Predicting Patient Readmission Within 30 days.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Explanation\n",
    "* It is important to know if a patient will be readmitted in some hospital. The reason is that you can change the treatment, in order to avoid a readmission.\n",
    "\n",
    "* In this database, you have 3 different outputs:\n",
    "\n",
    "* No readmission;\n",
    "* 1.A readmission in less than 30 days (this situation is not good, because maybe your treatment was not appropriate);\n",
    "* A readmission in more than 30 days (this one is not so good as well the last one, however, the reason can be the state of the patient.\n",
    "* In this context, you can see different objective functions for the problem. You can try to figure out situations where the patient will not be readmitted, or if their are going to be readmitted in less than 30 days (because the problem can the the treatment), etc… Make your choice and let's help them creating new approaches for the problem.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#### To understand the description of each column, visit this site https://www.hindawi.com/journals/bmri/2014/781670/tab1/\n",
    "#### This is a Kaggle dataset, and here is the link https://www.kaggle.com/brandao/diabetes\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Set Description\n",
    "#### VARIABLE NAMES: DESCRIPTION\n",
    "\n",
    "* **Encounter ID:** Unique identifier of an encounter\n",
    "* **Patient number:** Unique identifier of a patient\n",
    "* **Race Values:** Caucasian, Asian, African American, Hispanic, and other\n",
    "* **Gender Values:** male, female, and unknown/invalid\n",
    "* **Age Grouped in 10-year intervals:** 0, 10), 10, 20), …, 90, 100)\n",
    "* **Weight:** Weight in pounds\n",
    "* **Admission type:** Integer identifier corresponding to 9 distinct values, for example, emergency, urgent, elective, newborn, and not available\n",
    "* **Discharge disposition:** Integer identifier corresponding to 29 distinct values, for example, discharged to home, expired, and not available\n",
    "* **Admission source:** Integer identifier corresponding to 21 distinct values, for example, physician referral, emergency room, and transfer from a hospital\n",
    "* **Time in hospital:** Integer number of days between admission and discharge\n",
    "* **Payer code:** Integer identifier corresponding to 23 distinct values, for example, Blue Cross/Blue Shield, Medicare, and self-pay Medical\n",
    "* **Medical specialty:** Integer identifier of a specialty of the admitting physician, corresponding to 84 distinct values, for example, cardiology, internal medicine, family/general practice, and surgeon\n",
    "* **Number of lab procedures:** Number of lab tests performed during the encounter\n",
    "* **Number of procedures:** Numeric Number of procedures (other than lab tests) performed during the encounter\n",
    "* **Number of medications:** Number of distinct generic names administered during the encounter\n",
    "* **Number of outpatient visits:** Number of outpatient visits of the patient in the year preceding the encounter\n",
    "* **Number of emergency visits:** Number of emergency visits of the patient in the year preceding the encounter\n",
    "* **Number of inpatient visits:** Number of inpatient visits of the patient in the year preceding the encounter\n",
    "* **Diagnosis 1:** The primary diagnosis (coded as first three digits of ICD9); 848 distinct values\n",
    "* **Diagnosis 2:** Secondary diagnosis (coded as first three digits of ICD9); 923 distinct values\n",
    "* **Diagnosis 3:** Additional secondary diagnosis (coded as first three digits of ICD9); 954 distinct values\n",
    "* **Number of diagnoses:** Number of diagnoses entered to the system 0%\n",
    "* **Glucose serum test result:** Indicates the range of the result or if the test was not taken. Values: “>200,” “>300,” “normal,” and “none” if not measured\n",
    "* **A1c test result:** Indicates the range of the result or if the test was not taken. Values: “>8” if the result was greater than 8%, “>7” if the result was greater than 7% but less than 8%, “normal” if the result was less than 7%, and “none” if not measured.\n",
    "* **Change of medications:** Indicates if there was a change in diabetic medications (either dosage or generic name). Values: “change” and “no change”\n",
    "* **Diabetes medications:** Indicates if there was any diabetic medication prescribed. Values: “yes” and “no”\n",
    "* **24 features for medications:** For the generic names: metformin, repaglinide, nateglinide, chlorpropamide, glimepiride, acetohexamide, glipizide, glyburide, tolbutamide, pioglitazone, rosiglitazone, acarbose, miglitol, troglitazone, tolazamide, examide, sitagliptin, insulin, glyburide-metformin, glipizide-metformin, glimepiride- pioglitazone, metformin-rosiglitazone, and metformin- pioglitazone, the feature indicates whether the drug was prescribed or there was a change in the dosage. Values: “up” if the dosage was increased during the encounter, “down” if the dosage was decreased, “steady” if the dosage did not change, and “no” if the drug was not prescribed\n",
    "* **Readmitted: Days to inpatient readmission. Values:** “<30” if the patient was readmitted in less than 30 days, “>30” if the patient was readmitted in more than 30 days, and “No” for no record of readmission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('diabetic_data.csv')\n",
    "data.replace('?',np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Understanding the structure and the columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101766, 50)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>5</td>\n",
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       "      <td>8</td>\n",
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       "      <th>max_glu_serum</th>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <th>A1Cresult</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repaglinide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <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",
       "    <tr>\n",
       "      <th>nateglinide</th>\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",
       "    <tr>\n",
       "      <th>chlorpropamide</th>\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",
       "    <tr>\n",
       "      <th>glimepiride</th>\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>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acetohexamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <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",
       "    <tr>\n",
       "      <th>glipizide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide</th>\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>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolbutamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <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",
       "    <tr>\n",
       "      <th>pioglitazone</th>\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",
       "    <tr>\n",
       "      <th>rosiglitazone</th>\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>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acarbose</th>\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",
       "    <tr>\n",
       "      <th>miglitol</th>\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",
       "    <tr>\n",
       "      <th>troglitazone</th>\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",
       "    <tr>\n",
       "      <th>tolazamide</th>\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",
       "    <tr>\n",
       "      <th>examide</th>\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",
       "    <tr>\n",
       "      <th>citoglipton</th>\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",
       "    <tr>\n",
       "      <th>insulin</th>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide-metformin</th>\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",
       "    <tr>\n",
       "      <th>glipizide-metformin</th>\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",
       "    <tr>\n",
       "      <th>glimepiride-pioglitazone</th>\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",
       "    <tr>\n",
       "      <th>metformin-rosiglitazone</th>\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",
       "    <tr>\n",
       "      <th>metformin-pioglitazone</th>\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",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Ch</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Ch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetesMed</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readmitted</th>\n",
       "      <td>NO</td>\n",
       "      <td>&gt;30</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>&gt;30</td>\n",
       "      <td>NO</td>\n",
       "      <td>&gt;30</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 0          1  \\\n",
       "encounter_id                               2278392     149190   \n",
       "patient_nbr                                8222157   55629189   \n",
       "race                                     Caucasian  Caucasian   \n",
       "gender                                      Female     Female   \n",
       "age                                         [0-10)    [10-20)   \n",
       "weight                                         NaN        NaN   \n",
       "admission_type_id                                6          1   \n",
       "discharge_disposition_id                        25          1   \n",
       "admission_source_id                              1          7   \n",
       "time_in_hospital                                 1          3   \n",
       "payer_code                                     NaN        NaN   \n",
       "medical_specialty         Pediatrics-Endocrinology        NaN   \n",
       "num_lab_procedures                              41         59   \n",
       "num_procedures                                   0          0   \n",
       "num_medications                                  1         18   \n",
       "number_outpatient                                0          0   \n",
       "number_emergency                                 0          0   \n",
       "number_inpatient                                 0          0   \n",
       "diag_1                                      250.83        276   \n",
       "diag_2                                         NaN     250.01   \n",
       "diag_3                                         NaN        255   \n",
       "number_diagnoses                                 1          9   \n",
       "max_glu_serum                                 None       None   \n",
       "A1Cresult                                     None       None   \n",
       "metformin                                       No         No   \n",
       "repaglinide                                     No         No   \n",
       "nateglinide                                     No         No   \n",
       "chlorpropamide                                  No         No   \n",
       "glimepiride                                     No         No   \n",
       "acetohexamide                                   No         No   \n",
       "glipizide                                       No         No   \n",
       "glyburide                                       No         No   \n",
       "tolbutamide                                     No         No   \n",
       "pioglitazone                                    No         No   \n",
       "rosiglitazone                                   No         No   \n",
       "acarbose                                        No         No   \n",
       "miglitol                                        No         No   \n",
       "troglitazone                                    No         No   \n",
       "tolazamide                                      No         No   \n",
       "examide                                         No         No   \n",
       "citoglipton                                     No         No   \n",
       "insulin                                         No         Up   \n",
       "glyburide-metformin                             No         No   \n",
       "glipizide-metformin                             No         No   \n",
       "glimepiride-pioglitazone                        No         No   \n",
       "metformin-rosiglitazone                         No         No   \n",
       "metformin-pioglitazone                          No         No   \n",
       "change                                          No         Ch   \n",
       "diabetesMed                                     No        Yes   \n",
       "readmitted                                      NO        >30   \n",
       "\n",
       "                                        2          3          4          5  \\\n",
       "encounter_id                        64410     500364      16680      35754   \n",
       "patient_nbr                      86047875   82442376   42519267   82637451   \n",
       "race                      AfricanAmerican  Caucasian  Caucasian  Caucasian   \n",
       "gender                             Female       Male       Male       Male   \n",
       "age                               [20-30)    [30-40)    [40-50)    [50-60)   \n",
       "weight                                NaN        NaN        NaN        NaN   \n",
       "admission_type_id                       1          1          1          2   \n",
       "discharge_disposition_id                1          1          1          1   \n",
       "admission_source_id                     7          7          7          2   \n",
       "time_in_hospital                        2          2          1          3   \n",
       "payer_code                            NaN        NaN        NaN        NaN   \n",
       "medical_specialty                     NaN        NaN        NaN        NaN   \n",
       "num_lab_procedures                     11         44         51         31   \n",
       "num_procedures                          5          1          0          6   \n",
       "num_medications                        13         16          8         16   \n",
       "number_outpatient                       2          0          0          0   \n",
       "number_emergency                        0          0          0          0   \n",
       "number_inpatient                        1          0          0          0   \n",
       "diag_1                                648          8        197        414   \n",
       "diag_2                                250     250.43        157        411   \n",
       "diag_3                                V27        403        250        250   \n",
       "number_diagnoses                        6          7          5          9   \n",
       "max_glu_serum                        None       None       None       None   \n",
       "A1Cresult                            None       None       None       None   \n",
       "metformin                              No         No         No         No   \n",
       "repaglinide                            No         No         No         No   \n",
       "nateglinide                            No         No         No         No   \n",
       "chlorpropamide                         No         No         No         No   \n",
       "glimepiride                            No         No         No         No   \n",
       "acetohexamide                          No         No         No         No   \n",
       "glipizide                          Steady         No     Steady         No   \n",
       "glyburide                              No         No         No         No   \n",
       "tolbutamide                            No         No         No         No   \n",
       "pioglitazone                           No         No         No         No   \n",
