Comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a125b462",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1762e5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"C:/Users/SANIKA/Desktop/subject.csv\"\n",
    "df = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "830a5818",
   "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>subject_number</th>\n",
       "      <th>age</th>\n",
       "      <th>height/m</th>\n",
       "      <th>mass/kg</th>\n",
       "      <th>bmi</th>\n",
       "      <th>gender</th>\n",
       "      <th>previous_tobacco_use</th>\n",
       "      <th>current_tobacco_use</th>\n",
       "      <th>PREVIOUS ALCOHOL USE</th>\n",
       "      <th>Neuropathy AUTONOMIC SYMPTOMS</th>\n",
       "      <th>...</th>\n",
       "      <th>ACE INHIBITORS</th>\n",
       "      <th>ARBS</th>\n",
       "      <th>INSULIN(Yes_or_No)</th>\n",
       "      <th>ORAL HYPOGLYCEMIC</th>\n",
       "      <th>WBC K/uL</th>\n",
       "      <th>RBC m/uL</th>\n",
       "      <th>Hgb g/dL</th>\n",
       "      <th>Hgb&lt;12.5</th>\n",
       "      <th>GLUCOSE mg/dL</th>\n",
       "      <th>CHOLESTmg/dL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S0011</td>\n",
       "      <td>70</td>\n",
       "      <td>1.6383</td>\n",
       "      <td>71.667594</td>\n",
       "      <td>26.701519</td>\n",
       "      <td>F</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S0030</td>\n",
       "      <td>64</td>\n",
       "      <td>1.6256</td>\n",
       "      <td>72.574779</td>\n",
       "      <td>27.463655</td>\n",
       "      <td>F</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.65</td>\n",
       "      <td>14.1</td>\n",
       "      <td>Non-anemic</td>\n",
       "      <td>74.0</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S0033</td>\n",
       "      <td>65</td>\n",
       "      <td>1.8200</td>\n",
       "      <td>92.986000</td>\n",
       "      <td>28.072093</td>\n",
       "      <td>M</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.40</td>\n",
       "      <td>14.3</td>\n",
       "      <td>Non-anemic</td>\n",
       "      <td>76.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S0044</td>\n",
       "      <td>74</td>\n",
       "      <td>1.7018</td>\n",
       "      <td>68.038855</td>\n",
       "      <td>23.493080</td>\n",
       "      <td>F</td>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>6.1</td>\n",
       "      <td>4.54</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Non-anemic</td>\n",
       "      <td>66.0</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S0064</td>\n",
       "      <td>76</td>\n",
       "      <td>1.7018</td>\n",
       "      <td>68.038855</td>\n",
       "      <td>23.493080</td>\n",
       "      <td>M</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.48</td>\n",
       "      <td>14.9</td>\n",
       "      <td>Non-anemic</td>\n",
       "      <td>75.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>S0404</td>\n",
       "      <td>61</td>\n",
       "      <td>1.7200</td>\n",
       "      <td>77.800000</td>\n",
       "      <td>26.297999</td>\n",
       "      <td>M</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.09</td>\n",
       "      <td>16.3</td>\n",
       "      <td>Non-anemic</td>\n",
       "      <td>63.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>S0410</td>\n",
       "      <td>56</td>\n",
       "      <td>1.7700</td>\n",
       "      <td>72.700000</td>\n",
       "      <td>23.205337</td>\n",
       "      <td>M</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>S0412</td>\n",
       "      <td>65</td>\n",
       "      <td>1.5300</td>\n",
       "      <td>59.100000</td>\n",
       "      <td>25.246700</td>\n",
       "      <td>F</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5.27</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Non-anemic</td>\n",
       "      <td>76.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>S0413</td>\n",
       "      <td>60</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>96.200000</td>\n",
       "      <td>30.362328</td>\n",
       "      <td>M</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>S0414</td>\n",
       "      <td>63</td>\n",
       "      <td>1.7400</td>\n",
       "      <td>64.650000</td>\n",
       "      <td>21.353547</td>\n",
       "      <td>F</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.20</td>\n",
       "      <td>13.5</td>\n",
       "      <td>Non-anemic</td>\n",
       "      <td>93.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>159 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject_number  age  height/m    mass/kg        bmi gender  \\\n",
       "0            S0011   70    1.6383  71.667594  26.701519      F   \n",
       "1            S0030   64    1.6256  72.574779  27.463655      F   \n",
       "2            S0033   65    1.8200  92.986000  28.072093      M   \n",
       "3            S0044   74    1.7018  68.038855  23.493080      F   \n",
       "4            S0064   76    1.7018  68.038855  23.493080      M   \n",
       "..             ...  ...       ...        ...        ...    ...   \n",
       "154          S0404   61    1.7200  77.800000  26.297999      M   \n",
       "155          S0410   56    1.7700  72.700000  23.205337      M   \n",
       "156          S0412   65    1.5300  59.100000  25.246700      F   \n",
       "157          S0413   60    1.7800  96.200000  30.362328      M   \n",
       "158          S0414   63    1.7400  64.650000  21.353547      F   \n",
       "\n",
       "    previous_tobacco_use current_tobacco_use PREVIOUS ALCOHOL USE  \\\n",
       "0                    YES                  NO                  YES   \n",
       "1                     NO                  NO                  YES   \n",
       "2                    YES                  NO                  yes   \n",
       "3                    YES                 NaN                  YES   \n",
       "4                     NO                  NO                  YES   \n",
       "..                   ...                 ...                  ...   \n",
       "154                  YES                  NO                  YES   \n",
       "155                   NO                  NO                   NO   \n",
       "156                  YES                  NO                   NO   \n",
       "157                  YES                 YES                  YES   \n",
       "158                  YES                  NO                  YES   \n",
       "\n",
       "    Neuropathy AUTONOMIC SYMPTOMS  ... ACE INHIBITORS ARBS INSULIN(Yes_or_No)  \\\n",
       "0                              NO  ...              N    Y                  N   \n",
       "1                              NO  ...              N    N                  N   \n",
       "2                              no  ...              Y    N                  N   \n",
       "3                              NO  ...              N    N                  N   \n",
       "4                              NO  ...              N    N                  N   \n",
       "..                            ...  ...            ...  ...                ...   \n",
       "154                            NO  ...              N    N                  N   \n",
       "155                            NO  ...              N    N                  N   \n",
       "156                            NO  ...              N    N                  N   \n",
       "157                            NO  ...              Y    N                  N   \n",
       "158                           YES  ...              Y    N                  N   \n",
       "\n",
       "    ORAL HYPOGLYCEMIC WBC K/uL RBC m/uL Hgb g/dL    Hgb<12.5 GLUCOSE mg/dL  \\\n",
       "0                   N      NaN      NaN      NaN         NaN           NaN   \n",
       "1                   N      5.7     4.65     14.1  Non-anemic          74.0   \n",
       "2                   N      7.2     4.40     14.3  Non-anemic          76.0   \n",
       "3                   N      6.1     4.54     15.0  Non-anemic          66.0   \n",
       "4                   N      5.7     4.48     14.9  Non-anemic          75.0   \n",
       "..                ...      ...      ...      ...         ...           ...   \n",
       "154                 N      5.7     5.09     16.3  Non-anemic          63.0   \n",
       "155                 N      NaN      NaN      NaN         NaN           NaN   \n",
       "156                 N      9.2     5.27     15.0  Non-anemic          76.0   \n",
       "157                 N      NaN      NaN      NaN         NaN           NaN   \n",
       "158                 N      7.5     4.20     13.5  Non-anemic          93.0   \n",
       "\n",
       "    CHOLESTmg/dL  \n",
       "0            NaN  \n",
       "1          270.0  \n",
       "2          160.0  \n",
       "3          189.0  \n",
       "4          204.0  \n",
       "..           ...  \n",
       "154        124.0  \n",
       "155          NaN  \n",
       "156        190.0  \n",
       "157          NaN  \n",
       "158        185.0  \n",
       "\n",
       "[159 rows x 37 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1874263c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 159 entries, 0 to 158\n",
      "Data columns (total 37 columns):\n",
      " #   Column                                                     Non-Null Count  Dtype  \n",
      "---  ------                                                     --------------  -----  \n",
      " 0   subject_number                                             159 non-null    object \n",
      " 1   age                                                        159 non-null    int64  \n",
      " 2   height/m                                                   158 non-null    float64\n",
      " 3   mass/kg                                                    158 non-null    float64\n",
      " 4   bmi                                                        158 non-null    float64\n",
      " 5   gender                                                     159 non-null    object \n",
      " 6   previous_tobacco_use                                       159 non-null    object \n",
      " 7   current_tobacco_use                                        154 non-null    object \n",
      " 8   PREVIOUS ALCOHOL USE                                       159 non-null    object \n",
      " 9   Neuropathy AUTONOMIC SYMPTOMS                              158 non-null    object \n",
      " 10  Dizziness AUTONOMIC SYMPTOMS                               159 non-null    object \n",
      " 11  Numbness AUTONOMIC SYMPTOMS                                158 non-null    object \n",
      " 12  Painful feet AUTONOMIC SYMPTOMS                            159 non-null    object \n",
      " 13  Syncope AUTONOMIC SYMPTOMS                                 159 non-null    object \n",
      " 14  cancer,# FAMILY HISTORY                                    156 non-null    object \n",
      " 15  HeartDisease FAMILY HISTORY                                155 non-null    object \n",
      " 16  StrokeFAMILY HISTORY                                       156 non-null    object \n",
      " 17  HTN YRS PATIENT MEDICAL HISTORY                            159 non-null    int64  \n",
      " 18  CANCER PATIENT MEDICAL HISTORY                             157 non-null    object \n",
      " 19  STROKE PATIENT MEDICAL HISTORY                             157 non-null    object \n",
      " 20  ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY               157 non-null    object \n",
      " 21  HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY  157 non-null    object \n",
      " 22  HYPERLIPIDEMIA PATIENT MEDICAL HISTORY                     157 non-null    object \n",
      " 23  Stroke Group                                               153 non-null    object \n",
      " 24  ANTIHYPERLIPIDEMIC                                         159 non-null    object \n",
      " 25  STATINS                                                    159 non-null    object \n",
      " 26  ESTROGEN                                                   159 non-null    object \n",
      " 27  ACE INHIBITORS                                             158 non-null    object \n",
      " 28  ARBS                                                       159 non-null    object \n",
      " 29  INSULIN(Yes_or_No)                                         159 non-null    object \n",
      " 30  ORAL HYPOGLYCEMIC                                          159 non-null    object \n",
      " 31  WBC K/uL                                                   146 non-null    float64\n",
      " 32  RBC m/uL                                                   139 non-null    float64\n",
      " 33  Hgb g/dL                                                   140 non-null    float64\n",
      " 34  Hgb<12.5                                                   140 non-null    object \n",
      " 35  GLUCOSE mg/dL                                              144 non-null    float64\n",
      " 36  CHOLESTmg/dL                                               138 non-null    float64\n",
      "dtypes: float64(8), int64(2), object(27)\n",
      "memory usage: 46.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "442bebae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_number                                                0\n",
       "age                                                           0\n",
       "height/m                                                      1\n",
       "mass/kg                                                       1\n",
       "bmi                                                           1\n",
       "gender                                                        0\n",
       "previous_tobacco_use                                          0\n",
       "current_tobacco_use                                           5\n",
       "PREVIOUS ALCOHOL USE                                          0\n",
       "Neuropathy AUTONOMIC SYMPTOMS                                 1\n",
       "Dizziness AUTONOMIC SYMPTOMS                                  0\n",
       "Numbness AUTONOMIC SYMPTOMS                                   1\n",
       "Painful feet AUTONOMIC SYMPTOMS                               0\n",
       "Syncope AUTONOMIC SYMPTOMS                                    0\n",
       "cancer,# FAMILY HISTORY                                       3\n",
       "HeartDisease FAMILY HISTORY                                   4\n",
       "StrokeFAMILY HISTORY                                          3\n",
       "HTN YRS PATIENT MEDICAL HISTORY                               0\n",
       "CANCER PATIENT MEDICAL HISTORY                                2\n",
       "STROKE PATIENT MEDICAL HISTORY                                2\n",
       "ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY                  2\n",
       "HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY     2\n",
       "HYPERLIPIDEMIA PATIENT MEDICAL HISTORY                        2\n",
       "Stroke Group                                                  6\n",
       "ANTIHYPERLIPIDEMIC                                            0\n",
       "STATINS                                                       0\n",
       "ESTROGEN                                                      0\n",
       "ACE INHIBITORS                                                1\n",
       "ARBS                                                          0\n",
       "INSULIN(Yes_or_No)                                            0\n",
       "ORAL HYPOGLYCEMIC                                             0\n",
       "WBC K/uL                                                     13\n",
       "RBC m/uL                                                     20\n",
       "Hgb g/dL                                                     19\n",
       "Hgb<12.5                                                     19\n",
       "GLUCOSE mg/dL                                                15\n",
       "CHOLESTmg/dL                                                 21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ccc9566",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height/m</th>\n",
       "      <th>mass/kg</th>\n",
       "      <th>bmi</th>\n",
       "      <th>HTN YRS PATIENT MEDICAL HISTORY</th>\n",
       "      <th>WBC K/uL</th>\n",
       "      <th>RBC m/uL</th>\n",
       "      <th>Hgb g/dL</th>\n",
       "      <th>GLUCOSE mg/dL</th>\n",
