11.3.1.변수 구간화(Binning).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xverse in c:\\users\\angry\\anaconda3\\lib\\site-packages (1.0.5)\n",
      "Requirement already satisfied: scikit-learn>=0.19.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from xverse) (1.1.1)\n",
      "Requirement already satisfied: numpy>=1.11.3 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from xverse) (1.21.5)\n",
      "Requirement already satisfied: matplotlib>=3.0.3 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from xverse) (3.5.1)\n",
      "Requirement already satisfied: pandas>=0.21.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from xverse) (1.3.4)\n",
      "Requirement already satisfied: statsmodels>=0.6.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from xverse) (0.13.2)\n",
      "Requirement already satisfied: scipy>=0.19.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from xverse) (1.7.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.3->xverse) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.3->xverse) (0.11.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.3->xverse) (3.0.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.3->xverse) (21.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.3->xverse) (9.0.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.3->xverse) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.3->xverse) (1.3.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from pandas>=0.21.1->xverse) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.0.3->xverse) (1.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from scikit-learn>=0.19.0->xverse) (2.2.0)\n",
      "Requirement already satisfied: joblib>=1.0.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from scikit-learn>=0.19.0->xverse) (1.1.0)\n",
      "Requirement already satisfied: patsy>=0.5.2 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from statsmodels>=0.6.1->xverse) (0.5.2)\n"
     ]
    }
   ],
   "source": [
    "# 필요한 패키지 설치\n",
    "!pip install xverse\n",
    "from xverse.transformer import WOE\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
       "count    319795.000000\n",
       "mean         28.325399\n",
       "std           6.356100\n",
       "min          12.020000\n",
       "25%          24.030000\n",
       "50%          27.340000\n",
       "75%          31.420000\n",
       "max          94.850000\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BMI 컬럼의 분포 확인\n",
    "\n",
    "df['BMI'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1bbaf17f0a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BMI 컬럼 분포 시각화\n",
    "\n",
    "%matplotlib inline\n",
    "sns.displot(df['BMI'],height = 5, aspect = 3)"
   ]
  },
  {
   "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>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BMI_bin</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>a</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>b</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>b</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>b</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>b</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 BMI_bin Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "0           No  16.60       a     Yes              No     No             3.0   \n",
       "1           No  20.34       b      No              No    Yes             0.0   \n",
       "2           No  26.58       b     Yes              No     No            20.0   \n",
       "3           No  24.21       b      No              No     No             0.0   \n",
       "4           No  23.71       b      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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의로 단순 구간화\n",
    "\n",
    "df1 = df.copy() # 데이터셋 복사\n",
    "\n",
    "# 구간화용 빈 컬럼 생성 - 생략해도 되지만 바로 옆에 붙여 보기 위함\n",
    "df1.insert(2, 'BMI_bin', 0) \n",
    "\n",
    "df1.loc[df1['BMI'] <= 20, 'BMI_bin'] = 'a'\n",
    "df1.loc[(df1['BMI'] > 20) & (df1['BMI'] <= 30), 'BMI_bin'] = 'b'\n",
    "df1.loc[(df1['BMI'] > 30) & (df1['BMI'] <= 40), 'BMI_bin'] = 'c'\n",
    "df1.loc[(df1['BMI'] > 40) & (df1['BMI'] <= 50), 'BMI_bin'] = 'd'\n",
    "df1.loc[(df1['BMI'] > 50) & (df1['BMI'] <= 60), 'BMI_bin'] = 'e'\n",
    "df1.loc[(df1['BMI'] > 60) & (df1['BMI'] <= 70), 'BMI_bin'] = 'f'\n",
    "df1.loc[df1['BMI'] > 70, 'BMI_bin'] = 'g'\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1bbaf45e3d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 구간화 변수 분포 시각화\n",
    "\n",
    "sns.displot(df1['BMI_bin'],height = 5, aspect = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>BMI_bin</th>\n",