       "rosiglitazone                          No         No         No         No   \n",
       "acarbose                               No         No         No         No   \n",
       "miglitol                               No         No         No         No   \n",
       "troglitazone                           No         No         No         No   \n",
       "tolazamide                             No         No         No         No   \n",
       "examide                                No         No         No         No   \n",
       "citoglipton                            No         No         No         No   \n",
       "insulin                                No         Up     Steady     Steady   \n",
       "glyburide-metformin                    No         No         No         No   \n",
       "glipizide-metformin                    No         No         No         No   \n",
       "glimepiride-pioglitazone               No         No         No         No   \n",
       "metformin-rosiglitazone                No         No         No         No   \n",
       "metformin-pioglitazone                 No         No         No         No   \n",
       "change                                 No         Ch         Ch         No   \n",
       "diabetesMed                           Yes        Yes        Yes        Yes   \n",
       "readmitted                             NO         NO         NO        >30   \n",
       "\n",
       "                                  6          7          8                 9  \n",
       "encounter_id                  55842      63768      12522             15738  \n",
       "patient_nbr                84259809  114882984   48330783          63555939  \n",
       "race                      Caucasian  Caucasian  Caucasian         Caucasian  \n",
       "gender                         Male       Male     Female            Female  \n",
       "age                         [60-70)    [70-80)    [80-90)          [90-100)  \n",
       "weight                          NaN        NaN        NaN               NaN  \n",
       "admission_type_id                 3          1          2                 3  \n",
       "discharge_disposition_id          1          1          1                 3  \n",
       "admission_source_id               2          7          4                 4  \n",
       "time_in_hospital                  4          5         13                12  \n",
       "payer_code                      NaN        NaN        NaN               NaN  \n",
       "medical_specialty               NaN        NaN        NaN  InternalMedicine  \n",
       "num_lab_procedures               70         73         68                33  \n",
       "num_procedures                    1          0          2                 3  \n",
       "num_medications                  21         12         28                18  \n",
       "number_outpatient                 0          0          0                 0  \n",
       "number_emergency                  0          0          0                 0  \n",
       "number_inpatient                  0          0          0                 0  \n",
       "diag_1                          414        428        398               434  \n",
       "diag_2                          411        492        427               198  \n",
       "diag_3                          V45        250         38               486  \n",
       "number_diagnoses                  7          8          8                 8  \n",
       "max_glu_serum                  None       None       None              None  \n",
       "A1Cresult                      None       None       None              None  \n",
       "metformin                    Steady         No         No                No  \n",
       "repaglinide                      No         No         No                No  \n",
       "nateglinide                      No         No         No                No  \n",
       "chlorpropamide                   No         No         No                No  \n",
       "glimepiride                  Steady         No         No                No  \n",
       "acetohexamide                    No         No         No                No  \n",
       "glipizide                        No         No     Steady                No  \n",
       "glyburide                        No     Steady         No                No  \n",
       "tolbutamide                      No         No         No                No  \n",
       "pioglitazone                     No         No         No                No  \n",
       "rosiglitazone                    No         No         No            Steady  \n",
       "acarbose                         No         No         No                No  \n",
       "miglitol                         No         No         No                No  \n",
       "troglitazone                     No         No         No                No  \n",
       "tolazamide                       No         No         No                No  \n",
       "examide                          No         No         No                No  \n",
       "citoglipton                      No         No         No                No  \n",
       "insulin                      Steady         No     Steady            Steady  \n",
       "glyburide-metformin              No         No         No                No  \n",
       "glipizide-metformin              No         No         No                No  \n",
       "glimepiride-pioglitazone         No         No         No                No  \n",
       "metformin-rosiglitazone          No         No         No                No  \n",
       "metformin-pioglitazone           No         No         No                No  \n",
       "change                           Ch         No         Ch                Ch  \n",
       "diabetesMed                     Yes        Yes        Yes               Yes  \n",
       "readmitted                       NO        >30         NO                NO  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weight               96.86\n",
       "medical_specialty    49.08\n",
       "payer_code           39.56\n",
       "race                  2.23\n",
       "diag_3                1.40\n",
       "diag_2                0.35\n",
       "diag_1                0.02\n",
       "num_procedures        0.00\n",
       "max_glu_serum         0.00\n",
       "number_diagnoses      0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data.isna().mean().sort_values(ascending = False).head(10)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(data.isnull().T, cbar='rainbow')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['encounter_id', 'patient_nbr', 'race', 'gender', 'age', 'weight',\n",
       "       'admission_type_id', 'discharge_disposition_id', 'admission_source_id',\n",
       "       'time_in_hospital', 'payer_code', 'medical_specialty',\n",
       "       'num_lab_procedures', 'num_procedures', 'num_medications',\n",
       "       'number_outpatient', 'number_emergency', 'number_inpatient', 'diag_1',\n",
       "       'diag_2', 'diag_3', 'number_diagnoses', 'max_glu_serum', 'A1Cresult',\n",
       "       'metformin', 'repaglinide', 'nateglinide', 'chlorpropamide',\n",
       "       'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide',\n",
       "       'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone',\n",
       "       'tolazamide', 'examide', 'citoglipton', 'insulin',\n",
       "       'glyburide-metformin', 'glipizide-metformin',\n",
       "       'glimepiride-pioglitazone', 'metformin-rosiglitazone',\n",
       "       'metformin-pioglitazone', 'change', 'diabetesMed', 'readmitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def barplot(x, hue = None):\n",
    "    plt.figure(figsize = (10,6))\n",
    "    bar_plot1 = sns.countplot(x = x, data = data, hue = hue)\n",
    "    for p in bar_plot1.patches:\n",
    "        height = p.get_height()\n",
    "        bar_plot1.text(p.get_x()+ p.get_width()/2, 1.002*height,height, ha = 'center')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('race', 'readmitted')\n",
    "# Most of the data consists of race Causasian, white skin, and the readmission of every race are in almost same proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('gender', 'readmitted')\n",
    "# There is no class imbalance between male and female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dd51XYxORP0cJsoiIFEmtW7emYsWKWcrat2/vGQ/ZokULUlJSANfCBL169SIwMJDatWtTr1491qxZg7UWay1paWlYazl27FiWp+mfe+45hgwZQokSJbwXmIj8aUqQRUREsvHBBx/QqVMnIOeFCYoVK8aECROIioqiWrVqbNmyhf79+wOwfv169uzZQ5cuXXzSfxG5ckqQRURELjB8+HACAgLo06cPkPPCBGfPnmXChAmsX7+effv2ER0dzYgRIzh37hxPPvkkr776qre7LiJ5QAmyiIiIw5QpU1iwYAHTp0/3rM6V08IEiYmJANStWxdjDD179uS7777j+PHjbNq0ibZt2xIWFsbq1avp2rWrHtQTKSQ0zZuIiIjbokWLGDVqFN988w1BQUGe8q5du3LPPffw1FNPsW/fPpKTk2nevDkHDhxgy5YtHDx4kCpVqrBkyRLCw8MpV65cltks2rZty5gxY4iLi/NFWCJ/qNVbrfL0fKsez36KtsvRv39/EhISsNbSoEEDJk+eTOnSpbHWMnDgQBYuXEhQUBCTJ0+mWbNmedDr8/LtDrIx5gNjTKoxZpOjbLQx5idjTJIxZq4xprzj2DPGmO3GmJ+NMR0c5bHGmI3uY28a95/zxphAY8zH7vIfjDFh+RWLiIhcfXr37k3Lli35+eefCQ0N5f333+exxx7j+PHjxMfHExMTw8MPPwxAZGQkPXv2JCIigo4dOzJ+/Hj8/f2pVq0azz//PK1btyY6OprExET+7//+z8eRiRReZ86cIS0tDYCxY8eyYcMGkpKSqFmzpmeGmC+++ILk5GSSk5OZOHEijzzySJ73Iz/vIE8GxgFTHWVLgGestenGmFHAM8DTxpgIoBcQCVQDvjLGNLDWZgATgAHAamAh0BH4AugPHLbW1jPG9AJGAXfnYzwiInIVmTFjxkVl/fv3J3bw+f+2fgTHfg3K93gegGeXHeTZZZnlQQR1eQaAfUD7kf9h7ei+Wc67fPnyvO28yFVm69atvPfee8yZM4c5c+bQtGlTypYtC7ieATh58qRnyNO8efPo27cvxhhatGjBkSNH2L9/f54uN51vd5CttSuA3y4oW2ytTXfvrgZC3dvdgJnW2tPW2p3AdqC5MSYEKGut/d66npCYCtzuaDPFvf0p0C7z7rKIiIiIFGxpaWlMmjSJG264gYceeojw8HCSkpJo2rSpp84DDzzANddcw08//cTjjz8O5DyrTF7y5RjkB4GP3dvVcSXMmVLcZWfd2xeWZ7bZA+C+I30UqARkXcIIMMYMwHUXmpo1a+ZdBCIiIiJyRUJCQoiOjua9996jUaNG2daZNGkSGRkZPP7443z88cc88MADOc4qk5d8MouFMWYYkA5MzyzKppq9RPml2lxcaO1Ea22ctTauSpUql9tdEREREcljn376KdWrV6d79+689NJL/PLLL9nW8/f35+6772b27NlAzrPK5CWvJ8jGmH5AF6CPPf8nQApQw1EtFNdQrhTOD8NwlmdpY4wJAMpxwZAOERERESmY2rdvz8cff8zKlSspV64c3bp145ZbbmHXrl1Ya9m+fTvgGoP8+eefe+4yd+3alalTp2KtZfXq1ZQrVy5Pxx+Dl4dYGGM6Ak8Dbay1JxyH5gMfGWNew/WQXn1gjbU2wxhz3BjTAvgB6Au85WjTD/geuBNYZrO75y4iIiIil5QX07JdqUqVKjFw4EAGDhzImjVr8Pf3x1pLv379OHbsGNZamjRpwoQJEwC49dZbWbhwIfXq1SMoKIhJkybleZ/yLUE2xswA2gKVjTEpwPO4Zq0IBJa4x4qsttY+bK3dbIz5BNiCa+jF39wzWAA8gmtGjJK4Zq/4wl3+PjDNGLMd153jXvkVi4iIiIjkv+bNm3u2V63KPmk3xjB+/Ph87Ue+JcjW2t7ZFL9/ifrDgeHZlCcAjbMpPwXc9Wf6KCIiIiJyIS01LSIiIiLioARZREREpIgoqo9rXW7cSpBFREREioASJUpw6NChIpckW2s5dOgQJUqUyHUbXy4UIiIiIiJeEhoaSkpKCgcPHvR1V7yuRIkShIaG/nFFNyXIIiIiIkVAsWLFqF27tq+7UShoiIWIiIiIiIMSZBERERERByXIIiIiIiIOSpBFRERERByUIIuIiIiIOChBFhERERFxUIIsIiIiIuKgBFlERERExEEJsoiIiIiIgxJkEREREREHJcgiIiIiIg5KkEVEREREHJQgi4iIiIg4KEEWEREREXFQgiwiIiIi4qAEWURERETEQQmyiIiIiIiDEmQREREREQclyCIiIiIiDkqQRUREREQclCCLiIiIiDgoQRYRERERcVCCLCIiIiLioARZRERERMRBCbKIiIiIiIMSZBERERERByXIIiIiIiIOSpBFRERERByUIIuIiIiIOChBFhERERFxUIIsIiIiIuKgBFlERERExEEJsoiIiIiIgxJkEREREREHJcgiIiIiIg5KkEVEREREHJQgi4iIiIg4KEEWEREREXFQgiwiIiIi4pBvCbIx5gNjTKoxZpOjrKIxZokxJtn9tYLj2DPGmO3GmJ+NMR0c5bHGmI3uY28aY4y7PNAY87G7/AdjTFh+xSIiIiIiRUd+3kGeDHS8oGwosNRaWx9Y6t7HGBMB9AIi3W3eNsb4u9tMAAYA9d2vzHP2Bw5ba+sBY4FR+RaJiIiIiBQZ+ZYgW2tXAL9dUNwNmOLengLc7iifaa09ba3dCWwHmhtjQoCy1trvrbUWmHpBm8xzfQq0y7y7LCIiIiJypbw9BrmqtXY/gPtrsLu8OrDHUS/FXVbdvX1heZY21tp04ChQKd96LiIiIiJFQkF5SC+7O7/2EuWXanPxyY0ZYIxJMMYkHDx48Aq7KCIiIiJFgbcT5APuYRO4v6a6y1OAGo56ocA+d3loNuVZ2hhjAoByXDykAwBr7URrbZy1Nq5KlSp5FIqIyNXjjTfeoHHjxkRGRvL66697yt966y0aNmxIZGQkQ4YMAWD69OnExMR4Xn5+fiQmJgIwbNgwatSoQenSpX0QhYhI3vB2gjwf6Ofe7gfMc5T3cs9MURvXw3hr3MMwjhtjWrjHF/e9oE3mue4ElrnHKYuIyGXYtGkT7777LmvWrGHDhg0sWLCA5ORkvv76a+bNm0dSUhKbN29m0KBBAPTp04fExEQSExOZNm0aYWFhxMTEAHDbbbexZs0aH0YjIvLnBeTXiY0xM4C2QGVjTArwPDAS+MQY0x/YDdwFYK3dbIz5BNgCpAN/s9ZmuE/1CK4ZMUoCX7hfAO8D04wx23HdOe6VX7GIiFzNtm7dSosWLQgKCgKgTZs2zJ07l4SEBIYOHUpgYCAAwcHBF7WdMWMGvXv39uy3aNHCO50WEclH+ZYgW2t753CoXQ71hwPDsylPABpnU34Kd4ItIiJXrnHjxgwbNoxDhw5RsmRJFi5cSFxcHNu2bePbb79l2LBhlChRgjFjxnDttddmafvxxx8zb968HM4sIlI45VuCLCIihUN4eDhPP/008fHxlC5dmiZNmhAQEEB6ejqHDx9m9erV/Pjjj/Ts2ZMdO3aQOaPmDz/8QFBQEI0bX3QPQ0SkUCsos1iIiIgP9e/fn3Xr1rFixQoqVqxI/fr1CQ0NpUePHhhjaN68OX5+fvz666+eNjNnzswyvEIkde1itkz6P7ZMeibLw54AY8aMwRjjeQ9d6mHPtm3b0rBhQ8+x1NRURLxJd5BFRITU1FSCg4PZvXs3c+bM4fvvv8fPz49ly5bRtm1btm3bxpkzZ6hcuTIA586dY9asWaxYscLHPZeC4uTBFH5NWk6je5/H+AewYMF0OnfuTP369dmzZw9LliyhZs2anvp9+vShT58+AGzcuJFu3bp5HvYEVwIdFxfn7TBEACXIIiIC3HHHHRw6dIhdv/5O9ba9ueVfn3Muozi/LPqK0W9/gPEPILRtH+KGTAPg+O6tHD5XgrsmrARWes6T8s3HHN76PeknThAaGspDDz3ECy+84JugxKtO/baPUtXq4lfM9VBn5sOeQ4YM4cknn+SVV16hW7du2ba98GFPEV9TgiwiInz77bcAxA6e6inz8w+gdueHs61fpmY4jfr886Ly0DZ3E9rmbtaO7ps/HZUCq0TlUPat/JT0k7/jF1DM87Dn/PnzqV69Ok2aNMmxbXYPez7wwAP4+/tzxx138Oyzz3rGvot4gxJkERER+dNKVqpG1eadSZ71Cv7FA+nT+SYCAgIYPnw4ixcvzrFddg97Tp8+nerVq3P8+HHuuOMOpk2bRt+++qNLvEcP6YmIiEieqBzVhvC+L9Gg1zAqVqxIWFgYO3fupEmTJoSFhZGSkkKzZs343//+52mT3cOe1atXB6BMmTLcc889WnxGvE4JsoiIiOSJs2nHADhz7BBz5syhb9++pKamsmvXLnbt2kVoaCjr1q3jmmuuAc4/7Nmr1/m1vtLT0z0zXZw9e5YFCxZoKkHxOg2xEBERkTyxY/5bZJz8HePvz+czJlGhQoVL1l+xYgWhoaHUqVPHU3b69Gk6dOjA2bNnycjI4JZbbuEvf/lLfnddJAslyCIiIpInGvYe5tmuv+oJdq/KenzFg2U48fZN7Hbv1wE+uRV2vxSVpd5c92QXNf+5Of86K3IJGmIhIiIiIuKgBFlERERExEEJsoiIiIiIgxJkEREREREHJcgiIiIiIg5KkEVEREREHJQgi4iISJE3duxYIiMjady4Mb179+bUqVMkJibSokULYmJiiIuL86zod+bMGR544AGioqJo0qQJy5cv95znzJkzDBgwgAYNGtCoUSNmz57to4jkz9A8yCIiIlKk7d27lzfffJMtW7ZQsmRJevbsycyZM/noo494/vnn6dSpEwsXLmTIkCEsX76cd999F4CNGzeSmppKp06d+PHHH/Hz82P48OEEBwezbds2zp07x2+//ebj6ORKKEEWERGRIi89PZ2TJ09SrFgxTpw4QbVq1TDGcOyYa/nso0ePUq1aNQC2bNlCu3btAAgODqZ8+fIkJCTQvHlzPvjgA3766ScA/Pz8qFy5sm8Ckj9FCbKIiIgUadWrV2fQoEHUrFmTkiVL0r59e9q3b0+NGjXo0KEDgwYN4ty5c3z33XcANGnShHnz5tGrVy/27NnD2rVr2bNnDw0aNADgueeeY/ny5dStW5dx48ZRtWpVX4YnV0BjkEVERKRIO3z4MPPmzWPnzp3s27ePtLQ0PvzwQyZMmMDYsWPZs2cPY8eOpX///gA8+OCDhIaGEhcXxxNPPMH1119PQEAA6enppKSk0KpVK9atW0fLli0ZNGiQj6OTK6EEWURERIq0r776itq1a1OlShWKFStGjx49+O6775gyZQo9evQA4K677vI8pBcQEMDYsWNJTExk3rx5HDlyhPr161OpUiWCgoLo3r27p826det8FpdcOSXIIiIiUqTVrFmT1atXc+LECay1LF26lPDwcKpVq8Y333wDwLJly6hfvz4AJ06cIC0tDYAlS5YQEBBAREQExhhuu+02z6wWS5cuJSIiwicxyZ+jMcgiIiJSpF133XXceeedNGvWjICAAJo2bcqAAQNo2rQpAwcOJD09nRIlSjBx4kQAUlNT6dChA35+flSvXp1p06Z5zjVq1Cjuu+8+nnjiCapUqcKkSZN8FZb8CcZa6+s+eFVcXJxNSEjwdTdERAqk2MFT8+Q8a0f3zZPz+EJeXIOiHj/A3DKj//Q5av5zYx705PIV9fdAUWKMWWutjbuwXEMsREREREQclCCLiIiIiDgoQRYRERERcVCCLCIiIiLioARZRERERMRBCbKIiIiIiIMSZBERERERByXIIiIiIiIOSpBFRERERByUIIuIiIiIOChBFhERERFxUIIsIiIiIuKgBFlERERExEEJsoiIiIiIgxJkEREREREHJcgiIiIiIg5KkEVEREREHJQgi4iIiIg4KEEWERHJA6d+28/WKc+xdcpzxMTEULZsWV5//XUSExNp0aIFMTExxMXFsWbNGgDOnDnDAw88QFRUFE2aNGH58uUAHD9+nJiYGM+rcuXKPPHEE74LTKQICvB1B0RERK4GJSqGEN7vZQDWjOxD9erV6d69O3/5y194/vnn6dSpEwsXLmTIkCEsX76cd999F4CNGzeSmppKp06d+PHHHylTpgyJiYme88bGxtKjRw9fhCRSZPnkDrIx5kljzGZjzCZjzAxjTAljTEVjzBJjTLL7awVH/WeMMduNMT8bYzo4ymONMRvdx940xhhfxCMiIuK0dOlS6tatS61atTDGcOzYMQCOHj1KtWrVANiyZQvt2rUDIDg4mPLly5OQkJDlPMnJyaSmpnLjjTd6NwCRIs7rCbIxpjrwdyDOWtsY8Ad6AUOBpdba+sBS9z7GmAj38UigI/C2McbffboJwACgvvvV0YuhiIiIZGvmzJn07t0bgNdff53BgwdTo0YNBg0axIgRIwBo0qQJ8+bNIz09nZ07d7J27Vr27NmT5TwzZszg7rvvRvd/RLzLV2OQA4CSxpgAIAjYB3QDpriPTwFud293A2Zaa09ba3cC24HmxpgQoKy19ntrrQWmOtqIiIj4xLmMdObPn89dd90FwIQJExg7dix79uxh7Nix9O/fH4AHH3yQ0NBQ4uLieOKJJ7j++usJCMg68tGZaIuI93g9QbbW7gXGALuB/cBRa+1ioKq1dr+7zn4g2N2kOuD8kzrFXVbdvX1h+UWMMQOMMQnGmISDBw/mZTgiIiJZHNuZRLNmzahatSoAU6ZM8YwhvuuuuzwP6QUEBDB27FgSExOZN28eR44coX79+p7zbNiwgfT0dGJjY70fhEgR54shFhVw3RWuDVQDShlj7r1Uk2zK7CXKLy60dqK1Ns5aG1elSpXL7bKIiEiuHd66Ostd32rVqvHNN98AsGzZMk8SfOLECdLS0gBYsmQJAQEBREREeNrNmDFDd49FfMQXs1jcAuy01h4EMMbMAa4HDhhjQqy1+93DJ1Ld9VOAGo72obiGZKS4ty8sFxER8YlzZ09z7JdNWWadePfddxk4cCDp6emUKFGCiRMnApCamkqHDh3w8/OjevXqTJs2Lcu5PvnkExYuXOjV/ouIiy8S5N1AC2NMEHASaAckAGlAP2Ck++s8d/35wEfGmNdw3XGuD6yx1mYYY44bY1oAPwB9gbe8GomIiIiDX7FAmjz2NkffuIGj7rKawNxumTVOwH/uZ/d/XB/hLulT3F1+AKZ0YbfjXDt27PBSr0XkQl5PkK21PxhjPgXWAenAemAiUBr4xBjTH1cSfZe7/mZjzCfAFnf9v1lrM9ynewSYDJQEvnC/RERERESumE8WCrHWPg88f0HxaVx3k7OrPxwYnk15AtA4zzsoIiIiIkWWlpoWEREREXFQgiwiIiIi4qAEWURERETEQQmyiIiIiIiDEmQREREREQclyCIiIiIiDkqQRUREREQccpUgG2OW5qZMRERERKSwu+RCIcaYEkAQUNkYUwEw7kNlcS37LCIiIiJyVfmjlfT+CjyBKxley/kE+RgwPv+6JSIiIiLiG5dMkK21bwBvGGMet9a+5aU+iYiIiIj4zB/dQQbAWvuWMeZ6IMzZxlo7NZ/6JSIiIiLiE