13.2.1.로지스틱 회귀분석.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 패키지 설치\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.under_sampling import *\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>20.34</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>26.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>24.21</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>23.71</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "0           No  16.60     Yes              No     No             3.0   \n",
       "1           No  20.34      No              No    Yes             0.0   \n",
       "2           No  26.58     Yes              No     No            20.0   \n",
       "3           No  24.21      No              No     No             0.0   \n",
       "4           No  23.71      No              No     No            28.0   \n",
       "\n",
       "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
       "0          30.0          No  Female        55-59  White      Yes   \n",
       "1           0.0          No  Female  80 or older  White       No   \n",
       "2          30.0          No    Male        65-69  White      Yes   \n",
       "3           0.0          No  Female        75-79  White       No   \n",
       "4           0.0         Yes  Female        40-44  White       No   \n",
       "\n",
       "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
       "0              Yes  Very good        5.0    Yes            No        Yes  \n",
       "1              Yes  Very good        7.0     No            No         No  \n",
       "2              Yes       Fair        8.0    Yes            No         No  \n",
       "3               No       Good        6.0     No            No        Yes  \n",
       "4              Yes  Very good        8.0     No            No         No  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease\n",
    "df = pd.read_csv(\"datasets/heart_2020_cleaned.csv\")\n",
    "\n",
    "# 데이터 샘플 확인\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 319795 entries, 0 to 319794\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   HeartDisease      319795 non-null  object \n",
      " 1   BMI               319795 non-null  float64\n",
      " 2   Smoking           319795 non-null  object \n",
      " 3   AlcoholDrinking   319795 non-null  object \n",
      " 4   Stroke            319795 non-null  object \n",
      " 5   PhysicalHealth    319795 non-null  float64\n",
      " 6   MentalHealth      319795 non-null  float64\n",
      " 7   DiffWalking       319795 non-null  object \n",
      " 8   Sex               319795 non-null  object \n",
      " 9   AgeCategory       319795 non-null  object \n",
      " 10  Race              319795 non-null  object \n",
      " 11  Diabetic          319795 non-null  object \n",
      " 12  PhysicalActivity  319795 non-null  object \n",
      " 13  GenHealth         319795 non-null  object \n",
      " 14  SleepTime         319795 non-null  float64\n",
      " 15  Asthma            319795 non-null  object \n",
      " 16  KidneyDisease     319795 non-null  object \n",
      " 17  SkinCancer        319795 non-null  object \n",
      "dtypes: float64(4), object(14)\n",
      "memory usage: 43.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# 컬럼 정보 확인\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>BMI</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>HeartDisease_Yes</th>\n",
       "      <th>Smoking_Yes</th>\n",
       "      <th>AlcoholDrinking_Yes</th>\n",
       "      <th>Stroke_Yes</th>\n",
       "      <th>DiffWalking_Yes</th>\n",
       "      <th>Sex_Male</th>\n",
       "      <th>...</th>\n",
       "      <th>Diabetic_Yes</th>\n",
       "      <th>Diabetic_Yes (during pregnancy)</th>\n",
       "      <th>PhysicalActivity_Yes</th>\n",
       "      <th>GenHealth_Fair</th>\n",
       "      <th>GenHealth_Good</th>\n",
       "      <th>GenHealth_Poor</th>\n",
       "      <th>GenHealth_Very good</th>\n",
       "      <th>Asthma_Yes</th>\n",
       "      <th>KidneyDisease_Yes</th>\n",
       "      <th>SkinCancer_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.60</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.58</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.71</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BMI  PhysicalHealth  MentalHealth  SleepTime  HeartDisease_Yes  \\\n",
       "0  16.60             3.0          30.0        5.0                 0   \n",
       "1  20.34             0.0           0.0        7.0                 0   \n",
