real final2(HDL LDL중점적으로 분석하기).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. matplotlib을 import 하고 폰트가 깨져서 다른 폰트를 import 했습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gulim\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib.font_manager as fm\n",
    "font_list = fm.findSystemFonts(fontpaths=None, fontext='ttf')\n",
    "path = 'C:/Windows/Fonts/gulim.ttc'\n",
    "font_name = fm.FontProperties(fname=path, size=50).get_name()\n",
    "print(font_name)\n",
    "plt.rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 필요한 라이브러리 import 했습니다\n",
    "### 2. 혈액데이터의 칼럼을 height와 weight로 이름을 바꾸고(편의성을 위해) bmi라는 새로운 칼럼을 만들었습니다\n",
    "### 3. 그리고 허리둘레와 bmi를 나눠서 special bmi라는 칼럼도 생성했습니다. 왜냐하면 근육질인 사람은 bmi가 높아도 허리둘레는 상대적으로 낮기 때문입니다. 그래서 bmi만으로는 건강하다는것을 파악하기 힘들다고 생각했습니다.\n",
    "### 4. 그리고 필요없는 칼럼들은 삭제를 해주고 null값이 있는 행을 삭제합니다.\n",
    "### 5. 중요한 데이터인 hdl과 ldl에 이상치가 있어 제거를 해줬습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>허리둘레</th>\n",
       "      <th>수축기혈압</th>\n",
       "      <th>이완기혈압</th>\n",
       "      <th>식전혈당(공복혈당)</th>\n",
       "      <th>총콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>요단백</th>\n",
       "      <th>혈청크레아티닌</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
       "      <th>(혈청지오티)ALT</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>bmi</th>\n",
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       "      <td>90.0</td>\n",
       "      <td>91.0</td>\n",
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       "      <td>196.0</td>\n",
       "      <td>46.0</td>\n",
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       "      <td>30.0</td>\n",
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       "      <td>138.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>105.0</td>\n",
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       "      <td>138.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>13.6</td>\n",
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       "      <td>0.7</td>\n",
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       "      <td>76.0</td>\n",
       "      <td>93.0</td>\n",
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       "      <td>79.0</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>16.8</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>1.0</td>\n",
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      "text/plain": [
       "   성별코드  연령대코드(5세단위)  허리둘레  수축기혈압  이완기혈압  식전혈당(공복혈당)  총콜레스테롤  트리글리세라이드  \\\n",
       "0     1           10  86.0  140.0   90.0        91.0   263.0     196.0   \n",
       "1     2           11  85.0  138.0   80.0       105.0   185.0     262.0   \n",
       "2     2           11  80.0  138.0   88.0        93.0   246.0     186.0   \n",
       "3     1           12  85.0  124.0   76.0        93.0   179.0      79.0   \n",
       "4     2            6  67.0  121.0   68.0        96.0   223.0     104.0   \n",
       "\n",
       "   HDL콜레스테롤  LDL콜레스테롤   혈색소  요단백  혈청크레아티닌  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  \\\n",
       "0      46.0     177.0  16.0  1.0      0.9        27.0        30.0   45.0   \n",
       "1      35.0     122.0  13.1  1.0      0.9        18.0        17.0   19.0   \n",
       "2      59.0     150.0  13.6  1.0      0.7        23.0        19.0   64.0   \n",
       "3      40.0     123.0  16.8  1.0      1.0        20.0        39.0   69.0   \n",
       "4      72.0     130.0  12.8  1.0      0.7        13.0         7.0   20.0   \n",
       "\n",
       "   흡연상태        bmi  special_bmi  \n",
       "0   3.0  24.489796     3.511667  \n",
       "1   1.0  25.390625     3.347692  \n",
       "2   1.0  24.444444     3.272727  \n",
       "3   2.0  22.857143     3.718750  \n",
       "4   1.0  19.531250     3.430400  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "blood_data=pd.read_csv('blood.csv',engine='python',encoding='CP949')\n",
    "\n",
    "blood_data.rename(columns = {'신장(5Cm단위)' : 'height'}, inplace = True)\n",
    "blood_data.rename(columns = {'체중(5Kg단위)' : 'weight'}, inplace = True)\n",
    "\n",
    "blood_data['bmi']=blood_data['weight']/(blood_data['height']**2/10000)\n",
    "blood_data['special_bmi']=blood_data['허리둘레']/blood_data['bmi']\n",
    "blood_data=blood_data.drop(blood_data.columns[[8,9,10,11]], axis='columns')\n",
    "blood_data=blood_data.drop(blood_data.columns[[22,23,24,25,26,27,28,29]], axis='columns')\n",
    "blood_data=blood_data.drop(blood_data.columns[[0,1,4]], axis='columns')\n",
    "blood_data=blood_data.drop(blood_data.columns[[2,3]], axis='columns')\n",
    "blood_data=blood_data.dropna(axis=0)\n",
    "blood_data=blood_data[blood_data['LDL콜레스테롤']>75]\n",
    "blood_data=blood_data[blood_data['HDL콜레스테롤']<130]\n",
    "blood_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 허리둘레 칼럼도 special bmi에 들어가 있으므로 제거합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "blood_data=blood_data[blood_data['허리둘레']<300]\n",
    "blood_data=blood_data.drop(blood_data.columns[[2]], axis='columns') #허리둘레 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "b_data=blood_data\n",
    "\n",
    "\n",
    "b_data[\"LDL_cat\"] = pd.cut(b_data[\"LDL콜레스테롤\"],\n",
    "                               bins=[0., 100., 130.,160., 190., np.inf],\n",
    "                               labels=[1, 2, 3, 4, 5])\n",
    "\n",
    "b_data[\"HDL_cat\"] = pd.cut(b_data[\"HDL콜레스테롤\"],\n",
    "                               bins=[0.,40.,60., np.inf],\n",
    "                               labels=[0,1,2])\n",
    "\n",
    "b_data[\"TC_cat\"] = pd.cut(b_data[\"총콜레스테롤\"],\n",
    "                               bins=[0.,200.,240., np.inf],\n",
    "                               labels=[1,2,3])\n",
    "b_data[\"고혈압_1\"] = pd.cut(b_data[\"이완기혈압\"],\n",
    "                               bins=[0., 90.,np.inf],\n",
    "                               labels=[0,1])\n",
    "b_data[\"고혈압_2\"] = pd.cut(b_data[\"수축기혈압\"],\n",
    "                               bins=[0., 140.,np.inf],\n",
    "                               labels=[0,1])\n",
    "b_data['고혈압']=b_data['고혈압_1'].astype('float')+b_data['고혈압_2'].astype('float')\n",
    "b_data['고혈압']=np.ceil(b_data['고혈압']/2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>수축기혈압</th>\n",
       "      <th>이완기혈압</th>\n",
       "      <th>식전혈당(공복혈당)</th>\n",
       "      <th>총콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>...</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
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       "      <th>감마지티피</th>\n",
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       "      <td>59.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>76.0</td>\n",
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      ],
      "text/plain": [
       "   성별코드  연령대코드(5세단위)  수축기혈압  이완기혈압  식전혈당(공복혈당)  총콜레스테롤  트리글리세라이드  HDL콜레스테롤  \\\n",
       "0     1           10  140.0   90.0        91.0   263.0     196.0      46.0   \n",
       "1     2           11  138.0   80.0       105.0   185.0     262.0      35.0   \n",
       "2     2           11  138.0   88.0        93.0   246.0     186.0      59.0   \n",
       "3     1           12  124.0   76.0        93.0   179.0      79.0      40.0   \n",