       "      <th>BMI_bin2</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>a</td>\n",
       "      <td>a</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>b</td>\n",
       "      <td>b</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>b</td>\n",
       "      <td>b</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>b</td>\n",
       "      <td>b</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>b</td>\n",
       "      <td>b</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 BMI_bin BMI_bin2 Smoking AlcoholDrinking Stroke  \\\n",
       "0           No  16.60       a        a     Yes              No     No   \n",
       "1           No  20.34       b        b      No              No    Yes   \n",
       "2           No  26.58       b        b     Yes              No     No   \n",
       "3           No  24.21       b        b      No              No     No   \n",
       "4           No  23.71       b        b      No              No     No   \n",
       "\n",
       "   PhysicalHealth  MentalHealth DiffWalking     Sex  AgeCategory   Race  \\\n",
       "0             3.0          30.0          No  Female        55-59  White   \n",
       "1             0.0           0.0          No  Female  80 or older  White   \n",
       "2            20.0          30.0          No    Male        65-69  White   \n",
       "3             0.0           0.0          No  Female        75-79  White   \n",
       "4            28.0           0.0         Yes  Female        40-44  White   \n",
       "\n",
       "  Diabetic PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease  \\\n",
       "0      Yes              Yes  Very good        5.0    Yes            No   \n",
       "1       No              Yes  Very good        7.0     No            No   \n",
       "2      Yes              Yes       Fair        8.0    Yes            No   \n",
       "3       No               No       Good        6.0     No            No   \n",
       "4       No              Yes  Very good        8.0     No            No   \n",
       "\n",
       "  SkinCancer  \n",
       "0        Yes  \n",
       "1         No  \n",
       "2         No  \n",
       "3        Yes  \n",
       "4         No  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  cut() 함수 사용하여 임의로 구간화\n",
    "df1.insert(3, 'BMI_bin2', 0) # 구간화용 빈 컬럼 생성 \n",
    "\n",
    "df1['BMI_bin2'] = pd.cut(df1.BMI, bins=[0, 20, 30, 40, 50, 60, 70, 95]\n",
    "                         , labels=['a', 'b', 'c', 'd', 'e', 'f', 'g'])\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_db83b_row0_col0 {\n",
       "  background-color: #00ff80;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db83b_row1_col0 {\n",
       "  background-color: #006cc9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_db83b_row2_col0 {\n",
       "  background-color: #0012f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_db83b_row3_col0 {\n",
       "  background-color: #0011f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_db83b_row4_col0 {\n",
       "  background-color: #0002fe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_db83b_row5_col0, #T_db83b_row6_col0 {\n",
       "  background-color: #0000ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_db83b_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >BMI_bin2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_db83b_level0_row0\" class=\"row_heading level0 row0\" >b</th>\n",
       "      <td id=\"T_db83b_row0_col0\" class=\"data row0 col0\" >202548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db83b_level0_row1\" class=\"row_heading level0 row1\" >c</th>\n",
       "      <td id=\"T_db83b_row1_col0\" class=\"data row1 col0\" >86198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db83b_level0_row2\" class=\"row_heading level0 row2\" >a</th>\n",
       "      <td id=\"T_db83b_row2_col0\" class=\"data row2 col0\" >14699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db83b_level0_row3\" class=\"row_heading level0 row3\" >d</th>\n",
       "      <td id=\"T_db83b_row3_col0\" class=\"data row3 col0\" >13839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db83b_level0_row4\" class=\"row_heading level0 row4\" >e</th>\n",
       "      <td id=\"T_db83b_row4_col0\" class=\"data row4 col0\" >2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db83b_level0_row5\" class=\"row_heading level0 row5\" >f</th>\n",
       "      <td id=\"T_db83b_row5_col0\" class=\"data row5 col0\" >363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db83b_level0_row6\" class=\"row_heading level0 row6\" >g</th>\n",