7lKkI0x04C6QCKQ4S62gBJkEREREbmq5CpBBuKACGutzc/OiIiIiIj4Wm7nQd4EXJOfHRERERERKQhyewe5MrDFGLMGOJ1ZaK3tmi+9EhERERHxkdwmyC/kZydERERERAqK3M5i8U1+d0REREREpCDI7SwWx3HNWgFQHCgGpFlry+ZXx0REREREfCG3d5DLOPeNMbcDzfOjQyIiIiIivpTbWSyysNZ+Btyct10REREREfG93A6x6OHY9cM1L7LmRBYRERGRq05uZ7G4zbGdDuwCuuV5b0REREREfCy3Y5AfyO+OiIiIiIgUBLkag2yMCTXGzDXGpBpjDhhjZhtjQvO7cyIiIiIi3pbbh/QmAfOBakB14HN3mYiIiIjIVSW3CXIVa+0ka226+zUZqJKP/RIRERER8YncJsi/GmPuNcb4u1/3Aofys2MiIiIiIr6Q2wT5QaAn8D9gP3AnoAf3REREROSqk9tp3l4G+llrDwMYYyoCY3AlziIiIiIiV43c3kGOzkyOAay1vwFN86dLIiIiIiK+k9sE2c8YUyFzx30HObd3n0VERERECo3cJrmvAt8ZYz7FtcR0T2B4vvVKRERERMRHcruS3lRjTAJwM2CAHtbaLfnaMxERERERH8jtEAustVusteOstW8pORYRERG5WPqpNHbMe4tGjRoRHh7O999/z+DBg2nUqBHR0dF0796dI0eOALBkyRJiY2OJiooiNjaWZcuWec7TsWNHmjRpQmRkJA8//DAZGRk+iqhoynWCLCIiIiKXlrJsOmVrR/HTTz+xYcMGwsPDiY+PZ9OmTSQlJdGgQQNGjBgBQOXKlfn888/ZuHEjU6ZM4b777vOc55NPPmHDhg1s2rSJgwcPMmvWLF+FVCQpQRYRERHJAxmnT/J7ys9UimoDQPHixSlfvjzt27cnIMA1qrVFixakpKQA0LRpU6pVqwZAZGQkp06d4vTp0wCULVsWgPT0dM6cOYMxxtvhFGlKkEVERETywOmjqQQEleGXRe/RtGlTHnroIdLS0rLU+eCDD+jUqdNFbWfPnk3Tpk0JDAz0lHXo0IHg4GDKlCnDnXfeme/9l/OUIIuIiIjkAXvuHCcO/EKVmJtZv349pUqVYuTIkZ7jw4cPJyAggD59+mRpt3nzZp5++mn+/e9/Zyn/8ssv2b9/P6dPn84yPlnynxJkERERkTxQvEwFipepSKmQugDceeedrFu3DoApU6awYMECpk+fnmW4REpKCt27d2fq1KnUrVv3onOWKFGCrl27Mm/ePO8EIYASZBEREZE8UaxUeYqVqcip3/YDsHTpUiIiIli0aBGjRo1i/vz5BAUFeeofOXKEzp07M2LECFq1auUp//3339m/33WO9PR0Fi5cSKNGjbwbTBGn1fBERERE8kiNdvey6z/vEB09nTp16jBp0iSuvfZaTp8+TXx8POB6UO+dd95h3LhxbN++nZdffpmXX34ZgMWLF2OtpWvXrpw+fZqMjAxuvvlmHn74YV+GVeQoQRYRkTyxaeI/8CteAmP8iPv6TRISEtiwYQMPP/wwv//+O2FhYUyfPp2yZcty6NAh7rzzTn788Ufuv/9+xo0bB8Dx48e58cYbPedMSUnh3nvv5fXXX/dRVCKXJyi4Fo3ue5G5ZUYD/+X4W61Z1rckUBLInMt4FbtfiqIv0PfpOo5yOPVOOwBm3wY1/7nRu50XDw2xEBGRPNOg51DC+71MQkICAA899BAjR45k48aNdO/endGjRwOucZUvv/wyY8aMydK+TJkyJCYmel61atWiR48eXo9DRIo2nyTIxpjyxphPjTE/GWO2GmNaGmMqGmOWGGOS3V8rOOo/Y4zZboz52RjTwVEea4zZ6D72ptEkgSIiBcrPP/9M69atAYiPj2f27NkAlCpVihtuuIESJUrk2DY5OZnU1NQsd5RFRLzBV3eQ3wAWWWsbAU2ArcBQYKm1tj6w1L2PMSYC6AVEAh2Bt40x/u7zTAAGAPXdr47eDEJERLJK/nQ0W6f9k4kTJwLQuHFj5s+fD8CsWbPYs2dPrs81Y8YM7r77bi2QICJe5/UE2RhTFmgNvA9grT1jrT0CdAOmuKtNAW53b3cDZlprT1trdwLbgebGmBCgrLX2e2utBaY62oiIiJc1uOdZwvu+RL0egxg/fjwrVqzggw8+YPz48cTGxnL8+HGKFy+e6/PNnDmT3r1752OPRUSy54s7yHWAg8AkY8x6Y8x7xphSQFVr7X4A99dgd/3qgPOWQ4q7rLp7+8LyixhjBhhjEowxCQcPHszbaEREBIDipV0j44qVKkv37t1Zs2YNjRo1YvHixaxdu5bevXtnO89rdjZs2EB6ejqxsbH52WURkWz5IkEOAJoBE6y1TYE03MMpcpDdZ2v2EuUXF1o70VobZ62Nq1KlyuX2V0RE/kDGmdNknDnp2V68eDGNGzcmNTUVgHPnzvH//t//y/VUVTNmzNDdYxHxGV9M85YCpFhrf3Dvf4orQT5gjAmx1u53D59IddSv4WgfCuxzl4dmUy4iIl6WfuIoO+a9CYA9l8GzTz5Kx44deeONNxg/fjwAPXr04IEHHvC0CQsL49ixY5w5c4bPPvuMxYsXExERAcAnn3zCwoULvR+IiAg+SJCttf8zxuwxxjS01v4MtAO2uF/9gJHur5lrKs4HPjLGvAZUw/Uw3hprbYYx5rgxpgXwA9AXeMvL4YiICBBYPpjwfv/Ps39fxmh2vzST7kD3ewPdpf9hz8v/8dRZ8WAZoMz5k3x6N7vdm8vvL0VNrRwmIj7iq4VCHgemG2OKAzuAB3AN9/jEGNMf2A3cBWCt3WyM+QRXAp0O/M1amzmj9iPAZFyzb3/hfomIiIiIXDGfJMjW2kQgLptD7XKoPxwYnk15AtA4TzsnIiIiIkWaVtITEREREXFQgiwiIiIi4qAEWURERETEQQmyiIiIiIiDEmQRKfLCwsKIiooiJiaGuDjX88PPPfcc0dHRxMTE0L59e/btc02zfvbsWfr160dUVBTh4eGMGDHCc561a9cSFRVFvXr1+Pvf/4612a5dJCIiBZwSZBER4OuvvyYxMZGEhAQABg8eTFJSEomJiXTp0oWXXnoJgFmzZnH69Gk2btzI2rVr+fe//82uXbsAeOSRR5g4cSLJyckkJyezaNEiX4UjIiJ/ghJkEZFslC1b1rOdlpaGMa7V7Y0xpKWlkZ6ezsmTJylevDhly5Zl//79HDt2jJYtW2KMoW/fvnz22Wc+6r2IiPwZSpBFpMgzxtC+fXtiY2OZOHGip3zYsGHUqFGD6dOne+4g33nnnZQqVYqQkBBq1qzJoEGDqFixInv37iU0NNTTNjQ0lL1793o9FhER+fOUIItIkbdq1SrWrVvHF198wfjx41mxYgUAw4cPZ8+ePfTp04dx48YBsGbNGvz9/dm3bx87d+7k1VdfZceOHdmON8686ywiIoWLEmQRKfKqVasGQHBwMN27d2fNmjVZjt9zzz3Mnj0bgI8++oiOHTtSrFgxgoODadWqFQkJCYSGhpKSkuJpk5KS4jmviIgULkqQRaRIS0tL4/jx457txYsX07hxY5KTkz115s+fT6NGjQCoWbMmy5Ytw1pLWloaq1evplGjRoSEhFCmTBlWr16NtZapU6fSrVs3n8QkIiJ/ToCvOyAi4ksHDhyge/fuAKSnp3O4YjjDlqay4+/Pceq3/WAMxctWpmZ8P2IHTyXjTAV++W4zM6uEgoVKjW/kgWmJQCJpjbpwU5c7qVYukE6dOtGpUyefxiYiIldGCbKIFGl16tRhw4YNnv3YwVNd5d0ez7a+f/ES1On6WLbHSl1Tm4gH/sXa0X3zvqMiIuI1GmIhIiIiIuKgBFlERERExEEJsoiIiIiIgxJkEREREREHJcgiIiIiIg5KkEVEREREHJQgi4iIiIg4KEEWEREREXFQgiwiIiIi4qAEWURERETEQQmyiIiIiIiDEmQREREREQclyCIiIiIiDkqQRUREREQclCCLiIiIiDgoQRYRERERcVCCLCIiIiLioARZRERERMRBCbKIiIiIiIMSZBERERERByXIIiIiIiIOSpBFRERERByUIIuIiIiIOChBFhERERFxUIIsIiIiIuKgBFlERERExEEJsoiIiIiIgxJkEREREREHJcgiIiIiIg5KkEVEREREHJQgi4iIiIg4KEEWEREREXHwWYJsjPE3xqw3xixw71c0xiwxxiS7v1Zw1H3GGLPdGPOzMaaDozzWGLPRfexNY4zxRSwiIiIicvXw5R3kgcBWx/5QYKm1tj6w1L2PMSYC6AVEAh2Bt40x/u42E4ABQH33q6N3ui4iIiIiVyufJMjGmFCgM/Ceo7gbMMW9PQW43VE+01p72lq7E9gONDfGhABlrbXfW2stMNXRRkRERETkivjqDvLrwBDgnKOsqrV2P4D7a7C7vDqwx1EvxV1W3b19YflFjDEDjDEJxpiEgwcP5kkAIiIiInJ18nqCbIzpAqRaa9fmtkk2ZfYS5RcXWjvRWhtnrY2rUqVKLr+tiIiIiBRFAT74nq2ArsaYW4ESQFljzIfAAWNMiLV2v3v4RKq7fgpQw9E+FNjnLg/NplxERERE5Ip5/Q6ytfYZa22otTYM18N3y6y19wLzgX7uav2Aee7t+UAvY0ygMaY2rofx1riHYRw3xrRwz17R19FGREREROSK+OIOck5GAp8YY/oDu4G7AKy1m40xnwBbgHTgb9baDHebR4DJQEngC/dLREREROSK+TRBttYuB5a7tw8B7XKoNxwYnk15AtA4/3ooIiIiIkWNVtITEREREXFQgiwiIiIi4qAEWURERETEQQmyiIiIiIiDEmQREREREQclyCIiIiIiDkqQRUTywLn0M/z04QtsnfIskZGRPP/88wAMHjyYRo0aER0dTffu3Tly5AgA06dPJyYmxvPy8/MjMTERgBkzZhAVFUV0dDQdO3bk119/9VFUIiJFkxJkEZE8YPyLUb/nUML7/T8SExNZtGgRq1evJj4+nk2bNpGUlESDBg0YMWIEAH369CExMZHExESmTZtGWFgYMTExpKenM3DgQL7++muSkpKIjo5m3LhxPo5ORKRoUYIsIpIHjDH4Fy8BwNmzZzl79izGGNq3b09AgGtNphYtWpCSknJR2xkzZtC7d28ArLVYa0lLS8Nay7Fjx6hWrZr3AhERESXIIiJ5xZ47x9YpzxEcHEx8fDzXXXddluMffPABnTp1uqjdxx9/7EmQixUrxoQJE4iKiqJatWps2bKF/v37e6X/IiLiogRZRCSPGD8/wvu9TEpKCmvWrGHTpk2eY8OHDycgIIA+ffpkafPDDz8QFBRE48aNAdfd5wkTJrB+/Xr27dtHdHS0Z1iGiIh4hxJkEZE8Vr58edq2bcuiRYsAmDJlCgsWLGD69OkYY7LUnTlzpufuMeB5UK9u3boYY+jZsyffffed1/ou4kunzp6j68T/0vHt7dwyLtnzsOtvv/1GfHw89evXJz4+nsOHD3vaJCUl0bJlSyIjI4mKiuLUqVOA65OZ6OhoIiMjGTJkiE/ikcJLCbKISB44e+IY6afSADh58iRfffUVjRo1YtGiRYwaNYr58+cTFBSUpc25c+eYNWsWvXr18pRVr16dLVu2cPDgQQCWLFlCeHi49wIR8aHAAMOMfmEserQeXzxSz/Ow68iRI2nXrh3Jycm0a9eOkSNHApCens69997LO++8w+bNm1m+fDnFihXj0KFDDB48mKVLl7J582YOHDjA0qVLfRydFCYBvu6AiMjV4GzaEX754l3suXPETB1Al8hyRK97htZvbONM+jnaRtcEoGloSf51W3UAvt/5O8HmNwI+7MZux7kej/OjdevWFCtWjFq1ajF58mTvByTiA8YYSgX6A5CeYT0Pu86bN4/ly5cD0K9fP9q2bcuoUaNYvHgx0dHRNGnSBIBKlSoBsGPHDho0aECVKlUAuOWWW5g9ezbt2rXzflBSKClBFhHJA0FVahLe92UA5pYZ7SlfMbBBjm1a1i7NZ38pfVH5vddW5P8+35j3nRQpBDLOWbr8+7/s+u0Mjz0xiOuuu44DBw4QEhICQEhICKmpqQBs27YNYwwdOnTg4MGD9OrViyFDhlCvXj1++ukndu3aRWhoKJ999hlnzpzxZVhSyChBFhERkQLD38/wxSP1OHoyg7+vzvqw64XS09NZuXIlP/74I0FBQbRr147Y2FjatWvHhAkTuPvuu/Hz8+P6669nx44dXoxCCjuNQRYREZECp1xJf8/DrlWrVmX//v0A7N+/n+DgYABCQ0Np06YNlStXJigoiFtvvZV169YBcNttt/HDDz/w/fff07BhQ+rXr++zWKTwUYIsIiIiBcKhtHSOnswAXDNaZD7s2rVrV6ZMmQK4ZoXp1q0bAB06dCApKYkTJ06Qnp7ON998Q0REBIBnGMbhw4d5++23eeihh3wQkRRWGmIhIiIiBULq8XSempvCOWs5Z6HP3+6mS5cutGzZkp49e/L+++9Ts2ZNZs2aBUCFChV46qmnuPbaazHGcOutt9K5c2cABg4cyIYNGwD45z//SYMGOT8PIHIhJcgiIiJSIIRfU4IvHqnn2e9d4Uu+fOtL187tEEwwpzjFbdNvy9Ku/MPlAVjFKlq91cpVeD1UuL4Cqx5f5YWey9VGQyxERERERByUIIuIiIiIOChBFhERERFxUIIsIiIiIuKgBFlERERExEEJsoiIiIiIgxJkEREREREHJcgiIiIFxL6jZ7h70k5ufiuZyMhI3njjDQBeeOEFqlevTkxMDDExMSxcuNDTZsSIEdSrV4+GDRvy5ZeuOYNPnDhB586dadSoEZGRkQwdOtQn8YgUVlooREREpIDw9zM82+EaoqqVpMKT3xEbG0t8fDwATz75JIMGDcpSf8uWLcycOZPNmzezb98+brnlFrZt2wbAoEGDuOmmmzhz5gzt2rXjiy++oFOnTl6PSaQw0h1kERGRAqJqmWJEVSsJQJkyZQgPD2fv3r051p83bx69evUiMDCQ2rVrU69ePdasWUNQUBA33XQTAMWLF6dZs2akpKR4JQaRq4ESZBERkQJo165drF+/nuuuuw6AcePGER0dzYMPPsjhw4cB2Lt3LzVq1PC0CQ0NvSihPnLkCJ9//jnt2rXzXudFCjklyCIiIgVM2ukM7rjjDl5//XXKli3LI488wn//+18SExMJCQnhH//4BwDW2ovaGmM82+np6fTu3Zu///3v1KlTx2v9FynslCCLiIgUIGczLA9/vIc+ffrQo0cPAKpWrYq/vz9+fn785S9/Yc2aNYDrjvGePXs8bVNSUqhWrZpnf8CAAdSvX58nnnjCqzGIFHZKkEVERAoIay1D5u2lXpVAnnrqKU/5/v37Pdtz586lcePGAHTt2pWZM2dy+vRpdu7cSXJyMs2bNwfg2Wef5ejRo7z++utejUHkaqBZLERERAqIhN0nmLPhCI2qBlIqtBQAtbrU4te1v5K2Nw0MBFYMpO7ddWn1VisAjtQ4Qrnq5TD+hto9atP67dacPnyatcPX0qhRI5o1awbAY489xkMPPeSz2EQKEyXIIiIiBcS1tUrxy4uuu8O9K5T1lFeIrJBjm9AOoYR2CM1SFlghkOvfvJ5Vj6/Kn46KXOU0xEKkiNuzZw833XQT4eHhWRYmmDVrFpGRkfj5+ZGQkJClTVJSEi1btiQyMpKoqChOnTqV5XjXrl09HwGLiIgUNrqDLFLEBQQE8Oqrr9KsWTOOHz/uWZigcePGzJkzh7/+9a9Z6qenp3Pvvfcybdo0mjRpwqFDhyhWrJjn+Jw5cyhdurS3wxAREckzuoMsUsSFhIR4xig6FyYIDw+nYcOGF9VfvHgx0dHRNGnSBIBKlSrh7+8PwO+//85rr73Gs88+670ARESuIjktN/7bb78RHx9P/fr1iY+P98yFPX36dM8S5DExMfj5+ZGYmAjAjBkziIqKIjo6mo4dO/Lrr7/6KqxCRwmyiHhcuDBBdrZt24Yxhg4dOtCsWTNeeeUVz7HnnnuOf/zjHwQFBXmjuyIiV53M5caXPV6f1atXM378eLZs2cLIkSNp164dycnJtGvXjpEjRwLQp08fEhMTSUxMZNq0aYSFhRETE0N6ejoDBw7k66+/JikpiejoaMaNG+fj6AoPJcgiArju/joXJshJeno6K1euZPr06axcuZK5c+eydOlSEhMT2b59O927d/dir0VEri45LTc+b948+vXrB0C/fv347LPPLmo7Y8YMevfuDbimDLTWkpaWhrWWY8eOZZkjWy5NY5BFhLNnz3LHHXdkWZggJ6GhobRp04bKlSsDcOutt7Ju3TpKly7N2rVrCQsLIz09ndTUVNq2bcvy5cu9EIGIyNXH+anegQMHCAkJAVxD41JTUy+q//HHHzNv3jwAihUrxoQJE4iKiqJUqVLUr1+f8ePHe7X/hZnuIIsUcdZa+vfvT3h4eJaFCXLSoUMHkpKSOHHiBOnp6XzzzTdERETwyCOPsG/fPnbt2sXKlStp0KCBkmMRkSt04XLjf+SHH34gKCjIM4PQ2bNnmTBhAuvXr2ffvn1ER0czYsSI/O72VUN3kEWKuFWrVjFt2jSioqIICq4FQLUb78RmnGXP0g9JP3mclm3aUTK4JvXvHAzAbyHXUqlmfcBQtk4T/rn8EP9cPhWAtaP7+ioUEZGrgme58UdfzLLc+P79+wkJCWH//v0EBwdnaTNz5kzP8ArA86Be3bp1AejZs6dn3LL8Ma8nyMaYGsBU4BrgHDDRWvuGMaYi8DEQBuwCelprD7vbPAP0BzKAv1trv3SXxwKTgZLAQmCgtdZ6Mx6Rwu6GG24g88cmdvDULMfK14/Ltk2liFZUimiV4znDwsLYtGlT3nVSRKSIyGm58a5duzJlyhSGDh3KlClT6Natm+fYuXPnmDVrFitWrPCUVa9enS1btnDw4EGqVKnCkiVLCA8P92oshZkv7iCnA/+w1q4zxpQB1hpjlgD3A0uttSONMUOBocDTxpgIoBcQCVQDvjLGNLDWZgATgAHAalwJckfgC69HJCIiIpIHclpuvHRYabZN2saLr71IYIVAGjzQwLPc+NHkoxwNOMp9/7kvy7mCWgdRK6oW9YLrUatWLSZPnuztcAotryfI1tr9wH739nFjzFagOtANaOuuNgVYDjztLp9prT0N7DTGbAeaG2N2AWWttd8DGGOmArejBFlEREQKqZyWGweIfCwy2zbl6pcj+h/RF5Vfc8M1XHPDNVpy/Ar49CE9Y0wY0BT4AajqTp4zk+jMwTXVgT2OZinusuru7QvLs/s+A4wxCcaYhIMHD+ZpDCIiIiJydfFZgmyMKQ3MBp6w1h67VNVsyuwlyi8utHaitTbOWhtXpUqVy++siIiIiBQZPkmQjTHFcCXH0621c9zFB4wxIe7jIUDmBH8pQA1H81Bgn7s8NJtyEREppAZ9lkKzV7YSPz45S/lbb71Fw4YNiYyMZMiQIZ7ypKQkWrZsSWRkJFFRUZw6dQrQErsil+vBBx8kODjYM01cpux+9i61vHXbtm1p2LCh51h28zUXBl5PkI0xBngf2Gqtfc1xaD7Qz73dD5jnKO9ljAk0xtQG6gNr3MMwjhtjWrjP2dfRRkRECqG7Yiow5d6wLGVff/018+bNIykpic2bNzNo0CDAtarjvffeyzvvvMPmzZtZvnw5xYoV0xK7Ilfg/vvvZ9GiRVnKcvrZy2l560zTp0/3HL9wOrrCwhd3kFsB9wE3G2MS3a9bgZFAvDEmGYh372Ot3Qx8AmwBFgF/c89gAfAI8B6wHfgvekBPRKRQuy6sFOVL+mcpmzBhAkOHDiUwMBDA8x/u4sWLiY6OpkmTJgBUqlQJf39/LbErcgVat25NxYoVs5Tl9LPn5Fze+mri9QTZWrvSWmustdHW2hj3a6G19pC1tp21tr7762+ONsOttXWttQ2ttV84yhOstY3dxx7THMgiIlefbdu28e2333LdddfRpk0bfvzxR0+5MYYOHTrQrFkzXnnlFSDrErvVqlVjy5Yt9O/f35chiBRKOf3sOX388ccXJcgPPPAAMTExvPzyyxTW1ExLTYuISIGWnp7O4cOHWb16NaNHj6Znz55Ya0lPT2flypVMnz6dlStXMnfuXJYuXaoldkXySE4/e5kuXN4aXMMrNm7cyLfffsu3337LtGnTfNH1P00JsoiIFGihoaH06NEDYwzNmzfHz8+PX3/9ldDQUNq0aUPlypUJCgri1ltvZd26dVmW2DXG0LNnT7777jvfBiFSCOX0s5fpwuWtwbWCH0CZMmW45557WLNmjVf7nFeUIIuISIF2++23s2zZMsD1ke+ZM2eoXLkyHTp0ICkpiRMnTpCens4333xDREREliV2AS2xK3KFcvrZg/PLW/fq1ctTPz093ZNAnz17lgULFlw0K0Zh4YulpkVERLL1+Kw9fL8rjcMn0gksH0iNW2tQ5doqbP9iO2PeG4Ofvx9ht4dxw7gbAPi98e9UrlsZDFSIqMC/dvwLdmiJXZHL1bt3b5YvX+75dMa/cQcqRrTil0VfMfrtDzD+AYS27UPcENeQieO7t3L4XAnumrASWAlAxpnTbPt4ODYjA+w5BvS5k7/85S8+jOrKKUEWEZEC4627zk9771xmt0HfBtnWr3JtFapce/ECUFpiV+TyzJgxI8t+7OCpANTu/HC29cvUDKdRn39mKfMvHkj4fS959t8Y3TePe+k9GmIhIiIiIuKgBFlERERExEEJsoiIiIiIgxJkEREREREHJcgi8qf9sug9ksY/xpZJ/+cpmzVrFpGRkfj5+ZGQkOApP3v2LP369SMqKorw8HDPAg7Hjx8nJibG86pcuTJPPPGEt0MRERHRLBYi8udVjLyBKk1vYdfCiZ6yxo0bM2fOHP76179mqTtr1ixOnz7Nxo0bOXHiBBEREfTu3ZuwsDDPAg8AsbGx9OjRw1shiIiIeChBFpE/rUyNRpw+ejBLWU4LMxhjSEtLIz09nZMnT1K8eHHKli2bpU5ycjKpqanceOON+dZnERGRnGiIhYh41Z133kmpUqUICQmhZs2aDBo0iIoVK2apM2PGDO6++26MMT7qpYiIFGVKkEXEq9asWYO/vz/79u1j586dvPrqq+zYsSNLnZkzZ9K7d28f9dB3Bn2WQrNXthI/PtlT9ttvvxEfH0/9+vWJj4/n8OHDgGv55NjYWKKiooiNjfUsB6ux3CIif54SZBHxqo8++oiOHTtSrFgxgoODadWqVZaH+DZs2EB6ejqxsbE+7KVv3BVTgSn3hmUpGzlyJO3atSM5OZl27doxcuRIACpXrsznn3/Oxo0bmTJlCvfddx8AZcqUITEx0fOqVauWxnKLiFwmJcgi4lU1a9Zk2bJlWGtJS0tj9erVNGrUyHN8xowZRfLuMcB1YaUoX9I/S9m8efPo168fAP369eOzzz4DoGnTplSrVg2AyMhITp06xenTp7O01VhuEZErowRZxOGNN96gcePGREZG8vrrrwM5T1d26NAhbrrpJkqXLs1jjz3mox4XDDsXvM3PH73MqcP/I6RcMV7pVp2JvWsRUq4Y369cQae2LWhTrwy7X4qi67EppK6ZQ8OqJYmpXYnbQ3+j/Gd92P1SFLtfiuKjia/R5rcZvg6pwDhw4AAhISEAhISEkJqaelGd2bNn07RpUwIDA7OUayy3iMiV0SwWIm6bNm3i3XffZc2aNRQvXpyOHTvSuXPnHKcrK1GiBC+//DKbNm1i06ZNPup1wVC7y6Oe7bllRnu2O4aXvahuqUB/JtxdM8dzrXyiYd527iq3efNmnn76aRYvXnzRsZkzZzJt2jQf9EpEpHDTHWQRt61bt9KiRQuCgoIICAigTZs2zJ07l/DwcBo2vDhpK1WqFDfccAMlSpTwQW+lqKhatSr79+8HYP/+/QQHB3uOpaSk0L17d6ZOnUrdunWztCvKY7lFRP4sJcgibo0bN2bFihUcOnSIEydOsHDhQvbs2ePrbkkR17VrV6ZMmQLAlClT6NatGwBHjhyhc+fOjBgxglatWl3UriiP5RaRgmHs2LFERkbSuHFjevfuzalTp3KcmSenVVZ9RUMsRNzCw8N5+umniY+Pp3Tp0jRp0oSAAP2IiPc8PmsP3+9K4/CJdALLB1Lj1hpUjK7ItknbePG1FwmsEEiDBxrQ6q1W7PlyD3t/2kvfgX3pO7AvABGPRlC8THEA1r63lsSViT6MRkSKsjPHf+PNN99ky5YtlCxZkp49ezJz5ky2bNlCu3btGDp0KCNHjmTkyJGMGjXqkqus+oL+9xdx6N+/P/379wfg//7v/wgNDfVxj6QoeeuuGp7t3hXOj9+OfCzyoro1OtSgRocaF5Vnin0+NsvsICIi3pa5YmqxYsU4ceIE1apVY8SIESxfvhxwzczTtm1bRo0alatVVr1JQyxEHDJnCNi9ezdz5szRR9QiIiJXoHiZigwaNIiaNWsSEhJCuXLlaN++fY4z8+RmlVVv0h1kEYc6zW4k4+TvGH9/qrftzS3/+pwjyQnsWfoh6SeP07JNO0oG16T+nYMB2DTxH2ScOYnNSGfilI+od+dgtkx6xsdRiIiI+Fb6qTTmzZvHzp07KV++PHfddRcffvhhjvWdq6wePnyYG2+8kVtuuYU6dep4sdfnKUEWcWjYe9hFZeXrx1G+fly29RsPeDW/uyQiIlLoHP9lM3G1a1OlShUAevTowXfffeeZmSckJCTLzDw5rbLqqwRZQyxEREREJE8VL1uJ1atXc+LECay1LF26lPDw8Bxn5vmjVVa9TXeQRURERCRPlQqpy40pB4muVQF/P0PkNSXoWCuBE/7neHTiHv49+nmqlSvGhJ412P3SF3Q9ncGKNXtpWHUGFrgrpjzlP+sD0Rt90n8lyCL5ICwsjDJlyuDv709AQAAJCQnMmjWLF154ga1bt7JmzRri4s4P2xgxYgTvv/8+/v7+vPnmm3To0MGHvRcREfnznrq5Kk/dXDVLWWCAHzPur31R3T9aZdXblCCL5JOvv/6aypUre/ZzWrJ6y5YtzJw5k82bN7Nv3z5uueUWtm3bhr+/v7e7LCIiImgMsmQjIyODpk2b0qVLFwAGDx5Mo0aNiI6Opnv37hw5cgSA6dOnExMT43n5+fmRmJjou44XcDktWT1v3jx69epFYGAgtWvXpl69eqxZs8YHPSx4cvtezLR7925Kly7NmDFjfNBbERG5WihBlou88cYbhIeHe/bj4+PZtGkTSUlJNGjQwLP8Y58+fUhMTCQxMZFp06YRFhZGTEyMj3pdsBhjaN++PbGxsUycOPGSdffu3UuNGucXfAgNDWXv3r353cVCIbfvxUxPPvkknTp18nY3RUTkKqMEWbJISUnhP//5Dw899JCnrH379p4ll1u0aEFKSspF7WbMmKFFNRxWrVrFunXr+OKLLxg/fjwrVqzIsa619qIyY0x+dq9QuNz34meffUadOnWIjLx41TkREZHLoQRZsnjiiSd45ZVX8PPL/q3xwQcfZHuH7uOPP1aC7FCtWjUAgoOD6d69+yWHTISGhrJnzx7PfkpKiqd9UXY578W0tDRGjRrF888/780uiohIPjt16hTNmzenSZMmREZGen7Pz5o1i8jISPz8/EhISMjSZsSIEdSrV4+GDRvy5ZdfXtH3VYIsHgsWLCA4OJjY2Nhsjw8fPpyAgAD69OmTpfyHH34gKCiIxo0be6ObBV5aWhrHjx/3bC9evPiS16Zr167MnDmT06dPs3PnTpKTk2nevLm3ulsgLf352GW9F59//nmefPJJSpcu7c1uiohIPgsMDGTZsmVs2LCBxMREFi1axOrVqz0Pvrdu3TpLfeeD74sWLeLRRx8lIyPjsr+vZrEQj1WrVjF//nwWLlzIqVOnOHbsGPfeey8ffvghU6ZMYcGCBSxduvSij/9nzpypu8cO64c1Y8DM3QCkn7N0iypHxJrBTJzyV55fuI/f0jLo1LYFEdeUZFrfMMoA7Sun0iCkDAF+huc7hbBv3z769u3L//73P/z8/BgwYAADBw7kueeeY968efj5+REcHMzkyZM9d5uTkpL461//yrFjx/Dz8+PHH3+kRIkSPrwSVy5h9wnm78z9e/GHH37g008/ZciQIRw5cgQ/Pz9KlCjBY4895uNIRETkzzDGeG5+nD17lrNnz2KMyfJ8ilNOD763bNnysr6vEmTxGDFihOehp+XLlzNmzBg+/PBDFi1axKhRo/jmm28ICgrK0ubcuXPMmjXLM8Z2z5492SZ2gwcP5vPPP6d48eLUrVuXSZMmUb58ec95du/eTUREBC+88AKDBg3yWsz5oWbF4ix6tN5F5R3Dy9IxvGy2bR5vE8zjbYI9+wEBAbz66qs0a9aM48ePExsbS3x8PIMHD+bll18G4M033+Sll17inXfeIT09nXvvvZdp06bRpEkTDh06RLFixfInQC94Ov4axv/TNTl8bt6L3377rWf7hRdeoHTp0kqORUSuEhkZGcTGxrJ9+3b+9re/cd111+VYd+/evbRo0cKzf6UPvitBFo/YwVM928d3b+XA1hRiB09l83uDOZeRTq1I18IWparVpWb8/Z56h8+V4K4JK4GVLHgqPtvELj4+nhEjRhAQEMDTTz/NiBEjGDVqlOf7afaBrEJCQggJCQGgTJkyhIeHs3fvXiIiIjx10tLSPHdQFy9eTHR0NE2aNAGgUqVK3u90Hmv1VisAjiYfZd+ufbR6qxXrXlrHufRzhDUNA6BMWBnq3l03S7vdP+zGP9CfuYFzWfX4Km93W0RE8pi/vz+JiYkcOXKE7t27s2nTphyHLubVg+9KkB0efPBBzzjcTZs2AeT4kfaSJUsYOnQoZ86coXjx4owePZqbb77ZxxHknTI1wylT0/XxReRDoy9Zr1Gff3r2c0rs2rdv76nTokULPv30U89+5uwDpUqVyuswrgq7du1i/fr1nr+Yhw0bxtSpUylXrhxff/01ANu2bcMYQ4cOHTh48CC9evViyJAhvux2nilXvxzl6pcDoNk/m/1h/Zq3FpyVmEREJO+UL1+etm3bsmjRohwT5Lx68F0P6Tncf//9LFq0KEvZ4MGDSUpKIjExkS5duvDSSy8BULlyZT7//HM2btzIlClTuO+++3zR5QLtwsQuk2YfyL3ff/+dO+64g9dff52yZV3DM4YPH86ePXvo06cP48aNAyA9PZ2VK1cyffp0Vq5cydy5c1m6dKkvuy4iIvKnHTx40LMo1MmTJ/nqq69o1KhRjvXz6sF3JcgOrVu3pmLFilnKMpMSyPqRdtOmTT1/kURGRnLq1ClOnz7tvc4WcNkldqDZBy7H2bNnueOOO+jTpw89evS46Pg999zD7NmzAddfzG3atKFy5coEBQVx6623sm7dOm93WUREJE/t37+fm266iejoaK699lri4+Pp0qULc+fOJTQ0lO+//57OnTvToUMHwJWT9ezZk4iICDp27Mj48ePx9/e/7O+rIRa5kN1H2k6zZ8+madOmBAYGZinP6YE1gLfeeotx48YREBBA586deeWVV7wSizfklNj9mdkHMjIyiIuLo3r16ixYsIDffvuNu+++m127dhEWFsYnn3xChQoVvBpnfrLW0r9/f8LDw3nqqac85cnJydSvXx+A+fPne/6K7tChA6+88gonTpygePHifPPNNzz55JM+6buIiEheeeSbRwh68PxD2V/yJV++5ZrbuNbTtahFLQB+53fPsyuUhWueuAaA/7f9/9GJy3/GSQlyLgwfPpzhw4czYsQIxo0bx4svvug5tnnzZp5++mkWL158UbucZiI4cOAA8+bNIykpicDAQFJTU3PVj1OnTtG6dWtOnz5Neno6d955Z5a+FAQ5JXZ/dvaBzCWHjx07BsDIkSNp164dQ4cOZeTIkYwcOTLLQ3+FXfST0WyatomgakG8++m7ANTqUosDqw9wMvUkxhgCKwRS5+46nl8Ivzf+ncp1K4OBChEV6Ny5sy9DEBERKbSUIF+Ge+65h86dO3uS0pSUFLp3787UqVOpW7fuRfVzemDt3XffZejQoZ47zsHBwRe1zU7mZNmlS5fm7Nmz3HDDDXTq1CnLdCa+NuehukybtpNGVQOJ/PjfAAxuV5UXvtjPmfRztI12PUDVNLQk/7qtepa2R785wNni/uw+MYWa7im+4PySw8OGDeO1114DXPMcLl++HIB+/frRtm3bqypBLlu3LNe/ef1F5RUic75LXuXaKlS5tkp+dktERKRIUIL8B3L6SPvIkSN07tyZESNG0KpVqz88j/OBtcGDB/Ptt98ybNgwSpQowZgxY7j22mv/8Bw5TZZdkFxbqxS/vHjxk6U3Nyjzh22fvKlqtuWZSw5nrk4HcODAAc8fHyEhIbm+Cy8iIiLyR/SQnkPF8BbUbxzD5q0/UbxMRWp17E/zzvdQsnIoJavU4NnXP+DnyjcQO3gqEV36s2nLT9z3yFMEBdciKLgW0Y+Oy/a8Fz6wlp6ezuHDh1m9ejWjR4+mZ8+e2c7bl52MjAxiYmIIDg4mPj7+kpNlXw3+aPlrERERkbxW6O8gG2M6Am8A/sB71tqRV3qu2l0evaisclSbbOuGtOxGSMtuf3jO7B5YCw0NpUePHhhjaN68OX5+fvz6669UqfLHH49fzmTZV4Oclr+uWrUq+/fvJyQkhP379+d6mIqIiIjIHynUd5CNMf7AeKATEAH0NsZEXLqV9+T0wNrtt9/OsmXLANcCD2fOnKFy5cqXdW7nZNlXsxEjRpCSksKuXbuYOXMmN998Mx9++CFdu3ZlypQpgGt2jG7d/viPFREREZHcKOx3kJsD2621OwCMMTOBbsAWX3Vo90tRnu0ff0nL9oG1W+qUYvC8vTT89ysU8ze80v4a9rwc7WnnfEDN6eDBgxQrVozy5ct7Jst++umn8zcgH/FM1eLgXHL4bKWzbJu0jRdfe5HACoE0eKDBRW20zLCIiIhcCZPbsa8FkTHmTqCjtfYh9/59wHXW2scuqDcAGODebQj87NWOZlUZ+PUK25YEaru3DfAbsD8vOuVlf+YaXA2KevygawC6BkU9ftA1KOrxg65BQYi/lrX2ojGuhf0OcnZTOFyU8VtrJwIT8787f8wYk2CtjfN1P3ypqF+Doh4/6BqArkFRjx90DYp6/KBrUJDjL9RjkIEUoIZjPxTY56O+iIiIiMhVoLAnyD8C9Y0xtY0xxYFewHwf90lERERECrFCPcTCWptujHkM+BLXNG8fWGs3+7hbf6RADPXwsaJ+DYp6/KBrALoGRT1+0DUo6vGDrkGBjb9QP6QnIiIiIpLXCvsQCxERERGRPKUEWURERETEQQmyiIiIiIiDEuQrZIwJM8acNMYkuvc7GmN+NsZsN8YMvUS7D4wxqcaYTReUVzTGLDHGJLu/VnCXRxljJudnLLmVTcyXFUs25xttjPnJGJNkjJlrjCnvOPaM+1r+bIzp4Cj/KqfzeYPzGhhjahhjvjbGbDXGbDbGDHTUy+01eNkdf6IxZrExpprjWIG7BhfEX8IYs8YYs8Ed/4uOermK31F/kDHGGmMqO8oKXPzu75/l58Bd5m+MWW+MWeAoy+174AVjzF73NU00xtzqOFbgrkE2vwd2GWM2uvue4KiX6/eAMeZxd4ybjTGvOMoLXPzu73/hNShvjPnU/ftsqzGmpbs8t++Bjx3//rsueG8V+GtgjGno6H+iMeaYMeYJd73cXoMYY8zqzPeRMaa541iBuwbZvAeedL9/NxljZhhjSrjLcxt/E2PM9+6fpc+NMWUdxwpM/NnEPdAd8+bMf3N3eW7jvsvd9pwxJu6CYznFHeu+TtuNMW8aY4y7/DFjzAN5Fqy1Vq8reAFhwCb3tj/wX6AOUBzYAETk0K410CyzraP8FWCoe3soMMpx7CugZkGK+UpjuaBeeyDAvT0qsx4Q4b6GgbhWDvwv4O8+1g8YVhCuARACNHNvlwG2Zf67X8Y1KOvY/jvwTkG+BhfEb4DS7u1iwA9Ai8uJ3328Bq6ZaH4BKhfk+LP7OXCXPQV8BCy4gp+DF4BB2ZQXyGuQze+BXZn/bhfUy238N+H6HRfo3g8uyPHncA2mAA+5t4sD5S/nGlxw7leBfxa2a+Ao9wf+h2t1sst5HywGOrm3bwWWF+RrQNbfhdWBnUBJ9/4nwP2XGf+PQBv39oPAywUx/gvibgxsAoJwzYr2FVD/MuMOx7XC8XIgzlF+qbjXAC1x/R/0heN9EwSsz6tYdQc5bzQHtltrd1hrzwAzgW7ZVbTWrsC1RPSFuuH6JYv76+2OY5/jmuO5QLnCWJztF1tr0927q3Et9JLZfqa19rS1diewHdc1Btc8173/fO//PGvtfmvtOvf2cWArrl+UkPtrcMyxW4rzK0EW+GtgXX537xZzv5z9/8P43cYCQ8i6CmaBjz+TMSYU6Ay8d8Ghy7kG2Sk01yAHuY3/EWCktfY0gLU21dG+wMfvvtPXGngfwFp7xlp7xH34st4D7jthPYEZjvYF/hpcoB3wX2vtL+793F4DC2TeNS3H+UW/Css1CABKGmMCcCVqzv7nJv6GwAr39hLgDkf7ghp/OLDaWnvC/X/5N0B397Hc/h+41Vr7czaHso3bGBOC68bS99aVFU/NPLe19gSwy/npw5+hBDlvVAf2OPZTOJ8o5VZVa+1+cCVeQLDjWAJw45/qoXddKpacPIjrL0G4xPW01h4GAo0xlfKuu3+eMSYMaIrrLipcxjUwxgw3xuwB+gD/dBcXimtgXEMLEoFUYIm19rLiN8Z0BfZaazdccKhQxO/2Oq4E/9wF5Zfzc/CYcQ21+cDxUWRhuQYWWGyMWWuMGeAoz238DYAbjTE/GGO+McZc6y4vLPHXAQ4Ck4xrmM17xphS7mOX+7vwRuCAtTbZvV9YroFTL84n+JD7a/AEMNr9u3AM8Iy7vMBfA2vtXlx93g3sB45aaxe7D+c2/k1AV/f2XZxfJbggx78JaG2MqWSMCcJ15z+z31eSBzjlFHd19/aF5ZnyLF9Sgpw3TDZlNpuyK5UKVPvDWoWUMWYYkA5MzyzKpprzehao62GMKQ3MBp644I5wrlhrh1lra+CK/7HM02ZX1bFdIK6BtTbDWhuD6+5/c2NM49y2df9CHcb5PwqyHM7u2zm2C0T8xpguQKq1du2fOM0EoC4Qg+s/11czT59N3QJ3DYBW1tpmQCfgb8aY1pfZPgCoALQABgOfuO+kFpb4A3ANNZtgrW0KpOH6SPlK9CZrcllYrgEAxrWibVdg1hU0fwR40v278Encd+QpBNfA/UdtN1xDAaoBpYwx917maR7E9fOzFteQvTOZp8+mboGI31q7FdfwyCXAIlxDItIv2Sj3corba9dDCXLeSOH8X03gShb2GddDXJkPLTz8B+c44P7oAPfXVMexEsDJPO1x/so2FmPMJPe1WJhZ0RjTD+gC9HF/XAI5XE/HfoG5HsaYYriS4+nW2jmOQ7m+Bg4fcf5jtUJzDQDcHykvBzq6i3ITf11c/6FsMMbswhXjOmPMNRSe+FsBXd39nwncbIz50H0sV+8Ba+0B9x8a54B3Of/xaaG4Btbafe6vqcBczvc/tz8DKcAc95CdNbjuxFemkMSPq58pjk9PPsWVMMPl/S4MAHoAH19w7sJwDTJ1AtZZaw84ynJ7DfoBmb9DZ1G4fg5uAXZaaw9aa8/iiuN697Hc/h74yVrb3lobi+uPpP+62xfo+K2171trm1lrW+Macpn56ceV/B/olFPcKZwfjuksz5R31+NKBy8X9RdZB6oHADtw/Wef+ZBeZG7aOspGk3VA+yuOY3fgfniroMR8pbFcUK8jsAWockF5JFkH5+/g/OB8A+zF/XCfj//dDa7xT69nUy+316C+Y/tx4NOCfA0uiL8K5x9GKgl8C3S5nPgvOPcuzj+kVyDjz+k97y5vS9aH9HL7HghxbD+Ja9xdgb0GF7wHSgFlHNvfAR0vM/6HgZfc2w1wfaxqCmr82b0H3O/9hu7tF4DRl/tzgOv34TcXlBWaa+Aumwk8cEFZbt8HW4G27u12wNqCfA0u+Dm4DtiMa+yxwTXm9vHLjD/z4VQ/XP+vPFgQ48/mvZ/Z75rAT0CFy33vu+ssJ+tDepeK+0dcnzhlPqR3q6PdW0CvPInV22+qq+WVzZvkVlyzGPyXSzxViusvw/3AWVx/CfV3l1cCluL662spUNHRZhxwWwGM+bJjueB823H9Z5jofr3jODbMfS1/xv2Eqrs8DphdEK4BcAOuj3aSHDHcepnXYDaucVxJuB7GrF6Qr8EF8UcD691934T7yfvLif+Cc+/CMRtCQYw/u58DR3lbsibIuX0PTAM2uq/jfLImzAXuGlzwHqiD6z+xDbgShGGOermNvzjwofs9tA64uSDHn917ANfwmAT3v+FnnE8Scv1zAEwGHs6mvLBcgyDgEFDugnq5fR/cAKx1v5d+AGIL8jXIJv4XcSWIm9w/05mzsuQ2/oG4cohtwEjAFMT4s4n7W1w3ujYA7a7g3707rvzhNHAA+DKXcW9yHxt3wbVaRzaz6lzJy7hPKJfJ/VDWAmttrsdcXuH3CcT1ZOgN9vyMDz7hrZj/oA9vAPOttUt99P3DKMLXoKjH7/7+YRTha1DU43d//zB0DcIowtegqMZfEOLOiTGmKfCUtfa+vDifxiBfuQygnHFM6J5PauL6mMKnybGbt2K+lE2++g/Brahfg6IeP+gaFPX4QdcAdA2KavwFIe6cVAaey6uT6Q6yiIiIiIiD7iCLiIiIiDgoQRYRERERcVCCLCIiIiLioARZRERERMRBCbKIyFXIGPOZMWatMWazMWaAu6y/MWabMWa5MeZdY8w4d3kVY8xsY8yP7lcr3/ZeRMS3NIuFiMhVyBhT0Vr7mzGmJK6VpzoAq3Atg3wcWAZssNY+Zoz5CHjbWrvSGFMT12T94T7rvIiIjwX4ugMiIpIv/m6M6e7ergHch2sp498AjDGzcC3tDHALEGGMyWxb1hhTxlp73JsdFhEpKJQgi4hcZYwxbXElvS2ttSeMMctxLdea011hP3fdk17poIhIAacxyCIiV59ywGF3ctwIaAEEAW2MMRWMMQHAHY76i4HHMneMMTHe7KyISEGjBFlE5OqzCAgwxiQBLwOrgb3Av4AfgK+ALcBRd/2/A3HGmCRjzBbgYe93WUSk4NBDeiIiRYQxprS19nf3HeS5wAfW2rm+7peISEGjO8giIkXHC8aYRGATsBP4zKe9EREpoHQHWURERETEQXeQRUREREQclCCLiIiIiDgoQRYRERERcVCCLCIiIiLioARZRERERMTh/wO52mAe6xxkzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('age', 'readmitted')\n",
    "# Elder people more likely get admitted to hospital due to HbA1c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d599b415b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('weight', 'readmitted')\n",
    "plt.legend(loc = 1)\n",
    "# Most of the data is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('time_in_hospital','readmitted')\n",
    "# Comparatively more number of people are admitted for a fewer days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('max_glu_serum', 'readmitted')\n",
    "# For most of the patients, the glucose serum test is not conducted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('A1Cresult','readmitted')\n",
    "# For most of  the patients, A1c test result was not measured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('diabetesMed','readmitted')\n",
    "# Diabetic meds are prescribed for most of the patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('change', 'readmitted')\n",
    "# There is no high variation in the readmission data whether the medication is changed or not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing those columns which have high NAs and also removing ID columns they won't be much useful in model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing columns which have high NAs\n",
    "data.drop(['weight', 'payer_code', 'medical_specialty'], axis = 1, inplace=True)\n",
    "\n",
    "# Removing all ID columns\n",
    "data.drop(['encounter_id','patient_nbr', 'admission_type_id', 'discharge_disposition_id', 'admission_source_id'], axis = 1,  inplace=True)\n",
    "\n",
    "# In gender there are three datapoints which have invalid gender, it's a very minimal data so remove them\n",
    "data = data[data['gender'] != 'Unknown/Invalid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(sum(data.isna().sum())/data.shape[0] *100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 42 artists>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "missing_data = pd.DataFrame({'column':data.columns ,'na_percent':data.isna().mean() *100})\n",
    "missing_data.sort_values('na_percent',inplace=True)\n",
    "plt.barh(y = missing_data['column'],width=missing_data['na_percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98052, 42)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 98052 entries, 1 to 101765\n",
      "Data columns (total 42 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   race                      98052 non-null  object\n",
      " 1   gender                    98052 non-null  object\n",
      " 2   age                       98052 non-null  object\n",
      " 3   time_in_hospital          98052 non-null  int64 \n",
      " 4   num_lab_procedures        98052 non-null  int64 \n",
      " 5   num_procedures            98052 non-null  int64 \n",
      " 6   num_medications           98052 non-null  int64 \n",
      " 7   number_outpatient         98052 non-null  int64 \n",
      " 8   number_emergency          98052 non-null  int64 \n",
      " 9   number_inpatient          98052 non-null  int64 \n",
      " 10  diag_1                    98052 non-null  object\n",
      " 11  diag_2                    98052 non-null  object\n",
      " 12  diag_3                    98052 non-null  object\n",
      " 13  number_diagnoses          98052 non-null  int64 \n",
      " 14  max_glu_serum             98052 non-null  object\n",
      " 15  A1Cresult                 98052 non-null  object\n",
      " 16  metformin                 98052 non-null  object\n",
      " 17  repaglinide               98052 non-null  object\n",
      " 18  nateglinide               98052 non-null  object\n",
      " 19  chlorpropamide            98052 non-null  object\n",
      " 20  glimepiride               98052 non-null  object\n",
      " 21  acetohexamide             98052 non-null  object\n",
      " 22  glipizide                 98052 non-null  object\n",
      " 23  glyburide                 98052 non-null  object\n",
      " 24  tolbutamide               98052 non-null  object\n",
      " 25  pioglitazone              98052 non-null  object\n",
      " 26  rosiglitazone             98052 non-null  object\n",
      " 27  acarbose                  98052 non-null  object\n",
      " 28  miglitol                  98052 non-null  object\n",
      " 29  troglitazone              98052 non-null  object\n",
      " 30  tolazamide                98052 non-null  object\n",
      " 31  examide                   98052 non-null  object\n",
      " 32  citoglipton               98052 non-null  object\n",
      " 33  insulin                   98052 non-null  object\n",
      " 34  glyburide-metformin       98052 non-null  object\n",
      " 35  glipizide-metformin       98052 non-null  object\n",
      " 36  glimepiride-pioglitazone  98052 non-null  object\n",
      " 37  metformin-rosiglitazone   98052 non-null  object\n",
      " 38  metformin-pioglitazone    98052 non-null  object\n",
      " 39  change                    98052 non-null  object\n",
      " 40  diabetesMed               98052 non-null  object\n",
      " 41  readmitted                98052 non-null  object\n",
      "dtypes: int64(8), object(34)\n",
      "memory usage: 32.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Few of the variables in diag columns has E and V at the start\n",
    "diag_cols = ['diag_1','diag_2','diag_3']\n",
    "for col in diag_cols:\n",
    "    data[col] = data[col].str.replace('E','-')\n",
    "    data[col] = data[col].str.replace('V','-')\n",
    "    \n",
    "data[diag_cols] = data[diag_cols].astype(float)\n",
    "\n",
    "\n",
    "# diagnosis grouping\n",
    "for col in diag_cols:\n",
    "    data['temp']=np.nan\n",
    "    \n",
    "    condition = ((data[col] >=250) & (data[col] < 251))\n",
    "    data.loc[condition,'temp']='Diabetes'\n",
    "    \n",
    "    condition = (((data[col]>=390) & (data[col]<=458)) | (data[col]==785))\n",
    "    data.loc[condition,'temp']='Circulatory'\n",
    "    \n",
    "    condition = (((data[col]>=460) & (data[col]<=519)) | (data[col]==786))\n",
    "    data.loc[condition,'temp']='Respiratory'\n",
    "    \n",
    "    condition = (((data[col]>=520) & (data[col]<=579)) | (data[col]==787))\n",
    "    data.loc[condition,'temp']='Digestive'\n",
    "    \n",
    "    condition = (((data[col]>=580) & (data[col]<=629)) | (data[col]==788))\n",
    "    data.loc[condition,'temp']='Genitourinary'\n",
    "    \n",
    "    condition = ((data[col]>=800) & (data[col]<=999))\n",
    "    data.loc[condition,'temp']='Injury'\n",
    "    \n",
    "    condition = ((data[col]>=710) & (data[col]<=739))\n",
    "    data.loc[condition,'temp']='Muscoloskeletal'\n",
    "    \n",
    "    condition = ((data[col]>=140) & (data[col]<=239))\n",
    "    data.loc[condition,'temp']='Neoplasms'\n",
    "    \n",
    " \n",
    "    data['temp']=data['temp'].fillna('Others')\n",
    "    \n",
    "    data[col]=data['temp']\n",
    "data.drop('temp',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_dict = {'[0-10)' : 1,\n",
    "            '[10-20)' : 1,\n",
    "            '[20-30)' : 2,\n",
    "            '[30-40)' : 2,\n",
    "            '[40-50)' : 3,\n",
    "            '[50-60)' : 3,\n",
    "            '[60-70)' : 4,\n",
    "            '[70-80)' : 4,\n",
    "            '[80-90)' : 5,\n",
    "            '[90-100)': 5}\n",
    "\n",
    "data['age'] = data['age'].replace(age_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------\n",
      "\n",
      "\n",
      "metformin\n",
      "No        78807\n",
      "Steady    17677\n",
      "Up         1017\n",
      "Down        551\n",
      "Name: metformin, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "repaglinide\n",
      "No        96529\n",
      "Steady     1371\n",
      "Up          107\n",
      "Down         45\n",
      "Name: repaglinide, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "nateglinide\n",
      "No        97361\n",
      "Steady      657\n",
      "Up           23\n",
      "Down         11\n",
      "Name: nateglinide, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "chlorpropamide\n",
      "No        97969\n",
      "Steady       76\n",
      "Up            6\n",
      "Down          1\n",
      "Name: chlorpropamide, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "glimepiride\n",
      "No        93065\n",
      "Steady     4488\n",
      "Up          315\n",
      "Down        184\n",
      "Name: glimepiride, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "acetohexamide\n",
      "No        98051\n",
      "Steady        1\n",
      "Name: acetohexamide, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "glipizide\n",
      "No        85769\n",
      "Steady    10990\n",
      "Up          752\n",
      "Down        541\n",
      "Name: glipizide, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "glyburide\n",
      "No        87791\n",
      "Steady     8932\n",
      "Up          791\n",
      "Down        538\n",
      "Name: glyburide, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "tolbutamide\n",
      "No        98030\n",
      "Steady       22\n",
      "Name: tolbutamide, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "pioglitazone\n",
      "No        90955\n",
      "Steady     6755\n",
      "Up          227\n",
      "Down        115\n",
      "Name: pioglitazone, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "rosiglitazone\n",
      "No        91886\n",
      "Steady     5908\n",
      "Up          174\n",
      "Down         84\n",
      "Name: rosiglitazone, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "acarbose\n",
      "No        97753\n",
      "Steady      286\n",
      "Up           10\n",
      "Down          3\n",
      "Name: acarbose, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "miglitol\n",
      "No        98015\n",
      "Steady       31\n",
      "Down          4\n",
      "Up            2\n",
      "Name: miglitol, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "troglitazone\n",
      "No        98049\n",
      "Steady        3\n",
      "Name: troglitazone, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "tolazamide\n",
      "No        98015\n",
      "Steady       36\n",
      "Up            1\n",
      "Name: tolazamide, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "examide\n",
      "No    98052\n",
      "Name: examide, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "citoglipton\n",
      "No    98052\n",
      "Name: citoglipton, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "insulin\n",
      "No        45942\n",
      "Steady    29368\n",
      "Down      11843\n",
      "Up        10899\n",