       "4     2            6  121.0   68.0        96.0   223.0     104.0      72.0   \n",
       "\n",
       "   LDL콜레스테롤   혈색소  ...  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  흡연상태        bmi  \\\n",
       "0     177.0  16.0  ...        27.0        30.0   45.0   3.0  24.489796   \n",
       "1     122.0  13.1  ...        18.0        17.0   19.0   1.0  25.390625   \n",
       "2     150.0  13.6  ...        23.0        19.0   64.0   1.0  24.444444   \n",
       "3     123.0  16.8  ...        20.0        39.0   69.0   2.0  22.857143   \n",
       "4     130.0  12.8  ...        13.0         7.0   20.0   1.0  19.531250   \n",
       "\n",
       "   special_bmi  LDL_cat  HDL_cat TC_cat  고혈압  \n",
       "0     3.511667        4        1      3  0.0  \n",
       "1     3.347692        2        0      1  0.0  \n",
       "2     3.272727        3        1      3  0.0  \n",
       "3     3.718750        2        0      1  0.0  \n",
       "4     3.430400        2        2      2  0.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_data=b_data.drop(['고혈압_1','고혈압_2'], axis='columns')\n",
    "b_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>수축기혈압</th>\n",
       "      <th>이완기혈압</th>\n",
       "      <th>식전혈당(공복혈당)</th>\n",
       "      <th>총콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>...</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
       "      <th>(혈청지오티)ALT</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>bmi</th>\n",
       "      <th>special_bmi</th>\n",
       "      <th>LDL_cat</th>\n",
       "      <th>HDL_cat</th>\n",
       "      <th>TC_cat</th>\n",
       "      <th>고혈압</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.489796</td>\n",
       "      <td>3.511667</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>138.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.390625</td>\n",
       "      <td>3.347692</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>138.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.444444</td>\n",
       "      <td>3.272727</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>124.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>3.718750</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.531250</td>\n",
       "      <td>3.430400</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   성별코드  연령대코드(5세단위)  수축기혈압  이완기혈압  식전혈당(공복혈당)  총콜레스테롤  트리글리세라이드  HDL콜레스테롤  \\\n",
       "0     1           10  140.0   90.0        91.0   263.0     196.0      46.0   \n",
       "1     2           11  138.0   80.0       105.0   185.0     262.0      35.0   \n",
       "2     2           11  138.0   88.0        93.0   246.0     186.0      59.0   \n",
       "3     1           12  124.0   76.0        93.0   179.0      79.0      40.0   \n",
       "4     2            6  121.0   68.0        96.0   223.0     104.0      72.0   \n",
       "\n",
       "   LDL콜레스테롤   혈색소  ...  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  흡연상태        bmi  \\\n",
       "0     177.0  16.0  ...        27.0        30.0   45.0   3.0  24.489796   \n",
       "1     122.0  13.1  ...        18.0        17.0   19.0   1.0  25.390625   \n",
       "2     150.0  13.6  ...        23.0        19.0   64.0   1.0  24.444444   \n",
       "3     123.0  16.8  ...        20.0        39.0   69.0   2.0  22.857143   \n",
       "4     130.0  12.8  ...        13.0         7.0   20.0   1.0  19.531250   \n",
       "\n",
       "   special_bmi  LDL_cat  HDL_cat TC_cat  고혈압  \n",
       "0     3.511667        4        1      3  0.0  \n",
       "1     3.347692        2        0      1  0.0  \n",
       "2     3.272727        3        1      3  0.0  \n",
       "3     3.718750        2        0      1  0.0  \n",
       "4     3.430400        2        2      2  0.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(b_data['LDL콜레스테롤']<65)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LDL과 HDL의 건강과의 상관관계\n",
    "주장1 : HDL은 건강에 좋고 LDL은 나쁘다. 그래서 총 콜레스테롤이 높아도 HDL이 높고 LDL이 정상이면 bmi나 허리둘레나 다른 결과들이 좋게 나타날 것이다. \n",
    "\n",
    "주장2 : 반대로 LDL이높고 HDL이 낮은경우는 건강이 매우 안좋다(bmi)는 신호일것이다.\n",
    "\n",
    "주장3 : LDL수치와 HDL수치가 둘다 높거나 낮은경우도 건강이 좋지만은 않을것이다.\n",
    "\n",
    "LDL수치와 HDL수치가 둘다 높은 경우 HDL수치가 높고 LDL수치는 정상이거나 낮은경우 HDL수치가 정상이고 LDL수치가 높은 경우 HDL수치와 LDL수치가 둘다 낮은 경우(총콜레스테롤이 낮은경우) LDL-콜레스테롤\n",
    "\n",
    "< 100 정상 (optimal)\n",
    "\n",
    "100 ~ 129 정상 이상 (near optimal/above optimal)\n",
    "\n",
    "130 ~ 159 약간 높음 (borderline high)\n",
    "\n",
    "160 ~ 189 높음 (high)\n",
    "\n",
    "190 이상 아주 높음 (very high)\n",
    "\n",
    "총콜레스테롤\n",
    "\n",
    "< 200 적절 (desirable)\n",
    "\n",
    "200 ~ 239 약간 높음 (borderline high)\n",
    "\n",
    "240 이상 높음 (high)\n",
    "\n",
    "HDL-콜레스테롤\n",
    "\n",
    "< 40 낮음 (low)\n",
    "\n",
    "60 이상 높음 (high)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 주장1 : HDL은 건강에 좋고 LDL은 나쁘다. 그래서 총 콜레스테롤이 높아도 HDL이 높고 LDL이 정상이면 bmi나 허리둘레나 다른 결과들이 좋게 나타날 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(b_data[:100000], test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X=train_set[['HDL콜레스테롤','LDL콜레스테롤','트리글리세라이드','감마지티피','혈색소']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86485     123.0\n",
       "56221     119.0\n",
       "51659     133.0\n",
       "15627     125.0\n",
       "106588    132.0\n",
       "          ...  \n",
       "7239      130.0\n",
       "63066     120.0\n",
       "88305     102.0\n",
       "983       147.0\n",
       "18192     130.0\n",
       "Name: 수축기혈압, Length: 80000, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y=train_set['수축기혈압']\n",
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X=test_set[['HDL콜레스테롤','LDL콜레스테롤','트리글리세라이드','감마지티피','혈색소']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87055     110.0\n",
       "92164     130.0\n",
       "22833     124.0\n",
       "88164     130.0\n",
       "106886    123.0\n",
       "          ...  \n",
       "37530     100.0\n",
       "33743     110.0\n",
       "43559     118.0\n",
       "61381     139.0\n",
       "48762     160.0\n",
       "Name: 수축기혈압, Length: 20000, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y=test_set['수축기혈압']\n",
    "test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "stds = StandardScaler()\n",
    "train_scaled=stds.fit_transform(train_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid=[{'C': [1, 5, 10, 20]}], return_train_score=True,\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "param_grid = [\n",
    "    # try 12 (3×4) combinations of hyperparameters\n",
    "    {'C': [1,5,10,20], }]\n",
    "\n",
    "log_reg = LogisticRegression()\n",
    "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
    "grid_search = GridSearchCV(log_reg, param_grid, cv=5,\n",
    "                           scoring='roc_auc',\n",
    "                           return_train_score=True)\n",
    "grid_search.fit(train_scaled[:20000], train_y[:20000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_.fit(train_scaled, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00755219, 0.        , 0.00937538, 0.00443621]),\n",
       " 'std_fit_time': array([0.00700789, 0.        , 0.00765497, 0.00615212]),\n",