       "      <th>CHOLESTmg/dL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>159.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>138.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.779874</td>\n",
       "      <td>1.669446</td>\n",
       "      <td>75.293130</td>\n",
       "      <td>26.927546</td>\n",
       "      <td>17.874214</td>\n",
       "      <td>6.885616</td>\n",
       "      <td>4.494317</td>\n",
       "      <td>13.793571</td>\n",
       "      <td>82.659722</td>\n",
       "      <td>189.485507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.970394</td>\n",
       "      <td>0.089269</td>\n",
       "      <td>16.016221</td>\n",
       "      <td>4.887551</td>\n",
       "      <td>158.581239</td>\n",
       "      <td>1.901251</td>\n",
       "      <td>0.415172</td>\n",
       "      <td>1.343260</td>\n",
       "      <td>14.394620</td>\n",
       "      <td>39.006875</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>45.810000</td>\n",
       "      <td>18.246882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>61.500000</td>\n",
       "      <td>1.600200</td>\n",
       "      <td>64.410117</td>\n",
       "      <td>24.004820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.725000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>161.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>1.676400</td>\n",
       "      <td>72.574779</td>\n",
       "      <td>26.142826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>4.470000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>185.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>1.729300</td>\n",
       "      <td>84.353647</td>\n",
       "      <td>28.945327</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.075000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>14.700000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>214.500000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.854200</td>\n",
       "      <td>129.750000</td>\n",
       "      <td>43.587750</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>5.620000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>314.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              age    height/m     mass/kg         bmi  \\\n",
       "count  159.000000  158.000000  158.000000  158.000000   \n",
       "mean    66.779874    1.669446   75.293130   26.927546   \n",
       "std      7.970394    0.089269   16.016221    4.887551   \n",
       "min     50.000000    1.470000   45.810000   18.246882   \n",
       "25%     61.500000    1.600200   64.410117   24.004820   \n",
       "50%     67.000000    1.676400   72.574779   26.142826   \n",
       "75%     72.000000    1.729300   84.353647   28.945327   \n",
       "max     80.000000    1.854200  129.750000   43.587750   \n",
       "\n",
       "       HTN YRS PATIENT MEDICAL HISTORY    WBC K/uL    RBC m/uL    Hgb g/dL  \\\n",
       "count                       159.000000  146.000000  139.000000  140.000000   \n",
       "mean                         17.874214    6.885616    4.494317   13.793571   \n",
       "std                         158.581239    1.901251    0.415172    1.343260   \n",
       "min                           0.000000    2.500000    3.580000   10.000000   \n",
       "25%                           0.000000    5.725000    4.230000   12.800000   \n",
       "50%                           0.000000    6.500000    4.470000   13.800000   \n",
       "75%                           6.000000    8.075000    4.720000   14.700000   \n",
       "max                        2001.000000   13.200000    5.620000   17.200000   \n",
       "\n",
       "       GLUCOSE mg/dL  CHOLESTmg/dL  \n",
       "count     144.000000    138.000000  \n",
       "mean       82.659722    189.485507  \n",
       "std        14.394620     39.006875  \n",
       "min        54.000000     98.000000  \n",
       "25%        74.000000    161.000000  \n",
       "50%        81.000000    185.000000  \n",
       "75%        91.000000    214.500000  \n",
       "max       146.000000    314.000000  "
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.describe()"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56eea2ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_number                                                0\n",
       "age                                                           0\n",
       "height/m                                                      1\n",
       "mass/kg                                                       1\n",
       "bmi                                                           1\n",
       "gender                                                        0\n",
       "previous_tobacco_use                                          0\n",
       "current_tobacco_use                                           5\n",
       "PREVIOUS ALCOHOL USE                                          0\n",
       "Neuropathy AUTONOMIC SYMPTOMS                                 1\n",
       "Dizziness AUTONOMIC SYMPTOMS                                  0\n",
       "Numbness AUTONOMIC SYMPTOMS                                   1\n",
       "Painful feet AUTONOMIC SYMPTOMS                               0\n",
       "Syncope AUTONOMIC SYMPTOMS                                    0\n",
       "cancer,# FAMILY HISTORY                                       3\n",
       "HeartDisease FAMILY HISTORY                                   4\n",
       "StrokeFAMILY HISTORY                                          3\n",
       "HTN YRS PATIENT MEDICAL HISTORY                               0\n",
       "CANCER PATIENT MEDICAL HISTORY                                2\n",
       "STROKE PATIENT MEDICAL HISTORY                                2\n",
       "ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY                  2\n",
       "HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY     2\n",
       "HYPERLIPIDEMIA PATIENT MEDICAL HISTORY                        2\n",
       "Stroke Group                                                  6\n",
       "ANTIHYPERLIPIDEMIC                                            0\n",
       "STATINS                                                       0\n",
       "ESTROGEN                                                      0\n",
       "ACE INHIBITORS                                                1\n",
       "ARBS                                                          0\n",
       "INSULIN(Yes_or_No)                                            0\n",
       "ORAL HYPOGLYCEMIC                                             0\n",
       "WBC K/uL                                                     13\n",
       "RBC m/uL                                                     20\n",
       "Hgb g/dL                                                     19\n",
       "Hgb<12.5                                                     19\n",
       "GLUCOSE mg/dL                                                15\n",
       "CHOLESTmg/dL                                                 21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a19dccc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>S0033</td>\n",
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       "      <td>4.40</td>\n",
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       "      <td>76.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>S0064</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>4.48</td>\n",
       "      <td>14.9</td>\n",
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       "      <td>75.0</td>\n",
       "      <td>204.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>S0067</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.79</td>\n",
       "      <td>14.3</td>\n",
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       "      <td>60.0</td>\n",
       "      <td>192.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>S0068</td>\n",
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       "      <td>1.5748</td>\n",
       "      <td>64.863709</td>\n",
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       "      <td>N</td>\n",
       "      <td>11.4</td>\n",
       "      <td>4.57</td>\n",
       "      <td>14.3</td>\n",
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       "      <td>203.0</td>\n",
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       "      <td>NO</td>\n",
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       "      <td>N</td>\n",
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       "      <td>70.0</td>\n",
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       "      <th>153</th>\n",
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       "      <td>YES</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>4.39</td>\n",
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       "      <td>Non-anemic</td>\n",
       "      <td>81.0</td>\n",
       "      <td>137.0</td>\n",
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       "    <tr>\n",
       "      <th>154</th>\n",
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       "      <td>26.297999</td>\n",
       "      <td>M</td>\n",
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       "      <td>NO</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.09</td>\n",
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       "      <td>63.0</td>\n",
       "      <td>124.0</td>\n",
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       "      <th>156</th>\n",
       "      <td>S0412</td>\n",
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       "      <td>59.100000</td>\n",
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       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5.27</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Non-anemic</td>\n",
       "      <td>76.0</td>\n",
       "      <td>190.0</td>\n",
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       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>S0414</td>\n",
       "      <td>63</td>\n",
       "      <td>1.7400</td>\n",
       "      <td>64.650000</td>\n",
       "      <td>21.353547</td>\n",
       "      <td>F</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.20</td>\n",
       "      <td>13.5</td>\n",
       "      <td>Non-anemic</td>\n",
       "      <td>93.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject_number  age  height/m    mass/kg        bmi gender  \\\n",
       "1            S0030   64    1.6256  72.574779  27.463655      F   \n",
       "2            S0033   65    1.8200  92.986000  28.072093      M   \n",
       "4            S0064   76    1.7018  68.038855  23.493080      M   \n",
       "5            S0067   70    1.6764  68.492448  24.371788      M   \n",
       "6            S0068   79    1.5748  64.863709  26.154774      F   \n",
       "..             ...  ...       ...        ...        ...    ...   \n",
       "152          S0401   64    1.6800  88.800000  31.462585      M   \n",
       "153          S0402   54    1.8200  96.450000  29.117860      M   \n",
       "154          S0404   61    1.7200  77.800000  26.297999      M   \n",
       "156          S0412   65    1.5300  59.100000  25.246700      F   \n",
       "158          S0414   63    1.7400  64.650000  21.353547      F   \n",
       "\n",
       "    previous_tobacco_use current_tobacco_use PREVIOUS ALCOHOL USE  \\\n",
       "1                     NO                  NO                  YES   \n",
       "2                    YES                  NO                  yes   \n",
       "4                     NO                  NO                  YES   \n",
       "5                     NO                  NO                  YES   \n",
       "6                     NO                  NO                   NO   \n",
       "..                   ...                 ...                  ...   \n",
       "152                  YES                  NO                   NO   \n",
       "153                  YES                 YES                  YES   \n",
       "154                  YES                  NO                  YES   \n",
       "156                  YES                  NO                   NO   \n",
       "158                  YES                  NO                  YES   \n",
       "\n",
       "    Neuropathy AUTONOMIC SYMPTOMS  ... ACE INHIBITORS ARBS INSULIN(Yes_or_No)  \\\n",
       "1                              NO  ...              N    N                  N   \n",
       "2                              no  ...              Y    N                  N   \n",
       "4                              NO  ...              N    N                  N   \n",
       "5                              NO  ...              N    N                  N   \n",
       "6                              NO  ...              N    N                  N   \n",
       "..                            ...  ...            ...  ...                ...   \n",
       "152                            NO  ...              N    N                  N   \n",
       "153                            NO  ...              N    N                  N   \n",
       "154                            NO  ...              N    N                  N   \n",
       "156                            NO  ...              N    N                  N   \n",
       "158                           YES  ...              Y    N                  N   \n",
       "\n",
       "    ORAL HYPOGLYCEMIC WBC K/uL RBC m/uL Hgb g/dL    Hgb<12.5 GLUCOSE mg/dL  \\\n",
       "1                   N      5.7     4.65     14.1  Non-anemic          74.0   \n",
       "2                   N      7.2     4.40     14.3  Non-anemic          76.0   \n",
       "4                   N      5.7     4.48     14.9  Non-anemic          75.0   \n",
       "5                   N      5.5     4.79     14.3  Non-anemic          60.0   \n",
       "6                   N     11.4     4.57     14.3  Non-anemic          80.0   \n",
       "..                ...      ...      ...      ...         ...           ...   \n",
       "152                 N      6.6     4.83     16.0  Non-anemic          70.0   \n",
       "153                 N      6.3     4.39     13.1  Non-anemic          81.0   \n",
       "154                 N      5.7     5.09     16.3  Non-anemic          63.0   \n",
       "156                 N      9.2     5.27     15.0  Non-anemic          76.0   \n",
       "158                 N      7.5     4.20     13.5  Non-anemic          93.0   \n",
       "\n",
       "    CHOLESTmg/dL  \n",
       "1          270.0  \n",
       "2          160.0  \n",
       "4          204.0  \n",
       "5          192.0  \n",
       "6          203.0  \n",
       "..           ...  \n",
       "152        230.0  \n",
       "153        137.0  \n",
       "154        124.0  \n",
       "156        190.0  \n",
       "158        185.0  \n",
       "\n",
       "[118 rows x 37 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.dropna()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d7c01ed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_number                                               0\n",
       "age                                                          0\n",
       "height/m                                                     0\n",
       "mass/kg                                                      0\n",
       "bmi                                                          0\n",
       "gender                                                       0\n",
       "previous_tobacco_use                                         0\n",
       "current_tobacco_use                                          0\n",
       "PREVIOUS ALCOHOL USE                                         0\n",
       "Neuropathy AUTONOMIC SYMPTOMS                                0\n",
       "Dizziness AUTONOMIC SYMPTOMS                                 0\n",
       "Numbness AUTONOMIC SYMPTOMS                                  0\n",
       "Painful feet AUTONOMIC SYMPTOMS                              0\n",
       "Syncope AUTONOMIC SYMPTOMS                                   0\n",
       "cancer,# FAMILY HISTORY                                      0\n",
       "HeartDisease FAMILY HISTORY                                  0\n",
       "StrokeFAMILY HISTORY                                         0\n",
       "HTN YRS PATIENT MEDICAL HISTORY                              0\n",
       "CANCER PATIENT MEDICAL HISTORY                               0\n",
       "STROKE PATIENT MEDICAL HISTORY                               0\n",
       "ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY                 0\n",
       "HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY    0\n",
