RPA_4기_프로젝트3_6조.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 시작"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 모듈 불러오기\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.font_manager as fm  \n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 폰트조절\n",
    "\n",
    "from matplotlib import font_manager, rc\n",
    "f_path = \"c:/Windows/Fonts/malgun.ttf\"\n",
    "font_name = font_manager.FontProperties(fname=f_path).get_name()\n",
    "rc('font', family=font_name)  \n",
    "\n",
    "# 데이터 불러오기\n",
    "image = pd.read_csv('Medical_Image_Data_01.csv', encoding='cp949')\n",
    "diagnosis = pd.read_csv('Patient_Diagnosis_Data.csv')\n",
    "surgery = pd.read_csv('Patient_surgery_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>환자ID</th>\n",
       "      <th>전방디스크높이(mm)</th>\n",
       "      <th>후방디스크높이(mm)</th>\n",
       "      <th>지방축적도</th>\n",
       "      <th>Instability</th>\n",
       "      <th>MF + ES</th>\n",
       "      <th>Modic change</th>\n",
       "      <th>PI</th>\n",
       "      <th>PT</th>\n",
       "      <th>Seg Angle(raw)</th>\n",
       "      <th>Vaccum disc</th>\n",
       "      <th>골밀도</th>\n",
       "      <th>디스크단면적</th>\n",
       "      <th>디스크위치</th>\n",
       "      <th>척추이동척도</th>\n",
       "      <th>척추전방위증</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1PT</td>\n",
       "      <td>16.1</td>\n",
       "      <td>12.3</td>\n",
       "      <td>282.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1824.6</td>\n",
       "      <td>3</td>\n",
       "      <td>51.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>2048.50</td>\n",
       "      <td>4</td>\n",
       "      <td>Down</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2PT</td>\n",
       "      <td>13.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>177.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1737.5</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>1753.10</td>\n",
       "      <td>4</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3PT</td>\n",
       "      <td>13.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>256.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1188.5</td>\n",
       "      <td>0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>1749.00</td>\n",
       "      <td>4</td>\n",
       "      <td>Down</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4PT</td>\n",
       "      <td>10.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>250.1</td>\n",
       "      <td>0</td>\n",
       "      <td>2534.5</td>\n",
       "      <td>0</td>\n",
       "      <td>49.2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>19.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>1796.20</td>\n",
       "      <td>4</td>\n",
       "      <td>Down</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5PT</td>\n",
       "      <td>17.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>232.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1840.6</td>\n",
       "      <td>0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>14.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>2021.48</td>\n",
       "      <td>4</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  환자ID  전방디스크높이(mm)  후방디스크높이(mm)  지방축적도  Instability  MF + ES  Modic change  \\\n",
       "0  1PT         16.1         12.3  282.3            0   1824.6             3   \n",
       "1  2PT         13.7          6.4  177.3            0   1737.5             0   \n",
       "2  3PT         13.6          7.4  256.8            0   1188.5             0   \n",
       "3  4PT         10.6          7.3  250.1            0   2534.5             0   \n",
       "4  5PT         17.1          8.1  232.2            0   1840.6             0   \n",
       "\n",
       "     PI    PT  Seg Angle(raw)  Vaccum disc   골밀도   디스크단면적  디스크위치 척추이동척도  \\\n",
       "0  51.6  36.6            14.4            0 -1.01  2048.50      4   Down   \n",
       "1  40.8   7.2            17.8            0 -1.14  1753.10      4     Up   \n",
       "2  67.5  27.3            10.2            0 -1.77  1749.00      4   Down   \n",
       "3  49.2  18.7            19.9            0 -1.58  1796.20      4   Down   \n",
       "4  58.8  14.7             5.2            0 -0.79  2021.48      4     Up   \n",
       "\n",
       "   척추전방위증  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.head() # 이미지데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1894 entries, 0 to 1893\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   환자ID            1894 non-null   object \n",
      " 1   전방디스크높이(mm)     1894 non-null   float64\n",
      " 2   후방디스크높이(mm)     1894 non-null   float64\n",
      " 3   지방축적도           1891 non-null   float64\n",
      " 4   Instability     1894 non-null   int64  \n",
