX+OiM1p9XGgZ1qu7jPOe74o8P9Jk+SAIb8ewMqc9YqU1uykIdVDgCeAvSJiNWRBBdAtFavueDT143Q98APg47TeBXgr5z+L3P3Zuq8pf0Mq35SPwWeAN4Bb02WZWyS1owV9DyLiVeDnwCtkgcIGYCEt63uQq74++x5puWp6U3M22egI1P0Y1PT/SZPkgCG/fNfamt3tJJI+BdwFXBgRb9dUNE9a1JBe8iSdAKyJiIW5yXmKRoG8JnsMyP6yHgLcFBGHAO+RDUNXp9kdg3SN/kSyIea9gXbA8XmKNufvQW3Udb+b/PGQ9CNgMzC9MilPsWZ9DKpywJBfBdArZ70nsKpIfWkQknYlCxamR8TdKfn1NJRIel+T0qs7Hk35OB0BjJK0gmwIcQTZiEPHNDQN2+/P1n1N+R3IhjKb8jGoACoi4om0PossgGhJ34MvA/+MiDci4iPgbuDztKzvQa76+uwr2DaUn5veJKTJmycAZ6RLKlD3Y/Am1X+PmiQHDPn9HeiXZrjuRja56f4i96nepGtrk4ElEXFtTtb9QOUs57HAfTnpY9JM6WHAhjRc+RBwrKRO6S+1Y1NayYuIH0ZEz4joQ/b5/iUizgDmAaNTsarHoPLYjE7lI6WflmbP7wv0I5vsVfIi4jVgpaT9U9JI4Hla0PeA7FLEMEm7p38XlcegxXwPqqiXzz7lvSNpWDquY3LaKmmSvgJcCoyKiI05WdV9xnnPF+l7Ud33qGkq9qzLUn2RzQpeSjb79UfF7k8979sXyIbGngEWpddXya65zQWWpffOqbyAG9OxeBYYmtPW2WSTf8qBccXetx08Hkex7S6Jz5D9J1AO3Am0Tult0np5yv9MTv0fpWPzIiU4E7zAvg8GFqTvwr1kM91b1PcA+A/gBWAxcBvZLPhm/z0Abiebt/ER2V/J4+vzsweGpmP6EnAD6UGBpfSq5hiUk81JqPy/8VeFPmOqOV9U9z1qqi8/6dHMzMwK8iUJMzMzK8gBg5mZmRXkgMHMzMwKcsBgZmZmBTlgMDMzs4IcMJhZkybpQkm7F7sfZs2db6s0syYtPa1zaES8Wey+mDVnHmEwswYnaYykZyT9Q9JtkvaRNDelzZXUO5WbIml0Tr130/tRkh6RNEvSC5Kmp6cOfo/sNyDmSZpXnL0zaxlaFS5iZrbjJB1I9oS8IyLiTUmdyX7qd1pETJV0NjCJwj/9ewhwINnz+Oen9iZJugg42iMMZg3LIwxm1tBGALMqT+gRsQ4YDvw+5d9G9rjyQp6MiIqI+Jjskb19GqCvZlYNBwxm1tBE4Z/1rczfTPp/Kf1o0W45ZT7IWd6CR0jNGpUDBjNraHOBUyR1AUiXJP6H7Ff9AM4AHk3LK4BD0/KJwK61aP8doH19ddbM8nOEbmYNKiKek/SfwH9L2gI8DXwP+K2kS4A3gHGp+G+A+yQ9SRZovFeLTdwM/FHS6og4uv73wMzAt1WamZlZLfiShJmZmRXkgMHMzMwKcsBgZmZmBTlgMDMzs4IcMJiZmVlBDhjMzMysIAcMZmZmVpADBjMzMyvo/wMJAq1z6Edo2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,5))\n",
    "sns.countplot(y= df['age'], hue = df['readmitted']).set_title('Age of Patient VS. Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Race VS. Readmission')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,4))\n",
    "sns.countplot(y = df['race'], hue = df['readmitted']).set_title('Race VS. Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Num of Medications')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "sns.barplot(x = df['readmitted'], y = df['num_medications']).set_title(\"Number of medication used VS. Readmission\")\n",
    "\n",
    "plt.xlabel('Readmitted')\n",
    "plt.ylabel('Num of Medications')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Time in Hospital')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "sns.barplot(x = df['readmitted'], y = df['time_in_hospital']).set_title(\"Time in Hospital VS. Readmission\")\n",
    "\n",
    "plt.xlabel('Readmitted')\n",
    "plt.ylabel('Time in Hospital')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gender of Patient VS. Readmission')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "sns.countplot(y = df['gender'], hue = df['readmitted']).set_title(\"Gender of Patient VS. Readmission\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Change of Medication