       "2  26.58            20.0          30.0        8.0                 0   \n",
       "3  24.21             0.0           0.0        6.0                 0   \n",
       "4  23.71            28.0           0.0        8.0                 0   \n",
       "\n",
       "   Smoking_Yes  AlcoholDrinking_Yes  Stroke_Yes  DiffWalking_Yes  Sex_Male  \\\n",
       "0            1                    0           0                0         0   \n",
       "1            0                    0           1                0         0   \n",
       "2            1                    0           0                0         1   \n",
       "3            0                    0           0                0         0   \n",
       "4            0                    0           0                1         0   \n",
       "\n",
       "   ...  Diabetic_Yes  Diabetic_Yes (during pregnancy)  PhysicalActivity_Yes  \\\n",
       "0  ...             1                                0                     1   \n",
       "1  ...             0                                0                     1   \n",
       "2  ...             1                                0                     1   \n",
       "3  ...             0                                0                     0   \n",
       "4  ...             0                                0                     1   \n",
       "\n",
       "   GenHealth_Fair  GenHealth_Good  GenHealth_Poor  GenHealth_Very good  \\\n",
       "0               0               0               0                    1   \n",
       "1               0               0               0                    1   \n",
       "2               1               0               0                    0   \n",
       "3               0               1               0                    0   \n",
       "4               0               0               0                    1   \n",
       "\n",
       "   Asthma_Yes  KidneyDisease_Yes  SkinCancer_Yes  \n",
       "0           1                  0               1  \n",
       "1           0                  0               0  \n",
       "2           1                  0               0  \n",
       "3           0                  0               1  \n",
       "4           0                  0               0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 명목형 변수 더미처리\n",
    "\n",
    "# 하나의 가변수 범주 제거 옵션 적용\n",
    "df2 = pd.get_dummies(df, columns = ['HeartDisease','Smoking',\n",
    "                                    'AlcoholDrinking','Stroke',\n",
    "                                    'DiffWalking','Sex',\n",
    "                                    'AgeCategory','Race',\n",
    "                                    'Diabetic','PhysicalActivity',\n",
    "                                    'GenHealth','Asthma',\n",
    "                                    'KidneyDisease','SkinCancer']\n",
    "                     ,drop_first=True\n",
    "                    )\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>BMI</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>HeartDisease_Yes</th>\n",
       "      <th>Smoking_Yes</th>\n",
       "      <th>AlcoholDrinking_Yes</th>\n",
       "      <th>Stroke_Yes</th>\n",
       "      <th>DiffWalking_Yes</th>\n",
       "      <th>Sex_Male</th>\n",
       "      <th>...</th>\n",
       "      <th>Diabetic_Yes</th>\n",
       "      <th>Diabetic_Yes (during pregnancy)</th>\n",
       "      <th>PhysicalActivity_Yes</th>\n",
       "      <th>GenHealth_Fair</th>\n",
       "      <th>GenHealth_Good</th>\n",
       "      <th>GenHealth_Poor</th>\n",
       "      <th>GenHealth_Very good</th>\n",
       "      <th>Asthma_Yes</th>\n",
       "      <th>KidneyDisease_Yes</th>\n",
       "      <th>SkinCancer_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.453315</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.947226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.102842</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.423545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.491204</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BMI  PhysicalHealth  MentalHealth  SleepTime  HeartDisease_Yes  \\\n",
       "0 -1.453315             1.5          10.0       -1.0                 0   \n",
       "1 -0.947226             0.0           0.0        0.0                 0   \n",
       "2 -0.102842            10.0          10.0        0.5                 0   \n",
       "3 -0.423545             0.0           0.0       -0.5                 0   \n",
       "4 -0.491204            14.0           0.0        0.5                 0   \n",