      "Name: insulin, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "glyburide-metformin\n",
      "No        97383\n",
      "Steady      660\n",
      "Up            6\n",
      "Down          3\n",
      "Name: glyburide-metformin, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "glipizide-metformin\n",
      "No        98039\n",
      "Steady       13\n",
      "Name: glipizide-metformin, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "glimepiride-pioglitazone\n",
      "No        98051\n",
      "Steady        1\n",
      "Name: glimepiride-pioglitazone, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "metformin-rosiglitazone\n",
      "No    98052\n",
      "Name: metformin-rosiglitazone, dtype: int64\n",
      "------------------------\n",
      "\n",
      "\n",
      "metformin-pioglitazone\n",
      "No        98051\n",
      "Steady        1\n",
      "Name: metformin-pioglitazone, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "drug_cols = ['metformin',\n",
    "       'repaglinide', 'nateglinide', 'chlorpropamide', 'glimepiride',\n",
    "       'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide',\n",
    "       'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone',\n",
    "       'tolazamide', 'examide', 'citoglipton', 'insulin',\n",
    "       'glyburide-metformin', 'glipizide-metformin',\n",
    "       'glimepiride-pioglitazone', 'metformin-rosiglitazone',\n",
    "       'metformin-pioglitazone']\n",
    "\n",
    "for x in drug_cols:\n",
    "    print('------------------------')\n",
    "    print('\\n')\n",
    "    print(x)\n",
    "    print(data[x].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing those which has almost zero variance\n",
    "data.drop(['acetohexamide','tolbutamide','miglitol','troglitazone','tolazamide','examide','citoglipton', 'glipizide-metformin',\n",
    "          'glimepiride-pioglitazone','metformin-rosiglitazone','metformin-pioglitazone'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race                   object\n",
       "gender                 object\n",
       "age                     int64\n",
       "time_in_hospital        int64\n",
       "num_lab_procedures      int64\n",
       "num_procedures          int64\n",
       "num_medications         int64\n",
       "number_outpatient       int64\n",
       "number_emergency        int64\n",
       "number_inpatient        int64\n",
       "diag_1                 object\n",
       "diag_2                 object\n",
       "diag_3                 object\n",
       "number_diagnoses        int64\n",
       "max_glu_serum          object\n",
       "A1Cresult              object\n",
       "metformin              object\n",
       "repaglinide            object\n",
       "nateglinide            object\n",
       "chlorpropamide         object\n",
       "glimepiride            object\n",
       "glipizide              object\n",
       "glyburide              object\n",
       "pioglitazone           object\n",
       "rosiglitazone          object\n",
       "acarbose               object\n",
       "insulin                object\n",
       "glyburide-metformin    object\n",
       "change                 object\n",
       "diabetesMed            object\n",
       "readmitted             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = ['race', 'gender', 'age', 'diag_1', 'diag_2', 'diag_3','max_glu_serum', 'A1Cresult', 'metformin',\n",
    "       'repaglinide', 'nateglinide', 'chlorpropamide', 'glimepiride',\n",
    "       'glipizide', 'glyburide', 'pioglitazone', 'rosiglitazone', 'acarbose',\n",
    "       'insulin', 'glyburide-metformin', 'change', 'diabetesMed',\n",
    "       'readmitted']\n",
    "\n",
    "data[cat_cols] = data[cat_cols].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race                   category\n",
       "gender                 category\n",
       "age                    category\n",
       "time_in_hospital          int64\n",
       "num_lab_procedures        int64\n",
       "num_procedures            int64\n",
       "num_medications           int64\n",
       "number_outpatient         int64\n",
       "number_emergency          int64\n",
       "number_inpatient          int64\n",
       "diag_1                 category\n",
       "diag_2                 category\n",
       "diag_3                 category\n",
       "number_diagnoses          int64\n",
       "max_glu_serum          category\n",
       "A1Cresult              category\n",
       "metformin              category\n",
       "repaglinide            category\n",
       "nateglinide            category\n",
       "chlorpropamide         category\n",
       "glimepiride            category\n",
       "glipizide              category\n",
       "glyburide              category\n",
       "pioglitazone           category\n",
       "rosiglitazone          category\n",
       "acarbose               category\n",
       "insulin                category\n",
       "glyburide-metformin    category\n",
       "change                 category\n",
       "diabetesMed            category\n",
       "readmitted             category\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['race', 'gender', 'age', 'diag_1', 'diag_2', 'diag_3', 'max_glu_serum',\n",
       "       'A1Cresult', 'metformin', 'repaglinide', 'nateglinide',\n",
       "       'chlorpropamide', 'glimepiride', 'glipizide', 'glyburide',\n",
       "       'pioglitazone', 'rosiglitazone', 'acarbose', 'insulin',\n",
       "       'glyburide-metformin', 'change', 'diabetesMed', 'readmitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = data.select_dtypes('category').columns\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the dependency of the target on the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_chi2_independence(cat_col, target, df, alpha=0.05):\n",
    "    data = df[[target, cat_col]]\n",
    "    tab = sm.stats.Table.from_data(data)\n",
    "    tab = tab.table_orig.to_numpy()\n",
    "    print(f\"---------------{target} Vs {cat_col} Chi Square Test of Independence -------------------\")\n",
    "    print(f\"\\n Contingency table :\\n\")\n",
    "    print(tab)\n",
    "    \n",
    "    stat, p, dof, expected = chi2_contingency(tab)\n",
    "    print(f\"\\n Expected table :\\n\")\n",
    "    print(expected)\n",
    "    \n",
    "    print(f\"The p value returned = {p} and degrees of freedom returned = {dof}\")\n",
    "    \n",
    "    # interpret p-value\n",
    "    print('significance(alpha) = %.3f' % (alpha))\n",
    "\n",
    "    if p <= alpha:\n",
    "        print('Dependent (reject H0)')\n",
    "    else:\n",
    "        print('Independent (fail to reject H0)') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------readmitted Vs race Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[ 2140    65  8512   207   142]\n",
      " [ 6543   159 26874   633   440]\n",
      " [10198   401 39693  1144   901]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[ 2130.88102231    70.53655203  8473.30206421   223.91123078\n",
      "    167.36913067]\n",
      " [ 6672.04920858   220.85857504 26530.9455289    701.09346061\n",
      "    524.05322686]\n",
      " [10078.0697691    333.60487292 40074.75240689  1058.99530861\n",
      "    791.57764248]]\n",
      "The p value returned = 3.2699985670374004e-16 and degrees of freedom returned = 8\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs gender Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[ 6002  5064]\n",
      " [19060 15589]\n",
      " [27771 24566]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[ 5962.65224575  5103.34775425]\n",
      " [18669.79375229 15979.20624771]\n",
      " [28200.55400196 24136.44599804]]\n",
      "The p value returned = 9.05403281880493e-08 and degrees of freedom returned = 2\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs age Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[   32   621  2638  5464  2311]\n",
      " [  177  1602  8993 16998  6879]\n",
      " [  322  2803 14331 24652 10229]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[   59.92785461   567.22673683  2930.03194223  5317.21458002\n",
      "   2191.59888631]\n",
      " [  187.64144536  1776.05631706  9174.28852038 16648.84944723\n",
      "   6862.16426998]\n",
      " [  283.43070004  2682.71694611 13857.67953739 25147.93597275\n",
      "  10365.23684372]]\n",
      "The p value returned = 3.437136903946944e-23 and degrees of freedom returned = 8\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs diag_1 Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[ 3415  1075   993   547   830   459   339  2033  1375]\n",
      " [10612  3126  3292  1702  2192  1428   780  6130  5387]\n",
      " [15509  3764  4902  2734  3681  2852  2193  9471  7231]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[ 3333.38816138   898.91781912  1036.83088565   562.37382205\n",
      "    756.49041325   534.83635214   373.78729654  1990.1464937\n",
      "   1579.22875617]\n",
      " [10437.2461959   2814.62168033  3246.44436625  1760.86124709\n",
      "   2368.66404561  1674.6380594   1170.37376086  6231.39217966\n",
      "   4944.7584649 ]\n",
      " [15765.36564272  4251.46050055  4903.72474809  2659.76493085\n",
      "   3577.84554114  2529.52558846  1767.8389426   9412.46132664\n",
      "   7469.01277893]]\n",
      "The p value returned = 7.50612484653584e-110 and degrees of freedom returned = 16\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs diag_2 Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[ 3475  1300   464   966   275   174   375  2914  1123]\n",
      " [11398  3969  1369  2988   656   585   715  8983  3986]\n",
      " [16179  6593  2216  4249  1397   951  1403 13821  5528]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[ 3504.481622    1338.72732836   456.96399869   925.77813813\n",
      "    262.73454901   192.98800636   281.35619875  2902.49447232\n",
      "   1200.47568637]\n",
      " [10972.96075552  4191.71906743  1430.81019255  2898.72462571\n",
      "    822.65402032   604.26906131   880.96068413  9088.06533268\n",
      "   3758.83626035]\n",
      " [16574.55762249  6331.55360421  2161.22580875  4378.49723616\n",
      "   1242.61143067   912.74293232  1330.68311712 13727.440195\n",
      "   5677.68805328]]\n",
      "The p value returned = 4.846645876066012e-36 and degrees of freedom returned = 16\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs diag_3 Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[ 3206  1772   464   886   221   202   260  3183   872]\n",
      " [10771  5735  1399  2453   565   632   507  9913  2674]\n",
      " [15553  9188  1989  3188  1104  1028  1053 15581  3653]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[ 3332.71101048  1884.17237792   434.73087749   736.6273202\n",
      "    213.30253335   210.14249582   205.40243952  3236.44272427\n",
      "    812.46822094]\n",
      " [10435.12595358  5899.57425652  1361.1955697   2306.47027088\n",
      "    667.87633093   657.98186676   643.14017052 10133.69817036\n",
      "   2543.93741076]\n",
      " [15762.16303594  8911.25336556  2056.07355281  3483.90240893\n",
      "   1008.82113572   993.87563742   971.45738996 15306.85910537\n",
      "   3842.59436829]]\n",
      "The p value returned = 2.8872711924112223e-37 and degrees of freedom returned = 16\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs max_glu_serum Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[  184   179 10413   290]\n",
      " [  519   509 32755   866]\n",
      " [  746   539 49676  1376]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[  163.53194223   138.47735895 10478.23301921   285.7576796 ]\n",
      " [  512.03852038   433.58955452 32808.62966589   894.74225921]\n",
      " [  773.42953739   654.93308653 49557.13731489  1351.50006119]]\n",
      "The p value returned = 2.5379025056434998e-09 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs A1Cresult Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[  371   785  9436   474]\n",
      " [ 1279  2786 29018  1566]\n",
      " [ 2058  4060 43405  2814]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[  418.47925591   861.22308571  9238.48258067   547.81507771]\n",
      " [ 1310.30975401  2696.59485783 28926.81935096  1715.2760372 ]\n",
      " [ 1979.21099009  4073.18205646 43693.69806837  2590.90888508]]\n",
      "The p value returned = 1.2752611531707045e-12 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs metformin Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[   67  9181  1733    85]\n",
      " [  185 28108  6017   339]\n",
      " [  299 41518  9927   593]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[   62.18502427  8894.03848978  1994.99940848   114.77707747]\n",
      " [  194.70891976 27848.32275731  6246.58724962   359.38107331]\n",
      " [  294.10605597 42064.63875291  9435.4133419    542.84184922]]\n",
      "The p value returned = 1.1108821311257098e-19 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs repaglinide Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[    3 10862   181    20]\n",
      " [   19 34012   585    33]\n",
      " [   23 51655   605    54]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[5.07863175e+00 1.08941165e+04 1.54728981e+02 1.20758577e+01]\n",
      " [1.59018174e+01 3.41108118e+04 4.84475370e+02 3.78109880e+01]\n",
      " [2.40195509e+01 5.15240716e+04 7.31795649e+02 5.71131542e+01]]\n",
      "The p value returned = 3.7065065714243887e-10 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs nateglinide Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[    1 10986    78     1]\n",
      " [    4 34389   246    10]\n",
      " [    6 51986   333    12]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[1.24144332e+00 1.09880148e+04 7.41480235e+01 2.59574511e+00]\n",
      " [3.88711092e+00 3.44048188e+04 2.32166534e+02 8.12759556e+00]\n",
      " [5.87144576e+00 5.19681665e+04 3.50685442e+02 1.22766593e+01]]\n",
      "The p value returned = 0.7570043873321077 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Independent (fail to reject H0)\n",
      "---------------readmitted Vs chlorpropamide Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[    0 11061     5     0]\n",
      " [    0 34616    28     5]\n",
      " [    1 52292    43     1]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[1.12858483e-01 1.10566327e+04 8.57724473e+00 6.77150900e-01]\n",
      " [3.53373720e-01 3.46196700e+04 2.68564027e+01 2.12024232e+00]\n",
      " [5.33767797e-01 5.22926973e+04 4.05663525e+01 3.20260678e+00]]\n",
      "The p value returned = 0.1932775968054067 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Independent (fail to reject H0)\n",
      "---------------readmitted Vs glimepiride Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[   24 10556   451    35]\n",
      " [   65 32787  1696   101]\n",
      " [   95 49722  2341   179]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[2.07659609e+01 1.05031747e+04 5.06508873e+02 3.55504222e+01]\n",
      " [6.50207645e+01 3.28867253e+04 1.58594126e+03 1.11312722e+02]\n",
      " [9.82132746e+01 4.96751000e+04 2.39554987e+03 1.68136856e+02]]\n",
      "The p value returned = 0.0066178676049912216 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs glipizide Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[   84  9643  1240    99]\n",
      " [  205 30035  4131   278]\n",
      " [  252 46091  5619   375]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[   61.05643944  9679.75925019  1240.31473096    84.86957941]\n",
      " [  191.17518256 30308.51059642  3883.57718354   265.73703749]\n",
      " [  288.768378   45780.73015339  5866.10808551   401.3933831 ]]\n",
      "The p value returned = 5.060355898111835e-09 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs glyburide Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[   51  9964   966    85]\n",
      " [  214 31022  3142   271]\n",