       "HYPERLIPIDEMIA PATIENT MEDICAL HISTORY                       0\n",
       "Stroke Group                                                 0\n",
       "ANTIHYPERLIPIDEMIC                                           0\n",
       "STATINS                                                      0\n",
       "ESTROGEN                                                     0\n",
       "ACE INHIBITORS                                               0\n",
       "ARBS                                                         0\n",
       "INSULIN(Yes_or_No)                                           0\n",
       "ORAL HYPOGLYCEMIC                                            0\n",
       "WBC K/uL                                                     0\n",
       "RBC m/uL                                                     0\n",
       "Hgb g/dL                                                     0\n",
       "Hgb<12.5                                                     0\n",
       "GLUCOSE mg/dL                                                0\n",
       "CHOLESTmg/dL                                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "222f7a3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['previous_tobacco_use'] = df['previous_tobacco_use'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1,'unknown':0,'UNKNOWN':0})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['current_tobacco_use'] = df['current_tobacco_use'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1,'unknown':0,'UNKNOWN':0})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['PREVIOUS ALCOHOL USE'] = df['PREVIOUS ALCOHOL USE'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Neuropathy AUTONOMIC SYMPTOMS'] = df['Neuropathy AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Dizziness AUTONOMIC SYMPTOMS'] = df['Dizziness AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Numbness AUTONOMIC SYMPTOMS'] = df['Numbness AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Painful feet AUTONOMIC SYMPTOMS'] = df['Painful feet AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Syncope AUTONOMIC SYMPTOMS'] = df['Syncope AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['CANCER PATIENT MEDICAL HISTORY'] = df['CANCER PATIENT MEDICAL HISTORY'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['STROKE PATIENT MEDICAL HISTORY'] = df['STROKE PATIENT MEDICAL HISTORY'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY'] = df['ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY'].replace({'YE':1,'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'] = df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'].replace({'YE':1,'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['HYPERLIPIDEMIA PATIENT MEDICAL HISTORY'] = df['HYPERLIPIDEMIA PATIENT MEDICAL HISTORY'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Stroke Group'] = df['Stroke Group'].replace({'Non-Stroke': 0, 'Stroke': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ANTIHYPERLIPIDEMIC'] = df['ANTIHYPERLIPIDEMIC'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['STATINS'] = df['STATINS'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ESTROGEN'] = df['ESTROGEN'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ACE INHIBITORS'] = df['ACE INHIBITORS'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ARBS'] = df['ARBS'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['INSULIN(Yes_or_No)'] = df['INSULIN(Yes_or_No)'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ORAL HYPOGLYCEMIC'] = df['ORAL HYPOGLYCEMIC'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\1717661195.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Hgb<12.5'] = df['Hgb<12.5'].replace({'Non-anemic': 0, 'Anemic': 1,'no':0,'yes':1})\n"
     ]
    }
   ],
   "source": [
    "df['previous_tobacco_use'] = df['previous_tobacco_use'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1,'unknown':0,'UNKNOWN':0})\n",
    "df['current_tobacco_use'] = df['current_tobacco_use'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1,'unknown':0,'UNKNOWN':0})\n",
    "df['PREVIOUS ALCOHOL USE'] = df['PREVIOUS ALCOHOL USE'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['Neuropathy AUTONOMIC SYMPTOMS'] = df['Neuropathy AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['Dizziness AUTONOMIC SYMPTOMS'] = df['Dizziness AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['Numbness AUTONOMIC SYMPTOMS'] = df['Numbness AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['Painful feet AUTONOMIC SYMPTOMS'] = df['Painful feet AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['Syncope AUTONOMIC SYMPTOMS'] = df['Syncope AUTONOMIC SYMPTOMS'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['CANCER PATIENT MEDICAL HISTORY'] = df['CANCER PATIENT MEDICAL HISTORY'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['STROKE PATIENT MEDICAL HISTORY'] = df['STROKE PATIENT MEDICAL HISTORY'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY'] = df['ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY'].replace({'YE':1,'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'] = df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'].replace({'YE':1,'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['HYPERLIPIDEMIA PATIENT MEDICAL HISTORY'] = df['HYPERLIPIDEMIA PATIENT MEDICAL HISTORY'].replace({'NO': 0, 'YES': 1,'no':0,'yes':1})\n",
    "df['Stroke Group'] = df['Stroke Group'].replace({'Non-Stroke': 0, 'Stroke': 1,'no':0,'yes':1})\n",
    "df['ANTIHYPERLIPIDEMIC'] = df['ANTIHYPERLIPIDEMIC'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
    "df['STATINS'] = df['STATINS'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
    "\n",
    "df['ESTROGEN'] = df['ESTROGEN'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
    "df['ACE INHIBITORS'] = df['ACE INHIBITORS'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
    "df['ARBS'] = df['ARBS'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
    "df['INSULIN(Yes_or_No)'] = df['INSULIN(Yes_or_No)'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
    "df['ORAL HYPOGLYCEMIC'] = df['ORAL HYPOGLYCEMIC'].replace({'N': 0, 'Y': 1,'no':0,'yes':1})\n",
    "df['Hgb<12.5'] = df['Hgb<12.5'].replace({'Non-anemic': 0, 'Anemic': 1,'no':0,'yes':1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dd80804d",
   "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>subject_number</th>\n",
       "      <th>age</th>\n",
       "      <th>height/m</th>\n",
       "      <th>mass/kg</th>\n",
       "      <th>bmi</th>\n",
       "      <th>gender</th>\n",
       "      <th>previous_tobacco_use</th>\n",
       "      <th>current_tobacco_use</th>\n",
       "      <th>PREVIOUS ALCOHOL USE</th>\n",
       "      <th>Neuropathy AUTONOMIC SYMPTOMS</th>\n",
       "      <th>...</th>\n",
       "      <th>ACE INHIBITORS</th>\n",
       "      <th>ARBS</th>\n",
       "      <th>INSULIN(Yes_or_No)</th>\n",
       "      <th>ORAL HYPOGLYCEMIC</th>\n",
       "      <th>WBC K/uL</th>\n",
       "      <th>RBC m/uL</th>\n",
       "      <th>Hgb g/dL</th>\n",
       "      <th>Hgb&lt;12.5</th>\n",
       "      <th>GLUCOSE mg/dL</th>\n",
       "      <th>CHOLESTmg/dL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S0030</td>\n",
       "      <td>64</td>\n",
       "      <td>1.6256</td>\n",
       "      <td>72.574779</td>\n",
       "      <td>27.463655</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.65</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S0033</td>\n",
       "      <td>65</td>\n",
       "      <td>1.8200</td>\n",
       "      <td>92.986000</td>\n",
       "      <td>28.072093</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.40</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S0064</td>\n",
       "      <td>76</td>\n",
       "      <td>1.7018</td>\n",
       "      <td>68.038855</td>\n",
       "      <td>23.493080</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.48</td>\n",
       "      <td>14.9</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>S0067</td>\n",
       "      <td>70</td>\n",
       "      <td>1.6764</td>\n",
       "      <td>68.492448</td>\n",
       "      <td>24.371788</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.79</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S0068</td>\n",
       "      <td>79</td>\n",
       "      <td>1.5748</td>\n",
       "      <td>64.863709</td>\n",
       "      <td>26.154774</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>4.57</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>S0401</td>\n",
       "      <td>64</td>\n",
       "      <td>1.6800</td>\n",
       "      <td>88.800000</td>\n",
       "      <td>31.462585</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.83</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>S0402</td>\n",
       "      <td>54</td>\n",
       "      <td>1.8200</td>\n",
       "      <td>96.450000</td>\n",
       "      <td>29.117860</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4.39</td>\n",
       "      <td>13.1</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>S0404</td>\n",
       "      <td>61</td>\n",
       "      <td>1.7200</td>\n",
       "      <td>77.800000</td>\n",
       "      <td>26.297999</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.09</td>\n",
       "      <td>16.3</td>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>S0412</td>\n",
       "      <td>65</td>\n",
       "      <td>1.5300</td>\n",
       "      <td>59.100000</td>\n",
       "      <td>25.246700</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5.27</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>S0414</td>\n",
       "      <td>63</td>\n",
       "      <td>1.7400</td>\n",
       "      <td>64.650000</td>\n",
       "      <td>21.353547</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.20</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject_number  age  height/m    mass/kg        bmi gender  \\\n",
       "1            S0030   64    1.6256  72.574779  27.463655      F   \n",
       "2            S0033   65    1.8200  92.986000  28.072093      M   \n",
       "4            S0064   76    1.7018  68.038855  23.493080      M   \n",
       "5            S0067   70    1.6764  68.492448  24.371788      M   \n",
       "6            S0068   79    1.5748  64.863709  26.154774      F   \n",
       "..             ...  ...       ...        ...        ...    ...   \n",
       "152          S0401   64    1.6800  88.800000  31.462585      M   \n",
       "153          S0402   54    1.8200  96.450000  29.117860      M   \n",
       "154          S0404   61    1.7200  77.800000  26.297999      M   \n",
       "156          S0412   65    1.5300  59.100000  25.246700      F   \n",
       "158          S0414   63    1.7400  64.650000  21.353547      F   \n",
       "\n",
       "     previous_tobacco_use  current_tobacco_use  PREVIOUS ALCOHOL USE  \\\n",
       "1                       0                    0                     1   \n",
       "2                       1                    0                     1   \n",
       "4                       0                    0                     1   \n",
       "5                       0                    0                     1   \n",
       "6                       0                    0                     0   \n",
       "..                    ...                  ...                   ...   \n",
       "152                     1                    0                     0   \n",
       "153                     1                    1                     1   \n",
       "154                     1                    0                     1   \n",
       "156                     1                    0                     0   \n",
       "158                     1                    0                     1   \n",
       "\n",
       "     Neuropathy AUTONOMIC SYMPTOMS  ...  ACE INHIBITORS ARBS  \\\n",
       "1                                0  ...               0    0   \n",
       "2                                0  ...               1    0   \n",
       "4                                0  ...               0    0   \n",
       "5                                0  ...               0    0   \n",
       "6                                0  ...               0    0   \n",
       "..                             ...  ...             ...  ...   \n",
       "152                              0  ...               0    0   \n",
       "153                              0  ...               0    0   \n",
       "154                              0  ...               0    0   \n",
       "156                              0  ...               0    0   \n",
       "158                              1  ...               1    0   \n",
       "\n",
       "     INSULIN(Yes_or_No)  ORAL HYPOGLYCEMIC WBC K/uL RBC m/uL Hgb g/dL  \\\n",
       "1                     0                  0      5.7     4.65     14.1   \n",
       "2                     0                  0      7.2     4.40     14.3   \n",
       "4                     0                  0      5.7     4.48     14.9   \n",
       "5                     0                  0      5.5     4.79     14.3   \n",
       "6                     0                  0     11.4     4.57     14.3   \n",
       "..                  ...                ...      ...      ...      ...   \n",
       "152                   0                  0      6.6     4.83     16.0   \n",
       "153                   0                  0      6.3     4.39     13.1   \n",
       "154                   0                  0      5.7     5.09     16.3   \n",
       "156                   0                  0      9.2     5.27     15.0   \n",
       "158                   0                  0      7.5     4.20     13.5   \n",
       "\n",
       "     Hgb<12.5  GLUCOSE mg/dL  CHOLESTmg/dL  \n",
       "1           0           74.0         270.0  \n",
       "2           0           76.0         160.0  \n",
       "4           0           75.0         204.0  \n",
       "5           0           60.0         192.0  \n",
       "6           0           80.0         203.0  \n",
       "..        ...            ...           ...  \n",
       "152         0           70.0         230.0  \n",
       "153         0           81.0         137.0  \n",
       "154         0           63.0         124.0  \n",
       "156         0           76.0         190.0  \n",
       "158         0           93.0         185.0  \n",
       "\n",
       "[118 rows x 37 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e548f3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3294156851.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['gender'] = df['gender'].replace({'F': 0, 'M': 1})\n"
     ]
    }
   ],
   "source": [
    "df['gender'] = df['gender'].replace({'F': 0, 'M': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6ca8e9d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers in Hgb g/dL\n",
      "   subject_number  age  height/m  mass/kg        bmi  gender  \\\n",
      "82          S0244   71      1.57    72.57  29.441357       0   \n",
      "\n",
      "    previous_tobacco_use  current_tobacco_use  PREVIOUS ALCOHOL USE  \\\n",
      "82                     0                    0                     1   \n",
      "\n",
      "    Neuropathy AUTONOMIC SYMPTOMS  ...  ACE INHIBITORS ARBS  \\\n",
      "82                              0  ...               0    0   \n",
      "\n",
      "    INSULIN(Yes_or_No)  ORAL HYPOGLYCEMIC WBC K/uL RBC m/uL Hgb g/dL  \\\n",
      "82                   0                  0      2.9     3.75     10.0   \n",
      "\n",
      "    Hgb<12.5  GLUCOSE mg/dL  CHOLESTmg/dL  \n",
      "82         1          106.0         174.0  \n",
      "\n",
      "[1 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load your dataset into a DataFrame\n",
    "# df = pd.read_csv('your_dataset.csv')\n",
    "\n",
    "# Specify the column for which you want to detect outliers\n",
    "column_name = 'Hgb g/dL'\n",
    "\n",
    "# Calculate the IQR for the specified column\n",
    "Q1 = df[column_name].quantile(0.25)\n",
    "Q3 = df[column_name].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Define a lower bound and upper bound for outliers\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "# Detect outliers in the specified column\n",
    "outliers = df[(df[column_name] < lower_bound) | (df[column_name] > upper_bound)]\n",
    "\n",
    "# Display the outliers\n",
    "print(\"Outliers in\", column_name)\n",
    "print(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "161d72ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "column_name = 'WBC K/uL'\n",