      " 5   MF + ES         1894 non-null   float64\n",
      " 6   Modic change    1894 non-null   int64  \n",
      " 7   PI              1890 non-null   float64\n",
      " 8   PT              1890 non-null   float64\n",
      " 9   Seg Angle(raw)  1893 non-null   float64\n",
      " 10  Vaccum disc     1894 non-null   int64  \n",
      " 11  골밀도             998 non-null    float64\n",
      " 12  디스크단면적          1893 non-null   float64\n",
      " 13  디스크위치           1894 non-null   int64  \n",
      " 14  척추이동척도          1894 non-null   object \n",
      " 15  척추전방위증          1894 non-null   int64  \n",
      "dtypes: float64(9), int64(5), object(2)\n",
      "memory usage: 236.9+ KB\n"
     ]
    }
   ],
   "source": [
    "image.info() # 이미지 데이터 데이트타입확인   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>환자ID</th>\n",
       "      <th>Large Lymphocyte</th>\n",
       "      <th>Location of herniation</th>\n",
       "      <th>ODI</th>\n",
       "      <th>가족력</th>\n",
       "      <th>간질성폐질환</th>\n",
       "      <th>고혈압여부</th>\n",
       "      <th>과거수술횟수</th>\n",
       "      <th>당뇨여부</th>\n",
       "      <th>말초동맥질환여부</th>\n",
       "      <th>...</th>\n",
       "      <th>입원일자</th>\n",
       "      <th>종양진행여부</th>\n",
       "      <th>직업</th>\n",
       "      <th>체중</th>\n",
       "      <th>퇴원일자</th>\n",
       "      <th>헤모글로빈수치</th>\n",
       "      <th>혈전합병증여부</th>\n",
       "      <th>환자통증정도</th>\n",
       "      <th>흡연여부</th>\n",
       "      <th>통증기간(월)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1PT</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>20190713</td>\n",
       "      <td>0</td>\n",
       "      <td>자영업</td>\n",
       "      <td>60.3</td>\n",
       "      <td>20190716</td>\n",
       "      <td>13.66</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2PT</td>\n",
       "      <td>44.9</td>\n",
       "      <td>4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>20190715</td>\n",
       "      <td>0</td>\n",
       "      <td>운동선수</td>\n",
       "      <td>71.7</td>\n",
       "      <td>20190716</td>\n",
       "      <td>13.73</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3PT</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20190729</td>\n",
       "      <td>0</td>\n",
       "      <td>특수전문직</td>\n",
       "      <td>77.1</td>\n",
       "      <td>20190801</td>\n",
       "      <td>15.34</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4PT</td>\n",
       "      <td>54.7</td>\n",
       "      <td>4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>20190731</td>\n",
       "      <td>0</td>\n",
       "      <td>주부</td>\n",
       "      <td>74.2</td>\n",
       "      <td>20190803</td>\n",
       "      <td>15.38</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5PT</td>\n",
       "      <td>53.8</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>20190903</td>\n",
       "      <td>0</td>\n",
       "      <td>특수전문직</td>\n",
       "      <td>80.7</td>\n",
       "      <td>20190907</td>\n",
       "      <td>13.08</td>\n",
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      "text/plain": [
       "  환자ID  Large Lymphocyte  Location of herniation   ODI  가족력  간질성폐질환  고혈압여부  \\\n",
       "0  1PT              22.8                       3  51.0  0.0       0      0   \n",
       "1  2PT              44.9                       4  26.0  0.0       0      0   \n",
       "2  3PT              53.0                       3   NaN  0.0       0      0   \n",
       "3  4PT              54.7                       4  18.0  0.0       0      1   \n",
       "4  5PT              53.8                       4   NaN  0.0       0      1   \n",
       "\n",
       "   과거수술횟수  당뇨여부  말초동맥질환여부  ...      입원일자  종양진행여부     직업    체중      퇴원일자  \\\n",
       "0       0     0         0  ...  20190713       0    자영업  60.3  20190716   \n",
       "1       0     0         0  ...  20190715       0   운동선수  71.7  20190716   \n",
       "2       1     0         0  ...  20190729       0  특수전문직  77.1  20190801   \n",
       "3       0     0         0  ...  20190731       0     주부  74.2  20190803   \n",
       "4       0     0         0  ...  20190903       0  특수전문직  80.7  20190907   \n",
       "\n",
       "   헤모글로빈수치  혈전합병증여부  환자통증정도  흡연여부  통증기간(월)  \n",
       "0    13.66        0      10     0      1.0  \n",
       "1    13.73        0      10     0      1.0  \n",
       "2    15.34        0       7     0      1.0  \n",
       "3    15.38        0       7     0      2.0  \n",
       "4    13.08        0       7     0      1.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diagnosis.head() # 진단데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1894 entries, 0 to 1893\n",