      " [  273 46805  4824   435]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[   60.71786399  9907.95910333  1008.05197242    89.27106025]\n",
      " [  190.1150614  31023.03225839  3156.33406764   279.51861257]\n",
      " [  287.16707461 46860.00863827  4767.61395994   422.21032717]]\n",
      "The p value returned = 0.1750343160047786 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Independent (fail to reject H0)\n",
      "---------------readmitted Vs pioglitazone Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[   17 10314   707    28]\n",
      " [   44 31941  2574    90]\n",
      " [   54 48700  3474   109]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[1.29787256e+01 1.02650433e+04 7.62359054e+02 2.56188757e+01]\n",
      " [4.06379778e+01 3.21411067e+04 2.38703948e+03 8.02158345e+01]\n",
      " [6.13832966e+01 4.85488499e+04 3.60560147e+03 1.21165290e+02]]\n",
      "The p value returned = 3.206490536640204e-05 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs rosiglitazone Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[    5 10420   623    18]\n",
      " [   22 32284  2291    52]\n",
      " [   57 49182  2994   104]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[9.48011259e+00 1.03701146e+04 6.66767919e+02 1.96373761e+01]\n",
      " [2.96833925e+01 3.24700976e+04 2.08773194e+03 6.14870273e+01]\n",
      " [4.48364949e+01 4.90457878e+04 3.15350014e+03 9.28755966e+01]]\n",
      "The p value returned = 1.3044129760702972e-07 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------readmitted Vs acarbose Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[    1 11039    24     2]\n",
      " [    1 34497   147     4]\n",
      " [    1 52217   115     4]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[3.38575450e-01 1.10322553e+04 3.22775262e+01 1.12858483e+00]\n",
      " [1.06012116e+00 3.45433413e+04 1.01064884e+02 3.53373720e+00]\n",
      " [1.60130339e+00 5.21774034e+04 1.52657590e+02 5.33767797e+00]]\n",
      "The p value returned = 4.353097168154253e-06 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs insulin Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[ 1661  4658  3313  1434]\n",
      " [ 4632 15640 10155  4222]\n",
      " [ 5550 25644 15900  5243]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[ 1336.58301717  5184.94443765  3314.4279362   1230.04460898]\n",
      " [ 4185.00496675 16234.69544731 10377.87941092  3851.42017501]\n",
      " [ 6321.41201607 24522.36011504 15675.69265288  5817.53521601]]\n",
      "The p value returned = 8.213406350204559e-101 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs glyburide-metformin Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[    0 10995    71     0]\n",
      " [    0 34396   252     1]\n",
      " [    3 51992   337     5]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[3.38575450e-01 1.09904977e+04 7.44865989e+01 6.77150900e-01]\n",
      " [1.06012116e+00 3.44125930e+04 2.33226655e+02 2.12024232e+00]\n",
      " [1.60130339e+00 5.19799093e+04 3.52286746e+02 3.20260678e+00]]\n",
      "The p value returned = 0.29847245132829386 and degrees of freedom returned = 6\n",
      "significance(alpha) = 0.050\n",
      "Independent (fail to reject H0)\n",
      "---------------readmitted Vs change Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[ 5412  5654]\n",
      " [16798 17851]\n",
      " [23068 29269]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[ 5110.00640476  5955.99359524]\n",
      " [16000.05529719 18648.94470281]\n",
      " [24167.93829805 28169.06170195]]\n",
      "The p value returned = 3.525759205869931e-44 and degrees of freedom returned = 2\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs diabetesMed Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[ 2208  8858]\n",
      " [ 7092 27557]\n",
      " [13402 38935]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[ 2562.11328683  8503.88671317]\n",
      " [ 8022.29019296 26626.70980704]\n",
      " [12117.59652021 40219.40347979]]\n",
      "The p value returned = 1.6497385027265532e-83 and degrees of freedom returned = 2\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n",
      "---------------readmitted Vs readmitted Chi Square Test of Independence -------------------\n",
      "\n",
      " Contingency table :\n",
      "\n",
      "[[11066     0     0]\n",
      " [    0 34649     0]\n",
      " [    0     0 52337]]\n",
      "\n",
      " Expected table :\n",
      "\n",
      "[[ 1248.89197569  3910.43358626  5906.67443805]\n",
      " [ 3910.43358626 12244.0460266  18494.52038714]\n",
      " [ 5906.67443805 18494.52038714 27935.80517481]]\n",
      "The p value returned = 0.0 and degrees of freedom returned = 4\n",
      "significance(alpha) = 0.050\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "for col in cat_cols:\n",
    "    find_chi2_independence(col, 'readmitted', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['nateglinide', 'chlorpropamide', 'glyburide', 'glyburide-metformin'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98052, 27)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['race', 'gender', 'age', 'time_in_hospital', 'num_lab_procedures',\n",
       "       'num_procedures', 'num_medications', 'number_outpatient',\n",
       "       'number_emergency', 'number_inpatient', 'diag_1', 'diag_2', 'diag_3',\n",
       "       'number_diagnoses', 'max_glu_serum', 'A1Cresult', 'metformin',\n",
       "       'repaglinide', 'glimepiride', 'glipizide', 'pioglitazone',\n",
       "       'rosiglitazone', 'acarbose', 'insulin', 'change', 'diabetesMed',\n",
       "       'readmitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.discrete.discrete_model as lgm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "readmit_dict = {'NO' : 0,\n",
    "                '>30' : 0,\n",
    "                '<30' : 1}\n",
    "\n",
    "data['readmitted'] = data['readmitted'].replace(readmit_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['readmitted'] = data['readmitted'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['time_in_hospital', 'num_lab_procedures', 'num_procedures', 'num_medications', 'number_outpatient', 'number_emergency', 'number_inpatient', 'number_diagnoses', 'readmitted']\n"
     ]
    }
   ],
   "source": [
    "num_cols = list(data.select_dtypes('int64').columns)\n",
    "print(num_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This shows that all the numerical variables are individually significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.341176</td>\n",
       "      <td>time_in_hospital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.024442</td>\n",
       "      <td>num_lab_procedures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.928965</td>\n",
       "      <td>num_procedures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.424207</td>\n",
       "      <td>num_medications</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.114402</td>\n",
       "      <td>number_outpatient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.134192</td>\n",
       "      <td>number_emergency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.421691</td>\n",
       "      <td>number_inpatient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.684591</td>\n",
       "      <td>number_diagnoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.159935</td>\n",
       "      <td>readmitted</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VIF Factor            features\n",
       "0    4.341176    time_in_hospital\n",
       "1    6.024442  num_lab_procedures\n",
       "2    1.928965      num_procedures\n",
       "3    7.424207     num_medications\n",
       "4    1.114402   number_outpatient\n",
       "5    1.134192    number_emergency\n",
       "6    1.421691    number_inpatient\n",
       "7    7.684591    number_diagnoses\n",
       "8    1.159935          readmitted"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(data[num_cols].values, i) for i in range(data[num_cols].shape[1])]\n",
    "vif[\"features\"] = data[num_cols].columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_glu_dict = {'None' : 0,\n",
    "               'Norm' : 1,\n",
    "               '>200' : -1,\n",
    "                '>300' :-2\n",
    "               }\n",
    "data['max_glu_serum'] = data['max_glu_serum'].replace(max_glu_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "A1C_dict = {'None' : 0,\n",
    "            'Norm' : 1,\n",
    "            '>7' : -1,\n",
    "            '>8' : -2\n",
    "           }\n",
    "data['A1Cresult'] = data['A1Cresult'].replace(A1C_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_new_cols = ['metformin',\n",
    "       'repaglinide', 'glimepiride',\n",
    "       'glipizide',  'pioglitazone', 'rosiglitazone', 'acarbose',\n",
    "       'insulin']\n",
    "\n",
    "drug_dict = { 'No': 0,\n",
    "             'Steady' : 1,\n",
    "             'Down' : -1,\n",
    "             'Up' : 2}\n",
    "\n",
    "data[drug_new_cols] = data[drug_new_cols].replace(drug_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "change_dict = {'No' : 0,\n",
    "               'Ch' : 1}\n",
    "\n",
    "data['change'] = data['change'].replace(change_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabet_dict = {'No' : 0,\n",
    "               'Yes' : 1\n",
    "              }\n",
    "\n",
    "data['diabetesMed'] = data['diabetesMed'].replace(diabet_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Caucasian', 'AfricanAmerican', 'Other', 'Asian', 'Hispanic']\n",
       "Categories (5, object): ['Caucasian', 'AfricanAmerican', 'Other', 'Asian', 'Hispanic']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['race'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_dict = { 'Caucasian': 0,\n",
    "                'AfricanAmerican' : 1,\n",
    "                'Asian' : 2,\n",
    "                'Hispanic' : 3,\n",
    "                 'Other' : 4\n",
    "            }\n",
    "\n",
    "data['race'] = data['race'].replace(race_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_dict = { 'Female' : 0,\n",
    "               'Male': 1}\n",
    "\n",
    "data['gender'] = data['gender'].replace(gender_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Circulatory        29536\n",
       "Others             17634\n",
       "Respiratory        13993\n",
       "Digestive           9187\n",
       "Diabetes            7965\n",
       "Injury              6703\n",
       "Genitourinary       4983\n",
       "Muscoloskeletal     4739\n",
       "Neoplasms           3312\n",
       "Name: diag_1, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.diag_1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "diag_dict = {'Circulatory' :0,\n",
    "             'Respiratory' : 1,\n",
    "             'Digestive' : 2,\n",
    "             'Diabetes' : 3,\n",
    "             'Injury' : 4,\n",
    "             'Genitourinary' : 5,\n",
    "             'Muscoloskeletal' : 6,\n",
    "             'Neoplasms' : 7,\n",
    "             'Others' : 8}\n",
    "\n",
    "data[['diag_1', 'diag_2', 'diag_3']] = data[['diag_1', 'diag_2', 'diag_3']].replace(diag_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   race  gender age  time_in_hospital  num_lab_procedures  num_procedures  \\\n",
       "1     0       0   1                 3                  59               0   \n",
       "2     1       0   2                 2                  11               5   \n",
       "3     0       1   2                 2                  44               1   \n",
       "4     0       1   3                 1                  51               0   \n",
       "5     0       1   3                 3                  31               6   \n",
       "\n",
       "   num_medications  number_outpatient  number_emergency  number_inpatient  \\\n",
       "1               18                  0                 0                 0   \n",
       "2               13                  2                 0                 1   \n",
       "3               16                  0                 0                 0   \n",
       "4                8                  0                 0                 0   \n",
       "5               16                  0                 0                 0   \n",
       "\n",
       "   diag_1  diag_2  diag_3  number_diagnoses  max_glu_serum  A1Cresult  \\\n",
       "1       8       3       8                 9              0          0   \n",
       "2       8       3       8                 6              0          0   \n",
       "3       8       3       0                 7              0          0   \n",
       "4       7       7       3                 5              0          0   \n",
       "5       0       0       3                 9              0          0   \n",
       "\n",
       "   metformin  repaglinide  glimepiride  glipizide  pioglitazone  \\\n",
       "1          0            0            0          0             0   \n",
       "2          0            0            0          1             0   \n",
       "3          0            0            0          0             0   \n",
       "4          0            0            0          1             0   \n",
       "5          0            0            0          0             0   \n",
       "\n",
       "   rosiglitazone  acarbose  insulin  change  diabetesMed  readmitted  \n",
       "1              0         0        2       1            1           0  \n",
       "2              0         0        0       0            1           0  \n",
       "3              0         0        2       1            1           0  \n",
       "4              0         0        1       1            1           0  \n",
       "5              0         0        1       0            1           0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_columns',None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = ['race', 'gender', 'age', 'diag_1', 'diag_2', 'diag_3','max_glu_serum', 'A1Cresult', 'metformin',\n",
    "       'repaglinide', 'glimepiride',\n",
    "       'glipizide', 'pioglitazone', 'rosiglitazone', 'acarbose',\n",
    "       'insulin', 'change', 'diabetesMed',\n",
    "       'readmitted']\n",
    "\n",
    "data[cat_cols] = data[cat_cols].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race                  category\n",
       "gender                category\n",
       "age                   category\n",
       "time_in_hospital         int64\n",
       "num_lab_procedures       int64\n",
       "num_procedures           int64\n",
       "num_medications          int64\n",
       "number_outpatient        int64\n",
       "number_emergency         int64\n",
       "number_inpatient         int64\n",
       "diag_1                category\n",
       "diag_2                category\n",
       "diag_3                category\n",
       "number_diagnoses         int64\n",
       "max_glu_serum         category\n",
       "A1Cresult             category\n",
       "metformin             category\n",
       "repaglinide           category\n",
       "glimepiride           category\n",
       "glipizide             category\n",
       "pioglitazone          category\n",
       "rosiglitazone         category\n",
       "acarbose              category\n",
       "insulin               category\n",
       "change                category\n",
       "diabetesMed           category\n",
       "readmitted            category\n",
       "dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "  race gender age  time_in_hospital  num_lab_procedures  num_procedures  \\\n",
       "1    0      0   1                 3                  59               0   \n",
       "2    1      0   2                 2                  11               5   \n",
       "3    0      1   2                 2                  44               1   \n",
       "4    0      1   3                 1                  51               0   \n",
       "5    0      1   3                 3                  31               6   \n",
       "\n",