    "\n",
    "# Create a box plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(x=df[column_name])\n",
    "plt.title('Box Plot for ' + column_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7db57b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22b766625f0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "stroke_no = df[df['Stroke Group'] == 0]['age']\n",
    "\n",
    "stroke_yes=df[df['Stroke Group']==1]['age']\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number')\n",
    "plt.title('Prediction')\n",
    "plt.hist([stroke_no,stroke_yes],label=['No','Yes'])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "078ab891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22b766d3d00>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stroke_no = df[df['Stroke Group'] == 0]['STATINS']\n",
    "stroke_yes=df[df['Stroke Group']==1]['STATINS']\n",
    "plt.xlabel('Use of Statins')\n",
    "plt.ylabel('Number')\n",
    "plt.title('Prediction')\n",
    "plt.hist([stroke_no, stroke_yes], bins=[0, 1, 2], label=['No', 'Yes'], align='left')\n",
    "plt.xticks([0, 1])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3aac75da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "stroke_no = df[df['Stroke Group'] == 0]['previous_tobacco_use']\n",
    "stroke_yes = df[df['Stroke Group'] == 1]['previous_tobacco_use']\n",
    "\n",
    "plt.xlabel('Previous tobacco use')\n",
    "plt.ylabel('Number')\n",
    "plt.title('Prediction')\n",
    "plt.hist([stroke_no, stroke_yes], bins=[0, 1, 2], label=['No', 'Yes'], align='left')\n",
    "plt.xticks([0, 1])\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7151b1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22b76713580>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "stroke_no = df[df['Stroke Group'] == 0]['STROKE PATIENT MEDICAL HISTORY']\n",
    "\n",
    "stroke_yes=df[df['Stroke Group']==1]['STROKE PATIENT MEDICAL HISTORY']\n",
    "plt.xlabel('STROKE PATIENT MEDICAL HISTORY')\n",
    "plt.ylabel('Number')\n",
    "plt.title('Prediction')\n",
    "plt.hist([stroke_no, stroke_yes], bins=[0, 1, 2], label=['No', 'Yes'], align='left')\n",
    "plt.xticks([0, 1])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "784fbe9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22b767307f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stroke_no = df[df['Stroke Group'] == 0]['gender']\n",
    "stroke_yes=df[df['Stroke Group']==1]['gender']\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Number')\n",
    "plt.title('Prediction')\n",
    "plt.hist([stroke_no, stroke_yes], bins=[0, 1, 2], label=['Female', 'Male'], align='left')\n",
    "plt.xticks([0, 1])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "74fe1344",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\628451891.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['gender'] = df['gender'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "df['gender'] = df['gender'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ce13bf1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Numbness AUTONOMIC SYMPTOMS'] = df['Numbness AUTONOMIC SYMPTOMS'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Numbness AUTONOMIC SYMPTOMS'] = df['Numbness AUTONOMIC SYMPTOMS'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['cancer,# FAMILY HISTORY'] = df['cancer,# FAMILY HISTORY'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['cancer,# FAMILY HISTORY'] = df['cancer,# FAMILY HISTORY'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'] = df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'] = df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['HeartDisease FAMILY HISTORY'] = df['HeartDisease FAMILY HISTORY'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['HeartDisease FAMILY HISTORY'] = df['HeartDisease FAMILY HISTORY'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['StrokeFAMILY HISTORY'] = df['StrokeFAMILY HISTORY'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['StrokeFAMILY HISTORY'] = df['StrokeFAMILY HISTORY'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ESTROGEN'] = df['ESTROGEN'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ESTROGEN'] = df['ESTROGEN'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ACE INHIBITORS'] = df['ACE INHIBITORS'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ACE INHIBITORS'] = df['ACE INHIBITORS'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ARBS'] = df['ARBS'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ARBS'] = df['ARBS'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['INSULIN(Yes_or_No)'] = df['INSULIN(Yes_or_No)'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['INSULIN(Yes_or_No)'] = df['INSULIN(Yes_or_No)'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ORAL HYPOGLYCEMIC'] = df['ORAL HYPOGLYCEMIC'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ORAL HYPOGLYCEMIC'] = df['ORAL HYPOGLYCEMIC'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Stroke Group'] = df['Stroke Group'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Stroke Group'] = df['Stroke Group'].astype(int)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ANTIHYPERLIPIDEMIC'] = df['ANTIHYPERLIPIDEMIC'].astype(bool)\n",
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\3947613934.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['ANTIHYPERLIPIDEMIC'] = df['ANTIHYPERLIPIDEMIC'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "df['Numbness AUTONOMIC SYMPTOMS'] = df['Numbness AUTONOMIC SYMPTOMS'].astype(bool)\n",
    "df['Numbness AUTONOMIC SYMPTOMS'] = df['Numbness AUTONOMIC SYMPTOMS'].astype(int)\n",
    "df['cancer,# FAMILY HISTORY'] = df['cancer,# FAMILY HISTORY'].astype(bool)\n",
    "df['cancer,# FAMILY HISTORY'] = df['cancer,# FAMILY HISTORY'].astype(int)\n",
    "df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'] = df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'].astype(bool)\n",
    "df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'] = df['HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY'].astype(int)\n",
    "df['HeartDisease FAMILY HISTORY'] = df['HeartDisease FAMILY HISTORY'].astype(bool)\n",
    "df['HeartDisease FAMILY HISTORY'] = df['HeartDisease FAMILY HISTORY'].astype(int)\n",
    "df['StrokeFAMILY HISTORY'] = df['StrokeFAMILY HISTORY'].astype(bool)\n",
    "df['StrokeFAMILY HISTORY'] = df['StrokeFAMILY HISTORY'].astype(int)\n",
    "df['ESTROGEN'] = df['ESTROGEN'].astype(bool)\n",
    "df['ESTROGEN'] = df['ESTROGEN'].astype(int)\n",
    "df['ACE INHIBITORS'] = df['ACE INHIBITORS'].astype(bool)\n",
    "df['ACE INHIBITORS'] = df['ACE INHIBITORS'].astype(int)\n",
    "df['ARBS'] = df['ARBS'].astype(bool)\n",
    "df['ARBS'] = df['ARBS'].astype(int)\n",
    "df['INSULIN(Yes_or_No)'] = df['INSULIN(Yes_or_No)'].astype(bool)\n",
    "df['INSULIN(Yes_or_No)'] = df['INSULIN(Yes_or_No)'].astype(int)\n",
    "df['ORAL HYPOGLYCEMIC'] = df['ORAL HYPOGLYCEMIC'].astype(bool)\n",
    "df['ORAL HYPOGLYCEMIC'] = df['ORAL HYPOGLYCEMIC'].astype(int)\n",
    "df['Stroke Group'] = df['Stroke Group'].astype(bool)\n",
    "df['Stroke Group'] = df['Stroke Group'].astype(int)\n",
    "df['ANTIHYPERLIPIDEMIC'] = df['ANTIHYPERLIPIDEMIC'].astype(bool)\n",
    "df['ANTIHYPERLIPIDEMIC'] = df['ANTIHYPERLIPIDEMIC'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62256732",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 118 entries, 1 to 158\n",
      "Data columns (total 37 columns):\n",
      " #   Column                                                     Non-Null Count  Dtype  \n",
      "---  ------                                                     --------------  -----  \n",
      " 0   subject_number                                             118 non-null    object \n",
      " 1   age                                                        118 non-null    int64  \n",
      " 2   height/m                                                   118 non-null    float64\n",
      " 3   mass/kg                                                    118 non-null    float64\n",
      " 4   bmi                                                        118 non-null    float64\n",
      " 5   gender                                                     118 non-null    int32  \n",
      " 6   previous_tobacco_use                                       118 non-null    int64  \n",
      " 7   current_tobacco_use                                        118 non-null    int64  \n",
      " 8   PREVIOUS ALCOHOL USE                                       118 non-null    int64  \n",
      " 9   Neuropathy AUTONOMIC SYMPTOMS                              118 non-null    int64  \n",
      " 10  Dizziness AUTONOMIC SYMPTOMS                               118 non-null    int64  \n",
      " 11  Numbness AUTONOMIC SYMPTOMS                                118 non-null    int32  \n",
      " 12  Painful feet AUTONOMIC SYMPTOMS                            118 non-null    int64  \n",
      " 13  Syncope AUTONOMIC SYMPTOMS                                 118 non-null    int64  \n",
      " 14  cancer,# FAMILY HISTORY                                    118 non-null    int32  \n",
      " 15  HeartDisease FAMILY HISTORY                                118 non-null    int32  \n",
      " 16  StrokeFAMILY HISTORY                                       118 non-null    int32  \n",
      " 17  HTN YRS PATIENT MEDICAL HISTORY                            118 non-null    int64  \n",
      " 18  CANCER PATIENT MEDICAL HISTORY                             118 non-null    int64  \n",
      " 19  STROKE PATIENT MEDICAL HISTORY                             118 non-null    int64  \n",
      " 20  ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY               118 non-null    int64  \n",
      " 21  HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY  118 non-null    int32  \n",
      " 22  HYPERLIPIDEMIA PATIENT MEDICAL HISTORY                     118 non-null    int64  \n",
      " 23  Stroke Group                                               118 non-null    int32  \n",
      " 24  ANTIHYPERLIPIDEMIC                                         118 non-null    int32  \n",
      " 25  STATINS                                                    118 non-null    int64  \n",
      " 26  ESTROGEN                                                   118 non-null    int32  \n",
      " 27  ACE INHIBITORS                                             118 non-null    int32  \n",
      " 28  ARBS                                                       118 non-null    int32  \n",
      " 29  INSULIN(Yes_or_No)                                         118 non-null    int32  \n",
      " 30  ORAL HYPOGLYCEMIC                                          118 non-null    int32  \n",
      " 31  WBC K/uL                                                   118 non-null    float64\n",
      " 32  RBC m/uL                                                   118 non-null    float64\n",
      " 33  Hgb g/dL                                                   118 non-null    float64\n",
      " 34  Hgb<12.5                                                   118 non-null    int64  \n",
      " 35  GLUCOSE mg/dL                                              118 non-null    float64\n",
      " 36  CHOLESTmg/dL                                               118 non-null    float64\n",
      "dtypes: float64(8), int32(13), int64(15), object(1)\n",
      "memory usage: 29.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "18c5cbcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SANIKA\\AppData\\Local\\Temp\\ipykernel_17536\\2419203179.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[cols_to_scale] = scaler.fit_transform(df[cols_to_scale])\n"
     ]
    }
   ],
   "source": [
    "cols_to_scale = ['height/m','mass/kg','bmi','WBC K/uL','RBC m/uL','Hgb g/dL','GLUCOSE mg/dL','CHOLESTmg/dL','HTN YRS PATIENT MEDICAL HISTORY']\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "df[cols_to_scale] = scaler.fit_transform(df[cols_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9a354309",
   "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>subject_number</th>\n",
       "      <th>age</th>\n",
       "      <th>height/m</th>\n",
       "      <th>mass/kg</th>\n",
       "      <th>bmi</th>\n",
       "      <th>gender</th>\n",
       "      <th>previous_tobacco_use</th>\n",
       "      <th>current_tobacco_use</th>\n",
       "      <th>PREVIOUS ALCOHOL USE</th>\n",
       "      <th>Neuropathy AUTONOMIC SYMPTOMS</th>\n",
       "      <th>...</th>\n",
       "      <th>ACE INHIBITORS</th>\n",
       "      <th>ARBS</th>\n",
       "      <th>INSULIN(Yes_or_No)</th>\n",
       "      <th>ORAL HYPOGLYCEMIC</th>\n",
       "      <th>WBC K/uL</th>\n",
       "      <th>RBC m/uL</th>\n",
       "      <th>Hgb g/dL</th>\n",
       "      <th>Hgb&lt;12.5</th>\n",
       "      <th>GLUCOSE mg/dL</th>\n",
       "      <th>CHOLESTmg/dL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S0030</td>\n",
       "      <td>64</td>\n",
       "      <td>0.404997</td>\n",
       "      <td>0.318856</td>\n",
       "      <td>0.363712</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>0.524510</td>\n",
       "      <td>0.569444</td>\n",
       "      <td>0</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.796296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S0033</td>\n",
       "      <td>65</td>\n",
       "      <td>0.910984</td>\n",
       "      <td>0.562020</td>\n",
       "      <td>0.387722</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.439252</td>\n",
       "      <td>0.401961</td>\n",
       "      <td>0.597222</td>\n",
       "      <td>0</td>\n",
       "      <td>0.239130</td>\n",
       "      <td>0.287037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S0064</td>\n",
       "      <td>76</td>\n",
       "      <td>0.603332</td>\n",
       "      <td>0.264818</td>\n",
       "      <td>0.207025</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>0.441176</td>\n",
       "      <td>0.680556</td>\n",
       "      <td>0</td>\n",
       "      <td>0.228261</td>\n",
       "      <td>0.490741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>S0067</td>\n",
       "      <td>70</td>\n",
       "      <td>0.537220</td>\n",
       "      <td>0.270222</td>\n",
       "      <td>0.241701</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.280374</td>\n",
       "      <td>0.593137</td>\n",
       "      <td>0.597222</td>\n",
       "      <td>0</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.435185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S0068</td>\n",
       "      <td>79</td>\n",
       "      <td>0.272775</td>\n",
       "      <td>0.226992</td>\n",
       "      <td>0.312061</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.831776</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.597222</td>\n",
       "      <td>0</td>\n",
       "      <td>0.282609</td>\n",
       "      <td>0.486111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>S0401</td>\n",
       "      <td>64</td>\n",
       "      <td>0.546590</td>\n",
       "      <td>0.512152</td>\n",
       "      <td>0.521517</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.383178</td>\n",
       "      <td>0.612745</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>S0402</td>\n",
       "      <td>54</td>\n",
       "      <td>0.910984</td>\n",
       "      <td>0.603288</td>\n",
       "      <td>0.428990</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.355140</td>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.430556</td>\n",
       "      <td>0</td>\n",
       "      <td>0.293478</td>\n",
       "      <td>0.180556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>S0404</td>\n",
       "      <td>61</td>\n",
       "      <td>0.650703</td>\n",
       "      <td>0.381106</td>\n",
       "      <td>0.317713</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>0.740196</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.097826</td>\n",
       "      <td>0.120370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>S0412</td>\n",
       "      <td>65</td>\n",
       "      <td>0.156169</td>\n",
       "      <td>0.158327</td>\n",
       "      <td>0.276226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.626168</td>\n",
       "      <td>0.828431</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>0</td>\n",
       "      <td>0.239130</td>\n",
       "      <td>0.425926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>S0414</td>\n",
       "      <td>63</td>\n",
       "      <td>0.702759</td>\n",
       "      <td>0.224446</td>\n",
       "      <td>0.122595</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.467290</td>\n",