      "Data columns (total 30 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   환자ID                    1894 non-null   object \n",
      " 1   Large Lymphocyte        1894 non-null   float64\n",
      " 2   Location of herniation  1894 non-null   int64  \n",
      " 3   ODI                     462 non-null    float64\n",
      " 4   가족력                     1843 non-null   float64\n",
      " 5   간질성폐질환                  1894 non-null   int64  \n",
      " 6   고혈압여부                   1894 non-null   int64  \n",
      " 7   과거수술횟수                  1894 non-null   int64  \n",
      " 8   당뇨여부                    1894 non-null   int64  \n",
      " 9   말초동맥질환여부                1894 non-null   int64  \n",
      " 10  빈혈여부                    1894 non-null   int64  \n",
      " 11  성별                      1894 non-null   int64  \n",
      " 12  스테로이드치료                 1894 non-null   int64  \n",
      " 13  신부전여부                   1894 non-null   int64  \n",
      " 14  신장                      1894 non-null   int64  \n",
      " 15  심혈관질환                   1894 non-null   int64  \n",
      " 16  암발병여부                   1894 non-null   int64  \n",
      " 17  연령                      1894 non-null   int64  \n",
      " 18  우울증여부                   1894 non-null   int64  \n",
      " 19  입원기간                    1894 non-null   int64  \n",
      " 20  입원일자                    1894 non-null   int64  \n",
      " 21  종양진행여부                  1894 non-null   int64  \n",
      " 22  직업                      1479 non-null   object \n",
      " 23  체중                      1894 non-null   float64\n",
      " 24  퇴원일자                    1894 non-null   int64  \n",
      " 25  헤모글로빈수치                 1893 non-null   float64\n",
      " 26  혈전합병증여부                 1894 non-null   int64  \n",
      " 27  환자통증정도                  1894 non-null   int64  \n",
      " 28  흡연여부                    1894 non-null   int64  \n",
      " 29  통증기간(월)                 1890 non-null   float64\n",
      "dtypes: float64(6), int64(22), object(2)\n",
      "memory usage: 444.0+ KB\n"
     ]
    }
   ],
   "source": [
    "diagnosis.info() # 진단데이터 데이트타입확인   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>환자ID</th>\n",
       "      <th>수술기법</th>\n",
       "      <th>수술시간</th>\n",
       "      <th>수술실패여부</th>\n",
       "      <th>수술일자</th>\n",
       "      <th>신장</th>\n",
       "      <th>연령</th>\n",
       "      <th>입원일자</th>\n",
       "      <th>재발여부</th>\n",
       "      <th>체중</th>\n",
       "      <th>퇴원일자</th>\n",
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       "      <th>환자통증정도</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190715</td>\n",
       "      <td>163</td>\n",
       "      <td>66</td>\n",
       "      <td>20190713</td>\n",
       "      <td>0</td>\n",
       "      <td>60.3</td>\n",
       "      <td>20190716</td>\n",
       "      <td>13.66</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RH+A</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2PT</td>\n",
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       "      <td>47</td>\n",
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       "      <td>0</td>\n",
       "      <td>71.7</td>\n",
       "      <td>20190716</td>\n",
       "      <td>13.73</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RH+A</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190731</td>\n",
       "      <td>178</td>\n",
       "      <td>39</td>\n",
       "      <td>20190729</td>\n",
       "      <td>0</td>\n",
       "      <td>77.1</td>\n",
       "      <td>20190801</td>\n",
       "      <td>15.34</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RH+B</td>\n",
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       "    <tr>\n",
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       "      <td>TELD</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190802</td>\n",
       "      <td>174</td>\n",
       "      <td>40</td>\n",
       "      <td>20190731</td>\n",
       "      <td>0</td>\n",
       "      <td>74.2</td>\n",
       "      <td>20190803</td>\n",
       "      <td>15.38</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RH+O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190906</td>\n",
       "      <td>183</td>\n",
       "      <td>42</td>\n",
       "      <td>20190903</td>\n",
       "      <td>0</td>\n",
       "      <td>80.7</td>\n",
       "      <td>20190907</td>\n",
       "      <td>13.08</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RH+A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  환자ID  수술기법  수술시간  수술실패여부      수술일자   신장  연령      입원일자  재발여부    체중      퇴원일자  \\\n",
       "0  1PT  TELD  68.0       0  20190715  163  66  20190713     0  60.3  20190716   \n",
       "1  2PT  TELD  31.0       0  20190716  171  47  20190715     0  71.7  20190716   \n",
       "2  3PT  TELD  78.0       0  20190731  178  39  20190729     0  77.1  20190801   \n",
       "3  4PT  TELD  73.0       0  20190802  174  40  20190731     0  74.2  20190803   \n",
       "4  5PT  TELD  29.0       0  20190906  183  42  20190903     0  80.7  20190907   \n",
       "\n",
       "   헤모글로빈수치  환자통증정도  통증기간(월)   혈액형  \n",
       "0    13.66      10      1.0  RH+A  \n",
       "1    13.73      10      1.0  RH+A  \n",
       "2    15.34       7      1.0  RH+B  \n",
       "3    15.38       7      2.0  RH+O  \n",
       "4    13.08       7      1.0  RH+A  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surgery.head() # 수술데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1894 entries, 0 to 1893\n",
      "Data columns (total 15 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   환자ID     1894 non-null   object \n",
      " 1   수술기법     1813 non-null   object \n",
      " 2   수술시간     1840 non-null   float64\n",
      " 3   수술실패여부   1894 non-null   int64  \n",
      " 4   수술일자     1894 non-null   int64  \n",
      " 5   신장       1894 non-null   int64  \n",
      " 6   연령       1894 non-null   int64  \n",
      " 7   입원일자     1894 non-null   int64  \n",
      " 8   재발여부     1894 non-null   int64  \n",
      " 9   체중       1894 non-null   float64\n",
      " 10  퇴원일자     1894 non-null   int64  \n",
      " 11  헤모글로빈수치  1893 non-null   float64\n",
      " 12  환자통증정도   1894 non-null   int64  \n",
      " 13  통증기간(월)  1890 non-null   float64\n",
      " 14  혈액형      1894 non-null   object \n",
      "dtypes: float64(4), int64(8), object(3)\n",
      "memory usage: 222.1+ KB\n"
     ]
    }
   ],
   "source": [
    "surgery.info() #수술데이터 데이터타입확인"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>환자ID</th>\n",
       "      <th>전방디스크높이(mm)</th>\n",
       "      <th>후방디스크높이(mm)</th>\n",
       "      <th>지방축적도</th>\n",
       "      <th>Instability</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1PT</td>\n",
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       "      <td>12.3</td>\n",
       "      <td>282.3</td>\n",
       "      <td>0</td>\n",
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       "      <td>68.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2PT</td>\n",
       "      <td>13.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>177.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1737.5</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
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       "      <td>10</td>\n",
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       "      <td>TELD</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190716</td>\n",
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       "      <td>RH+A</td>\n",
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       "      <th>2</th>\n",
       "      <td>3PT</td>\n",
       "      <td>13.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>256.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1188.5</td>\n",
       "      <td>0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TELD</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190731</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+B</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4PT</td>\n",
       "      <td>10.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>250.1</td>\n",
       "      <td>0</td>\n",
       "      <td>2534.5</td>\n",
       "      <td>0</td>\n",
       "      <td>49.2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>19.9</td>\n",
       "      <td>...</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190802</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+O</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5PT</td>\n",
       "      <td>17.1</td>\n",
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       "      <td>232.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1840.6</td>\n",
       "      <td>0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>14.7</td>\n",
       "      <td>5.2</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TELD</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190906</td>\n",
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       "      <td>RH+A</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>1890PT</td>\n",
       "      <td>17.0</td>\n",
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       "      <td>237.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2795.7</td>\n",
       "      <td>2</td>\n",
       "      <td>59.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20170407</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>1891PT</td>\n",