VS. Readmission')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RJKnKBg0axLRp01iwYAEAK1asYP78+et+KK1nz5689dZbTJ48GYDtttuObt26MXXqVAAmTJiwWdvv2rUrb7755rrnHTp04N133wXgs5/9LJMnT+bll18G4LXXXuP555/nwAMP5KGHHmLp0qW8++673H777ZsVQ2NsUZEkqcq23357xo0bx9e//nVWrVoFwEUXXcSee+7Jqaeeyn777Ue/fv3WnX4BGDt27LrOtF/4whc2a/sjR47k8MMPp1evXkyZMoWRI0fSv39/BgwYwIQJE7jooos47LDDeO+99+jQoQPXXnstgwYNYvTo0QwePJhevXoxYMAA1q5du1lxNCRSSs2+0krYZqfd0l4n/rjaYUhqxbw8WdUyd+5c9t5772qHUTUN7X9EzEwpDdzYsp76kSRJhWWhIkmSCstCRZIkFZaFiiRJKiwLFUmSVFgWKpIkqbD8HRVJkqrsgPPGb3ymJmiOS/FPOeUUZsyYQUqJPffck3HjxtGlSxdWrVrFsGHDmDlzJj169GDSpEn069dv84NuhC0qkiS1Qa+//voGp1955ZU88cQTzJ49m759+3LNNdcAMGbMGLp3786CBQs455xzOP/88ysap4WKJElt0MCBAzn++OP54x//SEM//rrtttsC2b1+Vq5cue6+PnfffTfDhw8H4JhjjuHBBx9scPnmYqEiSVIbNH/+fI4//niuueYa9tlnHy6++GKWLFmy3jwjRoxgp512Yt68eYwaNQqAF154gT59+gDQvn17unXrxtKlSysWp4WKJEltUE1NDUcccQR33nknDz/8MM8++yx9+/Zl+vTp6+YZO3YsS5YsYe+992bSpEkADbaeVPIuyhYqkiS1UcuWLePGG29k6NChzJ8/nzFjxtC/f//15qmpqeG4447jjjvuAKB3794sWrQIgDVr1rBs2TJqa2srFqOFiiRJbdAJJ5zAgAEDePbZZxk/fjwPP/www4cPp1OnTqSUWLBgAZC1oNxzzz3stddeAAwdOpSbb74ZgMmTJ3PooYdWtEXFy5MlSaqyatzZ+9hjj2XcuHG0b//BUiClxPDhw1m+fDkpJT760Y9y3XXXAdllyyeeeCJ77LEHtbW1TJw4saJxWqhIktQGDR06tNFp7dq1Y9q0aQ1O69SpE7fffnulwvpgLC22JUmSpCayUJEkSYVloSJJkgrLQkWSJBWWhYokSSosCxVJklRYreby5N1rXuK3XS+rdhiSWrG/X+j/kKLqe8Ffqx1CVf39wv2adX3l5DMi+M53vsPll18OwM9//nPeeustRo8eDcCNN97IFVdcAWQ3KLziiisYMmRIs8ZZDltUJElqgzp27Midd97Jq6+++oFp9957LzfccANTp05l3rx5XH/99Rx//PG89NJLLR6nhYokSW1Q+/btGTlyJFdeeeUHpv3sZz/jsssuo2fPngAMGDCA4cOHc+2117Z0mBYqkiS1Vd/+9reZMGECy5YtW2/8k08+yQEHHLDeuIEDB/Lkk0+2ZHiAhYokSW3Wtttuy7Bhw7j66qs3Om9KqaI3H2yMhYokSW3Y2WefzZgxY3j77bfXjdtnn32YOXPmevM9/vjj7LPPPi0dnoWKJEltWW1tLcceeyxjxoxZN+573/se559/PkuXLgVg1qxZjBs3jtNPP73F42s1lydLkrSlqvbl2eeeey7XXHPNuudDhw7lhRde4JOf/CQRQdeuXbn11lvp1atXi8dmoSJJUhv01ltvrRvecccdWbFixXrTTzvtNE477bSWDusDPPUjSZIKy0JFkiQVloWKJEkqrIoWKhGxU0RMjIhnIuKpiPh9RIyMiHsruV1JkrRlqFihEtmvwvwWeCiltHtKaR/gB8COldqmJEnaslSyReUQ4N2U0vV1I1JKs4D/BbpExOSImBcRE6IaP3UnSZIKr5KXJ38EmNnItP2BfYElwDTgIGBq/ZkiYiQwEmCXbh0qE6UkSVV20H8d1KzrmzZq2mav45RTTmHGjBmklNhzzz0ZN24cXbp0YdWqVQwbNoyZM2fSo0cPJk2aRL9+/TY/6EZUqzPt9JTS4pTSe8AsoF9DM6WUbkwpDUwpDazdpqZFA5Qkqa1ZvXr1up/Sv/LKK3niiSeYPXs2ffv2XfeDcGPGjKF79+4sWLCAc845h/PPP7+iMVWyUHkSOKCRaatKhtfiD89JklQ1c+fO5dxzz+XDH/4w8+fPB7IbFkJ2M8KVK1euuyHh3XffzfDhwwE45phjePDBB0kpVSy2ShYqfwQ6RsSpdSMi4uPAZyq4TUmSVIa3336bsWPHMmTIEL75zW+y9957M3v2bPbff/9184wYMYKddtqJefPmMWrUKABeeOEF+vTpA0D79u3p1q3bunsCVULFWjJSSikijgauiojvA+8AC4G7KrVNSZJUnl69etG/f39++ctfstdeezU4z9ixY1m7di2jRo1i0qRJjBgxosHWk0peE1PRPioppSUppWPzy5P3TSn9c0rpppTSESXznJFSGlfJOCRJ0vomT57MLrvswtFHH82FF17I888/3+B8NTU1HHfccdxxxx0A9O7dm0WLFgGwZs0ali1bRm1tbcXi9JdpJUlqgw477DAmTZrE1KlT6datG0cddRSf+9znWLhwISklFixYAGR9VO655551rS5Dhw7l5ptvBrJi59BDD61oi4qdWCVJqrLmuJx4U/Xo0YOzzjqLs846i+nTp1NTU0NKieHDh7N8+XJSSnz0ox/luuuuA7LLlk888UT22GMPamtrmThxYkXjs1CRJEkAfOITn1g3PG1aw8VTp06duP3221sqJE/9SJKk4rJQkSRJhWWhIklSC6jkj6IV2ebut4WKJEkV1qlTJ5YuXdrmipWUEkuXLqVTp06bvA4700qSVGG9e/dm8eLFvPLKK9UOpcV16tSJ3r17b/LyraZQebamhq9337baYWyxqnlpnCRt6Tp06MBuu+1W7TBaJU/9SJKkwrJQkSRJhWWhIkmSCstCRZIkFZaFiiRJKiwLFUmSVFgWKpIkqbAsVCRJUmFZqEiSpMKyUJEkSYVloSJJkgrLQkWSJBWWhYokSSosCxVJklRYFiqSJKmwLFQkSVJhWahIkqTCslCRJEmFZaEiSZIKy0JFkiQVloWKJEkqLAsVSZJUWBYqkiSpsMouVCJi14j4XD68dUR0rVxYkiRJZRYqEXEqMBm4IR/VG7irUkFJkiRB+S0q3wYOApYDpJT+BuxQqaAkSZKg/EJlVUppdd2TiGgPpMqEJEmSlCm3UPlTRPwA2DoiPg/cDtxTubAkSZIgUtp4w0hEtANOAQ4DAvgD8MtUzsLNZODAgWnGjBkttTlJklRBETEzpTRwY/O1L2dlKaX3gJvyhyRJUosoq1CJiL/ywT4py4AZwEUppaXNHZgkSVJZhQpwH7AW+HX+/Gv53+XAOODI5g1LkiSp/ELloJTSQSXP/xoR01JKB0XECZUITJIkqdyrfrpExIF1TyLiE0CX/OmaZo9KkiSJ8ltUvgn8KiK6kF31sxz4ZkRsA1xSqeAkSVLbVu5VP38G9ouIbmSXNL9RMvk3FYlMkiS1eeVe9dMR+CrQD2gfEQCklC6sWGSSJKnNK/fUz91klyPPBFZVLhxJkqT3lVuo9E4pfbGikUiSJNVT7lU/j0TEfhWNRJIkqZ5yW1SGACdFxHNkp34CSCml/hWLTJIktXnlFiqHVzQKSZKkBpR7efLzABGxA9CpohFJkiTlyuqjEhFDI+JvwHPAn4CFZPf/kSRJqphyO9P+BzAImJ9S2g34LDCtYlFJkiRRfqHybkppKdAuItqllKYAH6tgXJIkSWV3pn0jv8/Pw8CEiHgZb0YoSZIqrNwWlaOAlcA5wP8DngGOrFRQkiRJUP5VP2+XPL25QrFIkiStp9yrfr4SEX+LiGURsTwi3oyI5ZUOTpIktW3l9lG5FDgypTS3ksFsyNzFSzngvPHV2rwkATDzsmHVDkFqU8rto/KPahYpkiSpbdpgi0pEfCUfnBERk4C7yO71A0BK6c4KxiZJktq4jZ36qbuyJwErgMNKpiXAQkWSJFXMBguVlNIIgIi4GTgrpfRG/rw7cHnlw5MkSW1ZuX1U+tcVKQAppdeB/SsTkiRJUqbcQqVd3ooCQETUUv4VQ5IkSZuk3GLjcuCRiJhM1jflWOAnFYtKkiSJ8n+ZdnxEzAAOBQL4SkrpqYpGJkmS2ryyT9/khYnFiSRJajHl9lGRJElqcRYqkiSpsCxUJElSYVmoSJKkwrJQkSRJhWWhIkmSCstCRZIkFZaFiiRJKiwLFUmSVFgWKpIkqbAsVCRJUmFZqEiSpMKyUJEkSYVloSJJkgrLQkWSJBWWhYokSSosCxVJklRYFiqSJKmwLFQkSVJhRUqp2jGUpf8uW6d7v7VHtcOQJKlN6HvBXyu6/oiYmVIauLH5bFGRJEmFZaEiSZIKy0JFkiQVloWKJEkqLAsVSZJUWBYqkiSpsCxUJElSYVmoSJKkwrJQkSRJhWWhIkmSCqtihUpEpIi4vOT5dyNidKW2J0mStjyVbFFZBXwlInpWcBuSJGkLVslCZQ1wI3BO/QkRsWtEPBgRs/O/fSsYhyRJaqUq3UflWuAbEdGt3vhrgPEppf7ABODqhhaOiJERMSMiZrz29toKhypJkoqmooVKSmk5MB44s96kwcCv8+FbgCGNLH9jSmlgSmlg7TY1lQtUkiQVUktc9XMVcAqwzQbmSS0QhyRJamUqXqiklF4DfkNWrNR5BPhaPvwNYGql45AkSa1PS/2OyuVA6dU/ZwIjImI2cCJwVgvFIUmSWpH2lVpxSqlLyfA/gM4lzxcCh1Zq25IkacvgL9NKkqTCslCRJEmFZaEiSZIKy0JFkiQVloWKJEkqLAsVSZJUWBYqkiSpsCxUJElSYVmoSJKkwqrYL9M2t2dravh6922rHUabMW3UtGqHIEmSLSqSJKm4LFQkSVJhWahIkqTCslCRJEmFZaEiSZIKy0JFkiQVloWKJEkqLAsVSZJUWBYqkiSpsCxUJElSYVmoSJKkwrJQkSRJhWWhIkmSCstCRZIkFZaFiiRJKiwLFUmSVFgWKpIkqbAsVCRJUmFZqEiSpMKyUJEkSYVloSJJkgrLQkWSJBWWhYokSSosCxVJklRYFiqSJKmwLFQkSVJhWahIkqTCslCRJEmF1b7aAZRrrx32YtqoadUOQ5IktSBbVCRJUmFZqEiSpMKyUJEkSYVloSJJkgrLQkWSJBWWhYokSSosCxVJklRYFiqSJKmwLFQkSVJhWahIkqTCipRStWMoS0S8CTxd7ThauZ7Aq9UOYgtgHpuHeWwe5nHzmcPm0dQ87ppS2n5jM7Wae/0AT6eUBlY7iNYsImaYw81nHpuHeWwe5nHzmcPmUak8eupHkiQVloWKJEkqrNZUqNxY7QC2AOaweZjH5mEem4d53HzmsHlUJI+tpjOtJElqe1pTi4okSWpjLFQkSVJhFb5QiYgvRsTTEbEgIr5f7XiKKCIWRsRfI2JWRMzIx9VGxP0R8bf8b/d8fETE1Xk+Z0fEgJL1DM/n/1tEDK/W/rSUiPhVRLwcEXNKxjVb3iLigPx1WZAvGy27h5XXSA5HR8QL+fE4KyK+VDLt3/J8PB0RXygZ3+D7PCJ2i4jH8txOioitWm7vWk5E9ImIKRExNyKejIiz8vEej2XaQA49HpsgIjpFxPSIeCLP44/z8Q3ue0R0zJ8vyKf3K1lXk/LbqJRSYR9ADfAM8CFgK+AJYJ9qx1W0B7AQ6Flv3KXA9/Ph7wM/y4e/BNwHBDAIeCwfXws8m//tng93r/a+VThvnwYGAHMqkTdgOjA4X+Y+4PBq73ML5XA08N0G5t0nfw93BHbL39s1G3qfA78BvpYPXw+cVu19rlAeewED8uGuwPw8Xx6Pm59Dj8em5TGALvlwB+Cx/BhrcN+B04Hr8+GvAZM2Nb+NPYreovIJYEFK6dmU0mpgInBUlWNqLY4Cbs6Hbwa+XDJ+fMr8H7BdRPQCvgDcn1J6LaX0OnA/8MWWDrolpZQeBl6rN7pZ8pZP2zal9GjK3rXjS9a1xWgkh405CpiYUlqVUnoOWED2Hm/wfZ5/4z8UmJwvX/p6bFFSSq0mMmwAAAR/SURBVC+mlB7Ph98E5gK74PFYtg3ksDEejw3Ij6m38qcd8kei8X0vPUYnA5/Nc9Wk/G4opqIXKrsAi0qeL2bDB15blYD/iYiZETEyH7djSulFyN7AwA75+MZyaq4zzZW3XfLh+uPbijPyUxK/qjtdQdNz2AN4I6W0pt74LVredL4/2TdZj8dNUC+H4PHYJBFRExGzgJfJit1naHzf1+Urn76MLFfN9llT9EKloXOoXk/9QQellAYAhwPfjohPb2DexnJqrjesqXlry/m8Dtgd+BjwInB5Pt4cbkREdAHuAM5OKS3f0KwNjDOXNJhDj8cmSimtTSl9DOhN1gKyd0Oz5X8rnseiFyqLgT4lz3sDS6oUS2GllJbkf18Gfkt2YP0jb+4l//tyPntjOTXXmebK2+J8uP74LV5K6R/5P7r3gJvIjkdoeg5fJTul0b7e+C1SRHQg+4CdkFK6Mx/t8dgEDeXQ43HTpZTeAB4i66PS2L6vy1c+vRvZ6eBm+6wpeqHyZ+Cf8t7GW5F11PldlWMqlIjYJiK61g0DhwFzyPJU1+N/OHB3Pvw7YFh+1cAgYFnepPwH4LCI6J43jR6Wj2trmiVv+bQ3I2JQfr52WMm6tmh1H6y5o8mOR8hy+LX8KoHdgH8i6+DZ4Ps870sxBTgmX7709dii5MfIGGBuSumKkkkej2VqLIcej00TEdtHxHb58NbA58j6+zS276XH6DHAH/NcNSm/Gwyqkr2Hm+NB1rt9Ptk5sh9WO56iPch6Tj+RP56syxHZOcIHgb/lf2vT+z26r83z+VdgYMm6Tibr8LQAGFHtfWuB3N1G1hT8LlmVf0pz5g0YSPZP8RngGvJfgt6SHo3k8JY8R7Pzf0C9Sub/YZ6Ppym56qSx93l+fE/Pc3s70LHa+1yhPA4ha/6eDczKH1/yeGyWHHo8Ni2P/YG/5PmaA1ywoX0HOuXPF+TTP7Sp+W3s4U/oS5Kkwir6qR9JktSGWahIkqTCslCRJEmFZaEiSZIKy0JFkiQVloWKpFYpIs6OiM7VjkNSZXl5sqRWKSIWkv1+yKvVjkVS5diiIqliImJYfjO4JyLilojYNSIezMc9GBF98/nGRcQxJcu9lf89OCIeiojJETEvIibkv8Z6JrAzMCUiplRn7yS1hPYbn0WSmi4i9iX7ZcqDUkqvRkQt2e3gx6eUbo6Ik4Gref928Y3ZH9iX7H4g0/L1XR0R3wEOsUVF2rLZoiKpUg4FJtcVEiml14DBwK/z6beQ/ez5xkxPKS1O2U3lZgH9KhCrpIKyUJFUKcFGbt9eMn0N+f+j/OZyW5XMs6pkeC22BEttioWKpEp5EDg2InoA5Kd+HiG7WyrAN4Cp+fBC4IB8+CigQxnrfxPo2lzBSiomv5lIqoiU0pMR8RPgTxGxluyOrGcCv4qI84BXgBH57DcBd0fEdLIC5+0yNnEjcF9EvJhSOqT590BSEXh5siRJKixP/UiSpMKyUJEkSYVloSJJkgrLQkWSJBWWhYokSSosCxVJklRYFiqSJKmw/j9yCUd/mTjlIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9,4))\n",
    "sns.countplot(y = df['change'], hue = df['readmitted']).set_title('Change of Medication VS. Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Diabetes Medication prescribed VS Readmission')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "sns.countplot(  df['diabetesMed'], hue = df['readmitted']).set_title('Diabetes Medication prescribed VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Insulin VS Readmission')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "sns.countplot(x = df['insulin'], hue = df['readmitted']).set_title('Insulin VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'metformin VS Readmission')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "sns.countplot(x = df['metformin'], hue = df['readmitted']).set_title('metformin VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'chlorpropamide VS Readmission')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hIhamn4sk/YnsnOo/JXWOiHfSW/5FqfoCoGvB6mXAwlR+VLXy6am8rEj9Yu24CbgJsvtkt22vzGxTDv33CSXd3qxfnbXZOu+99x7t27evcfmoUaNo27YtAJdeeimjR4/miiuuYMyYMbRv356KigomTZrE5ZdfzuTJk0vWdsixJytpZ0ltKqeBAcDLwFSg8g6BcuCeND0VOCvdZdAfWJZOJzwIDJDUPl3wGgA8mJZ9KKl/uqvgrIJtmdl2pE+fPnzrW9/ikUceodgHrCoDNiJYuXJl1RgF99xzD+XlWRwNHjyYadOmFV1/W+R5umAPYIakvwHPAP8TEf8LXA18WdLrwJfTPMD9wFygAvgDcD5ARCwFfgI8mx4jUxnAecDNaZ1/ABuG7TGz7cacOXP41re+xejRo+nRowc/+9nPWLhw4ze2Q4cOZc899+S1117joosuAuDtt9+ma9fsDXSLFi1o164dS5YsKWnbcjtdEBFzgYOLlC8Bji1SHsAFNWzrFuCWIuUzgS37jgoza3KaN2/OwIEDGThwIIsXL+bKK69kr7324oknnqBv375ANjbC+vXrueiii5g8eTJDhw4t2mst9e32/sSXmTUJy5Yt46abbmLQoEHMmTOHMWPG0KtXr43qNG/enCFDhnDXXXcBUFZWxvz52c1L69atY9myZXTo0KGk7XLImlmjd8YZZ9C7d2/mzp3LhAkTeOyxxygvL6d169ZERNWQixHBvffeywEHHADAoEGDGD8+u21/ypQpHHPMMSXvyXqoQzNr9E499VTGjRtHixafjLTKQbo/+OADIoKDDz6YG264Achu7TrzzDPp3r07HTp0YNKkSSVvm0PWzEqqNrdcldqgQYNqXNasWTMef/zxostat25dssG5a3z+XLduZradc8iameXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmjZ4khg8fXjX/61//mhEjRlTN33TTTRxwwAEccMAB9O3blxkzZtRZ23yfrJmV1FsjDyrp9vb64UubrdOqVSv++Mc/cuWVV9KxY8eNlt133338/ve/Z8aMGXTs2JHnnnuOk08+mWeeeYY999yzpG