       "      <td>0.303922</td>\n",
       "      <td>0.486111</td>\n",
       "      <td>0</td>\n",
       "      <td>0.423913</td>\n",
       "      <td>0.402778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject_number  age  height/m   mass/kg       bmi  gender  \\\n",
       "1            S0030   64  0.404997  0.318856  0.363712       0   \n",
       "2            S0033   65  0.910984  0.562020  0.387722       1   \n",
       "4            S0064   76  0.603332  0.264818  0.207025       1   \n",
       "5            S0067   70  0.537220  0.270222  0.241701       1   \n",
       "6            S0068   79  0.272775  0.226992  0.312061       0   \n",
       "..             ...  ...       ...       ...       ...     ...   \n",
       "152          S0401   64  0.546590  0.512152  0.521517       1   \n",
       "153          S0402   54  0.910984  0.603288  0.428990       1   \n",
       "154          S0404   61  0.650703  0.381106  0.317713       1   \n",
       "156          S0412   65  0.156169  0.158327  0.276226       0   \n",
       "158          S0414   63  0.702759  0.224446  0.122595       0   \n",
       "\n",
       "     previous_tobacco_use  current_tobacco_use  PREVIOUS ALCOHOL USE  \\\n",
       "1                       0                    0                     1   \n",
       "2                       1                    0                     1   \n",
       "4                       0                    0                     1   \n",
       "5                       0                    0                     1   \n",
       "6                       0                    0                     0   \n",
       "..                    ...                  ...                   ...   \n",
       "152                     1                    0                     0   \n",
       "153                     1                    1                     1   \n",
       "154                     1                    0                     1   \n",
       "156                     1                    0                     0   \n",
       "158                     1                    0                     1   \n",
       "\n",
       "     Neuropathy AUTONOMIC SYMPTOMS  ...  ACE INHIBITORS  ARBS  \\\n",
       "1                                0  ...               0     0   \n",
       "2                                0  ...               1     0   \n",
       "4                                0  ...               0     0   \n",
       "5                                0  ...               0     0   \n",
       "6                                0  ...               0     0   \n",
       "..                             ...  ...             ...   ...   \n",
       "152                              0  ...               0     0   \n",
       "153                              0  ...               0     0   \n",
       "154                              0  ...               0     0   \n",
       "156                              0  ...               0     0   \n",
       "158                              1  ...               1     0   \n",
       "\n",
       "     INSULIN(Yes_or_No)  ORAL HYPOGLYCEMIC  WBC K/uL  RBC m/uL  Hgb g/dL  \\\n",
       "1                     0                  0  0.299065  0.524510  0.569444   \n",
       "2                     0                  0  0.439252  0.401961  0.597222   \n",
       "4                     0                  0  0.299065  0.441176  0.680556   \n",
       "5                     0                  0  0.280374  0.593137  0.597222   \n",
       "6                     0                  0  0.831776  0.485294  0.597222   \n",
       "..                  ...                ...       ...       ...       ...   \n",
       "152                   0                  0  0.383178  0.612745  0.833333   \n",
       "153                   0                  0  0.355140  0.397059  0.430556   \n",
       "154                   0                  0  0.299065  0.740196  0.875000   \n",
       "156                   0                  0  0.626168  0.828431  0.694444   \n",
       "158                   0                  0  0.467290  0.303922  0.486111   \n",
       "\n",
       "     Hgb<12.5  GLUCOSE mg/dL  CHOLESTmg/dL  \n",
       "1           0       0.217391      0.796296  \n",
       "2           0       0.239130      0.287037  \n",
       "4           0       0.228261      0.490741  \n",
       "5           0       0.065217      0.435185  \n",
       "6           0       0.282609      0.486111  \n",
       "..        ...            ...           ...  \n",
       "152         0       0.173913      0.611111  \n",
       "153         0       0.293478      0.180556  \n",
       "154         0       0.097826      0.120370  \n",
       "156         0       0.239130      0.425926  \n",
       "158         0       0.423913      0.402778  \n",
       "\n",
       "[118 rows x 37 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4e1d6d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 118 entries, 1 to 158\n",
      "Data columns (total 37 columns):\n",
      " #   Column                                                     Non-Null Count  Dtype  \n",
      "---  ------                                                     --------------  -----  \n",
      " 0   subject_number                                             118 non-null    object \n",
      " 1   age                                                        118 non-null    int64  \n",
      " 2   height/m                                                   118 non-null    float64\n",
      " 3   mass/kg                                                    118 non-null    float64\n",
      " 4   bmi                                                        118 non-null    float64\n",
      " 5   gender                                                     118 non-null    int32  \n",
      " 6   previous_tobacco_use                                       118 non-null    int64  \n",
      " 7   current_tobacco_use                                        118 non-null    int64  \n",
      " 8   PREVIOUS ALCOHOL USE                                       118 non-null    int64  \n",
      " 9   Neuropathy AUTONOMIC SYMPTOMS                              118 non-null    int64  \n",
      " 10  Dizziness AUTONOMIC SYMPTOMS                               118 non-null    int64  \n",
      " 11  Numbness AUTONOMIC SYMPTOMS                                118 non-null    int32  \n",
      " 12  Painful feet AUTONOMIC SYMPTOMS                            118 non-null    int64  \n",
      " 13  Syncope AUTONOMIC SYMPTOMS                                 118 non-null    int64  \n",
      " 14  cancer,# FAMILY HISTORY                                    118 non-null    int32  \n",
      " 15  HeartDisease FAMILY HISTORY                                118 non-null    int32  \n",
      " 16  StrokeFAMILY HISTORY                                       118 non-null    int32  \n",
      " 17  HTN YRS PATIENT MEDICAL HISTORY                            118 non-null    float64\n",
      " 18  CANCER PATIENT MEDICAL HISTORY                             118 non-null    int64  \n",
      " 19  STROKE PATIENT MEDICAL HISTORY                             118 non-null    int64  \n",
      " 20  ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY               118 non-null    int64  \n",
      " 21  HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY  118 non-null    int32  \n",
      " 22  HYPERLIPIDEMIA PATIENT MEDICAL HISTORY                     118 non-null    int64  \n",
      " 23  Stroke Group                                               118 non-null    int32  \n",
      " 24  ANTIHYPERLIPIDEMIC                                         118 non-null    int32  \n",
      " 25  STATINS                                                    118 non-null    int64  \n",
      " 26  ESTROGEN                                                   118 non-null    int32  \n",
      " 27  ACE INHIBITORS                                             118 non-null    int32  \n",
      " 28  ARBS                                                       118 non-null    int32  \n",
      " 29  INSULIN(Yes_or_No)                                         118 non-null    int32  \n",
      " 30  ORAL HYPOGLYCEMIC                                          118 non-null    int32  \n",
      " 31  WBC K/uL                                                   118 non-null    float64\n",
      " 32  RBC m/uL                                                   118 non-null    float64\n",
      " 33  Hgb g/dL                                                   118 non-null    float64\n",
      " 34  Hgb<12.5                                                   118 non-null    int64  \n",
      " 35  GLUCOSE mg/dL                                              118 non-null    float64\n",
      " 36  CHOLESTmg/dL                                               118 non-null    float64\n",
      "dtypes: float64(9), int32(13), int64(14), object(1)\n",
      "memory usage: 29.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5993b1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('subject_number', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2435c8f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height/m</th>\n",
       "      <th>mass/kg</th>\n",
       "      <th>bmi</th>\n",
       "      <th>gender</th>\n",
       "      <th>previous_tobacco_use</th>\n",
       "      <th>current_tobacco_use</th>\n",
       "      <th>PREVIOUS ALCOHOL USE</th>\n",
       "      <th>Neuropathy AUTONOMIC SYMPTOMS</th>\n",
       "      <th>Dizziness AUTONOMIC SYMPTOMS</th>\n",
       "      <th>...</th>\n",
       "      <th>ACE INHIBITORS</th>\n",
       "      <th>ARBS</th>\n",
       "      <th>INSULIN(Yes_or_No)</th>\n",
       "      <th>ORAL HYPOGLYCEMIC</th>\n",
       "      <th>WBC K/uL</th>\n",
       "      <th>RBC m/uL</th>\n",
       "      <th>Hgb g/dL</th>\n",
       "      <th>Hgb&lt;12.5</th>\n",
       "      <th>GLUCOSE mg/dL</th>\n",
       "      <th>CHOLESTmg/dL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>0.404997</td>\n",
       "      <td>0.318856</td>\n",
       "      <td>0.363712</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.299065</td>\n",
       "      <td>0.524510</td>\n",
       "      <td>0.569444</td>\n",
       "      <td>0</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.796296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>0.910984</td>\n",
       "      <td>0.562020</td>\n",
       "      <td>0.387722</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.439252</td>\n",
       "      <td>0.401961</td>\n",
       "      <td>0.597222</td>\n",
       "      <td>0</td>\n",
       "      <td>0.239130</td>\n",
       "      <td>0.287037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76</td>\n",
       "      <td>0.603332</td>\n",
       "      <td>0.264818</td>\n",
       "      <td>0.207025</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>0.441176</td>\n",
       "      <td>0.680556</td>\n",
       "      <td>0</td>\n",
       "      <td>0.228261</td>\n",
       "      <td>0.490741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70</td>\n",
       "      <td>0.537220</td>\n",
       "      <td>0.270222</td>\n",
       "      <td>0.241701</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.280374</td>\n",
       "      <td>0.593137</td>\n",
       "      <td>0.597222</td>\n",
       "      <td>0</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.435185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79</td>\n",
       "      <td>0.272775</td>\n",
       "      <td>0.226992</td>\n",
       "      <td>0.312061</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.831776</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.597222</td>\n",
       "      <td>0</td>\n",
       "      <td>0.282609</td>\n",
       "      <td>0.486111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>64</td>\n",
       "      <td>0.546590</td>\n",
       "      <td>0.512152</td>\n",
       "      <td>0.521517</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.383178</td>\n",
       "      <td>0.612745</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>54</td>\n",
       "      <td>0.910984</td>\n",
       "      <td>0.603288</td>\n",
       "      <td>0.428990</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.355140</td>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.430556</td>\n",
       "      <td>0</td>\n",
       "      <td>0.293478</td>\n",
       "      <td>0.180556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>61</td>\n",
       "      <td>0.650703</td>\n",
       "      <td>0.381106</td>\n",
       "      <td>0.317713</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>0.740196</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.097826</td>\n",
       "      <td>0.120370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>65</td>\n",
       "      <td>0.156169</td>\n",
       "      <td>0.158327</td>\n",
       "      <td>0.276226</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.626168</td>\n",
       "      <td>0.828431</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>0</td>\n",
       "      <td>0.239130</td>\n",
       "      <td>0.425926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>63</td>\n",
       "      <td>0.702759</td>\n",
       "      <td>0.224446</td>\n",
       "      <td>0.122595</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.467290</td>\n",
       "      <td>0.303922</td>\n",
       "      <td>0.486111</td>\n",
       "      <td>0</td>\n",
       "      <td>0.423913</td>\n",
       "      <td>0.402778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  height/m   mass/kg       bmi  gender  previous_tobacco_use  \\\n",
       "1     64  0.404997  0.318856  0.363712       0                     0   \n",
       "2     65  0.910984  0.562020  0.387722       1                     1   \n",
       "4     76  0.603332  0.264818  0.207025       1                     0   \n",
       "5     70  0.537220  0.270222  0.241701       1                     0   \n",
       "6     79  0.272775  0.226992  0.312061       0                     0   \n",
       "..   ...       ...       ...       ...     ...                   ...   \n",
       "152   64  0.546590  0.512152  0.521517       1                     1   \n",
       "153   54  0.910984  0.603288  0.428990       1                     1   \n",
       "154   61  0.650703  0.381106  0.317713       1                     1   \n",
       "156   65  0.156169  0.158327  0.276226       0                     1   \n",
       "158   63  0.702759  0.224446  0.122595       0                     1   \n",
       "\n",
       "     current_tobacco_use  PREVIOUS ALCOHOL USE  Neuropathy AUTONOMIC SYMPTOMS  \\\n",
       "1                      0                     1                              0   \n",
       "2                      0                     1                              0   \n",
       "4                      0                     1                              0   \n",
       "5                      0                     1                              0   \n",
       "6                      0                     0                              0   \n",
       "..                   ...                   ...                            ...   \n",
       "152                    0                     0                              0   \n",
       "153                    1                     1                              0   \n",
       "154                    0                     1                              0   \n",
       "156                    0                     0                              0   \n",
       "158                    0                     1                              1   \n",
       "\n",
       "     Dizziness AUTONOMIC SYMPTOMS  ...  ACE INHIBITORS  ARBS  \\\n",
       "1                               0  ...               0     0   \n",
       "2                               0  ...               1     0   \n",
       "4                               0  ...               0     0   \n",
       "5                               0  ...               0     0   \n",
       "6                               0  ...               0     0   \n",
       "..                            ...  ...             ...   ...   \n",
       "152                             0  ...               0     0   \n",
       "153                             0  ...               0     0   \n",
       "154                             0  ...               0     0   \n",