       "   num_medications  number_outpatient  number_emergency  number_inpatient  \\\n",
       "1               18                  0                 0                 0   \n",
       "2               13                  2                 0                 1   \n",
       "3               16                  0                 0                 0   \n",
       "4                8                  0                 0                 0   \n",
       "5               16                  0                 0                 0   \n",
       "\n",
       "  diag_1 diag_2 diag_3  number_diagnoses max_glu_serum A1Cresult metformin  \\\n",
       "1      8      3      8                 9             0         0         0   \n",
       "2      8      3      8                 6             0         0         0   \n",
       "3      8      3      0                 7             0         0         0   \n",
       "4      7      7      3                 5             0         0         0   \n",
       "5      0      0      3                 9             0         0         0   \n",
       "\n",
       "  repaglinide glimepiride glipizide pioglitazone rosiglitazone acarbose  \\\n",
       "1           0           0         0            0             0        0   \n",
       "2           0           0         1            0             0        0   \n",
       "3           0           0         0            0             0        0   \n",
       "4           0           0         1            0             0        0   \n",
       "5           0           0         0            0             0        0   \n",
       "\n",
       "  insulin change diabetesMed readmitted  \n",
       "1       2      1           1          0  \n",
       "2       0      0           1          0  \n",
       "3       2      1           1          0  \n",
       "4       1      1           1          0  \n",
       "5       1      0           1          0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def histo_plot(x,data):\n",
    "    data[x].hist(bins = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8U+ApYC+wtW22FbivLe8FtiQ5Ocm59N48fryqDgNvJbm4nV10Td8cSdJxspR94jHgW+0M0RXAf6mqP0ryfeCeJNcBLwBXAVTVgST3AE8DU8ANVfVeu6/rgTuBlfTeV3hgCbkkSYuw6EKoqj8FfnGW8f8DbDrKnJ3AzlnG9wEXLDaLJGnp/KSyJAmwECRJjYUgSQIsBElSYyFIkgALQZLUWAiSJMBCkCQ1FoIkCbAQJEmNhSBJAiwESVJjIUiSAAtBktRYCJIkwEKQJDUWgiQJsBAkSc1S/p/KkkbM2h33s339FNfuuP+4Pu7Bmy47ro+nY8M9BEkSYCFIkhoLQZIEWAiSpMZCkCQBFoIkqbEQJEmAhSBJaiwESRJgIUiSGgtBkgSMUCEk2Zzk2SSTSXYMO48knWhG4svtkpwE/Afgl4FDwPeT7K2qp4ebTNJCrF2GL9Nb7Jfy+cV6y2dU9hA2ApNV9adV9RfAHuDyIWeSpBNKqmrYGUhyJbC5qv5Zu/0Z4O9V1edmbLcN2NZu/gLw7IAPdTbw2hLjDkNXc0N3s3c1N3Q3e1dzQ7ey/82q+vnZVozEISMgs4x9oKmqahewa9EPkuyrqg2LnT8sXc0N3c3e1dzQ3exdzQ3dzt5vVA4ZHQLO6bu9Bnh5SFkk6YQ0KoXwfWBdknOT/FVgC7B3yJkk6YQyEoeMqmoqyeeA/wacBHylqg4cg4da9OGmIetqbuhu9q7mhu5m72pu6Hb2nxqJN5UlScM3KoeMJElDZiFIkoATpBC69LUYSc5J8p0kzyQ5kOTzbfzMJA8mea5dnzHsrLNJclKS/57kD9vtruT+cJJ7k/yw/ez/fheyJ/kX7XXyVJKvJflro5o7yVeSvJrkqb6xo2ZNcmP7nX02yaXDSX3U3P+6vVb+JMm3kny4b91I5F6Mn/lC6PtajF8BzgM+neS84aaa0xSwvao+BlwM3NDy7gAeqqp1wEPt9ij6PPBM3+2u5P53wB9V1d8GfpHecxjp7ElWA/8c2FBVF9A7IWMLo5v7TmDzjLFZs7bX/Bbg/Dbntva7PAx38sHcDwIXVNXfAf4HcCOMXO6B/cwXAh37WoyqOlxVP2jLb9H7h2k1vcy722a7gSuGEnAOSdYAlwG/3zfchdynAf8YuAOgqv6iqv6cDmSnd6bgyiQrgJ+j9/mdkcxdVd8FXp8xfLSslwN7quqdqnoemKT3u3zczZa7qv64qqbazUfpfXYKRij3YpwIhbAaeLHv9qE2NvKSrAU+DjwGjFXVYeiVBvCRIUY7mn8L/EvgJ31jXcj9t4A/A/5zO9z1+0lOYcSzV9VLwL8BXgAOA29W1R8z4rlnOFrWLv3efhZ4oC13KfcHnAiFsKCvxRg1SU4FvgF8oap+NOw880nyKeDVqnpi2FkWYQXwd4Hbq+rjwNuMzmGWo2rH2y8HzgX+BnBKkl8fbqpl04nf2yRfpHeY96vTQ7NsNnK5j+ZEKITOfS1Gkg/RK4OvVtU32/ArSVa19auAV4eV7yguAX41yUF6h+X+SZI/YPRzQ+81cqiqHmu376VXEKOe/ZeA56vqz6rqXeCbwD9g9HP3O1rWkf+9TbIV+BRwdf3lB7pGPvdcToRC6NTXYiQJvWPZz1TVl/tW7QW2tuWtwH3HO9tcqurGqlpTVWvp/YwfrqpfZ8RzA1TV/wZeTPILbWgT8DSjn/0F4OIkP9deN5vovec06rn7HS3rXmBLkpOTnAusAx4fQr5ZJdkM/Cbwq1X1f/tWjXTueVXVz/wF+CS9MwH+J/DFYeeZJ+s/pLeL+SfAk+3ySeAsemdhPNeuzxx21jmewzjwh225E7mBC4F97ef+X4EzupAd+FfAD4GngLuBk0c1N/A1eu91vEvvL+nr5soKfLH9zj4L/MqI5Z6k917B9O/ofxy13Iu5+NUVkiTgxDhkJElaAAtBkgRYCJKkxkKQJAEWgiSpsRAkSYCFIElq/j8sN30vZ/apagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histo_plot('num_lab_procedures',data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['race', 'gender', 'age', 'time_in_hospital', 'num_lab_procedures',\n",
       "       'num_procedures', 'num_medications', 'number_outpatient',\n",
       "       'number_emergency', 'number_inpatient', 'diag_1', 'diag_2', 'diag_3',\n",
       "       'number_diagnoses', 'max_glu_serum', 'A1Cresult', 'metformin',\n",
       "       'repaglinide', 'glimepiride', 'glipizide', 'pioglitazone',\n",
       "       'rosiglitazone', 'acarbose', 'insulin', 'change', 'diabetesMed',\n",
       "       'readmitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>number_diagnoses</th>\n",
       "      <th>max_glu_serum</th>\n",
       "      <th>A1Cresult</th>\n",
       "      <th>metformin</th>\n",
       "      <th>repaglinide</th>\n",
       "      <th>glimepiride</th>\n",
       "      <th>glipizide</th>\n",
       "      <th>pioglitazone</th>\n",
       "      <th>rosiglitazone</th>\n",
       "      <th>acarbose</th>\n",
       "      <th>insulin</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  race gender age  time_in_hospital  num_lab_procedures  num_procedures  \\\n",
       "1    0      0   1                 3                  59               0   \n",
       "2    1      0   2                 2                  11               5   \n",
       "3    0      1   2                 2                  44               1   \n",
       "4    0      1   3                 1                  51               0   \n",
       "5    0      1   3                 3                  31               6   \n",
       "\n",
       "   num_medications  number_outpatient  number_emergency  number_inpatient  \\\n",
       "1               18                  0                 0                 0   \n",
       "2               13                  2                 0                 1   \n",
       "3               16                  0                 0                 0   \n",
       "4                8                  0                 0                 0   \n",
       "5               16                  0                 0                 0   \n",
       "\n",
       "  diag_1 diag_2 diag_3  number_diagnoses max_glu_serum A1Cresult metformin  \\\n",
       "1      8      3      8                 9             0         0         0   \n",
       "2      8      3      8                 6             0         0         0   \n",
       "3      8      3      0                 7             0         0         0   \n",
       "4      7      7      3                 5             0         0         0   \n",
       "5      0      0      3                 9             0         0         0   \n",
       "\n",
       "  repaglinide glimepiride glipizide pioglitazone rosiglitazone acarbose  \\\n",
       "1           0           0         0            0             0        0   \n",
       "2           0           0         1            0             0        0   \n",
       "3           0           0         0            0             0        0   \n",
       "4           0           0         1            0             0        0   \n",
       "5           0           0         0            0             0        0   \n",
       "\n",
       "  insulin change diabetesMed readmitted  \n",
       "1       2      1           1          0  \n",
       "2       0      0           1          0  \n",
       "3       2      1           1          0  \n",
       "4       1      1           1          0  \n",
       "5       1      0           1          0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import f_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = data.select_dtypes(['int64','float64']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = f_classif(data[num_cols],data['readmitted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(x).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>185.017732</td>\n",
       "      <td>4.248086e-42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.850302</td>\n",
       "      <td>4.594371e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.248803</td>\n",
       "      <td>9.429954e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140.100085</td>\n",
       "      <td>2.662873e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.236579</td>\n",
       "      <td>8.184481e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>360.892506</td>\n",
       "      <td>2.511366e-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2703.614127</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>212.383437</td>\n",
       "      <td>4.658282e-48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0             1\n",
       "0   185.017732  4.248086e-42\n",
       "1    38.850302  4.594371e-10\n",
       "2    15.248803  9.429954e-05\n",
       "3   140.100085  2.662873e-32\n",
       "4    33.236579  8.184481e-09\n",
       "5   360.892506  2.511366e-80\n",
       "6  2703.614127  0.000000e+00\n",
       "7   212.383437  4.658282e-48"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.rename(columns = {0:'F_statistic',1:'p-values'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "Name: p-values, dtype: bool"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['p-values'] > 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race                  category\n",
       "gender                category\n",
       "age                   category\n",
       "time_in_hospital         int64\n",
       "num_lab_procedures       int64\n",
       "num_procedures           int64\n",
       "num_medications          int64\n",
       "number_outpatient        int64\n",
       "number_emergency         int64\n",
       "number_inpatient         int64\n",
       "diag_1                category\n",
       "diag_2                category\n",
       "diag_3                category\n",
       "number_diagnoses         int64\n",
       "max_glu_serum         category\n",
       "A1Cresult             category\n",
       "metformin             category\n",
       "repaglinide           category\n",
       "glimepiride           category\n",
       "glipizide             category\n",
       "pioglitazone          category\n",
       "rosiglitazone         category\n",
       "acarbose              category\n",
       "insulin               category\n",
       "change                category\n",
       "diabetesMed           category\n",
       "readmitted            category\n",
       "dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the data into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score \n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn_pandas import DataFrameMapper\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# !pip install imblearn\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data[[\"readmitted\"]]\n",
    "X=data.drop('readmitted', axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,random_state=200, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2,random_state=340, stratify=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['readmitted_0', 'readmitted_1']\n"
     ]
    }
   ],
   "source": [
    "ohe = OneHotEncoder(handle_unknown='error')\n",
    "\n",
    "ohe.fit(y_train)\n",
    "\n",
    "columns_ohe = list(ohe.get_feature_names(y_train.columns))\n",
    "print(columns_ohe)\n",
    "\n",
    "y_train_ohe = ohe.transform(y_train)\n",
    "y_val_ohe = ohe.transform(y_val)\n",
    "y_test_ohe = ohe.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_ohe = pd.DataFrame(y_train_ohe.todense(), columns=columns_ohe)\n",
    "y_val_ohe = pd.DataFrame(y_val_ohe.todense(), columns=columns_ohe)\n",
    "y_test_ohe = pd.DataFrame(y_test_ohe.todense(), columns=columns_ohe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_ohe = y_train_ohe.values\n",
    "y_val_ohe = y_val_ohe.values\n",
    "y_test_ohe = y_test_ohe.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70596, 26)\n",
      "(70596, 2)\n",
      "(17650, 26)\n",
      "(17650, 2)\n",
      "(9806, 26)\n",
      "(9806, 2)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train_ohe.shape)\n",
    "print(X_val.shape)\n",
    "print(y_val_ohe.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test_ohe.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['time_in_hospital', 'num_lab_procedures', 'num_procedures',\n",
       "       'num_medications', 'number_outpatient', 'number_emergency',\n",
       "       'number_inpatient', 'number_diagnoses'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attr=X_train.select_dtypes(['int64','float64']).columns\n",
    "num_attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['race', 'gender', 'age', 'diag_1', 'diag_2', 'diag_3', 'max_glu_serum',\n",
       "       'A1Cresult', 'metformin', 'repaglinide', 'glimepiride', 'glipizide',\n",
       "       'pioglitazone', 'rosiglitazone', 'acarbose', 'insulin', 'change',\n",
       "       'diabetesMed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_attr = X_train.select_dtypes('category').columns\n",
    "cat_attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='median')\n",
    "\n",
    "imputer = imputer.fit(X_train[num_attr])\n",
    "\n",
    "X_train[num_attr] = imputer.transform(X_train[num_attr])\n",
    "X_val[num_attr] = imputer.transform(X_val[num_attr])\n",
    "X_test[num_attr] = imputer.transform(X_test[num_attr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race                  category\n",
       "gender                category\n",
       "age                   category\n",
       "time_in_hospital       float64\n",
       "num_lab_procedures     float64\n",
       "num_procedures         float64\n",
       "num_medications        float64\n",
       "number_outpatient      float64\n",
       "number_emergency       float64\n",
       "number_inpatient       float64\n",
       "diag_1                category\n",
       "diag_2                category\n",
       "diag_3                category\n",
       "number_diagnoses       float64\n",
       "max_glu_serum         category\n",
       "A1Cresult             category\n",
       "metformin             category\n",
       "repaglinide           category\n",
       "glimepiride           category\n",
       "glipizide             category\n",
       "pioglitazone          category\n",
       "rosiglitazone         category\n",
       "acarbose              category\n",
       "insulin               category\n",
       "change                category\n",
       "diabetesMed           category\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "\n",
    "imputer = imputer.fit(X_train[cat_attr])\n",
    "\n",
    "X_train[cat_attr] = imputer.transform(X_train[cat_attr])\n",
    "X_val[cat_attr] = imputer.transform(X_val[cat_attr])\n",
    "X_test[cat_attr] = imputer.transform(X_test[cat_attr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper = DataFrameMapper(\n",
    "  [([continuous_col], StandardScaler()) for continuous_col in num_attr] +\n",
    "  [([categorical_col], OneHotEncoder(handle_unknown='error')) for categorical_col in cat_attr]\n",
    ", df_out=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper.fit(X_train)\n",
    "\n",
    "X_train_final = mapper.transform(X_train)\n",
    "X_val_final = mapper.transform(X_val)\n",
    "X_test_final = mapper.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70596, 91)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['time_in_hospital', 'num_lab_procedures', 'num_procedures',\n",
       "       'num_medications', 'number_outpatient', 'number_emergency',\n",
       "       'number_inpatient', 'number_diagnoses', 'race_x0_0', 'race_x0_1',\n",
       "       'race_x0_2', 'race_x0_3', 'race_x0_4', 'gender_x0_0', 'gender_x0_1',\n",
       "       'age_x0_1', 'age_x0_2', 'age_x0_3', 'age_x0_4', 'age_x0_5',\n",
       "       'diag_1_x0_0', 'diag_1_x0_1', 'diag_1_x0_2', 'diag_1_x0_3',\n",