       "      <td id=\"T_db83b_row6_col0\" class=\"data row6 col0\" >129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bbaf643bb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BMI_bin2 구간 별 관측치 수 집계\n",
    "\n",
    "df1.BMI_bin2.value_counts().to_frame().style.background_gradient(cmap='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BMI_bin</th>\n",
       "      <th>BMI_bin2</th>\n",
       "      <th>BMI_bin3</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>...</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>a</td>\n",
       "      <td>a</td>\n",
       "      <td>a</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>...</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>b</td>\n",
       "      <td>b</td>\n",
       "      <td>a</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>...</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>b</td>\n",
       "      <td>b</td>\n",
       "      <td>d</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>...</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>b</td>\n",
       "      <td>b</td>\n",
       "      <td>b</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>...</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>b</td>\n",
       "      <td>b</td>\n",
       "      <td>b</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>...</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",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  HeartDisease    BMI BMI_bin BMI_bin2 BMI_bin3 Smoking AlcoholDrinking  \\\n",
       "0           No  16.60       a        a        a     Yes              No   \n",
       "1           No  20.34       b        b        a      No              No   \n",
       "2           No  26.58       b        b        d     Yes              No   \n",
       "3           No  24.21       b        b        b      No              No   \n",
       "4           No  23.71       b        b        b      No              No   \n",
       "\n",
       "  Stroke  PhysicalHealth  MentalHealth  ...     Sex  AgeCategory   Race  \\\n",
       "0     No             3.0          30.0  ...  Female        55-59  White   \n",
       "1    Yes             0.0           0.0  ...  Female  80 or older  White   \n",
       "2     No            20.0          30.0  ...    Male        65-69  White   \n",
       "3     No             0.0           0.0  ...  Female        75-79  White   \n",
       "4     No            28.0           0.0  ...  Female        40-44  White   \n",
       "\n",
       "  Diabetic PhysicalActivity  GenHealth SleepTime  Asthma KidneyDisease  \\\n",
       "0      Yes              Yes  Very good       5.0     Yes            No   \n",
       "1       No              Yes  Very good       7.0      No            No   \n",
       "2      Yes              Yes       Fair       8.0     Yes            No   \n",
       "3       No               No       Good       6.0      No            No   \n",
       "4       No              Yes  Very good       8.0      No            No   \n",
       "\n",
       "  SkinCancer  \n",
       "0        Yes  \n",
       "1         No  \n",
       "2         No  \n",
       "3        Yes  \n",
       "4         No  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# qcut() 함수 사용하여 자동 구간화\n",
    "df1.insert(4, 'BMI_bin3', 0) # 구간화용 빈 컬럼 생성 \n",
    "\n",
    "df1['BMI_bin3'] = pd.qcut(df1.BMI, q=7, labels=['a', 'b', 'c', 'd', 'e', 'f', 'g'])\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7bc7d_row0_col0 {\n",
       "  background-color: #00ff80;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7bc7d_row1_col0 {\n",
       "  background-color: #0085bc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7bc7d_row2_col0 {\n",
       "  background-color: #0082be;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7bc7d_row3_col0 {\n",
       "  background-color: #0075c4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7bc7d_row4_col0 {\n",
       "  background-color: #0043de;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7bc7d_row5_col0 {\n",
       "  background-color: #0029eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7bc7d_row6_col0 {\n",
       "  background-color: #0000ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7bc7d_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >BMI_bin3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7bc7d_level0_row0\" class=\"row_heading level0 row0\" >b</th>\n",
       "      <td id=\"T_7bc7d_row0_col0\" class=\"data row0 col0\" >47309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7bc7d_level0_row1\" class=\"row_heading level0 row1\" >a</th>\n",