       "156                             0  ...               0     0   \n",
       "158                             1  ...               1     0   \n",
       "\n",
       "     INSULIN(Yes_or_No)  ORAL HYPOGLYCEMIC  WBC K/uL  RBC m/uL  Hgb g/dL  \\\n",
       "1                     0                  0  0.299065  0.524510  0.569444   \n",
       "2                     0                  0  0.439252  0.401961  0.597222   \n",
       "4                     0                  0  0.299065  0.441176  0.680556   \n",
       "5                     0                  0  0.280374  0.593137  0.597222   \n",
       "6                     0                  0  0.831776  0.485294  0.597222   \n",
       "..                  ...                ...       ...       ...       ...   \n",
       "152                   0                  0  0.383178  0.612745  0.833333   \n",
       "153                   0                  0  0.355140  0.397059  0.430556   \n",
       "154                   0                  0  0.299065  0.740196  0.875000   \n",
       "156                   0                  0  0.626168  0.828431  0.694444   \n",
       "158                   0                  0  0.467290  0.303922  0.486111   \n",
       "\n",
       "     Hgb<12.5  GLUCOSE mg/dL  CHOLESTmg/dL  \n",
       "1           0       0.217391      0.796296  \n",
       "2           0       0.239130      0.287037  \n",
       "4           0       0.228261      0.490741  \n",
       "5           0       0.065217      0.435185  \n",
       "6           0       0.282609      0.486111  \n",
       "..        ...            ...           ...  \n",
       "152         0       0.173913      0.611111  \n",
       "153         0       0.293478      0.180556  \n",
       "154         0       0.097826      0.120370  \n",
       "156         0       0.239130      0.425926  \n",
       "158         0       0.423913      0.402778  \n",
       "\n",
       "[118 rows x 36 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1a5089c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 118 entries, 1 to 158\n",
      "Data columns (total 36 columns):\n",
      " #   Column                                                     Non-Null Count  Dtype  \n",
      "---  ------                                                     --------------  -----  \n",
      " 0   age                                                        118 non-null    int64  \n",
      " 1   height/m                                                   118 non-null    float64\n",
      " 2   mass/kg                                                    118 non-null    float64\n",
      " 3   bmi                                                        118 non-null    float64\n",
      " 4   gender                                                     118 non-null    int32  \n",
      " 5   previous_tobacco_use                                       118 non-null    int64  \n",
      " 6   current_tobacco_use                                        118 non-null    int64  \n",
      " 7   PREVIOUS ALCOHOL USE                                       118 non-null    int64  \n",
      " 8   Neuropathy AUTONOMIC SYMPTOMS                              118 non-null    int64  \n",
      " 9   Dizziness AUTONOMIC SYMPTOMS                               118 non-null    int64  \n",
      " 10  Numbness AUTONOMIC SYMPTOMS                                118 non-null    int32  \n",
      " 11  Painful feet AUTONOMIC SYMPTOMS                            118 non-null    int64  \n",
      " 12  Syncope AUTONOMIC SYMPTOMS                                 118 non-null    int64  \n",
      " 13  cancer,# FAMILY HISTORY                                    118 non-null    int32  \n",
      " 14  HeartDisease FAMILY HISTORY                                118 non-null    int32  \n",
      " 15  StrokeFAMILY HISTORY                                       118 non-null    int32  \n",
      " 16  HTN YRS PATIENT MEDICAL HISTORY                            118 non-null    float64\n",
      " 17  CANCER PATIENT MEDICAL HISTORY                             118 non-null    int64  \n",
      " 18  STROKE PATIENT MEDICAL HISTORY                             118 non-null    int64  \n",
      " 19  ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY               118 non-null    int64  \n",
      " 20  HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDICAL HISTORY  118 non-null    int32  \n",
      " 21  HYPERLIPIDEMIA PATIENT MEDICAL HISTORY                     118 non-null    int64  \n",
      " 22  Stroke Group                                               118 non-null    int32  \n",
      " 23  ANTIHYPERLIPIDEMIC                                         118 non-null    int32  \n",
      " 24  STATINS                                                    118 non-null    int64  \n",
      " 25  ESTROGEN                                                   118 non-null    int32  \n",
      " 26  ACE INHIBITORS                                             118 non-null    int32  \n",
      " 27  ARBS                                                       118 non-null    int32  \n",
      " 28  INSULIN(Yes_or_No)                                         118 non-null    int32  \n",
      " 29  ORAL HYPOGLYCEMIC                                          118 non-null    int32  \n",
      " 30  WBC K/uL                                                   118 non-null    float64\n",
      " 31  RBC m/uL                                                   118 non-null    float64\n",
      " 32  Hgb g/dL                                                   118 non-null    float64\n",
      " 33  Hgb<12.5                                                   118 non-null    int64  \n",
      " 34  GLUCOSE mg/dL                                              118 non-null    float64\n",
      " 35  CHOLESTmg/dL                                               118 non-null    float64\n",
      "dtypes: float64(9), int32(13), int64(14)\n",
      "memory usage: 28.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "896271d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data into features and variables\n",
    "X = df.drop(columns=['Stroke Group'])\n",
    "y = df['Stroke Group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e09c92f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#Splitting data into training and validation sets\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6c15f951",
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>STROKE PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.449983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.054413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CHOLESTmg/dL</td>\n",
       "      <td>0.052481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmi</td>\n",
       "      <td>0.050010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HTN YRS PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.041212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mass/kg</td>\n",
       "      <td>0.034255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Hgb g/dL</td>\n",
       "      <td>0.034152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>STATINS</td>\n",
       "      <td>0.031154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>height/m</td>\n",
       "      <td>0.030649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GLUCOSE mg/dL</td>\n",
       "      <td>0.028598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WBC K/uL</td>\n",
       "      <td>0.027089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>RBC m/uL</td>\n",
       "      <td>0.026860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PREVIOUS ALCOHOL USE</td>\n",
       "      <td>0.021265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>previous_tobacco_use</td>\n",
       "      <td>0.018282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ACE INHIBITORS</td>\n",
       "      <td>0.017720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dizziness AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.014120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ARBS</td>\n",
       "      <td>0.011788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HYPERLIPIDEMIA PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.007409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Syncope AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.007308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.005975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Hgb&lt;12.5</td>\n",
       "      <td>0.005759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ESTROGEN</td>\n",
       "      <td>0.005008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Numbness AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.004571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>current_tobacco_use</td>\n",
       "      <td>0.004311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ANTIHYPERLIPIDEMIC</td>\n",
       "      <td>0.003817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CANCER PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.003059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDI...</td>\n",
       "      <td>0.002946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.002605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Neuropathy AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.001842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Painful feet AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ORAL HYPOGLYCEMIC</td>\n",
       "      <td>0.000416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>INSULIN(Yes_or_No)</td>\n",
       "      <td>0.000406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>StrokeFAMILY HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HeartDisease FAMILY HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cancer,# FAMILY HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Feature  Importance\n",
       "18                     STROKE PATIENT MEDICAL HISTORY    0.449983\n",
       "0                                                 age    0.054413\n",
       "34                                       CHOLESTmg/dL    0.052481\n",
       "3                                                 bmi    0.050010\n",
       "16                    HTN YRS PATIENT MEDICAL HISTORY    0.041212\n",
       "2                                             mass/kg    0.034255\n",
       "31                                           Hgb g/dL    0.034152\n",
       "23                                            STATINS    0.031154\n",
       "1                                            height/m    0.030649\n",
       "33                                      GLUCOSE mg/dL    0.028598\n",
       "29                                           WBC K/uL    0.027089\n",
       "30                                           RBC m/uL    0.026860\n",
       "7                                PREVIOUS ALCOHOL USE    0.021265\n",
       "5                                previous_tobacco_use    0.018282\n",
       "25                                     ACE INHIBITORS    0.017720\n",
       "9                        Dizziness AUTONOMIC SYMPTOMS    0.014120\n",
       "26                                               ARBS    0.011788\n",
       "21             HYPERLIPIDEMIA PATIENT MEDICAL HISTORY    0.007409\n",
       "12                         Syncope AUTONOMIC SYMPTOMS    0.007308\n",
       "19       ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY    0.005975\n",
       "32                                           Hgb<12.5    0.005759\n",
       "24                                           ESTROGEN    0.005008\n",
       "10                        Numbness AUTONOMIC SYMPTOMS    0.004571\n",
       "6                                 current_tobacco_use    0.004311\n",
       "22                                 ANTIHYPERLIPIDEMIC    0.003817\n",
       "17                     CANCER PATIENT MEDICAL HISTORY    0.003059\n",
       "20  HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDI...    0.002946\n",
       "4                                              gender    0.002605\n",
       "8                       Neuropathy AUTONOMIC SYMPTOMS    0.001842\n",
       "11                    Painful feet AUTONOMIC SYMPTOMS    0.000537\n",
       "28                                  ORAL HYPOGLYCEMIC    0.000416\n",
       "27                                 INSULIN(Yes_or_No)    0.000406\n",
       "15                               StrokeFAMILY HISTORY    0.000000\n",
       "14                        HeartDisease FAMILY HISTORY    0.000000\n",
       "13                            cancer,# FAMILY HISTORY    0.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Extract feature importances\n",
    "feature_importances = clf.feature_importances_\n",
    "\n",
    "# Get the corresponding feature names\n",
    "feature_names = X.columns\n",
    "\n",
    "# Create a DataFrame to display the feature names and importances\n",
    "feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
    "\n",
    "# Sort the DataFrame by importance in descending order\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "feature_importance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7e41fbcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_=['Importance']\n",
    "feature_importance_df[col_] = scaler.fit_transform(feature_importance_df[col_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "85e9dcb5",
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>STROKE PATIENT MEDICAL HISTORY</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.120922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CHOLESTmg/dL</td>\n",
       "      <td>0.116628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmi</td>\n",
       "      <td>0.111138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HTN YRS PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.091586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mass/kg</td>\n",
       "      <td>0.076125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Hgb g/dL</td>\n",
       "      <td>0.075897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>STATINS</td>\n",
       "      <td>0.069234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>height/m</td>\n",
       "      <td>0.068111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GLUCOSE mg/dL</td>\n",
       "      <td>0.063554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WBC K/uL</td>\n",
       "      <td>0.060201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>RBC m/uL</td>\n",
       "      <td>0.059692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PREVIOUS ALCOHOL USE</td>\n",
       "      <td>0.047258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>previous_tobacco_use</td>\n",
       "      <td>0.040628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ACE INHIBITORS</td>\n",
       "      <td>0.039379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dizziness AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.031379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ARBS</td>\n",
       "      <td>0.026198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HYPERLIPIDEMIA PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.016465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Syncope AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.016240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.013277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Hgb&lt;12.5</td>\n",
       "      <td>0.012799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ESTROGEN</td>\n",
       "      <td>0.011129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Numbness AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.010159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>current_tobacco_use</td>\n",
       "      <td>0.009580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ANTIHYPERLIPIDEMIC</td>\n",
       "      <td>0.008483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CANCER PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.006798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDI...</td>\n",
       "      <td>0.006547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.005789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Neuropathy AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.004094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Painful feet AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.001193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ORAL HYPOGLYCEMIC</td>\n",
       "      <td>0.000924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>INSULIN(Yes_or_No)</td>\n",