       "\n",
       "   Smoking_Yes  AlcoholDrinking_Yes  Stroke_Yes  DiffWalking_Yes  Sex_Male  \\\n",
       "0            1                    0           0                0         0   \n",
       "1            0                    0           1                0         0   \n",
       "2            1                    0           0                0         1   \n",
       "3            0                    0           0                0         0   \n",
       "4            0                    0           0                1         0   \n",
       "\n",
       "   ...  Diabetic_Yes  Diabetic_Yes (during pregnancy)  PhysicalActivity_Yes  \\\n",
       "0  ...             1                                0                     1   \n",
       "1  ...             0                                0                     1   \n",
       "2  ...             1                                0                     1   \n",
       "3  ...             0                                0                     0   \n",
       "4  ...             0                                0                     1   \n",
       "\n",
       "   GenHealth_Fair  GenHealth_Good  GenHealth_Poor  GenHealth_Very good  \\\n",
       "0               0               0               0                    1   \n",
       "1               0               0               0                    1   \n",
       "2               1               0               0                    0   \n",
       "3               0               1               0                    0   \n",
       "4               0               0               0                    1   \n",
       "\n",
       "   Asthma_Yes  KidneyDisease_Yes  SkinCancer_Yes  \n",
       "0           1                  0               1  \n",
       "1           0                  0               0  \n",
       "2           1                  0               0  \n",
       "3           0                  0               1  \n",
       "4           0                  0               0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RobustScaler 적용\n",
    "\n",
    "# 숫자형 변수 분리\n",
    "df_num = df[['BMI','PhysicalHealth','MentalHealth','SleepTime']]\n",
    "df_nom = df2.drop(['BMI','PhysicalHealth','MentalHealth','SleepTime'],axis=1)\n",
    "\n",
    "# 숫자형 변수 RobustScaler 적용\n",
    "RobustScaler = RobustScaler()\n",
    "df_robust = RobustScaler.fit_transform(df_num)\n",
    "\n",
    "# 컬럼명 결합\n",
    "df_num2 = pd.DataFrame(data=df_robust, columns=df_num.columns)\n",
    "\n",
    "# 숫자형 테이블과 더미화 문자형 테이블 결합\n",
    "df3 = pd.concat([df_num2,df_nom],axis=1)\n",
    "\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data 개수:  239846\n",
      "test data 개수:  79949\n"
     ]
    }
   ],
   "source": [
    "# 독립변수와 종속변수 분리하여 생성\n",
    "X = df3.drop(['HeartDisease_Yes'],axis=1)\n",
    "y = df3[['HeartDisease_Yes']]\n",
    "\n",
    "# 학습셋과 테스트셋 분리하여 생성(7.5:2.5)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,y,test_size=0.25,random_state=10)\n",
    "\n",
    "# 학습셋과 검증셋이 잘 나뉘었는지 확인\n",
    "print('train data 개수: ', len(X_train))\n",
    "print('test data 개수: ', len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HeartDisease_Yes 컬럼 클래스 분포 시각화\n",
    "sns.countplot(x=\"HeartDisease_Yes\", data=y_train)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X28</th>\n",
       "      <th>X29</th>\n",
       "      <th>X30</th>\n",
       "      <th>X31</th>\n",
       "      <th>X32</th>\n",
       "      <th>X33</th>\n",
       "      <th>X34</th>\n",
       "      <th>X35</th>\n",
       "      <th>X36</th>\n",
       "      <th>X37</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163572</th>\n",
       "      <td>-0.202977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150901</th>\n",
       "      <td>1.476319</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164527</th>\n",
       "      <td>-0.248985</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260971</th>\n",
       "      <td>0.805142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262287</th>\n",
       "      <td>0.612991</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              X1    X2    X3   X4  X5  X6  X7  X8  X9  X10  ...  X28  X29  \\\n",
       "163572 -0.202977   0.0   0.0 -0.5   0   0   0   0   0    0  ...    0    0   \n",
       "150901  1.476319   0.0   0.0  0.5   1   0   1   0   1    0  ...    0    0   \n",
       "164527 -0.248985  15.0  10.0 -2.0   1   0   0   1   1    0  ...    0    0   \n",
       "260971  0.805142   0.0   0.0  0.0   0   0   0   0   0    0  ...    1    0   \n",