       " 'mean_score_time': array([0.        , 0.00312548, 0.        , 0.        ]),\n",
       " 'std_score_time': array([0.        , 0.00625095, 0.        , 0.        ]),\n",
       " 'param_C': masked_array(data=[1, 5, 10, 20],\n",
       "              mask=[False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 1}, {'C': 5}, {'C': 10}, {'C': 20}],\n",
       " 'split0_test_score': array([0.72300114, 0.72297341, 0.72297341, 0.72297341]),\n",
       " 'split1_test_score': array([0.70906385, 0.70905461, 0.70904999, 0.70903151]),\n",
       " 'split2_test_score': array([0.71101397, 0.71101397, 0.71101397, 0.71101397]),\n",
       " 'split3_test_score': array([0.68209041, 0.68208117, 0.68207654, 0.68207192]),\n",
       " 'split4_test_score': array([0.69856532, 0.6985422 , 0.6985422 , 0.6985422 ]),\n",
       " 'mean_test_score': array([0.70474694, 0.70473307, 0.70473122, 0.7047266 ]),\n",
       " 'std_test_score': array([0.01373068, 0.01372786, 0.0137291 , 0.01372946]),\n",
       " 'rank_test_score': array([1, 2, 3, 4]),\n",
       " 'split0_train_score': array([0.70233386, 0.70232029, 0.70231624, 0.70231451]),\n",
       " 'split1_train_score': array([0.70435834, 0.70434303, 0.70434072, 0.70433927]),\n",
       " 'split2_train_score': array([0.70597087, 0.70596191, 0.70595903, 0.705957  ]),\n",
       " 'split3_train_score': array([0.71258854, 0.7125767 , 0.71257294, 0.71257179]),\n",
       " 'split4_train_score': array([0.70728675, 0.70727173, 0.70727231, 0.70727173]),\n",
       " 'mean_train_score': array([0.70650767, 0.70649473, 0.70649225, 0.70649086]),\n",
       " 'std_train_score': array([0.00346157, 0.00346219, 0.00346225, 0.00346247])}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8663943990665111"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "test_scaled=stds.transform(test_X)\n",
    "b_predictions = model.predict(test_scaled)\n",
    "accuracy_score(test_y, b_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1483    7]\n",
      " [ 222    2]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(test_y,b_predictions)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>수축기혈압</th>\n",
       "      <th>이완기혈압</th>\n",
       "      <th>식전혈당(공복혈당)</th>\n",
       "      <th>총콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>...</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
       "      <th>(혈청지오티)ALT</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>bmi</th>\n",
       "      <th>special_bmi</th>\n",
       "      <th>LDL_cat</th>\n",
       "      <th>HDL_cat</th>\n",
       "      <th>TC_cat</th>\n",
       "      <th>고혈압</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>153.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.221453</td>\n",
       "      <td>3.798286</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>131.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.403092</td>\n",
       "      <td>3.317278</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>144.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.484375</td>\n",
       "      <td>3.723636</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>1</td>\n",
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       "      <td>150.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.484375</td>\n",
       "      <td>3.397818</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>145.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.057851</td>\n",
       "      <td>2.873750</td>\n",
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       "    <tr>\n",
       "      <th>997771</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>155.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.489796</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997988</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>135.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.390625</td>\n",
       "      <td>2.914462</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>998287</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>142.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.224490</td>\n",
       "      <td>4.193269</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998797</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>155.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.491349</td>\n",
       "      <td>3.734769</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998830</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>142.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.296875</td>\n",
       "      <td>3.345067</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1134 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        성별코드  연령대코드(5세단위)  수축기혈압  이완기혈압  식전혈당(공복혈당)  총콜레스테롤  트리글리세라이드  \\\n",
       "777        1           13  153.0   85.0       101.0   229.0     306.0   \n",
       "914        2           14  131.0  103.0        89.0   217.0      89.0   \n",
       "951        1            9  144.0   97.0        98.0   236.0     300.0   \n",
       "1182       1           11  150.0   90.0       101.0   211.0     253.0   \n",
       "2853       1           10  145.0   95.0        97.0   222.0     440.0   \n",
       "...      ...          ...    ...    ...         ...     ...       ...   \n",
       "997771     1            8  155.0  100.0       108.0   216.0     261.0   \n",
       "997988     2           12  135.0   91.0        96.0   219.0     217.0   \n",
       "998287     1            8  142.0   95.0       117.0   210.0      78.0   \n",
       "998797     1           12  155.0   94.0       102.0   211.0     284.0   \n",
       "998830     2            6  142.0   84.0       111.0   234.0     259.0   \n",
       "\n",
       "        HDL콜레스테롤  LDL콜레스테롤   혈색소  ...  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  흡연상태  \\\n",
       "777         67.0     100.0  15.2  ...        30.0        22.0   43.0   2.0   \n",
       "914        105.0      94.0  14.8  ...        53.0        74.0   32.0   1.0   \n",
       "951         79.0      97.0  14.1  ...        37.0        43.0   55.0   3.0   \n",
       "1182        80.0      80.0  14.4  ...        35.0        17.0   76.0   3.0   \n",
       "2853        68.0      78.0  13.7  ...        40.0        58.0   66.0   2.0   \n",
       "...          ...       ...   ...  ...         ...         ...    ...   ...   \n",
       "997771      63.0     100.0  15.5  ...        44.0        61.0  351.0   2.0   \n",
       "997988      79.0      97.0  12.7  ...        29.0        24.0   27.0   1.0   \n",
       "998287      96.0      98.0  14.8  ...        26.0        21.0  190.0   2.0   \n",
       "998797      63.0      91.0  15.3  ...        31.0        46.0  182.0   3.0   \n",
       "998830      85.0      97.0  13.6  ...        17.0        20.0  128.0   1.0   \n",
       "\n",
       "              bmi  special_bmi  LDL_cat  HDL_cat TC_cat  고혈압  \n",
       "777     24.221453     3.798286        1        2      2  1.0  \n",
       "914     21.403092     3.317278        1        2      2  1.0  \n",
       "951     21.484375     3.723636        1        2      2  1.0  \n",
       "1182    21.484375     3.397818        1        2      2  1.0  \n",