0txj1ZM2v0WrRowbBhwxg1atQnlv3iF7/gV7/6VVX49u7dm/Lycq677ro6aZtD1syahAsuuICJEyeybNmyjcpnz57NoYceulFZnz59mD17dp20yyFrZk1C27ZtOeuss7j22ms3WzciSj4QTE0csmbWZFxyySWMGTOGjz76qKqsR48ezJo1a6N6zz33HD169KiTNjlkzazJ6NChA6eeeipjxoypKvv+97/P5ZdfXvWNBy+88ALjxo3j/PPPr5M2+e4CM2tShg8fzujRo6vmBw0axNtvv83hhx+OJNq0acNtt91G586d66Q9DlkzK6na3HJVapVf8Q2wxx57sGLFio2Wn3feeZx33nl13SzApwvMzHLlkDUzy5FD1swsRw5ZM7McOWTNzHLkkDUzy5FD1syavHPOOYeDDz6YXr16MXjw4KpbvlavXs2QIUPo3r07/fr1Y968eSV/bt8na2YldcTvjijp9h6/qPjXeW/OmjVrWLt2LTvvvDOjRo2ibdu2AFx66aWMHj2aK664gjFjxtC+fXsqKiqYNGkSl19+OZMnTy5l892TNbOm5dVXX2X48OF85jOfYc6cOQBVARsRrFy5smpwmHvuuYfy8nIABg8ezLRp04iIkrbHIWtmjd5HH33E2LFjOfLIIzn33HM58MADefHFF/nc5z5XVWfo0KHsueeevPbaa1x00UUAvP3223Tt2hXIxqRt165d1RgHpeLTBWbW6HXu3JlevXpx8803c8ABBxStM3bsWNavX89FF13E5MmTGTp0aNFea6mHQHRP1swavSlTptClSxe+/vWvM3LkSN58882i9Zo3b86QIUO46667ACgrK2P+/PkArFu3jmXLltGhQ4eSts0ha2aN3oABA5g8eTIzZsygXbt2nHTSSRx33HHMmzePiKCiogLIzsnee++9Vb3dQYMGMX78eCAL6mOOOabkPVmfLjCzJmO33Xbj4osv5uKLL+aZZ56hefPmRATl5eV88MEHRAQHH3wwN9xwA5Dd2nXmmWfSvXt3OnTowKRJk0reJoesmZXU1t5yVWp9+/atmn788eJtat26NXfeeWeu7fDpAjOzHDlkzcxy5JA1M8uRQ9bMtkqpPxnVWGzpfjtkzWyLtW7dmiVLlmx3QRsRLFmyhNatW9d6Hd9dYGZbrKysjAULFrB48eL6bkqda926NWVlZbWu75A1sy3WsmVL9tlnn/puRqOQ++kCSc0lPS/pvjS/j6SnJb0uabKkHVJ5qzRfkZZ3K9jGlan875KOLyg/IZVVSLoi730xM9tSdXFO9mLg1YL5XwCjImI/4D3gnFR+DvBeRHQHRqV6SOoBfBPoCZwAXJ+CuzlwHXAi0AM4LdU1M2swcg1ZSWXAV4Gb07yAY4Apqcp44OQ0fVKaJy0/NtU/CZgUEasj4g2gAuibHhURMTci1gCTUl0zswYj757sNcD3gY/T/G7A+xGxLs0vALqk6S7AfIC0fFmqX1VebZ2ays3MGozcQlbSQGBRRMwqLC5SNTazbEvLi7VlmKSZkmZuj1dDzaz+5NmTPQIYJGke2Vv5Y8h6trtKqryroQxYmKYXAF0B0vJ2wNLC8mrr1FT+CRFxU0T0iYg+nTp12vY9MzOrpdxCNiKujIiyiOhGduHqkYg4HXgUGJyqlQP3pOmpaZ60/JHI7nSeCnwz3X2wD7Af8AzwLLBfulthh/QcU/PaHzOzrVEf98leDkySdBXwPDAmlY8BbpVUQdaD/SZARMyWdAfwCrAOuCAi1gNIuhB4EGgO3BIRs+t0T8zMNqNOQjYipgPT0/RcsjsDqtdZBZxSw/o/BX5apPx+4P4SNtXMrKQ8doGZWY4csmZmOXLImpnlyCFrZpYjh6yZWY4csmZmOXLImpnlyCFrZpYjh6yZWY4csmZmOXLImpnlyCFrZpYjh6yZWY4csmZmOXLImpnlyCFrZpYjh6yZWY4csmZmOXLImpnlyCFrZpYjh6yZWY7q4yvBG6xD/31CSbbzpzYl2YyZNQHuyZqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWo9xCVlJrSc9I+puk2ZJ+nMr3kfS0pNclTZa0QypvleYr0vJuBdu6MpX/XdLxBeUnpLIKSVfktS9mZlsrz57sauCYiDgYOAQ4QVJ/4BfAqIjYD3gPOCfVPwd4LyK6A6NSPST1AL4J9AROAK6X1FxSc+A64ESgB3Baqmtm1mDkFrKRWZ5mW6ZHAMcAU1L5eODkNH1SmictP1aSUvmkiFgdEW8AFUDf9KiIiLkRsQaYlOqamTUYuZ6TTT3OF4BFwMPAP4D3I2JdqrIA6JKmuwDzAdLyZcBuheXV1qmp3Myswcg1ZCNifUQcApSR9TwPLFYt/VQNy7a0/BMkDZM0U9LMxYsXb77hZmYlUquQlTStNmU1iYj3gelAf2BXSS3SojJgYZpeAHRN224BtAOWFpZXW6em8mLPf1NE9ImIPp06dapts83MttkmQzbdIdAB6CipvaQO6dEN+NRm1u0kadc0vSNwHPAq8CgwOFUrB+5J01PTPGn5IxERqfyb6e6DfYD9gGeAZ4H90t0KO5BdHJta+103M8tfi80s/1fgErJAncWGt+gfkF3Z35TOwPh0F0Az4I6IuE/SK8AkSVcBzwNjUv0xwK2SKsh6sN8EiIjZku4AXgHWARdExHoASRcCDwLNgVsiYnbtdtvMrG5sMmQj4r+A/5J0UUT8bks2HBEvAp8rUj6X7Pxs9fJVwCk1bOunwE+LlN8P3L8l7TIzq0ub68kCEBG/k3Q40K1wnYiYkFO7zMyahFqFrKRbgX2BF4D1qTgAh6yZ2SbUKmSBPkCPdCHKzMxqqbb3yb4M7JlnQ8zMmqLa9mQ7Aq9IeoZsTAIAImJQLq0yM2siahuyI/JshJlZU1Xbuwv+kndDzMyaotreXfAhG8YF2IFsRK2PIqJtXg0zM2sKatuTbVM4L+lkinygwMzMNrZVo3BFxN1k48Kamdkm1PZ0wTcKZpuR3Tfre2bNzDajtncXfK1geh0wD38LgZnZZtX2nOzQvBtiZtYU1XbQ7jJJf5K0SNI/Jd0lqSzvxpmZNXa1vfA1lmxA7E+RfY/WvanMzMw2obYh2ykixkbEuvQYB/h7XMzMNqO2IfuupDPSt882l3QGsCTPhpmZNQW1DdlvA6cC/we8Q/YdXL4YZma2GbW9hesnQHlEvAeQvlzx12Tha2ZmNahtT7ZXZcACRMRSinx/l5mZbay2IdtMUvvKmdSTrW0v2Mxsu1XboPwN8ISkKWQfpz2VIt8ea2ZmG6vtJ74mSJpJNiiMgG9ExCu5tszMrAmo9Vv+FKoOVjOzLbBVQx2amVntOGTNzHLkkDUzy5FD1swsRw5ZM7McOWTNzHLkkDUzy5FD1swsRw5ZM7McOWTNzHLkkDUzy5FD1swsRw5ZM7McOWTNzHLkkDUzy5FD1swsRw5ZM7Mc5RaykrpKelTSq5JmS7o4lXeQ9LCk19PP9qlckq6VVCHpRUm9C7ZVnuq/Lqm8oPxQSS+lda6VpLz2x8xsa+TZk10HDI+IA4H+wAWSegBXANMiYj9gWpoHOBHYLz2GATdA1Tfj/gjoB/QFflTwzbk3pLqV652Q4/6YmW2x3EI2It6JiOfS9IfAq0AX4CRgfKo2Hjg5TZ8ETIjMU8CukjoDxwMPR8TSiHgPeBg4IS1rGxFPRkQAEwq2ZWbWINTJOVlJ3YDPAU8De0TEO5AFMbB7qtYFmF+w2oJUtqnyBUXKzcwajNxDVtIuwF3AJRHxwaaqFimLrSgv1oZhkmZKmrl48eLNNdnMrGRyDVlJLckCdmJE/DEV/zO91Sf9XJTKFwBdC1YvAxZuprysSPknRMRNEdEnIvp06tRp23bKzGwL5Hl3gYAxwKsR8duCRVOByjsEyoF7CsrPSncZ9AeWpdMJDwIDJLVPF7wGAA+mZR9K6p+e66yCbZmZNQgtctz2EcCZwEuSXkhl/wFcDdwh6RzgLeCUtOx+4CtABbACGAoQEUsl/QR4NtUbGRFL0/R5wDhgR+CB9DAzazByC9mImEHx86YAxxapH8AFNWzrFuCWIuUzgc9uQzPNzHLlT3yZmeXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmliOHrJlZjhyyZmY5csiameXIIWtmliOHrJlZjlrUdwNs04743REl2c7jFz1eku2Y2ZbJrScr6RZJiyS9XFDWQdLDkl5PP9unckm6VlKFpBcl9S5YpzzVf11SeUH5oZJeSutcK0l57YuZ2dbK83TBOOCEamVXANMiYj9gWpoHOBHYLz2GATdAFsrAj4B+QF/gR5XBnOoMK1iv+nOZmdW73EI2Ih4DllYrPgkYn6bHAycXlE+IzFPArpI6A8cDD0fE0oh4D3gYOCEtaxsRT0ZEABMKtmVm1mDU9YWvPSLiHYD0c/dU3gWYX1BvQSrbVPmCIuVmZg1KQ7m7oNj51NiK8uIbl4ZJmilp5uLFi7eyiWZmW66uQ/af6a0+6eeiVL4A6FpQrwxYuJnysiLlRUXETRHRJyL6dOrUaZt3wsystuo6ZKcClXcIlAP3FJSfle4y6A8sS6cTHgQGSGqfLngNAB5Myz6U1D/dVXBWwbbMzBqM3O6TlXQ7cBTQUdICsrsErgbukHQO8BZwSqp+P/AVoAJYAQwFiIilkn4CPJvqjYyIyotp55HdwbAj8EB6mJk1KLmFbEScVsOiY4vUDeCCGrZzC3BLkfKZwCqRn5oAAAlQSURBVGe3pY1mZnlrKBe+zMyaJIesmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWI4esmVmOHLJmZjlyyJqZ5cgha2aWo0YfspJOkPR3SRWSrqjv9piZFWrUISupOXAdcCLQAzhNUo/6bZWZ2QaNOmSBvkBFRMyNiDXAJOCkem6TmVmVxh6yXYD5BfMLUpmZWYPQor4bsI1UpCw+UUkaBgxLs8sl/T3PRu1du2odgXfzbEchfa/YoWqQ6vS4NCI+LsU1pONS9KXf2EN2AdC1YL4MWFi9UkTcBNxUV42qDUkzI6JPfbejofFxKc7HpbjGcFwa++mCZ4H9JO0jaQfgm8DUem6TmVmVRt2TjYh1ki4EHgSaA7dExOx6bpaZWZVGHbIAEXE/cH99t2MrNKjTFw2Ij0txPi7FNfjjoohPXCcyM7MSaeznZM3MGjSHbB2QFJJ+UzB/maQR9dikXEn6T0mzJb0o6QVJ/SRdImmnEm2/m6SXS7Gt+iBpfTousyX9TdKlkvxa3IRiv3NJIyRdVl9tqi3/YuvGauAbkjrWd0PyJukwYCDQOyJ6AceRfWDkEqAkIdsErIyIQyKiJ/Bl4CvAj+q5TZYTh2zdWEd2gv7fqi+QtLekaanXN03SXnXfvJLqDLwbEasBIuJdYDDwKeBRSY8CSBog6UlJz0m6U9IuqfyHkp6V9LKkmyQplR+aen1PAhdUPpmkv0o6pGD+cUm96mxvt1FELCL7oMyFyrSWNFbSS5Kel3Q0gKT7K/crlf8wTf9E0rmSjpI0XdIUSa9Jmlh57Jq6tN/XSHoi/d30re82FXLI1p3rgNMltatWPhqYkHp9E4Fr67xlpfUQ0FXSHEnXS/pSRFxL9iGRoyPi6NSj/wFwXET0BmYCl6b1R0fE5yPis8COZL1igLHA9yLisGrPdzNwNoCk/YFWEfFinjtYahExl+y1uDvpH0hEHAScBoyX1Bp4DPiCpLZk/7SPSKsfCfw1TX+O7B1DD+DTBXW2BztHxOHA+cAt9d2YQg7ZOhIRHwATgO9VW3QY8N9p+layF02jFRHLgUPJemeLgcmSzq5WrT9ZEDwu6QWgnA0fSTxa0tOSXgKOAXqmf0y7RsRfUp1bC7Z1JzBQUkvg28C40u9VnajsdR5J2r+IeA14E9ifLEi/mJb/D7BLOsfdLSIqPyb+TEQsiIiPgReAbnXX/NzVdBtUZfntABHxGNBW0q510qpaaPT3yTYy1wDPkfXKatLo76mLiPXAdGB6CsvyalUEPBwRp21UmPXYrgf6RMT8dHGwdapf9LhExApJD5ONvnYq0KA/YlmMpE8D64FFFB+PA7JPN/YB5gIPk31m/zvArII6qwum19O0Xt9LgPbVyjoAb6Tp6n8fDeZ15J5sHYqIpcAdwDkFxU+QfRwY4HRgRl23q5QkfUbSfgVFh5D1xj4E2qSyp4AjJHVP6+yU3uq3TsvfTedoBwNExPvAMkmVvfzTqz3tzWSnWZ5Nx7jRkNQJuJHsNEmQnRY4PS3bH9gL+HsaynM+2T+Sp8h6tpex4VRBk5