       "262287  0.612991   5.0   0.0  0.0   0   0   0   0   1    0  ...    1    0   \n",
       "\n",
       "        X30  X31  X32  X33  X34  X35  X36  X37  \n",
       "163572    1    0    0    0    0    0    0    0  \n",
       "150901    1    0    0    0    1    0    0    0  \n",
       "164527    1    0    0    1    0    0    0    0  \n",
       "260971    1    0    0    0    0    0    0    0  \n",
       "262287    1    0    1    0    0    0    0    0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임시 변수명 적용\n",
    "\n",
    "X_train_re = X_train.copy()\n",
    "y_train_re = y_train.copy()\n",
    "\n",
    "X_temp_name = ['X1','X2','X3','X4','X5','X6','X7','X8','X9','X10',\n",
    "            'X11','X12','X13','X14','X15','X16','X17','X18','X19','X20',\n",
    "            'X21','X22','X23','X24','X25','X26','X27','X28','X29','X30',\n",
    "            'X31','X32','X33','X34','X35','X36','X37']\n",
    "y_temp_name = ['y1']\n",
    "\n",
    "\n",
    "X_train_re.columns = X_temp_name\n",
    "y_train_re.columns = y_temp_name\n",
    "\n",
    "X_train_re.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomUnderSampler 적용 전 학습셋 변수/레이블 데이터 세트:  (239846, 37) (239846, 1)\n",
      "RandomUnderSampler 적용 후 학습셋 변수/레이블 데이터 세트:  (41036, 37) (41036, 1)\n",
      "RandomUnderSampler 적용 전 레이블 값 분포: \n",
      " 0    219328\n",
      "1     20518\n",
      "Name: y1, dtype: int64\n",
      "RandomUnderSampler 적용 후 레이블 값 분포: \n",
      " 1    20518\n",
      "0    20518\n",
      "Name: y1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 언더샘플링 적용\n",
    "\n",
    "X_train_under, y_train_under = RandomUnderSampler(\n",
    "    random_state=0).fit_resample(X_train_re,y_train_re)\n",
    "\n",
    "print('RandomUnderSampler 적용 전 학습셋 변수/레이블 데이터 세트: '\n",
    "      , X_train_re.shape, y_train_re.shape)\n",
    "print('RandomUnderSampler 적용 후 학습셋 변수/레이블 데이터 세트: '\n",
    "      , X_train_under.shape, y_train_under.shape)\n",
    "print('RandomUnderSampler 적용 전 레이블 값 분포: \\n'\n",
    "      , pd.Series(y_train_re['y1']).value_counts())\n",
    "print('RandomUnderSampler 적용 후 레이블 값 분포: \\n'\n",
    "      , pd.Series(y_train_under['y1']).value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>BMI</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Smoking_Yes</th>\n",
       "      <th>AlcoholDrinking_Yes</th>\n",
       "      <th>Stroke_Yes</th>\n",
       "      <th>DiffWalking_Yes</th>\n",
       "      <th>Sex_Male</th>\n",
       "      <th>AgeCategory_25-29</th>\n",
       "      <th>...</th>\n",
       "      <th>Diabetic_Yes</th>\n",
       "      <th>Diabetic_Yes (during pregnancy)</th>\n",
       "      <th>PhysicalActivity_Yes</th>\n",
       "      <th>GenHealth_Fair</th>\n",
       "      <th>GenHealth_Good</th>\n",
       "      <th>GenHealth_Poor</th>\n",
       "      <th>GenHealth_Very good</th>\n",
       "      <th>Asthma_Yes</th>\n",
       "      <th>KidneyDisease_Yes</th>\n",
       "      <th>SkinCancer_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.572395</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.365359</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.5</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>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.964817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.246279</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.668471</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BMI  PhysicalHealth  MentalHealth  SleepTime  Smoking_Yes  \\\n",
       "0 -0.572395             0.0      1.666667        0.5            0   \n",
       "1  2.365359             6.0      0.333333       -0.5            0   \n",
       "2 -0.964817             0.0      2.666667        0.5            0   \n",
       "3 -1.246279             0.0      0.000000       -0.5            1   \n",
       "4  0.668471            15.0      0.000000        0.5            1   \n",
       "\n",
       "   AlcoholDrinking_Yes  Stroke_Yes  DiffWalking_Yes  Sex_Male  \\\n",
       "0                    0           0                0         1   \n",
       "1                    0           0                0         0   \n",
       "2                    0           0                0         1   \n",
       "3                    0           0                1         1   \n",
       "4                    0           0                0         1   \n",
       "\n",
       "   AgeCategory_25-29  ...  Diabetic_Yes  Diabetic_Yes (during pregnancy)  \\\n",
       "0                  0  ...             0                                0   \n",