       "2853    33.057851     2.873750        1        2      2  1.0  \n",
       "...           ...          ...      ...      ...    ...  ...  \n",
       "997771  24.489796     3.920000        1        2      2  1.0  \n",
       "997988  25.390625     2.914462        1        2      2  1.0  \n",
       "998287  21.224490     4.193269        1        2      2  1.0  \n",
       "998797  22.491349     3.734769        1        2      2  1.0  \n",
       "998830  29.296875     3.345067        1        2      2  1.0  \n",
       "\n",
       "[1134 rows x 22 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highH_normalL[highH_normalL['고혈압']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>수축기혈압</th>\n",
       "      <th>이완기혈압</th>\n",
       "      <th>식전혈당(공복혈당)</th>\n",
       "      <th>총콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>...</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
       "      <th>(혈청지오티)ALT</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>bmi</th>\n",
       "      <th>special_bmi</th>\n",
       "      <th>LDL_cat</th>\n",
       "      <th>HDL_cat</th>\n",
       "      <th>TC_cat</th>\n",
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       "      <th>20</th>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.365473</td>\n",
       "      <td>3.648150</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>135.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.031142</td>\n",
       "      <td>4.203636</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>135.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.109375</td>\n",
       "      <td>3.045053</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>100.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.811655</td>\n",
       "      <td>3.267400</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.730489</td>\n",
       "      <td>3.950778</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999764</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>111.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.761246</td>\n",
       "      <td>3.997833</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999768</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.892820</td>\n",
       "      <td>3.276136</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999813</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.484375</td>\n",
       "      <td>3.351273</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999827</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>108.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.777778</td>\n",
       "      <td>3.656250</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999898</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>139.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.484375</td>\n",
       "      <td>3.584000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7434 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        성별코드  연령대코드(5세단위)  수축기혈압  이완기혈압  식전혈당(공복혈당)  총콜레스테롤  트리글리세라이드  \\\n",
       "20         1           10  138.0   84.0        95.0   204.0     125.0   \n",
       "42         1           12  135.0   81.0       105.0   215.0      31.0   \n",
       "375        1            8  135.0   87.0        99.0   217.0     253.0   \n",
       "606        2            9  100.0   70.0        91.0   212.0      86.0   \n",
       "626        2            7  100.0   67.0        77.0   215.0     171.0   \n",
       "...      ...          ...    ...    ...         ...     ...       ...   \n",
       "999764     1           10  111.0   69.0       100.0   206.0     391.0   \n",
       "999768     2            7  110.0   60.0        81.0   214.0      89.0   \n",
       "999813     2            8  120.0   80.0        90.0   205.0      52.0   \n",
       "999827     2            6  108.0   63.0        94.0   203.0      65.0   \n",
       "999898     2           12  139.0   84.0       101.0   205.0     133.0   \n",
       "\n",
       "        HDL콜레스테롤  LDL콜레스테롤   혈색소  ...  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  흡연상태  \\\n",
       "20          91.0      88.0  15.9  ...        19.0        11.0   37.0   3.0   \n",
       "42         120.0      82.0  15.7  ...        33.0        18.0   46.0   3.0   \n",
       "375         73.0      93.0  16.9  ...        34.0        57.0  144.0   3.0   \n",
       "606        105.0      89.0  12.5  ...        17.0        15.0   12.0   1.0   \n",
       "626         86.0      94.0  11.7  ...        20.0        14.0   19.0   1.0   \n",
       "...          ...       ...   ...  ...         ...         ...    ...   ...   \n",
       "999764      61.0      66.0  16.5  ...        23.0        37.0  118.0   3.0   \n",
       "999768      99.0      97.0  12.5  ...        16.0        10.0   10.0   1.0   \n",
       "999813     100.0      95.0  14.5  ...        26.0        17.0   18.0   1.0   \n",
       "999827      95.0      95.0  13.5  ...        13.0        10.0   16.0   1.0   \n",
       "999898      85.0      93.0  13.4  ...        31.0        45.0   47.0   1.0   \n",
       "\n",
       "              bmi  special_bmi  LDL_cat  HDL_cat TC_cat  고혈압  \n",
       "20      18.365473     3.648150        1        2      2  0.0  \n",
       "42      19.031142     4.203636        1        2      2  0.0  \n",
       "375     37.109375     3.045053        1        2      2  0.0  \n",
       "606     20.811655     3.267400        1        2      2  0.0  \n",
       "626     18.730489     3.950778        1        2      2  0.0  \n",
       "...           ...          ...      ...      ...    ...  ...  \n",
       "999764  20.761246     3.997833        1        2      2  0.0  \n",
       "999768  22.892820     3.276136        1        2      2  0.0  \n",
       "999813  21.484375     3.351273        1        2      2  0.0  \n",
       "999827  17.777778     3.656250        1        2      2  0.0  \n",
       "999898  21.484375     3.584000        1        2      2  0.0  \n",
       "\n",
       "[7434 rows x 22 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highH_normalL[highH_normalL['고혈압']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalH_highL=b_data[(b_data['LDL_cat']>2) & (b_data['HDL_cat']==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalH_highL=normalH_highL[normalH_highL['TC_cat']>1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>수축기혈압</th>\n",
       "      <th>이완기혈압</th>\n",
       "      <th>식전혈당(공복혈당)</th>\n",
       "      <th>총콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>...</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
       "      <th>(혈청지오티)ALT</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>bmi</th>\n",
       "      <th>special_bmi</th>\n",
       "      <th>LDL_cat</th>\n",
       "      <th>HDL_cat</th>\n",
       "      <th>TC_cat</th>\n",
       "      <th>고혈압</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.489796</td>\n",
       "      <td>3.511667</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>138.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.444444</td>\n",
       "      <td>3.272727</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>124.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.031142</td>\n",