beIb0j6VgASR2AE9jwehmSyo8ElkXEsnppaBFN6T9dY/Eb4MKC+e8Bt0j6d7K310PrpVWlswvwu/R2bR1QQXbq4DTgAUnvpPOyZwO3S2qV1vtBRMyR9AfgJWAeWe+t0lCy47SC7GPUVSJilqQP2PQ7hIZkx3SapCXZMboV+G1adj1wY3oHsA44u/IiIlmgHpt6738lGxBpuwjZ5CzguoLbIX8cEf9I1/fek/QE0JbstFGD4U98WaMn6VNkpycOSOcjbTsiaTpwWUTMrO+2FOPTBdaoSToLeBr4TwesNUTuyZqZ5cg9WTOzHDlkzcxy5JA1M8uRQ9YaHEnjJA0uUt6oR9+qTtLNknoUKT9b0uj6aJOVnu+TtSZLUouIWFfLus3TJ9XqTEScW5fPZ/XDPVmrd5LOSqOQ/U1S5bgEX0yjKs2toVdb02hVZ6dRve4FHkqjUz0m6U+SXpF0o9LYrZKWSxop6WngMEnHpm29JOmWyg9KSJon6ReSnkmPyk+qfS2Ns/C8pD9L2iOVj5A0XtJDad1vSPpl2u7/pnEWKkeP6pOmh6ZBdf5CwcAukjpJukvZyGTPStqeBn1pGiLCDz/q7QH0BP4OdEzzHcgGebmTrBPQA6hIy7oBL6fp4cDYNH0A8BbZx3LPJvuq+A5p2VHAKrJRqZqTfe5/cFoWwKlpujXZx1b3T/MTgEvS9Dyy+3Ah+9TRfWm6PRtugzwX+E2aHkH2cc+WwMHACuDEtOxPwMlpejrZeASdU/s7ATsAj5N9zBaywYOOTNN7Aa/W9+/Mjy17uCdr9e0YYEpk484SG8YeuDsiPo6IV4A9iqxX02hVkA0+UziGwTMRMTey0wG3s2Gks/XAXWn6M8AbETEnzY8nG/Wq0u0FPyuHWywDHkwfgf13sn8YlR6IiLVkHxFuDvxvKn+JT46O1Q+YHhGLIxujYHLBsuOA0eljuFPJRphqgzUaPidr9a2mEbZWV6tTbL2afFRtvqYRmlbFhvOwmxvgOopM/w74bURMlXQUWQ+2UuWg5R9LWhupKwp8TPHXXU2fCmoGHBYRKzfTPmug3JO1+jYNOFXSblA1ulJtFB2tqoa6fSXtk87FDqH4SGevAd0qz7cCZwJ/KVg+pODnk2m6HfB2mq4+nOOWeBo4StJu6XztKQXLHqJgQCEVfAuENQ7uyVq9iojZkn4K/EXSeuD5Wq5adLQqFf/GlSeBq4GDyML5T0XasUrSUOBOSS3IRgC7saBKq3SBrBnZiGKQ9VzvlPQ22fCD+9Sy7dWf+500du6TwDtkYw43T4u/Rzby1Itkr9fHgO9uzfNY/fDYBdakpbfxl0XEwM3V3cQ25pENJP5uqdpl2w+fLjAzy5F7smZmOXJP1swsRw5ZM7McOWTNzHLkkDUzy5FD1swsRw5ZM7Mc/X+nhkGmXMgB/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "sns.countplot(x = df['chlorpropamide'], hue = df['readmitted']).set_title('chlorpropamide VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'glimepiride VS Readmission')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "sns.countplot(x = df['glimepiride'], hue = df['readmitted']).set_title('glimepiride VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'tolazamide VS Readmission')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "sns.countplot(x = df['tolazamide'], hue = df['readmitted']).set_title('tolazamide VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'admission_source_id VS Readmission')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,5))\n",
    "sns.countplot(x = df['admission_source_id'], hue = df['readmitted']).set_title('admission_source_id VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'admission_type_id VS Readmission')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "sns.countplot(x = df['admission_type_id'], hue = df['readmitted']).set_title('admission_type_id VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'max_glu_serum VS Readmission')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "sns.countplot(x = df['max_glu_serum'], hue = df['readmitted']).set_title('max_glu_serum VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'A1Cresult VS Readmission')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "sns.countplot(x = df['A1Cresult'], hue = df['readmitted']).set_title('A1Cresult VS Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No        98015\n",
       "Steady       36\n",
       "Up            1\n",