       "      <td>0.000902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>StrokeFAMILY HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HeartDisease FAMILY HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cancer,# FAMILY HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Feature  Importance\n",
       "18                     STROKE PATIENT MEDICAL HISTORY    1.000000\n",
       "0                                                 age    0.120922\n",
       "34                                       CHOLESTmg/dL    0.116628\n",
       "3                                                 bmi    0.111138\n",
       "16                    HTN YRS PATIENT MEDICAL HISTORY    0.091586\n",
       "2                                             mass/kg    0.076125\n",
       "31                                           Hgb g/dL    0.075897\n",
       "23                                            STATINS    0.069234\n",
       "1                                            height/m    0.068111\n",
       "33                                      GLUCOSE mg/dL    0.063554\n",
       "29                                           WBC K/uL    0.060201\n",
       "30                                           RBC m/uL    0.059692\n",
       "7                                PREVIOUS ALCOHOL USE    0.047258\n",
       "5                                previous_tobacco_use    0.040628\n",
       "25                                     ACE INHIBITORS    0.039379\n",
       "9                        Dizziness AUTONOMIC SYMPTOMS    0.031379\n",
       "26                                               ARBS    0.026198\n",
       "21             HYPERLIPIDEMIA PATIENT MEDICAL HISTORY    0.016465\n",
       "12                         Syncope AUTONOMIC SYMPTOMS    0.016240\n",
       "19       ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY    0.013277\n",
       "32                                           Hgb<12.5    0.012799\n",
       "24                                           ESTROGEN    0.011129\n",
       "10                        Numbness AUTONOMIC SYMPTOMS    0.010159\n",
       "6                                 current_tobacco_use    0.009580\n",
       "22                                 ANTIHYPERLIPIDEMIC    0.008483\n",
       "17                     CANCER PATIENT MEDICAL HISTORY    0.006798\n",
       "20  HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDI...    0.006547\n",
       "4                                              gender    0.005789\n",
       "8                       Neuropathy AUTONOMIC SYMPTOMS    0.004094\n",
       "11                    Painful feet AUTONOMIC SYMPTOMS    0.001193\n",
       "28                                  ORAL HYPOGLYCEMIC    0.000924\n",
       "27                                 INSULIN(Yes_or_No)    0.000902\n",
       "15                               StrokeFAMILY HISTORY    0.000000\n",
       "14                        HeartDisease FAMILY HISTORY    0.000000\n",
       "13                            cancer,# FAMILY HISTORY    0.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "82405b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,f1_score,precision_score,recall_score,roc_auc_score\n",
    "\n",
    "models={\n",
    "    'Logistic Regression':LogisticRegression(),\n",
    "    'SVM':SVC(),\n",
    "    'Decision Tree':DecisionTreeClassifier(),\n",
    "    'Random Classifier':RandomForestClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f9657fe7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SANIKA\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "*******************\n",
      "Test performance:\n",
      "------------------\n",
      "model_test_score =  1.0\n",
      "model_test_f1_score =  1.0\n",
      "model_test_precision =  1.0\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SANIKA\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM\n",
      "*******************\n",
      "Test performance:\n",
      "------------------\n",
      "model_test_score =  0.375\n",
      "model_test_f1_score =  0.2045454545454545\n",
      "model_test_precision =  0.140625\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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RI0fqoYcecm47YsQILV26VLfccotGjhypG2+8UdnZ2Tpw4IBWr16t0aNHq0WLFmrdurX69u2rF154QX/++ac6d+4su92uHTt2KCwsTI8++miesTz00EMKDQ3VTTfdpPLlyystLU3JycmKiIhQs2bN8twnICBAkydPVp8+fdS5c2cNGjRIDodDL7/8sk6ePFmgv2dfe/3113XzzTerdevWGjx4sKpUqaLTp09r7969+uSTT7Ru3TpJBb++sDA/TtKDG3bu3GkkJCQYlSpVMoKDg43w8HCjUaNGxvjx443Dhw87t8vKyjImTZpkXH/99UZQUJBRtmxZ4/777zd+//13l+O1adPGqFu3bq5+EhISjMqVK7u0KZ+Z0N9++63RqlUrIzw83KhQoYKRlJRkvP322y6z3zdv3mzcddddRuXKlQ273W5ERUUZbdq0MVasWJGrj7/PfjcMw/jhhx+MLl26GBEREUZwcLDRoEEDY+7cuS7b5Mx4/uc//+nSnpqaakjKtf2l/j77/XLymsE+Z84co1atWobdbjeqVatmJCcnG++8847L+RuGYezbt8/o2LGjUapUKUOS8/rmF/vf1+XM5F69erUREBCQ6xodO3bMqFSpktGsWTPD4XBc9hwKQ5JzCQgIMEqXLm3Ur1/fePjhh/N9ouDMmTPG008/bdSqVcsIDg42IiIijPr16xsjR4400tLSnNtlZWUZr732mlGvXj3ndi1btjQ++eQT5zaXzn6fP3++0a5dOyMmJsYIDg42YmNjjfvuu89l5n9+TwwsX77caNGihRESEmKEh4cb7du3N77++muXbXJmvx85csSl/dKnOQoiv9nv+T1NkJqaagwYMMCoUKGCERQUZFx77bVGq1atjBdeeMFlu4JeX1iTzTB4sBEAADPgnjoAACZBUgcAwCRI6gAAmARJHQAAkyCpAwBgEiR1AABMgqQOAIBJmPKNcqGNhvk7BMDnTqRM83cIgM+F+DhLeZIvzu0ofv8PmjKpAwBQIDZzDViT1AEA1lWIb0e8GpDUAQDWZbJK3VxnAwCAhVGpAwCsi+F3AABMwmTD7yR1AIB1UakDAGASVOoAAJiEySp1c/2KAgCAhVGpAwCsi+F3AABMwmTD7yR1AIB1UakDAGASVOoAAJiEySp1c50NAADF0MaNG9WlSxfFxsbKZrNp+fLl+W47aNAg2Ww2TZkypdD9kNQBANZlC3B/KYSMjAw1aNBA06ZNu+x2y5cv1zfffKPY2Fi3TofhdwCAdQUUzT31uLg4xcXFXXab//73vxo2bJg+//xzxcfHu9UPSR0AYF0e3FN3OBxyOBwubXa7XXa7vdDHys7OVt++fTVmzBjVrVvX7ZgYfgcAWJfN5vaSnJysiIgIlyU5OdmtMCZNmqQSJUpo+PDhHp0OlToAwLo8qNQTExM1atQolzZ3qvRt27bp9ddf1/bt22Xz8BE7KnUAANxgt9tVunRpl8WdpP7ll1/q8OHDqlSpkkqUKKESJUpo//79Gj16tKpUqVKoY1GpAwCsqxi8fKZv377q0KGDS9vtt9+uvn37qn///oU6FkkdAGBdRfTymTNnzmjv3r3Oz6mpqdq5c6ciIyNVqVIlRUVFuWwfFBSkcuXKqVatWoXqh6QOALCuIqrUt27dqnbt2jk/59yLT0hI0Lx587zWD0kdAGBdRVSpt23bVoZhFHj7ffv2udUPSR0AYF3F4J66NzH7HQAAk6BSBwBYl8m+pY2kDgCwLpMNv5PUAQDWRaUOAIBJkNQBADAJkw2/m+tXFAAALIxKHQBgXQy/AwBgEiYbfiepAwCsi0odAACToFIHAMAcbCZL6uYadwAAwMKo1AEAlmW2Sp2kDgCwLnPldJI6AMC6qNQBADAJkjoAACZhtqTO7HcAAEyCSh0AYFlmq9RJ6gAA6zJXTiepAwCsi0odAACTIKkDAGASZkvqzH4HAMAkqNQBAJZltkqdpA4AsC5z5XSSOgDAuqjUAQAwCbMldSbKAQAsy2azub0UxsaNG9WlSxfFxsbKZrNp+fLlznWZmZkaO3as6tevr/DwcMXGxuqBBx7QwYMHC30+JHUAAHwsIyNDDRo00LRp03KtO3v2rLZv365nnnlG27dv10cffaQ9e/aoa9euhe6H4XcAgHUV0eh7XFyc4uLi8lwXERGhNWvWuLRNnTpVzZs314EDB1SpUqUC90NSBwBYlif31B0OhxwOh0ub3W6X3W73NCylp6fLZrPpmmuuKdR+DL8DACzLk3vqycnJioiIcFmSk5M9jun8+fN68skn1bt3b5UuXbpQ+1KpAwAsy5NKPTExUaNGjXJp87RKz8zMVM+ePZWdna3p06cXen+SOgDAsjxJ6t4aas+RmZmp++67T6mpqVq3bl2hq3SJpA4AgN/lJPRffvlF69evV1RUlFvHIakDAKyriGa/nzlzRnv37nV+Tk1N1c6dOxUZGanY2Fjdc8892r59uz799FNlZWUpLS1NkhQZGang4OAC90NSBwBYVlG9UW7r1q1q166d83POvfiEhARNmDBBK1askCQ1bNjQZb/169erbdu2Be6HpA4AsKyiSupt27aVYRj5rr/cusIgqQMALIt3vwMAgGKJSh0AYF3mKtRJ6ii4mxpX18gHOqhxnUoqf22E7hs5S5988b1z/axn71ffrv9w2efb71PVJuH/ijpUwOuWLHpf8+a+o6NHjqh6jZp64smn1LhJU3+HBQ+ZbfidpI4CCw+164c9/9WCFVu0+P8eynObz7/epUFJ7zk/X8jMKqrwAJ/5bNVKTZ6YrHHPJKlho8b68IPFGjLoIS1b8S+Vj431d3jwAEkdlrX6691a/fXuy25z4cJF/XnsdBFFBBSNBfPn6q7u3XX3PfdKkp5IHKdNm77SB0sW6bGRo/0cHTxhtqTORDl4VeumNbV/bbK+Xz5ebz7TS9eWKenvkACPZF64oJ9271LLVje7tLdsdZO+27nDT1HBWzz5QpfiyK+V+h9//KEZM2Zo06ZNSktLk81mU0xMjFq1aqVHHnlEFStW9Gd4KKTVX+/WR2t26MCh46pSIUrjh3TWqlnD1ar3ZF3IvOjv8AC3nDh5QllZWble2xkVVVZHjx7xU1RA3vyW1L/66ivFxcWpYsWK6tixozp27CjDMHT48GEtX75cU6dO1apVq3TTTTdd9jh5fZ+tkZ0lW0CgL8NHHj5cvd3537t/PaTtuw/o55XPKa51XX287js/RgZ47tLKzDCMYlutoRBM9lfot6Q+cuRIPfjgg3rttdfyXT9ixAilpKRc9jjJycl69tlnXdoCY5opqHxzr8UK96QdPaUDh46rRqVr/R0K4LYy15RRYGCgjh496tJ+/PgxRUWV9VNU8Baz/WLmt3vqP/74ox555JF81w8aNEg//vjjFY+TmJio9PR0l6VETBNvhgo3RUaE67qYMjp09JS/QwHcFhQcrNp16mrLpq9d2rds2qQGDRv5KSp4C/fUvaR8+fLatGmTatWqlef6zZs3q3z58lc8Tl7fZ8vQu2+EhwaresX/Vd1VKkTpxusr6MSpszqenqGnH4nX8rU7dehIuirHRum5R7vo2MkzWsHQO65yfRP6a9yTT6hOvXpq0KCRlv5ziQ4dOqR7e/T0d2jwUDHNzW7zW1J//PHH9cgjj2jbtm267bbbFBMTI5vNprS0NK1Zs0Zvv/22pkyZ4q/wkIfGdSpr9duPOT9Pfry7JGnBii0a/tIS1a0Rq96dm+uaUqFKO3pKG1L2qO/YOTpz1pHfIYGrwh1xnZR+8oRmzZiuI0cOq0bN6/XmzFmKja3g79DgoeJacbvLZnjrq2HcsGTJEr322mvatm2bsrL+eklJYGCgmjRpolGjRum+++5z67ihjYZ5M0ygWDqRMs3fIQA+F+Lj0rPmmM/c3veXl+/wYiTe4ddH2nr06KEePXooMzPTOQmlbNmyCgoK8mdYAACLMFmhXjzeKBcUFFSg++cAAHiT2Ybfi0VSBwDAH0yW00nqAADrCggwV1YnqQMALMtslTpf6AIAgElQqQMALIuJcgAAmITJcjpJHQBgXVTqAACYBEkdAACTMFlOZ/Y7AABmQaUOALAsht8BADAJk+V0kjoAwLqo1AEAMAmT5XSSOgDAusxWqTP7HQAAH9u4caO6dOmi2NhY2Ww2LV++3GW9YRiaMGGCYmNjFRoaqrZt22rXrl2F7oekDgCwLJvN/aUwMjIy1KBBA02bNi3P9ZMnT9arr76qadOmKSUlReXKldNtt92m06dPF6ofht8BAJblyfC7w+GQw+FwabPb7bLb7bm2jYuLU1xcXJ7HMQxDU6ZM0bhx43T33XdLkubPn6+YmBgtXLhQgwYNKnBMVOoAAMvypFJPTk5WRESEy5KcnFzoGFJTU5WWlqaOHTs62+x2u9q0aaNNmzYV6lhU6gAAy/KkUk9MTNSoUaNc2vKq0q8kLS1NkhQTE+PSHhMTo/379xfqWCR1AIBleTL5Pb+hdvdjcQ3GMIxC/9LB8DsAAH5Urlw5Sf+r2HMcPnw4V/V+JSR1AIBl2Ww2txdvqVq1qsqVK6c1a9Y42y5cuKANGzaoVatWhToWw+8AAMsqqnfPnDlzRnv37nV+Tk1N1c6dOxUZGalKlSppxIgReumll1SzZk3VrFlTL730ksLCwtS7d+9C9UNSBwBYVlG9UW7r1q1q166d83POBLuEhATNmzdPTzzxhM6dO6chQ4boxIkTatGihVavXq1SpUoVqh+bYRiGVyMvBkIbDfN3CIDPnUjJ+yUWgJmE+Lj0vOXVr93ed+Oom7wYiXdQqQMALMtkr35nohwAAGZBpQ4AsCyzfUsbSR0AYFkmy+kkdQCAdVGpAwBgEibL6SR1AIB1BZgsqzP7HQAAk6BSBwBYlskKdZI6AMC6mCgHAIBJBJgrp5PUAQDWRaUOAIBJmCynM/sdAACzoFIHAFiWTeYq1UnqAADLYqIcAAAmwUQ5AABMwmQ5naQOALAu3v0OAACKJSp1AIBlmaxQJ6kDAKyLiXIAAJiEyXI6SR0AYF1mmyhHUgcAWJa5UnoBk/qKFSsKfMCuXbu6HQwAAHBfgZJ6t27dCnQwm82mrKwsT+IBAKDIWHKiXHZ2tq/jAACgyPHudwAATMKSlfqlMjIytGHDBh04cEAXLlxwWTd8+HCvBAYAgK+ZLKcXPqnv2LFDnTp10tmzZ5WRkaHIyEgdPXpUYWFhio6OJqkDAK4aRVWpX7x4URMmTND777+vtLQ0lS9fXv369dPTTz+tgADvvbG90EcaOXKkunTpouPHjys0NFRbtmzR/v371aRJE73yyiteCwwAALOYNGmSZs6cqWnTpumnn37S5MmT9fLLL2vq1Kle7afQlfrOnTv11ltvKTAwUIGBgXI4HKpWrZomT56shIQE3X333V4NEAAAX/FkopzD4ZDD4XBps9vtstvtubbdvHmz7rzzTsXHx0uSqlSpokWLFmnr1q3uB5CHQlfqQUFBzuGKmJgYHThwQJIUERHh/G8AAK4GNpvN7SU5OVkREREuS3Jycp793HzzzVq7dq327NkjSfruu+/01VdfqVOnTl49n0JX6o0aNdLWrVt1/fXXq127dho/fryOHj2qBQsWqH79+l4NDgAAX/LkjnpiYqJGjRrl0pZXlS5JY8eOVXp6um644QYFBgYqKytLL774onr16uVBBLkVOqm/9NJLOn36tCTp+eefV0JCggYPHqwaNWpo7ty5Xg0OAABf8uTd7/kNtedlyZIleu+997Rw4ULVrVtXO3fu1IgRIxQbG6uEhAS3Y7hUoZN606ZNnf997bXXauXKlV4LBgAAMxozZoyefPJJ9ezZU5JUv3597d+/X8nJyf5N6gAAmEVRPad+9uzZXI+uBQYGev2NrYVO6lWrVr3sc32//fabRwEBAFBUiuo59S5duujFF19UpUqVVLduXe3YsUOvvvqqBgwY4NV+Cp3UR4wY4fI5MzNTO3bs0GeffaYxY8Z4Ky4AAHyuqCr1qVOn6plnntGQIUN0+PBhxcbGatCgQRo/frxX+yl0Un/sscfybH/zzTe9/rwdAAC+5MlEucIoVaqUpkyZoilTpvi0H6+9my4uLk5Lly711uEAAPA5m839pTjyWlL/8MMPFRkZ6a3DAQCAQnLr5TN/n1hgGIbS0tJ05MgRTZ8+3avBAQDgS5b/6tU777zT5SIEBATo2muvVdu2bXXDDTd4NTh3nUiZ5u8QAJ/r/s63/g4B8Ll/DWru0+N77/vRiodCJ/UJEyb4IAwAAIqe2Sr1Qv+SEhgYqMOHD+dqP3bsmAIDA70SFAAARSHA5v5SHBW6UjcMI892h8Oh4OBgjwMCAKCoFNfk7K4CJ/U33nhD0l9DFW+//bZKlizpXJeVlaWNGzcWm3vqAABYUYGT+muvvSbpr0p95syZLkPtwcHBqlKlimbOnOn9CAEA8BGz3VMvcFJPTU2VJLVr104fffSRypQp47OgAAAoCpYdfs+xfv16X8QBAECRM1mhXvjZ7/fcc48mTpyYq/3ll1/Wvffe65WgAAAoCgE2m9tLcVTopL5hwwbFx8fnar/jjju0ceNGrwQFAEBRCPBgKY4KHdeZM2fyfHQtKChIp06d8kpQAACg8Aqd1OvVq6clS5bkal+8eLHq1KnjlaAAACgKZvuWtkJPlHvmmWfUvXt3/frrr7r11lslSWvXrtXChQv14Ycfej1AAAB8pbjeG3dXoZN6165dtXz5cr300kv68MMPFRoaqgYNGmjdunUqXbq0L2IEAMAnTJbTC5/UJSk+Pt45We7kyZN6//33NWLECH333XfKysryaoAAAPiK2Z5Td3sC37p163T//fcrNjZW06ZNU6dOnbR161ZvxgYAgE+Z7ZG2QlXqf/zxh+bNm6c5c+YoIyND9913nzIzM7V06VImyQEA4GcFrtQ7deqkOnXqaPfu3Zo6daoOHjyoqVOn+jI2AAB8yrKz31evXq3hw4dr8ODBqlmzpi9jAgCgSFj2nvqXX36p06dPq2nTpmrRooWmTZumI0eO+DI2AAB8yubBn+KowEm9ZcuWmj17tg4dOqRBgwZp8eLFqlChgrKzs7VmzRqdPn3al3ECAOB1ATb3l+Ko0LPfw8LCNGDAAH311Vf64YcfNHr0aE2cOFHR0dHq2rWrL2IEAMAnLJ/U/65WrVqaPHmy/vjjDy1atMhbMQEAADe49fKZSwUGBqpbt27q1q2bNw4HAECRsBXXaexu8kpSBwDgalRch9HdRVIHAFiWyQp1kjoAwLqK6+te3eXRRDkAAK5mRTn7/b///a/uv/9+RUVFKSwsTA0bNtS2bdu8ej5U6gAA+NiJEyd00003qV27dlq1apWio6P166+/6pprrvFqPyR1AIBlFdXo+6RJk1SxYkXNnTvX2ValShWv98PwOwDAsgJkc3txOBw6deqUy+JwOPLsZ8WKFWratKnuvfdeRUdHq1GjRpo9e7YPzgcAAIvy5FvakpOTFRER4bIkJyfn2c9vv/2mGTNmqGbNmvr888/1yCOPaPjw4Xr33Xe9ez6GYRhePWIxcP6ivyMAfK/7O9/6OwTA5/41qLlPjz9z8z639+3fuHyuytxut8tut+faNjg4WE2bNtWmTZucbcOHD1dKSoo2b97sdgyX4p46AMCyPHmkLb8Enpfy5curTp06Lm21a9fW0qVL3e4/Lwy/AwDgYzfddJN+/vlnl7Y9e/aocuXKXu2HpA4AsCxP7qkXxsiRI7Vlyxa99NJL2rt3rxYuXKhZs2Zp6NChXj0fkjoAwLICbDa3l8Jo1qyZli1bpkWLFqlevXp6/vnnNWXKFPXp08er58M9dQCAZRXlW2I7d+6szp07+7QPkjoAwLLMNlxNUgcAWJbZvk/dbL+kAABgWVTqAADLMledTlIHAFiY2b5PnaQOALAsc6V0kjoAwMJMVqiT1AEA1sXsdwAAUCxRqQMALMtslS1JHQBgWWYbfiepAwAsy1wpnaQOALAwKnUAAEzCbPfUzXY+AABYFpU6AMCyGH4HAMAkzJXSSeoAAAszWaFOUgcAWFeAyWp1kjoAwLLMVqkz+x0AAJOgUgcAWJaN4XcAAMzBbMPvJHUAgGUxUQ4AAJOgUgcAwCTMltSZ/Q4AgElQqQMALIvZ7wAAmESAuXI6SR0AYF1mq9S5pw4AsCybzf3FXcnJybLZbBoxYoTXziMHSR0AgCKSkpKiWbNm6cYbb/TJ8UnqAADLsnnwp7DOnDmjPn36aPbs2SpTpowPzoakDi9Ysuh9xXW8Vc0a1VfPe+/W9m1b/R0S4FWhQQF6qFUlze3dQB8NbKpX7qytmteG+zsseEGAzf3F4XDo1KlTLovD4ci3r6FDhyo+Pl4dOnTw3fn47MiwhM9WrdTkicl66OHBWvLhcjVu3ERDBj2kQwcP+js0wGuGt6mqRhVK65X1v2noP3/Q9j9O6cX4WooKC/J3aPCQJ5V6cnKyIiIiXJbk5OQ8+1m8eLG2b9+e73pvIanDIwvmz9Vd3bvr7nvuVbXq1fVE4jiVK19OHyxZ5O/QAK8IDrTppqqRmvvN79p16LQOnXJo4bb/6s/TDnWqG+3v8OAhTybKJSYmKj093WVJTEzM1cfvv/+uxx57TO+9955CQkJ8ej480ga3ZV64oJ9279KABx92aW/Z6iZ9t3OHn6ICvCswwKbAAJsuZBku7Y4sQ3XKlfJTVPAWTx5os9vtstvtV9xu27ZtOnz4sJo0aeJsy8rK0saNGzVt2jQ5HA4FBgZ6EMn/kNThthMnTygrK0tRUVEu7VFRZXX06BE/RQV417nMbP2Udlo9G8fq9xPndPJcptrUiFKt6HAdTD/v7/BwFWjfvr1++OEHl7b+/fvrhhtu0NixY72W0KVintR///13JSUlac6cOflu43A4ck1MMAIL9tsTvMN2yQObhmHkagOuZq+s/00j2lTVgr6NlJVtaO/RDG3Ye0zVyzJZ7moXUAT/VpUqVUr16tVzaQsPD1dUVFSudk8V63vqx48f1/z58y+7TV4TFV6e5NuJCPhLmWvKKDAwUEePHnVpP378mKKiyvopKsD70k459OQn/9Hd72xVwvs7NWrZbgUG2PTnqfxnOuPqYPNgKY78WqmvWLHisut/++23Kx4jMTFRo0aNcmkzAqnSi0JQcLBq16mrLZu+VvsOtznbt2zapLa3tvdjZIBvOC5my3ExWyWDA9X4ugjN/eZ3f4cET/kpO3/xxRc+Oa5fk3q3bt1ks9lkGEa+21xpGDeviQrnL3olPBRA34T+GvfkE6pTr54aNGikpf9cokOHDuneHj39HRrgNY2vi5DNJv1x8pzKlw7RwH9U1H9Pntean49eeWcUa2Z797tfk3r58uX15ptvqlu3bnmu37lzp8tsQRQ/d8R1UvrJE5o1Y7qOHDmsGjWv15szZyk2toK/QwO8Jiw4UP2aX6eyJYN1+vxFfZ16Qu+m/KGs7PwLElwdzDb9x69JvUmTJtq+fXu+Sf1KVTyKhx69+qhHrz7+DgPwma9+O66vfjvu7zCAK/JrUh8zZowyMjLyXV+jRg2tX7++CCMCAFiJyQp1/yb11q1bX3Z9eHi42rRpU0TRAAAsx2RZvVg/pw4AgC8xUQ4AAJNgohwAACZhspxevN8oBwAACo5KHQBgXSYr1UnqAADLYqIcAAAmwUQ5AABMwmQ5naQOALAwk2V1Zr8DAGASVOoAAMtiohwAACbBRDkAAEzCZDmdpA4AsDCTZXWSOgDAssx2T53Z7wAAmASVOgDAspgoBwCASZgsp5PUAQAWZrKsTlIHAFiW2SbKkdQBAJZltnvqzH4HAMAkqNQBAJZlskKdpA4AsDCTZXWSOgDAssw2UY576gAAy7LZ3F8KIzk5Wc2aNVOpUqUUHR2tbt266eeff/b6+ZDUAQCWZfNgKYwNGzZo6NCh2rJli9asWaOLFy+qY8eOysjI8NKZ/IXhdwAAfOyzzz5z+Tx37lxFR0dr27ZtuuWWW7zWD0kdAGBdHtxSdzgccjgcLm12u112u/2K+6anp0uSIiMj3Q8gDwy/AwAsy+bBn+TkZEVERLgsycnJV+zTMAyNGjVKN998s+rVq+fV86FSBwBYlidvlEtMTNSoUaNc2gpSpQ8bNkzff/+9vvrqK/c7zwdJHQBgWZ480FbQofa/e/TRR7VixQpt3LhR1113nQe9542kDgCwriJ6TN0wDD366KNatmyZvvjiC1WtWtUn/ZDUAQDwsaFDh2rhwoX6+OOPVapUKaWlpUmSIiIiFBoa6rV+mCgHALAsTybKFcaMGTOUnp6utm3bqnz58s5lyZIlXj0fKnUAgGUV1VevGoZRJP2Q1AEAlmWuN7+T1AEAFlZUlXpRIakDACzMXFmdiXIAAJgElToAwLIYfgcAwCRMltNJ6gAA66JSBwDAJAr7EpnijqQOALAuc+V0Zr8DAGAWVOoAAMsyWaFOUgcAWBcT5QAAMAkmygEAYBbmyukkdQCAdZkspzP7HQAAs6BSBwBYFhPlAAAwCSbKAQBgEmar1LmnDgCASVCpAwAsi0odAAAUS1TqAADLYqIcAAAmYbbhd5I6AMCyTJbTSeoAAAszWVZnohwAACZBpQ4AsCwmygEAYBJMlAMAwCRMltO5pw4AsDCbB4sbpk+frqpVqyokJERNmjTRl19+6ekZuCCpAwAsy+bBn8JasmSJRowYoXHjxmnHjh1q3bq14uLidODAAa+dD0kdAIAi8Oqrr2rgwIF68MEHVbt2bU2ZMkUVK1bUjBkzvNYH99QBAJblyUQ5h8Mhh8Ph0ma322W323Nte+HCBW3btk1PPvmkS3vHjh21adMm94O4hCmTeogpz6r4cjgcSk5OVmJiYp4/zPCNfw1q7u8QLIWfc3PyJF9MeCFZzz77rEtbUlKSJkyYkGvbo0ePKisrSzExMS7tMTExSktLcz+IS9gMwzC8djRY0qlTpxQREaH09HSVLl3a3+EAPsHPOS5VmEr94MGDqlChgjZt2qSWLVs621988UUtWLBA//nPf7wSEzUtAABuyC+B56Vs2bIKDAzMVZUfPnw4V/XuCSbKAQDgY8HBwWrSpInWrFnj0r5mzRq1atXKa/1QqQMAUARGjRqlvn37qmnTpmrZsqVmzZqlAwcO6JFHHvFaHyR1eMxutyspKYnJQzA1fs7hqR49eujYsWN67rnndOjQIdWrV08rV65U5cqVvdYHE+UAADAJ7qkDAGASJHUAAEyCpA4AgEmQ1AEAMAmSOjzm668SBPxp48aN6tKli2JjY2Wz2bR8+XJ/hwTki6QOjxTFVwkC/pSRkaEGDRpo2rRp/g4FuCIeaYNHWrRoocaNG7t8dWDt2rXVrVs3JScn+zEywPtsNpuWLVumbt26+TsUIE9U6nBbzlcJduzY0aXd218lCAAoGJI63FZUXyUIACgYkjo8ZrPZXD4bhpGrDQDgeyR1uK2ovkoQAFAwJHW4rai+ShAAUDB8Sxs8UhRfJQj405kzZ7R3717n59TUVO3cuVORkZGqVKmSHyMDcuORNnhs+vTpmjx5svOrBF977TXdcsst/g4L8IovvvhC7dq1y9WekJCgefPmFX1AwGWQ1AEAMAnuqQMAYBIkdQAATIKkDgCASZDUAQAwCZI6AAAmQVIHAMAkSOoAAJgESR0AAJMgqQNXgQkTJqhhw4bOz/369VO3bt2KPI59+/bJZrNp586dRd43gCsjqQMe6Nevn2w2m2w2m4KCglStWjU9/vjjysjI8Gm/r7/+eoFfUUoiBqyDL3QBPHTHHXdo7ty5yszM1JdffqkHH3xQGRkZmjFjhst2mZmZCgoK8kqfERERXjkOAHOhUgc8ZLfbVa5cOVWsWFG9e/dWnz59tHz5cueQ+Zw5c1StWjXZ7XYZhqH09HQ9/PDDio6OVunSpXXrrbfqu+++cznmxIkTFRMTo1KlSmngwIE6f/68y/pLh9+zs7M1adIk1ahRQ3a7XZUqVdKLL74oSapataokqVGjRrLZbGrbtq1zv7lz56p27doKCQnRDTfcoOnTp7v08+2336pRo0YKCQlR06ZNtWPHDi9eOQDeRqUOeFloaKgyMzMlSXv37tUHH3ygpUuXKjAwUJIUHx+vyMhIrVy5UhEREXrrrbfUvn177dmzR5GRkfrggw+UlJSkN998U61bt9aCBQv0xhtvqFq1avn2mZiYqNmzZ+u1117TzTffrEOHDuk///mPpL8Sc/PmzfXvf/9bdevWVXBwsCRp9uzZSkpK0rRp09SoUSPt2LFDDz30kMLDw5WQkKCMjAx17txZt956q9577z2lpqbqscce8/HVA+ARA4DbEhISjDvvvNP5+ZtvvjGioqKM++67z0hKSjKCgoKMw4cPO9evXbvWKF26tHH+/HmX41SvXt146623DMMwjJYtWxqPPPKIy/oWLVoYDRo0yLPfU6dOGXa73Zg9e3aeMaamphqSjB07dri0V6xY0Vi4cKFL2/PPP2+0bNnSMAzDeOutt4zIyEgjIyPDuX7GjBl5HgtA8cDwO+ChTz/9VCVLllRISIhatmypW265RVOnTpUkVa5cWddee61z223btunMmTOKiopSyZIlnUtqaqp+/fVXSdJPP/2kli1buvRx6ee/++mnn+RwONS+ffsCx3zkyBH9/vvvGjhwoEscL7zwgkscDRo0UFhYWIHiAOB/DL8DHmrXrp1mzJihoKAgxcbGukyGCw8Pd9k2Oztb5cuX1xdffJHrONdcc41b/YeGhhZ6n+zsbEl/DcG3aNHCZV3ObQLDMNyKB4D/kNQBD4WHh6tGjRoF2rZx48ZKS0tTiRIlVKVKlTy3qV27trZs2aIHHnjA2bZly5Z8j1mzZk2FhoZq7dq1evDBB3Otz7mHnpWV5WyLiYlRhQoV9Ntvv6lPnz55HrdOnTpasGCBzp075/zF4XJxAPA/ht+BItShQwe1bNlS3bp10+eff659+/Zp06ZNevrpp7V161ZJ0mOPPaY5c+Zozpw52rNnj5KSkrRr1658jxkSEqKxY8fqiSee0Lvvvqtff/1VW7Zs0TvvvCNJio6OVmhoqD777DP9+eefSk9Pl/TXC22Sk5P1+uuva8+ePfrhhx80d+5cvfrqq5Kk3r17KyAgQAMHDtTu3bu1cuVKvfLKKz6+QgA8QVIHipDNZtPKlSt1yy23aMCAAbr++uvVs2dP7du3TzExMZKkHj16aPz48Ro7dqyaNGmi/fv3a/DgwZc97jPPPKPRo0dr/Pjxql27tnr06KHDhw9LkkqUKKE33nhDb731lmJjY3XnnXdKkh588EG9/fbbmjdvnurXr682bdpo3rx5zkfgSpYsqU8++US7d+9Wo0aNNG7cOE2aNMmHVweAp2wGN84AADAFKnUAAEyCpA4AgEmQ1AEAMAmSOgAAJkFSBwDAJEjqAACYBEkdAACTIKkDAGASJHUAAEyCpA4AgEmQ1AEAMIn/B/xylm35Zn6xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree\n",