       "      <td>3.362909</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>114.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>3.937500</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>128.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.681661</td>\n",
       "      <td>3.323500</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999967</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>145.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.218408</td>\n",
       "      <td>3.148700</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999972</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>138.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>3.330000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999977</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>127.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.221304</td>\n",
       "      <td>3.138882</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999984</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>121.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.491349</td>\n",
       "      <td>3.556923</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>129.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.055151</td>\n",
       "      <td>3.104769</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164868 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        성별코드  연령대코드(5세단위)  수축기혈압  이완기혈압  식전혈당(공복혈당)  총콜레스테롤  트리글리세라이드  \\\n",
       "0          1           10  140.0   90.0        91.0   263.0     196.0   \n",
       "2          2           11  138.0   88.0        93.0   246.0     186.0   \n",
       "5          1            9  124.0   79.0        90.0   319.0      45.0   \n",
       "6          1           10  114.0   76.0        85.0   207.0      93.0   \n",
       "12         1            7  128.0   80.0       125.0   230.0     214.0   \n",
       "...      ...          ...    ...    ...         ...     ...       ...   \n",
       "999967     1            7  145.0   98.0        96.0   219.0     111.0   \n",
       "999972     2           16  138.0   80.0       100.0   227.0     169.0   \n",
       "999977     1           13  127.0   79.0        91.0   260.0     111.0   \n",
       "999984     1            9  121.0   74.0       118.0   254.0      98.0   \n",
       "999996     2           11  129.0   79.0       105.0   248.0      82.0   \n",
       "\n",
       "        HDL콜레스테롤  LDL콜레스테롤   혈색소  ...  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  흡연상태  \\\n",
       "0           46.0     177.0  16.0  ...        27.0        30.0   45.0   3.0   \n",
       "2           59.0     150.0  13.6  ...        23.0        19.0   64.0   1.0   \n",
       "5           53.0     257.0  15.8  ...        20.0        14.0   12.0   1.0   \n",
       "6           50.0     138.0  13.9  ...        25.0        30.0   24.0   3.0   \n",
       "12          42.0     145.0  15.3  ...        22.0        22.0  101.0   1.0   \n",
       "...          ...       ...   ...  ...         ...         ...    ...   ...   \n",
       "999967      47.0     150.0  17.3  ...        27.0        28.0   26.0   3.0   \n",
       "999972      56.0     137.0  13.9  ...        19.0        29.0   36.0   1.0   \n",
       "999977      41.0     196.0  15.3  ...        23.0        26.0   38.0   1.0   \n",
       "999984      59.0     175.0  15.3  ...        15.0        16.0   19.0   3.0   \n",
       "999996      58.0     172.0  13.2  ...        29.0        34.0  143.0   3.0   \n",
       "\n",
       "              bmi  special_bmi  LDL_cat  HDL_cat TC_cat  고혈압  \n",
       "0       24.489796     3.511667        4        1      3  0.0  \n",
       "2       24.444444     3.272727        3        1      3  0.0  \n",
       "5       19.031142     3.362909        5        1      3  0.0  \n",
       "6       22.857143     3.937500        3        1      2  0.0  \n",
       "12      27.681661     3.323500        3        1      2  0.0  \n",
       "...           ...          ...      ...      ...    ...  ...  \n",
       "999967  29.218408     3.148700        3        1      2  1.0  \n",
       "999972  22.222222     3.330000        3        1      2  0.0  \n",
       "999977  31.221304     3.138882        5        1      3  0.0  \n",
       "999984  22.491349     3.556923        4        1      3  0.0  \n",
       "999996  27.055151     3.104769        4        1      3  0.0  \n",
       "\n",
       "[164868 rows x 22 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalH_highL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>수축기혈압</th>\n",
       "      <th>이완기혈압</th>\n",
       "      <th>식전혈당(공복혈당)</th>\n",
       "      <th>총콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>...</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
       "      <th>(혈청지오티)ALT</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>bmi</th>\n",
       "      <th>special_bmi</th>\n",
       "      <th>LDL_cat</th>\n",
       "      <th>HDL_cat</th>\n",
       "      <th>TC_cat</th>\n",
       "      <th>고혈압</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.489796</td>\n",
       "      <td>3.511667</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>138.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.444444</td>\n",
       "      <td>3.272727</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>124.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.031142</td>\n",
       "      <td>3.362909</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>114.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>3.937500</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>128.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.681661</td>\n",
       "      <td>3.323500</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999960</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>106.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.484375</td>\n",
       "      <td>3.537455</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999972</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>138.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>3.330000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999977</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>127.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.221304</td>\n",
       "      <td>3.138882</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999984</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>121.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.491349</td>\n",
       "      <td>3.556923</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>129.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.055151</td>\n",
       "      <td>3.104769</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146453 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        성별코드  연령대코드(5세단위)  수축기혈압  이완기혈압  식전혈당(공복혈당)  총콜레스테롤  트리글리세라이드  \\\n",
       "0          1           10  140.0   90.0        91.0   263.0     196.0   \n",
       "2          2           11  138.0   88.0        93.0   246.0     186.0   \n",
       "5          1            9  124.0   79.0        90.0   319.0      45.0   \n",