       "Name: tolazamide, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tolazamide'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time in hospital VS. Readmission')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4),)\n",
    "ax=sns.kdeplot(df.loc[(df['readmitted'] == 2),'time_in_hospital'] , color='b',shade=True,label='Not readmitted')\n",
    "ax=sns.kdeplot(df.loc[(df['readmitted'] == 1),'time_in_hospital'] , color='r',shade=True, label='readmitted')\n",
    "ax.set(xlabel='time in hospital', ylabel='Frequency')\n",
    "plt.title('Time in hospital VS. Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Diagnoses VS. Readmission')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4),)\n",
    "ax=sns.kdeplot(df.loc[(df['readmitted'] == 2),'number_diagnoses'] , color='b',shade=True,label='Not readmitted')\n",
    "ax=sns.kdeplot(df.loc[(df['readmitted'] == 1),'number_diagnoses'] , color='r',shade=True, label='readmitted')\n",
    "ax.set(xlabel='Number of Diagnoses', ylabel='Frequency')\n",
    "plt.title('Number of Diagnoses VS. Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'num_medications  VS. Readmission')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4),)\n",
    "ax=sns.kdeplot(df.loc[(df['readmitted'] == 2),'num_medications'] , color='b',shade=True,label='Not readmitted')\n",
    "ax=sns.kdeplot(df.loc[(df['readmitted'] == 1),'num_medications'] , color='r',shade=True, label='readmitted')\n",
    "ax.set(xlabel='num_medications', ylabel='Frequency')\n",
    "plt.title('num_medications  VS. Readmission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['race'] = df['race'].replace('Caucasian', 0)\n",
    "df['race'] = df['race'].replace('AfricanAmerican', 1)\n",
    "df['race'] = df['race'].replace('Hispanic', 2)\n",
    "df['race'] = df['race'].replace('Asian', 3)\n",
    "df['race'] = df['race'].replace('Other', 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raza\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "keys = ['metformin', 'repaglinide', 'nateglinide', 'chlorpropamide', 'glimepiride', 'glipizide', 'glyburide', 'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'insulin', 'glyburide-metformin', 'tolazamide', 'metformin-pioglitazone','metformin-rosiglitazone', 'glimepiride-pioglitazone', 'glipizide-metformin', 'troglitazone', 'tolbutamide', 'acetohexamide']\n",
    "for col in keys:\n",
    "    df[col] = df[col].apply(lambda x: 0 if (x == 'No' or x == 'Steady') else 1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raza\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\Raza\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\Raza\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Raza\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\Raza\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\Raza\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df['change'] = df['change'].replace('Ch', 1)\n",
    "df['change'] = df['change'].replace('No', 0)\n",
    "df['gender'] = df['gender'].replace('Male', 1)\n",
    "df['gender'] = df['gender'].replace('Female', 0)\n",
    "df['diabetesMed'] = df['diabetesMed'].replace('Yes', 1)\n",
    "df['diabetesMed'] = df['diabetesMed'].replace('No', 0)"
   ]
  }
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