       "1                  0  ...             1                                0   \n",
       "2                  0  ...             0                                0   \n",
       "3                  0  ...             0                                0   \n",
       "4                  0  ...             1                                0   \n",
       "\n",
       "   PhysicalActivity_Yes  GenHealth_Fair  GenHealth_Good  GenHealth_Poor  \\\n",
       "0                     1               0               1               0   \n",
       "1                     1               1               0               0   \n",
       "2                     1               0               0               0   \n",
       "3                     1               0               1               0   \n",
       "4                     1               1               0               0   \n",
       "\n",
       "   GenHealth_Very good  Asthma_Yes  KidneyDisease_Yes  SkinCancer_Yes  \n",
       "0                    0           0                  0               0  \n",
       "1                    0           0                  0               0  \n",
       "2                    1           1                  0               0  \n",
       "3                    0           1                  0               0  \n",
       "4                    0           0                  0               0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 컬럼명 복구\n",
    "X_train_under.columns = list(X_train)\n",
    "y_train_under.columns = list(y_train)\n",
    "\n",
    "X_train_under.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "학습셋 모델 정확도: 0.7645725704259675\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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"
     ]
    }
   ],
   "source": [
    "# 학습셋 모델 학습\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train_under, y_train_under)\n",
    "\n",
    "print('학습셋 모델 정확도:', model.score(X_train_under, y_train_under))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트셋 모델 정확도: 0.7501532226794582\n"
     ]
    }
   ],
   "source": [
    "# 테스트셋 모델 적합\n",
    "\n",
    "print('테스트셋 모델 정확도:', model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7.94849270e-02  1.19088541e-02  2.93860460e-02 -5.45000754e-02\n",
      "   4.06112134e-01 -2.46744807e-01  1.28152611e+00  2.00070634e-01\n",
      "   7.60268121e-01 -5.74375739e-02  3.97070744e-01  4.15952140e-01\n",
      "   7.88601253e-01  1.07642238e+00  1.52874797e+00  1.76634364e+00\n",
      "   2.04612912e+00  2.33265548e+00  2.68501408e+00  2.84155732e+00\n",
      "   3.20283092e+00 -5.28873063e-01 -2.43639982e-01 -1.83580485e-01\n",
      "  -9.67382420e-02 -6.00065344e-02  2.81263387e-01  4.80312738e-01\n",
      "   4.37448259e-01 -2.02598119e-03  1.49954560e+00  1.02784311e+00\n",
      "   1.81095933e+00  4.45426020e-01  2.94367112e-01  6.17215689e-01\n",
      "   1.41569511e-01]]\n"
     ]
    }
   ],
   "source": [
    "# 계수값 확인\n",
    "\n",
    "print(model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1799: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1852: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>HeartDisease_Yes</td> <th>  No. Observations:  </th>  <td> 41036</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td> 40999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>    36</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 14 Jun 2022</td> <th>  Pseudo R-squ.:     </th>  <td>   inf</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:24:44</td>     <th>  Log-Likelihood:    </th> <td>    -inf</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td>  0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td> 1.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BMI</th>                              <td>    0.0918</td> <td>    0.015</td> <td>    6.056</td> <td> 0.000</td> <td>    0.062</td> <td>    0.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PhysicalHealth</th>                   <td>    0.0114</td> <td>    0.003</td> <td>    3.367</td> <td> 0.001</td> <td>    0.005</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MentalHealth</th>                     <td>    0.0170</td> <td>    0.005</td> <td>    3.478</td> <td> 0.001</td> <td>    0.007</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SleepTime</th>                        <td>   -0.0733</td> <td>    0.016</td> <td>   -4.619</td> <td> 0.000</td> <td>   -0.104</td> <td>   -0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Smoking_Yes</th>                      <td>    0.3696</td> <td>    0.025</td> <td>   14.807</td> <td> 0.000</td> <td>    0.321</td> <td>    0.419</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AlcoholDrinking_Yes</th>              <td>   -0.2732</td> <td>    0.053</td> <td>   -5.107</td> <td> 0.000</td> <td>   -0.378</td> <td>   -0.