       "      <td id=\"T_7bc7d_row1_col0\" class=\"data row1 col0\" >45980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7bc7d_level0_row2\" class=\"row_heading level0 row2\" >d</th>\n",
       "      <td id=\"T_7bc7d_row2_col0\" class=\"data row2 col0\" >45944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7bc7d_level0_row3\" class=\"row_heading level0 row3\" >f</th>\n",
       "      <td id=\"T_7bc7d_row3_col0\" class=\"data row3 col0\" >45805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7bc7d_level0_row4\" class=\"row_heading level0 row4\" >g</th>\n",
       "      <td id=\"T_7bc7d_row4_col0\" class=\"data row4 col0\" >45255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7bc7d_level0_row5\" class=\"row_heading level0 row5\" >e</th>\n",
       "      <td id=\"T_7bc7d_row5_col0\" class=\"data row5 col0\" >44977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7bc7d_level0_row6\" class=\"row_heading level0 row6\" >c</th>\n",
       "      <td id=\"T_7bc7d_row6_col0\" class=\"data row6 col0\" >44525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bbafd14850>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BMI_bin3 구간 별 관측치 수 집계\n",
    "\n",
    "df1.BMI_bin3.value_counts().to_frame().style.background_gradient(cmap='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18b99787b20>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BMI_bin3 분포 시각화\n",
    "\n",
    "sns.displot(df1['BMI_bin3'],height = 5, aspect = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Variable_Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Count</th>\n",
       "      <th>Event</th>\n",
       "      <th>Non_Event</th>\n",
       "      <th>Event_Rate</th>\n",
       "      <th>Non_Event_Rate</th>\n",
       "      <th>Event_Distribution</th>\n",
       "      <th>Non_Event_Distribution</th>\n",
       "      <th>WOE</th>\n",
       "      <th>Information_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PhysicalHealth</td>\n",
       "      <td>(-0.001, 1.0]</td>\n",
       "      <td>237078</td>\n",
       "      <td>5719.0</td>\n",
       "      <td>231359.0</td>\n",
       "      <td>0.024123</td>\n",
       "      <td>0.975877</td>\n",
       "      <td>0.485525</td>\n",
       "      <td>0.751127</td>\n",
       "      <td>-0.436343</td>\n",
       "      <td>0.308774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PhysicalHealth</td>\n",
       "      <td>(1.0, 30.0]</td>\n",
       "      <td>82717</td>\n",
       "      <td>6060.0</td>\n",
       "      <td>76657.0</td>\n",
       "      <td>0.073262</td>\n",
       "      <td>0.926738</td>\n",
       "      <td>0.514475</td>\n",
       "      <td>0.248873</td>\n",
       "      <td>0.726202</td>\n",
       "      <td>0.308774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Variable_Name       Category   Count   Event  Non_Event  Event_Rate  \\\n",
       "0  PhysicalHealth  (-0.001, 1.0]  237078  5719.0   231359.0    0.024123   \n",
       "1  PhysicalHealth    (1.0, 30.0]   82717  6060.0    76657.0    0.073262   \n",
       "\n",
       "   Non_Event_Rate  Event_Distribution  Non_Event_Distribution       WOE  \\\n",
       "0        0.975877            0.485525                0.751127 -0.436343   \n",
       "1        0.926738            0.514475                0.248873  0.726202   \n",
       "\n",
       "   Information_Value  \n",
       "0           0.308774  \n",
       "1           0.308774  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# WOE를 사용한 변수 구간화\n",
    "\n",
    "df2 = df.copy()  # 데이터셋 복사\n",
    "\n",
    "\n",
    "# xverse 함수 적용을 위한 더미변수 변환\n",
    "df2=pd.get_dummies(df)\n",
    "\n",
    "# 구간화 할 컬럼(X), 기준 컬럼(y) 지정\n",
    "X = df2[['PhysicalHealth']]\n",
    "y = df2[['KidneyDisease_Yes']]\n",
    "\n",
    "y = y.T.squeeze() # 차원 축소\n",
    "\n",
    "# WOE 모델 설정 및 적용\n",
    "clf = WOE()\n",
    "clf.fit(X, y)\n",
    "\n",
    "# 구간 기준점 및 eight of Evidence 값 테이블 생성\n",
    "a=clf.woe_df \n",
    "\n",
    "#Information Value 데이블 생성\n",
    "b=clf.iv_df\n",
    "\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Variable_Name</th>\n",
       "      <th>Information_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PhysicalHealth</td>\n",
       "      <td>0.308774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Variable_Name  Information_Value\n",
       "0  PhysicalHealth           0.308774"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Information Value 확인\n",
    "\n",
    "b.head()"
   ]
  }
 ],
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