       "6          1           10  114.0   76.0        85.0   207.0      93.0   \n",
       "12         1            7  128.0   80.0       125.0   230.0     214.0   \n",
       "...      ...          ...    ...    ...         ...     ...       ...   \n",
       "999960     2            9  106.0   66.0       117.0   221.0     148.0   \n",
       "999972     2           16  138.0   80.0       100.0   227.0     169.0   \n",
       "999977     1           13  127.0   79.0        91.0   260.0     111.0   \n",
       "999984     1            9  121.0   74.0       118.0   254.0      98.0   \n",
       "999996     2           11  129.0   79.0       105.0   248.0      82.0   \n",
       "\n",
       "        HDL콜레스테롤  LDL콜레스테롤   혈색소  ...  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  흡연상태  \\\n",
       "0           46.0     177.0  16.0  ...        27.0        30.0   45.0   3.0   \n",
       "2           59.0     150.0  13.6  ...        23.0        19.0   64.0   1.0   \n",
       "5           53.0     257.0  15.8  ...        20.0        14.0   12.0   1.0   \n",
       "6           50.0     138.0  13.9  ...        25.0        30.0   24.0   3.0   \n",
       "12          42.0     145.0  15.3  ...        22.0        22.0  101.0   1.0   \n",
       "...          ...       ...   ...  ...         ...         ...    ...   ...   \n",
       "999960      54.0     137.0  12.5  ...        19.0        10.0   16.0   1.0   \n",
       "999972      56.0     137.0  13.9  ...        19.0        29.0   36.0   1.0   \n",
       "999977      41.0     196.0  15.3  ...        23.0        26.0   38.0   1.0   \n",
       "999984      59.0     175.0  15.3  ...        15.0        16.0   19.0   3.0   \n",
       "999996      58.0     172.0  13.2  ...        29.0        34.0  143.0   3.0   \n",
       "\n",
       "              bmi  special_bmi  LDL_cat  HDL_cat TC_cat  고혈압  \n",
       "0       24.489796     3.511667        4        1      3  0.0  \n",
       "2       24.444444     3.272727        3        1      3  0.0  \n",
       "5       19.031142     3.362909        5        1      3  0.0  \n",
       "6       22.857143     3.937500        3        1      2  0.0  \n",
       "12      27.681661     3.323500        3        1      2  0.0  \n",
       "...           ...          ...      ...      ...    ...  ...  \n",
       "999960  21.484375     3.537455        3        1      2  0.0  \n",
       "999972  22.222222     3.330000        3        1      2  0.0  \n",
       "999977  31.221304     3.138882        5        1      3  0.0  \n",
       "999984  22.491349     3.556923        4        1      3  0.0  \n",
       "999996  27.055151     3.104769        4        1      3  0.0  \n",
       "\n",
       "[146453 rows x 22 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalH_highL[normalH_highL['고혈압']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>수축기혈압</th>\n",
       "      <th>이완기혈압</th>\n",
       "      <th>식전혈당(공복혈당)</th>\n",
       "      <th>총콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>...</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
       "      <th>(혈청지오티)ALT</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>bmi</th>\n",
       "      <th>special_bmi</th>\n",
       "      <th>LDL_cat</th>\n",
       "      <th>HDL_cat</th>\n",
       "      <th>TC_cat</th>\n",
       "      <th>고혈압</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>149.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.221453</td>\n",
       "      <td>3.798286</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>150.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>...</td>\n",
       "      <td>43.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.755102</td>\n",
       "      <td>3.278676</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>167.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.020408</td>\n",
       "      <td>3.223684</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>160.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.548209</td>\n",
       "      <td>3.121800</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>150.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.159334</td>\n",
       "      <td>3.669818</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999730</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>146.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.875115</td>\n",
       "      <td>3.727731</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999798</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>170.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.875115</td>\n",
       "      <td>4.230346</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999809</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>152.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.711662</td>\n",
       "      <td>3.344786</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999883</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>150.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.390625</td>\n",
       "      <td>3.387077</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999967</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>145.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.218408</td>\n",
       "      <td>3.148700</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18415 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        성별코드  연령대코드(5세단위)  수축기혈압  이완기혈압  식전혈당(공복혈당)  총콜레스테롤  트리글리세라이드  \\\n",
       "24         1           13  149.0   85.0       120.0   234.0     272.0   \n",
       "100        1            9  150.0   82.0        92.0   216.0     153.0   \n",
       "101        1           10  167.0   90.0       149.0   231.0     145.0   \n",
       "106        1            8  160.0   90.0       276.0   294.0     286.0   \n",
       "174        1           14  150.0   80.0        96.0   225.0      73.0   \n",
       "...      ...          ...    ...    ...         ...     ...       ...   \n",
       "999730     1           14  146.0   95.0        85.0   204.0      99.0   \n",
       "999798     1           15  170.0  110.0        88.0   263.0     247.0   \n",
       "999809     1            9  152.0   87.0        81.0   224.0     226.0   \n",
       "999883     1           15  150.0   90.0       145.0   263.0     367.0   \n",
       "999967     1            7  145.0   98.0        96.0   219.0     111.0   \n",
       "\n",
       "        HDL콜레스테롤  LDL콜레스테롤   혈색소  ...  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  흡연상태  \\\n",
       "24          42.0     136.0  15.4  ...        57.0        40.0   61.0   2.0   \n",
       "100         46.0     139.0  14.5  ...        43.0        68.0  440.0   2.0   \n",
       "101         41.0     161.0  14.8  ...        20.0        41.0   32.0   2.0   \n",
       "106         53.0     183.0  16.8  ...        14.0        25.0   38.0   3.0   \n",
       "174         48.0     162.0  12.2  ...        22.0        12.0   22.0   2.0   \n",
       "...          ...       ...   ...  ...         ...         ...    ...   ...   \n",
       "999730      45.0     139.0  15.8  ...        23.0        20.0   32.0   3.0   \n",