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Stroke_Yes</th>                       <td>    1.2616</td> <td>    0.054</td> <td>   23.555</td> <td> 0.000</td> <td>    1.157</td> <td>    1.367</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DiffWalking_Yes</th>                  <td>    0.1708</td> <td>    0.035</td> <td>    4.907</td> <td> 0.000</td> <td>    0.103</td> <td>    0.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sex_Male</th>                         <td>    0.6680</td> <td>    0.025</td> <td>   26.580</td> <td> 0.000</td> <td>    0.619</td> <td>    0.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_25-29</th>                <td>   -1.4044</td> <td>    0.127</td> <td>  -11.088</td> <td> 0.000</td> <td>   -1.653</td> <td>   -1.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_30-34</th>                <td>   -0.9200</td> <td>    0.103</td> <td>   -8.905</td> <td> 0.000</td> <td>   -1.122</td> <td>   -0.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_35-39</th>                <td>   -0.9326</td> <td>    0.097</td> <td>   -9.634</td> <td> 0.000</td> <td>   -1.122</td> <td>   -0.743</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_40-44</th>                <td>   -0.5486</td> <td>    0.089</td> <td>   -6.190</td> <td> 0.000</td> <td>   -0.722</td> <td>   -0.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_45-49</th>                <td>   -0.2661</td> <td>    0.082</td> <td>   -3.244</td> <td> 0.001</td> <td>   -0.427</td> <td>   -0.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_50-54</th>                <td>    0.1993</td> <td>    0.076</td> <td>    2.636</td> <td> 0.008</td> <td>    0.051</td> <td>    0.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_55-59</th>                <td>    0.4354</td> <td>    0.072</td> <td>    6.049</td> <td> 0.000</td> <td>    0.294</td> <td>    0.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_60-64</th>                <td>    0.7295</td> <td>    0.070</td> <td>   10.414</td> <td> 0.000</td> <td>    0.592</td> <td>    0.867</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_65-69</th>                <td>    1.0211</td> <td>    0.070</td> <td>   14.680</td> <td> 0.000</td> <td>    0.885</td> <td>    1.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_70-74</th>                <td>    1.3695</td> <td>    0.070</td> <td>   19.549</td> <td> 0.000</td> <td>    1.232</td> <td>    1.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_75-79</th>                <td>    1.5302</td> <td>    0.073</td> <td>   20.958</td> <td> 0.000</td> <td>    1.387</td> <td>    1.673</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AgeCategory_80 or older</th>          <td>    1.8668</td> <td>    0.072</td> <td>   25.936</td> <td> 0.000</td> <td>    1.726</td> <td>    2.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Race_Asian</th>                       <td>   -2.6688</td> <td>    0.120</td> <td>  -22.307</td> <td> 0.000</td> <td>   -2.903</td> <td>   -2.434</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Race_Black</th>                       <td>   -2.3371</td> <td>    0.081</td> <td>  -28.721</td> <td> 0.000</td> <td>   -2.497</td> <td>   -2.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Race_Hispanic</th>                    <td>   -2.3334</td> <td>    0.080</td> <td>  -29.136</td> <td> 0.000</td> <td>   -2.490</td> <td>   -2.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Race_Other</th>                       <td>   -2.2070</td> <td>    0.093</td> <td>  -23.681</td> <td> 0.000</td> <td>   -2.390</td> <td>   -2.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Race_White</th>                       <td>   -2.1554</td> <td>    0.069</td> <td>  -31.379</td> <td> 0.000</td> <td>   -2.290</td> <td>   -2.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diabetic_No, borderline diabetes</th> <td>    0.2903</td> <td>    0.078</td> <td>    3.734</td> <td> 0.000</td> <td>    0.138</td> <td>    0.443</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diabetic_Yes</th>                     <td>    0.4801</td> <td>    0.032</td> <td>   14.907</td> <td> 0.000</td> <td>    0.417</td> <td>    0.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diabetic_Yes (during pregnancy)</th>  <td>    0.2256</td> <td>    0.164</td> <td>    1.376</td> <td> 0.169</td> <td>   -0.096</td> <td>    0.