       "       'diag_1_x0_4', 'diag_1_x0_5', 'diag_1_x0_6', 'diag_1_x0_7',\n",
       "       'diag_1_x0_8', 'diag_2_x0_0', 'diag_2_x0_1', 'diag_2_x0_2',\n",
       "       'diag_2_x0_3', 'diag_2_x0_4', 'diag_2_x0_5', 'diag_2_x0_6',\n",
       "       'diag_2_x0_7', 'diag_2_x0_8', 'diag_3_x0_0', 'diag_3_x0_1',\n",
       "       'diag_3_x0_2', 'diag_3_x0_3', 'diag_3_x0_4', 'diag_3_x0_5',\n",
       "       'diag_3_x0_6', 'diag_3_x0_7', 'diag_3_x0_8', 'max_glu_serum_x0_-2',\n",
       "       'max_glu_serum_x0_-1', 'max_glu_serum_x0_0', 'max_glu_serum_x0_1',\n",
       "       'A1Cresult_x0_-2', 'A1Cresult_x0_-1', 'A1Cresult_x0_0',\n",
       "       'A1Cresult_x0_1', 'metformin_x0_-1', 'metformin_x0_0', 'metformin_x0_1',\n",
       "       'metformin_x0_2', 'repaglinide_x0_-1', 'repaglinide_x0_0',\n",
       "       'repaglinide_x0_1', 'repaglinide_x0_2', 'glimepiride_x0_-1',\n",
       "       'glimepiride_x0_0', 'glimepiride_x0_1', 'glimepiride_x0_2',\n",
       "       'glipizide_x0_-1', 'glipizide_x0_0', 'glipizide_x0_1', 'glipizide_x0_2',\n",
       "       'pioglitazone_x0_-1', 'pioglitazone_x0_0', 'pioglitazone_x0_1',\n",
       "       'pioglitazone_x0_2', 'rosiglitazone_x0_-1', 'rosiglitazone_x0_0',\n",
       "       'rosiglitazone_x0_1', 'rosiglitazone_x0_2', 'acarbose_x0_-1',\n",
       "       'acarbose_x0_0', 'acarbose_x0_1', 'acarbose_x0_2', 'insulin_x0_-1',\n",
       "       'insulin_x0_0', 'insulin_x0_1', 'insulin_x0_2', 'change_x0_0',\n",
       "       'change_x0_1', 'diabetesMed_x0_0', 'diabetesMed_x0_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = pd.DataFrame(columns=['Model','Train_Recall','Val_Recall','Train_Precision','Val_Precision','Train_F1_Score','Val_F1_Score','Train_Accuracy','Val_Accuracy',])\n",
    "\n",
    "def get_metrics(train_actual,train_predicted,val_actual,val_predicted,model_description,dataframe):\n",
    "    classifcation_report_and_CM_train_test(train_actual,train_predicted,val_actual,val_predicted)\n",
    "    \n",
    "    x = pd.crosstab(train_actual,train_predicted, rownames=['Actual'], colnames=['Predicted'])\n",
    "    \n",
    "    train_accuracy = round(accuracy_score(train_actual,train_predicted),4)\n",
    "    \n",
    "    train_recall   = round((x.iloc[1,1]/(x.iloc[1,0]+x.iloc[1,1])),4)\n",
    "        \n",
    "    train_precision= round((x.iloc[1,1]/(x.iloc[0,1]+x.iloc[1,1])),4)\n",
    "    \n",
    "    train_f1score  = round(2*(train_recall*train_precision)/(train_recall+train_precision),4)\n",
    "    \n",
    "    y = pd.crosstab(val_actual,val_predicted, rownames=['Actual'], colnames=['Predicted'])\n",
    "    \n",
    "    val_accuracy = round(accuracy_score(val_actual,val_predicted),4)\n",
    "            \n",
    "    val_recall   = round((y.iloc[1,1]/(y.iloc[1,0]+y.iloc[1,1])),4)\n",
    "    \n",
    "    val_precision= round((y.iloc[1,1]/(y.iloc[0,1]+y.iloc[1,1])),4)\n",
    "    \n",
    "    val_f1score  = round(2*(val_recall*val_precision)/(val_recall+val_precision),4)\n",
    "    \n",
    "    dataframe = dataframe.append(pd.Series([model_description, train_recall,val_recall,train_precision,val_precision,train_f1score,\n",
    "                                            val_f1score, train_accuracy,val_accuracy],\n",
    "                                           index=scores.columns ), ignore_index=True)\n",
    "    \n",
    "    return(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classifcation_report_and_CM_train_test(train, train_pred, val, val_pred):\n",
    "\n",
    "    print('''\n",
    "            =========================================================\n",
    "            CLASSIFICATION REPORT AND CONFUSION MATRIX FOR TRAIN DATA\n",
    "            =========================================================\n",
    "            ''')\n",
    "    print(pd.crosstab(train, train_pred, rownames=['Actual'], colnames=['Predicted']))\n",
    "    print(classification_report(train, train_pred, digits=4))\n",
    "    \n",
    "\n",
    "    print('''\n",
    "            =========================================================\n",
    "            CLASSIFICATION REPORT AND CONFUSION MATRIX FOR Val DATA\n",
    "            =========================================================\n",
    "            ''')\n",
    "    print(pd.crosstab(val, val_pred, rownames=['Actual'], colnames=['Predicted']))\n",
    "    print(classification_report(val, val_pred, digits=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Model</th>\n",
       "      <th>Train_Recall</th>\n",
       "      <th>Val_Recall</th>\n",
       "      <th>Train_Precision</th>\n",
       "      <th>Val_Precision</th>\n",
       "      <th>Train_F1_Score</th>\n",
       "      <th>Val_F1_Score</th>\n",
       "      <th>Train_Accuracy</th>\n",
       "      <th>Val_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Model, Train_Recall, Val_Recall, Train_Precision, Val_Precision, Train_F1_Score, Val_F1_Score, Train_Accuracy, Val_Accuracy]\n",
       "Index: []"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras import Input, Model\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation\n",
    "from tensorflow.keras.regularizers import l2, l1\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(340)\n",
    "tf.random.set_seed(340)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape = X_train_final.shape[1]\n",
    "input_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(800, input_dim=input_shape, kernel_initializer=tf.keras.initializers.glorot_normal(), activation='relu',kernel_regularizer=l2(2e-4)))\n",
    "\n",
    "model.add(Dense(750, kernel_initializer='glorot_normal', activation='relu'))\n",
    "\n",
    "model.add(Dense(500, kernel_initializer='glorot_normal', activation='relu'))\n",
    "\n",
    "model.add(Dense(250, kernel_initializer='glorot_normal', activation='relu'))\n",
    "\n",
    "model.add(Dense(125, kernel_initializer='glorot_normal', activation='relu'))\n",
    "\n",
    "model.add(Dense(100, kernel_initializer='glorot_normal', activation='relu'))\n",
    "\n",
    "model.add(Dense(75, kernel_initializer='glorot_normal', activation='relu'))\n",
    "\n",
    "model.add(Dense(50, kernel_initializer='glorot_normal', activation='relu'))\n",
    "\n",
    "model.add(Dense(35, kernel_initializer='glorot_normal', activation='relu'))\n",
    "\n",
    "model.add(Dense(2, activation='softmax',kernel_initializer='glorot_normal'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 800)               73600     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 750)               600750    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 500)               375500    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 250)               125250    \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 125)               31375     \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 100)               12600     \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 75)                7575      \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 50)                3800      \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 35)                1785      \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 2)                 72        \n",
      "=================================================================\n",
      "Total params: 1,232,307\n",
      "Trainable params: 1,232,307\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "readmitted\n",
       "0             62629\n",
       "1              7967\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "              optimizer= tf.keras.optimizers.Adam(),\n",
    "              metrics=tf.keras.metrics.Recall())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, mode='min', verbose=1, min_delta=0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks=[early_stopping]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "readmitted    1.127209\n",
      "dtype: float64\n",
      "readmitted    8.861052\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "w0 = y_train.count()/y_train[y_train==0].count()\n",
    "print(w0)\n",
    "w1 = y_train.count()/y_train[y_train==1].count()\n",
    "print(w1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "883/883 [==============================] - 24s 26ms/step - loss: 1.3781 - recall: 0.5745 - val_loss: 0.6737 - val_recall: 0.6665\n",
      "Epoch 2/100\n",
      "883/883 [==============================] - 20s 23ms/step - loss: 1.3462 - recall: 0.6008 - val_loss: 0.7250 - val_recall: 0.5568\n",
      "Epoch 3/100\n",
      "883/883 [==============================] - 21s 24ms/step - loss: 1.3387 - recall: 0.6175 - val_loss: 0.7143 - val_recall: 0.4875\n",
      "Epoch 4/100\n",
      "883/883 [==============================] - 24s 27ms/step - loss: 1.3296 - recall: 0.6158 - val_loss: 0.6841 - val_recall: 0.5734\n",
      "Epoch 5/100\n",
      "883/883 [==============================] - 23s 27ms/step - loss: 1.3280 - recall: 0.6180 - val_loss: 0.6756 - val_recall: 0.5354\n",
      "Epoch 6/100\n",
      "883/883 [==============================] - 21s 24ms/step - loss: 1.3218 - recall: 0.6044 - val_loss: 0.5891 - val_recall: 0.7528\n",
      "Epoch 7/100\n",
      "883/883 [==============================] - 22s 25ms/step - loss: 1.3257 - recall: 0.6022 - val_loss: 0.6896 - val_recall: 0.5919\n",
      "Epoch 8/100\n",
      "883/883 [==============================] - 23s 26ms/step - loss: 1.3179 - recall: 0.6080 - val_loss: 0.6023 - val_recall: 0.6964\n",
      "Epoch 9/100\n",
      "883/883 [==============================] - 21s 24ms/step - loss: 1.3132 - recall: 0.6236 - val_loss: 0.5977 - val_recall: 0.6516\n",
      "Epoch 10/100\n",
      "883/883 [==============================] - 22s 25ms/step - loss: 1.3098 - recall: 0.6038 - val_loss: 0.6927 - val_recall: 0.4799\n",
      "Epoch 11/100\n",
      "883/883 [==============================] - 22s 25ms/step - loss: 1.3223 - recall: 0.5649 - val_loss: 0.6927 - val_recall: 0.5713\n",
      "Epoch 00011: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d5abcc6ac0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_final, y_train_ohe,epochs=100,batch_size=64,class_weight= {0: w0, 1:w1},\\\n",
    "          validation_split=0.2,callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred=model.predict(X_train_final)\n",
    "val_pred=model.predict(X_val_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6916891 , 0.3083109 ],\n",
       "       [0.4802494 , 0.5197506 ],\n",
       "       [0.35516196, 0.64483804],\n",
       "       ...,\n",
       "       [0.32315338, 0.6768466 ],\n",
       "       [0.5882623 , 0.41173762],\n",
       "       [0.5562289 , 0.44377112]], dtype=float32)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_indices(array):\n",
    "    x = []\n",
    "    for i in array:\n",
    "        x.append(np.argmax(i))\n",
    "    return np.array(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_original = get_indices(y_train_ohe)\n",
    "train_predictions = get_indices(train_pred)\n",
    "val_original = get_indices(y_val_ohe)\n",
    "val_predictions = get_indices(val_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "            =========================================================\n",
      "            CLASSIFICATION REPORT AND CONFUSION MATRIX FOR TRAIN DATA\n",
      "            =========================================================\n",
      "            \n",
      "Predicted      0      1\n",
      "Actual                 \n",
      "0          35520  27109\n",
      "1           2518   5449\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9338    0.5671    0.7057     62629\n",
      "           1     0.1674    0.6839    0.2689      7967\n",
      "\n",
      "    accuracy                         0.5803     70596\n",
      "   macro avg     0.5506    0.6255    0.4873     70596\n",
      "weighted avg     0.8473    0.5803    0.6564     70596\n",
      "\n",
      "\n",
      "            =========================================================\n",
      "            CLASSIFICATION REPORT AND CONFUSION MATRIX FOR Val DATA\n",
      "            =========================================================\n",
      "            \n",
      "Predicted     0     1\n",
      "Actual               \n",
      "0          8747  6911\n",
      "1           736  1256\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9224    0.5586    0.6958     15658\n",
      "           1     0.1538    0.6305    0.2473      1992\n",
      "\n",
      "    accuracy                         0.5667     17650\n",
      "   macro avg     0.5381    0.5946    0.4716     17650\n",
      "weighted avg     0.8356    0.5667    0.6452     17650\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scores = get_metrics(train_original, train_predictions, val_original, val_predictions,'NN',scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>Model</th>\n",
       "      <th>Train_Recall</th>\n",
       "      <th>Val_Recall</th>\n",
       "      <th>Train_Precision</th>\n",
       "      <th>Val_Precision</th>\n",
       "      <th>Train_F1_Score</th>\n",
       "      <th>Val_F1_Score</th>\n",
       "      <th>Train_Accuracy</th>\n",
       "      <th>Val_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NN</td>\n",
       "      <td>0.6839</td>\n",
       "      <td>0.6305</td>\n",
       "      <td>0.1674</td>\n",
       "      <td>0.1538</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.2473</td>\n",
       "      <td>0.5803</td>\n",
       "      <td>0.5667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model  Train_Recall  Val_Recall  Train_Precision  Val_Precision  \\\n",
       "0    NN        0.6839      0.6305           0.1674         0.1538   \n",
       "\n",
       "   Train_F1_Score  Val_F1_Score  Train_Accuracy  Val_Accuracy  \n",
       "0           0.269        0.2473          0.5803        0.5667  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = model.predict(X_test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7001039 , 0.29989618],\n",
       "       [0.12086729, 0.87913275],\n",
       "       [0.4845445 , 0.5154555 ],\n",
       "       ...,\n",
       "       [0.45021588, 0.5497842 ],\n",
       "       [0.67340535, 0.32659465],\n",
       "       [0.6036472 , 0.3963528 ]], dtype=float32)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_original = get_indices(y_test_ohe)\n",
    "test_predictions = get_indices(test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4871</td>\n",
       "      <td>3828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>424</td>\n",
       "      <td>683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted     0     1\n",
       "Actual               \n",
       "0          4871  3828\n",
       "1           424   683"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(test_original,test_predictions, rownames=['Actual'], colnames=['Predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.56      0.70      8699\n",
      "           1       0.15      0.62      0.24      1107\n",
      "\n",
      "    accuracy                           0.57      9806\n",
      "   macro avg       0.54      0.59      0.47      9806\n",
      "weighted avg       0.83      0.57      0.65      9806\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_original,test_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This data is highly imbalanced, it was challenging to balance both Precision and Recall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For this dataset, our main intention is to predict correcly those who are admitting to the hospital within 30 days, so we concentrated on Recall.\n",
    "* Recall of 0.53 is the highest I got on this data.\n",
    "* As I have used neural net, it's not possible to extract the important features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###                                                              THE END"
   ]
  }
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