       "999798      60.0     154.0  17.0  ...        45.0        69.0  160.0   3.0   \n",
       "999809      44.0     135.0  14.9  ...        37.0        75.0   36.0   3.0   \n",
       "999883      43.0     146.0  14.0  ...        28.0        24.0   74.0   1.0   \n",
       "999967      47.0     150.0  17.3  ...        27.0        28.0   26.0   3.0   \n",
       "\n",
       "              bmi  special_bmi  LDL_cat  HDL_cat TC_cat  고혈압  \n",
       "24      24.221453     3.798286        3        1      2  1.0  \n",
       "100     27.755102     3.278676        3        1      2  1.0  \n",
       "101     31.020408     3.223684        4        1      2  1.0  \n",
       "106     27.548209     3.121800        4        1      3  1.0  \n",
       "174     26.159334     3.669818        4        1      2  1.0  \n",
       "...           ...          ...      ...      ...    ...  ...  \n",
       "999730  23.875115     3.727731        3        1      2  1.0  \n",
       "999798  23.875115     4.230346        3        1      3  1.0  \n",
       "999809  25.711662     3.344786        3        1      2  1.0  \n",
       "999883  25.390625     3.387077        3        1      3  1.0  \n",
       "999967  29.218408     3.148700        3        1      2  1.0  \n",
       "\n",
       "[18415 rows x 22 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalH_highL[normalH_highL['고혈압']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "stds = StandardScaler()\n",
    "train_scaled=stds.fit_transform(train_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8968"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neigh = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2)\n",
    "neigh.fit(train_X, train_y)\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = neigh.predict(test_X)\n",
    "accuracy_score(test_y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90035"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(train_X, train_y)\n",
    "from sklearn.metrics import accuracy_score\n",
    "b_predictions = gnb.predict(test_X)\n",
    "accuracy_score(test_y, b_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9016"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC()\n",
    "svc.fit(train_X, train_y)\n",
    "from sklearn.metrics import accuracy_score\n",
    "b_predictions = svc.predict(test_X)\n",
    "accuracy_score(test_y, b_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.905"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_clf=RandomForestClassifier(n_estimators= 200)\n",
    "rf_clf.fit(train_scaled, train_y)\n",
    "y_pred = rf_clf.predict(train_X)\n",
    "from sklearn.metrics import accuracy_score\n",
    "test_scaled=stds.transform(test_X)\n",
    "b_predictions = rf_clf.predict(test_X)\n",
    "accuracy_score(test_y, b_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.16378653, 0.19259849, 0.20966954, 0.16626266, 0.16591521,\n",
       "       0.10176757])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('quantiletransformer', QuantileTransformer()),\n",
       "                ('mlpregressor',\n",
       "                 MLPRegressor(early_stopping=True, hidden_layer_sizes=(50, 50),\n",
       "                              learning_rate_init=0.01))])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "from time import time\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "\n",
    "est = make_pipeline(QuantileTransformer(),\n",
    "                    MLPRegressor(hidden_layer_sizes=(50, 50),\n",
    "                                 learning_rate_init=0.01,\n",
    "                                 early_stopping=True))\n",
    "est.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190.69467289810652"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=est.predict(test_X)\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_pred,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HistGradientBoostingRegressor()"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.experimental import enable_hist_gradient_boosting  # noqa\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "\n",
    "\n",
    "est = HistGradientBoostingRegressor()\n",
    "\n",
    "est.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08812344961473881"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=est.predict(test_X)\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_pred,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "est=RandomForestRegressor()\n",
    "est.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "565.7950172778692"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=est.predict(test_X)\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_pred,test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이거하고 끝내자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.inspection import plot_partial_dependence\n",
    "features = ['HDL콜레스테롤','LDL콜레스테롤','트리글리세라이드','감마지티피','혈색소']\n",
    "plot_partial_dependence(est, train_X[:50000], features) \n",
    "fig = plt.gcf()\n",
    "fig.suptitle('Partial dependence')\n",
    "fig.subplots_adjust(hspace=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>419196</th>\n",
       "      <td>161.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795693</th>\n",
       "      <td>68.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495761</th>\n",
       "      <td>130.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982486</th>\n",
       "      <td>121.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137629</th>\n",
       "      <td>91.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662143</th>\n",
       "      <td>95.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597811</th>\n",
       "      <td>105.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620525</th>\n",
       "      <td>113.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98358</th>\n",
       "      <td>69.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845997</th>\n",
       "      <td>95.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6854 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HDL콜레스테롤  LDL콜레스테롤  트리글리세라이드  감마지티피   혈색소  연령대코드(5세단위)\n",
       "419196     161.0      44.0     154.0   15.0  11.1            7\n",
       "795693      68.0      98.0     202.0  856.0  15.4           10\n",
       "495761     130.0      97.0      52.0   11.0  12.9           17\n",
       "982486     121.0      99.0      96.0   28.0  12.9           12\n",
       "137629      91.0      91.0     148.0   24.0  12.9           11\n",
       "...          ...       ...       ...    ...   ...          ...\n",
       "662143      95.0      86.0     128.0   21.0  13.5           10\n",
       "597811     105.0      91.0      40.0   25.0  12.2           12\n",
       "620525     113.0      79.0      60.0   17.0  12.5           11\n",
       "98358       69.0      98.0     213.0   75.0  16.6            6\n",
       "845997      95.0      97.0     107.0   29.0  14.3           14\n",
       "\n",
       "[6854 rows x 6 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_yb=train_set['special_bmi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_yb=test_set['special_bmi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('quantiletransformer', QuantileTransformer()),\n",