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PhysicalActivity_Yes</th>             <td>   -0.1670</td> <td>    0.029</td> <td>   -5.835</td> <td> 0.000</td> <td>   -0.223</td> <td>   -0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GenHealth_Fair</th>                   <td>    1.2327</td> <td>    0.050</td> <td>   24.510</td> <td> 0.000</td> <td>    1.134</td> <td>    1.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GenHealth_Good</th>                   <td>    0.7566</td> <td>    0.041</td> <td>   18.541</td> <td> 0.000</td> <td>    0.677</td> <td>    0.837</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GenHealth_Poor</th>                   <td>    1.5592</td> <td>    0.074</td> <td>   21.061</td> <td> 0.000</td> <td>    1.414</td> <td>    1.704</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GenHealth_Very good</th>              <td>    0.1865</td> <td>    0.041</td> <td>    4.559</td> <td> 0.000</td> <td>    0.106</td> <td>    0.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asthma_Yes</th>                       <td>    0.2378</td> <td>    0.035</td> <td>    6.809</td> <td> 0.000</td> <td>    0.169</td> <td>    0.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>KidneyDisease_Yes</th>                <td>    0.6159</td> <td>    0.053</td> <td>   11.598</td> <td> 0.000</td> <td>    0.512</td> <td>    0.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SkinCancer_Yes</th>                   <td>    0.1745</td> <td>    0.037</td> <td>    4.707</td> <td> 0.000</td> <td>    0.102</td> <td>    0.247</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:       HeartDisease_Yes   No. Observations:                41036\n",
       "Model:                          Logit   Df Residuals:                    40999\n",
       "Method:                           MLE   Df Model:                           36\n",
       "Date:                Tue, 14 Jun 2022   Pseudo R-squ.:                     inf\n",
       "Time:                        21:24:44   Log-Likelihood:                   -inf\n",
       "converged:                       True   LL-Null:                        0.0000\n",
       "Covariance Type:            nonrobust   LLR p-value:                     1.000\n",
       "====================================================================================================\n",
       "                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------------\n",
       "BMI                                  0.0918      0.015      6.056      0.000       0.062       0.121\n",
       "PhysicalHealth                       0.0114      0.003      3.367      0.001       0.005       0.018\n",
       "MentalHealth                         0.0170      0.005      3.478      0.001       0.007       0.027\n",
       "SleepTime                           -0.0733      0.016     -4.619      0.000      -0.104      -0.042\n",
       "Smoking_Yes                          0.3696      0.025     14.807      0.000       0.321       0.419\n",
       "AlcoholDrinking_Yes                 -0.2732      0.053     -5.107      0.000      -0.378      -0.168\n",
       "Stroke_Yes                           1.2616      0.054     23.555      0.000       1.157       1.367\n",
       "DiffWalking_Yes                      0.1708      0.035      4.907      0.000       0.103       0.239\n",
       "Sex_Male                             0.6680      0.025     26.580      0.000       0.619       0.717\n",
       "AgeCategory_25-29                   -1.4044      0.127    -11.088      0.000      -1.653      -1.156\n",
       "AgeCategory_30-34                   -0.9200      0.103     -8.905      0.000      -1.122      -0.717\n",
       "AgeCategory_35-39                   -0.9326      0.097     -9.634      0.000      -1.122      -0.743\n",
       "AgeCategory_40-44                   -0.5486      0.089     -6.190      0.000      -0.722      -0.375\n",
       "AgeCategory_45-49                   -0.2661      0.082     -3.244      0.001      -0.427      -0.105\n",
       "AgeCategory_50-54                    0.1993      0.076      2.636      0.008       0.051       0.348\n",
       "AgeCategory_55-59                    0.4354      0.072      6.049      0.000       0.294       0.576\n",
       "AgeCategory_60-64                    0.7295      0.070     10.414      0.000       0.592       0.867\n",
       "AgeCategory_65-69                    1.0211      0.070     14.680      0.000       0.885       1.157\n",
       "AgeCategory_70-74                    1.3695      0.070     19.549      0.000       1.232       1.507\n",