      "*******************\n",
      "Test performance:\n",
      "------------------\n",
      "model_test_score =  1.0\n",
      "model_test_f1_score =  1.0\n",
      "model_test_precision =  1.0\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Classifier\n",
      "*******************\n",
      "Test performance:\n",
      "------------------\n",
      "model_test_score =  1.0\n",
      "model_test_f1_score =  1.0\n",
      "model_test_precision =  1.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate performance metrics\n",
    "    model_test_accuracy = accuracy_score(y_test, y_pred)\n",
    "    model_test_f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "    model_test_precision = precision_score(y_test, y_pred, average='weighted')\n",
    "    \n",
    "    # Calculate confusion matrix\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    # Visualize confusion matrix\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.title(f'Confusion Matrix - {model_name}')\n",
    "    plt.show()\n",
    "    \n",
    "    # Display model performance metrics\n",
    "    print(model_name)\n",
    "    print('*' * 19)\n",
    "    print('Test performance:')\n",
    "    print('------------------')\n",
    "    print('model_test_score = ', model_test_accuracy)\n",
    "    print('model_test_f1_score = ', model_test_f1)\n",
    "    print('model_test_precision = ', model_test_precision)\n",
    "    print('\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "450ca0e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>STROKE PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.917508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ACE INHIBITORS</td>\n",
       "      <td>0.043258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmi</td>\n",
       "      <td>0.039234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ARBS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDI...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HYPERLIPIDEMIA PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ANTIHYPERLIPIDEMIC</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>STATINS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ESTROGEN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>INSULIN(Yes_or_No)</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ORAL HYPOGLYCEMIC</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WBC K/uL</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>RBC m/uL</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Hgb g/dL</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Hgb&lt;12.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GLUCOSE mg/dL</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CANCER PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>height/m</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HTN YRS PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>StrokeFAMILY HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HeartDisease FAMILY HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cancer,# FAMILY HISTORY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Syncope AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Painful feet AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Numbness AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dizziness AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Neuropathy AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PREVIOUS ALCOHOL USE</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>current_tobacco_use</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>previous_tobacco_use</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mass/kg</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CHOLESTmg/dL</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Feature  Importance\n",
       "18                     STROKE PATIENT MEDICAL HISTORY    0.917508\n",
       "25                                     ACE INHIBITORS    0.043258\n",
       "3                                                 bmi    0.039234\n",
       "0                                                 age    0.000000\n",
       "26                                               ARBS    0.000000\n",
       "20  HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDI...    0.000000\n",
       "21             HYPERLIPIDEMIA PATIENT MEDICAL HISTORY    0.000000\n",
       "22                                 ANTIHYPERLIPIDEMIC    0.000000\n",
       "23                                            STATINS    0.000000\n",
       "24                                           ESTROGEN    0.000000\n",
       "27                                 INSULIN(Yes_or_No)    0.000000\n",
       "28                                  ORAL HYPOGLYCEMIC    0.000000\n",
       "29                                           WBC K/uL    0.000000\n",
       "30                                           RBC m/uL    0.000000\n",
       "31                                           Hgb g/dL    0.000000\n",
       "32                                           Hgb<12.5    0.000000\n",
       "33                                      GLUCOSE mg/dL    0.000000\n",
       "19       ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY    0.000000\n",
       "17                     CANCER PATIENT MEDICAL HISTORY    0.000000\n",
       "1                                            height/m    0.000000\n",
       "16                    HTN YRS PATIENT MEDICAL HISTORY    0.000000\n",
       "15                               StrokeFAMILY HISTORY    0.000000\n",
       "14                        HeartDisease FAMILY HISTORY    0.000000\n",
       "13                            cancer,# FAMILY HISTORY    0.000000\n",
       "12                         Syncope AUTONOMIC SYMPTOMS    0.000000\n",
       "11                    Painful feet AUTONOMIC SYMPTOMS    0.000000\n",
       "10                        Numbness AUTONOMIC SYMPTOMS    0.000000\n",
       "9                        Dizziness AUTONOMIC SYMPTOMS    0.000000\n",
       "8                       Neuropathy AUTONOMIC SYMPTOMS    0.000000\n",
       "7                                PREVIOUS ALCOHOL USE    0.000000\n",
       "6                                 current_tobacco_use    0.000000\n",
       "5                                previous_tobacco_use    0.000000\n",
       "4                                              gender    0.000000\n",
       "2                                             mass/kg    0.000000\n",
       "34                                       CHOLESTmg/dL    0.000000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Initialize the Decision Tree classifier (CART)\n",
    "cart_clf = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# Fit the classifier to the data\n",
    "cart_clf.fit(X_train, y_train)\n",
    "\n",
    "# Extract feature importances\n",
    "cart_feature_importances = cart_clf.feature_importances_\n",
    "\n",
    "# Create a DataFrame to display the feature names and importances\n",
    "cart_feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': cart_feature_importances})\n",
    "\n",
    "# Sort the DataFrame by importance in descending order\n",
    "cart_feature_importance_df = cart_feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "cart_feature_importance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "02722137",
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>STROKE PATIENT MEDICAL HISTORY</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmi</td>\n",
       "      <td>5.298864e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>RBC m/uL</td>\n",
       "      <td>2.083762e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GLUCOSE mg/dL</td>\n",
       "      <td>1.010488e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ACE INHIBITORS</td>\n",
       "      <td>5.977998e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ARBS</td>\n",
       "      <td>1.546598e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDI...</td>\n",
       "      <td>2.827508e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WBC K/uL</td>\n",
       "      <td>1.316254e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Hgb g/dL</td>\n",
       "      <td>8.775025e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>STATINS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HYPERLIPIDEMIA PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ANTIHYPERLIPIDEMIC</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>INSULIN(Yes_or_No)</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ESTROGEN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ORAL HYPOGLYCEMIC</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Hgb&lt;12.5</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CANCER PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>height/m</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HTN YRS PATIENT MEDICAL HISTORY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>StrokeFAMILY HISTORY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HeartDisease FAMILY HISTORY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cancer,# FAMILY HISTORY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Syncope AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Painful feet AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Numbness AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dizziness AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Neuropathy AUTONOMIC SYMPTOMS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PREVIOUS ALCOHOL USE</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>current_tobacco_use</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>previous_tobacco_use</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mass/kg</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CHOLESTmg/dL</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Feature    Importance\n",
       "18                     STROKE PATIENT MEDICAL HISTORY  1.000000e+00\n",
       "3                                                 bmi  5.298864e-02\n",
       "30                                           RBC m/uL  2.083762e-02\n",
       "33                                      GLUCOSE mg/dL  1.010488e-02\n",
       "25                                     ACE INHIBITORS  5.977998e-03\n",
       "26                                               ARBS  1.546598e-16\n",
       "20  HEART FAILURE =CHF /IFARCTION=-MI PATIENT MEDI...  2.827508e-17\n",
       "29                                           WBC K/uL  1.316254e-17\n",
       "31                                           Hgb g/dL  8.775025e-18\n",
       "0                                                 age  0.000000e+00\n",
       "23                                            STATINS  0.000000e+00\n",
       "21             HYPERLIPIDEMIA PATIENT MEDICAL HISTORY  0.000000e+00\n",
       "22                                 ANTIHYPERLIPIDEMIC  0.000000e+00\n",
       "27                                 INSULIN(Yes_or_No)  0.000000e+00\n",
       "24                                           ESTROGEN  0.000000e+00\n",
       "28                                  ORAL HYPOGLYCEMIC  0.000000e+00\n",
       "32                                           Hgb<12.5  0.000000e+00\n",
       "19       ATRIAL FIBTRILLATION PATIENT MEDICAL HISTORY  0.000000e+00\n",
       "17                     CANCER PATIENT MEDICAL HISTORY  0.000000e+00\n",
       "1                                            height/m  0.000000e+00\n",
       "16                    HTN YRS PATIENT MEDICAL HISTORY  0.000000e+00\n",
       "15                               StrokeFAMILY HISTORY  0.000000e+00\n",
       "14                        HeartDisease FAMILY HISTORY  0.000000e+00\n",
       "13                            cancer,# FAMILY HISTORY  0.000000e+00\n",
       "12                         Syncope AUTONOMIC SYMPTOMS  0.000000e+00\n",
       "11                    Painful feet AUTONOMIC SYMPTOMS  0.000000e+00\n",
       "10                        Numbness AUTONOMIC SYMPTOMS  0.000000e+00\n",
       "9                        Dizziness AUTONOMIC SYMPTOMS  0.000000e+00\n",
       "8                       Neuropathy AUTONOMIC SYMPTOMS  0.000000e+00\n",
       "7                                PREVIOUS ALCOHOL USE  0.000000e+00\n",
       "6                                 current_tobacco_use  0.000000e+00\n",
       "5                                previous_tobacco_use  0.000000e+00\n",
       "4                                              gender  0.000000e+00\n",
       "2                                             mass/kg  0.000000e+00\n",
       "34                                       CHOLESTmg/dL  0.000000e+00"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Initialize the GBM classifier\n",
    "gbm_clf = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Fit the classifier to the data\n",
    "gbm_clf.fit(X_train, y_train)\n",
    "\n",
    "# Extract feature importances\n",
    "gbm_feature_importances = gbm_clf.feature_importances_\n",
    "\n",
    "# Create a DataFrame to display the feature names and importances\n",
    "gbm_feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': gbm_feature_importances})\n",
    "\n",
    "# Sort the DataFrame by importance in descending order\n",
    "gbm_feature_importance_df = gbm_feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "col__=['Importance']\n",
    "gbm_feature_importance_df[col__] = scaler.fit_transform(gbm_feature_importance_df[col__])\n",
    "gbm_feature_importance_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d22e49fd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Initialize the GBM classifier\n",
    "gbm_clf = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Fit the classifier to the data\n",
    "gbm_clf.fit(X_train, y_train)\n",
    "\n",
    "# Initialize the SHAP explainer with the trained GBM model\n",
    "explainer = shap.Explainer(gbm_clf, X_train)\n",
    "\n",
    "# Calculate SHAP values for all instances in the training set\n",
    "shap_values = explainer(X_train)\n",
    "\n",
    "# Summary plot for overall feature importance\n",
    "shap.summary_plot(shap_values, X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "925a36e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,f1_score,precision_score,recall_score,roc_auc_score\n",
    "\n",
    "models={\n",
    "    'Logistic Regression':LogisticRegression(),\n",
    "    'SVM':SVC(),\n",
    "    'Decision Tree':DecisionTreeClassifier(),\n",
    "    'Random Classifier':RandomForestClassifier()\n",
    "}\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate performance metrics\n",
    "    model_test_accuracy = accuracy_score(y_test, y_pred)\n",
    "    model_test_f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "    model_test_precision = precision_score(y_test, y_pred, average='weighted')\n",
    "    \n",
    "    # Calculate confusion matrix\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    # Visualize confusion matrix\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.title(f'Confusion Matrix - {model_name}')\n",
    "    plt.show()\n",
    "    \n",
    "    # Display model performance metrics\n",
    "    print(model_name)\n",
    "    print('*' * 19)\n",
    "    print('Test performance:')\n",
    "    print('------------------')\n",
    "    print('model_test_score = ', model_test_accuracy)\n",
    "    print('model_test_f1_score = ', model_test_f1)\n",
    "    print('model_test_precision = ', model_test_precision)\n",
    "    print('\\n')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}