       "                ('mlpregressor',\n",
       "                 MLPRegressor(early_stopping=True, hidden_layer_sizes=(50, 50),\n",
       "                              learning_rate_init=0.01))])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from time import time\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "\n",
    "est = make_pipeline(QuantileTransformer(),\n",
    "                    MLPRegressor(hidden_layer_sizes=(50, 50),\n",
    "                                 learning_rate_init=0.01,\n",
    "                                 early_stopping=True))\n",
    "est.fit(train_X, train_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "510.2285160041491"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=est.predict(test_X)\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_pred,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HistGradientBoostingRegressor()"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.experimental import enable_hist_gradient_boosting  # noqa\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "\n",
    "\n",
    "est = HistGradientBoostingRegressor()\n",
    "\n",
    "est.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "511.09973812116766"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=est.predict(test_X)\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_pred,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "est=RandomForestRegressor()\n",
    "est.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "565.2404587509471"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=est.predict(test_X)\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_pred,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.anaconda.org/scipy-wheels-nightly/simple\n",
      "Requirement already satisfied: scikit-learn in c:\\wanje\\anaconda3\\lib\\site-packages (0.23.1)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\wanje\\anaconda3\\lib\\site-packages (from scikit-learn) (0.16.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in c:\\wanje\\anaconda3\\lib\\site-packages (from scikit-learn) (1.5.0)\n",
      "Requirement already satisfied: numpy>=1.13.3 in c:\\wanje\\anaconda3\\lib\\site-packages (from scikit-learn) (1.18.5)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\wanje\\anaconda3\\lib\\site-packages (from scikit-learn) (2.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing partial dependence plots...\n",
      "done in 5.659s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.inspection import partial_dependence\n",
    "from sklearn.inspection import plot_partial_dependence\n",
    "\n",
    "print('Computing partial dependence plots...')\n",
    "tic = time()\n",
    "features = ['HDL콜레스테롤','LDL콜레스테롤']\n",
    "display = plot_partial_dependence(\n",
    "       est, train_X, features, \n",
    ")\n",
    "print(f\"done in {time() - tic:.3f}s\")\n",
    "display.figure_.suptitle(\n",
    "    'PDP'\n",
    ")\n",
    "display.figure_.subplots_adjust(hspace=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing partial dependence plots...\n",
      "done in 0.133s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.inspection import partial_dependence\n",
    "from sklearn.inspection import plot_partial_dependence\n",
    "\n",
    "print('Computing partial dependence plots...')\n",
    "tic = time()\n",
    "features = ['HDL콜레스테롤','LDL콜레스테롤','트리글리세라이드']\n",
    "display = plot_partial_dependence(\n",
    "       est, train_X, features, \n",
    ")\n",
    "print(f\"done in {time() - tic:.3f}s\")\n",
    "display.figure_.suptitle(\n",
    "    'PDP'\n",
    ")\n",
    "display.figure_.subplots_adjust(hspace=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1ff208e2880>,\n",
       "  <matplotlib.lines.Line2D at 0x1ff208e22b0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1ff208e2df0>,\n",
       "  <matplotlib.lines.Line2D at 0x1ff208e2280>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1ff208d48b0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1ff0ffe36d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1ff0ffe32e0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(train_X['LDL콜레스테롤'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "419196    44.0\n",
       "795693    98.0\n",
       "495761    97.0\n",
       "982486    99.0\n",
       "137629    91.0\n",
       "          ... \n",
       "662143    86.0\n",
       "597811    91.0\n",
       "620525    79.0\n",
       "98358     98.0\n",
       "845997    97.0\n",
       "Name: LDL콜레스테롤, Length: 6854, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X['LDL콜레스테롤']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>HDL콜레스테롤</th>\n",
       "      <th>LDL콜레스테롤</th>\n",
       "      <th>트리글리세라이드</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>혈색소</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42140</th>\n",
       "      <td>64.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33289</th>\n",
       "      <td>39.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48813</th>\n",
       "      <td>59.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17661</th>\n",
       "      <td>46.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14710</th>\n",
       "      <td>80.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12142</th>\n",
       "      <td>62.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48229</th>\n",
       "      <td>58.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41134</th>\n",
       "      <td>46.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>47.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17022</th>\n",
       "      <td>83.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HDL콜레스테롤  LDL콜레스테롤  트리글리세라이드  감마지티피   혈색소  연령대코드(5세단위)\n",
       "42140      64.0     151.0      77.0   22.0  12.1           15\n",
       "33289      39.0     147.0     189.0   95.0  16.2           10\n",
       "48813      59.0      92.0      68.0   10.0   9.0            9\n",
       "17661      46.0     136.0     146.0   20.0  14.8           11\n",
       "14710      80.0      92.0      73.0   45.0  15.8            8\n",
       "...         ...       ...       ...    ...   ...          ...\n",
       "12142      62.0     142.0     107.0   13.0  14.3            9\n",
       "48229      58.0      78.0      65.0   10.0  12.6            5\n",
       "41134      46.0     133.0      95.0   10.0  13.7           16\n",
       "921        47.0     169.0     178.0   58.0  15.6           11\n",
       "17022      83.0     130.0      72.0   35.0  14.7           13\n",
       "\n",
       "[40000 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1d61c5b3d30>,\n",
       "  <matplotlib.lines.Line2D at 0x1d61c5c20d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1d61c5c2430>,\n",
       "  <matplotlib.lines.Line2D at 0x1d61c5c2790>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1d61c5b39d0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1d61c5c2af0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1d61c5c2df0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(b_data['HDL콜레스테롤'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}