       "AgeCategory_75-79                    1.5302      0.073     20.958      0.000       1.387       1.673\n",
       "AgeCategory_80 or older              1.8668      0.072     25.936      0.000       1.726       2.008\n",
       "Race_Asian                          -2.6688      0.120    -22.307      0.000      -2.903      -2.434\n",
       "Race_Black                          -2.3371      0.081    -28.721      0.000      -2.497      -2.178\n",
       "Race_Hispanic                       -2.3334      0.080    -29.136      0.000      -2.490      -2.176\n",
       "Race_Other                          -2.2070      0.093    -23.681      0.000      -2.390      -2.024\n",
       "Race_White                          -2.1554      0.069    -31.379      0.000      -2.290      -2.021\n",
       "Diabetic_No, borderline diabetes     0.2903      0.078      3.734      0.000       0.138       0.443\n",
       "Diabetic_Yes                         0.4801      0.032     14.907      0.000       0.417       0.543\n",
       "Diabetic_Yes (during pregnancy)      0.2256      0.164      1.376      0.169      -0.096       0.547\n",
       "PhysicalActivity_Yes                -0.1670      0.029     -5.835      0.000      -0.223      -0.111\n",
       "GenHealth_Fair                       1.2327      0.050     24.510      0.000       1.134       1.331\n",
       "GenHealth_Good                       0.7566      0.041     18.541      0.000       0.677       0.837\n",
       "GenHealth_Poor                       1.5592      0.074     21.061      0.000       1.414       1.704\n",
       "GenHealth_Very good                  0.1865      0.041      4.559      0.000       0.106       0.267\n",
       "Asthma_Yes                           0.2378      0.035      6.809      0.000       0.169       0.306\n",
       "KidneyDisease_Yes                    0.6159      0.053     11.598      0.000       0.512       0.720\n",
       "SkinCancer_Yes                       0.1745      0.037      4.707      0.000       0.102       0.247\n",
       "====================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다른 로지스틱 모델로 학습\n",
    "\n",
    "model2 = sm.Logit(y_train_under, X_train_under)\n",
    "results = model2.fit(method = \"newton\") \n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BMI                                 1.096091\n",
       "PhysicalHealth                      1.011469\n",
       "MentalHealth                        1.017101\n",
       "SleepTime                           0.929300\n",
       "Smoking_Yes                         1.447148\n",
       "AlcoholDrinking_Yes                 0.760970\n",
       "Stroke_Yes                          3.530911\n",
       "DiffWalking_Yes                     1.186292\n",
       "Sex_Male                            1.950422\n",
       "AgeCategory_25-29                   0.245524\n",
       "AgeCategory_30-34                   0.398530\n",
       "AgeCategory_35-39                   0.393534\n",
       "AgeCategory_40-44                   0.577770\n",
       "AgeCategory_45-49                   0.766331\n",
       "AgeCategory_50-54                   1.220555\n",
       "AgeCategory_55-59                   1.545532\n",
       "AgeCategory_60-64                   2.074125\n",
       "AgeCategory_65-69                   2.776307\n",
       "AgeCategory_70-74                   3.933446\n",
       "AgeCategory_75-79                   4.619038\n",
       "AgeCategory_80 or older             6.467417\n",
       "Race_Asian                          0.069335\n",
       "Race_Black                          0.096610\n",
       "Race_Hispanic                       0.096964\n",
       "Race_Other                          0.110032\n",
       "Race_White                          0.115859\n",
       "Diabetic_No, borderline diabetes    1.336833\n",
       "Diabetic_Yes                        1.616245\n",
       "Diabetic_Yes (during pregnancy)     1.253032\n",
       "PhysicalActivity_Yes                0.846179\n",
       "GenHealth_Fair                      3.430625\n",
       "GenHealth_Good                      2.130947\n",
       "GenHealth_Poor                      4.755009\n",
       "GenHealth_Very good                 1.205017\n",
       "Asthma_Yes                          1.268454\n",
       "KidneyDisease_Yes                   1.851397\n",
       "SkinCancer_Yes                      1.190609\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 오즈비 확인\n",
    "\n",
    "np.exp(results.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}