       "      <td>9.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>288.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1473.0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.7</td>\n",
       "      <td>20.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20170427</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>1892PT</td>\n",
       "      <td>13.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>148.5</td>\n",
       "      <td>0</td>\n",
       "      <td>3864.1</td>\n",
       "      <td>0</td>\n",
       "      <td>44.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>IELD</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20170411</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>1893PT</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2481.8</td>\n",
       "      <td>2</td>\n",
       "      <td>32.2</td>\n",
       "      <td>11.1</td>\n",
       "      <td>17.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>1894PT</td>\n",
       "      <td>16.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>251.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1796.1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>27.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20170412</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+AB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1894 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        환자ID  전방디스크높이(mm)  후방디스크높이(mm)  지방축적도  Instability  MF + ES  \\\n",
       "0        1PT         16.1         12.3  282.3            0   1824.6   \n",
       "1        2PT         13.7          6.4  177.3            0   1737.5   \n",
       "2        3PT         13.6          7.4  256.8            0   1188.5   \n",
       "3        4PT         10.6          7.3  250.1            0   2534.5   \n",
       "4        5PT         17.1          8.1  232.2            0   1840.6   \n",
       "...      ...          ...          ...    ...          ...      ...   \n",
       "1889  1890PT         17.0         10.7  237.5            0   2795.7   \n",
       "1890  1891PT          9.4          8.2  288.0            0   1473.0   \n",
       "1891  1892PT         13.5          5.5  148.5            0   3864.1   \n",
       "1892  1893PT         14.0         10.0   89.0            0   2481.8   \n",
       "1893  1894PT         16.1          9.5  251.4            0   1796.1   \n",
       "\n",
       "      Modic change    PI    PT  Seg Angle(raw)  ...  혈전합병증여부  환자통증정도  흡연여부  \\\n",
       "0                3  51.6  36.6            14.4  ...        0      10     0   \n",
       "1                0  40.8   7.2            17.8  ...        0      10     0   \n",
       "2                0  67.5  27.3            10.2  ...        0       7     0   \n",
       "3                0  49.2  18.7            19.9  ...        0       7     0   \n",
       "4                0  58.8  14.7             5.2  ...        0       7     0   \n",
       "...            ...   ...   ...             ...  ...      ...     ...   ...   \n",
       "1889             2  59.5  23.0            21.8  ...        0       7     0   \n",
       "1890             0  47.7  20.2             5.0  ...        0       7     0   \n",
       "1891             0  44.6  15.0            17.4  ...        0       7     0   \n",
       "1892             2  32.2  11.1            17.7  ...        0       7     0   \n",
       "1893             0  38.9   6.8            27.8  ...        0       8     0   \n",
       "\n",
       "      통증기간(월)  수술기법  수술시간  수술실패여부      수술일자  재발여부    혈액형  \n",
       "0         1.0  TELD  68.0       0  20190715     0   RH+A  \n",
       "1         1.0  TELD  31.0       0  20190716     0   RH+A  \n",
       "2         1.0  TELD  78.0       0  20190731     0   RH+B  \n",
       "3         2.0  TELD  73.0       0  20190802     0   RH+O  \n",
       "4         1.0  TELD  29.0       0  20190906     0   RH+A  \n",
       "...       ...   ...   ...     ...       ...   ...    ...  \n",
       "1889     12.0   NaN  80.0       0  20170407     0   RH+A  \n",
       "1890      6.0   NaN  20.0       0  20170427     0   RH+B  \n",
       "1891      1.0  IELD  50.0       0  20170411     0   RH+O  \n",
       "1892     24.0   NaN  25.0       0  20170410     0   RH+A  \n",
       "1893      6.0   NaN  45.0       0  20170412     0  RH+AB  \n",
       "\n",
       "[1894 rows x 51 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(image, diagnosis) # 이미지,진단,수술 데이터 결합\n",
    "data = pd.merge(data, surgery)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['환자ID', '전방디스크높이(mm)', '후방디스크높이(mm)', '지방축적도', 'Instability', 'MF + ES',\n",
       "       'Modic change', 'PI', 'PT', 'Seg Angle(raw)', 'Vaccum disc', '골밀도',\n",
       "       '디스크단면적', '디스크위치', '척추이동척도', '척추전방위증', 'Large Lymphocyte',\n",
       "       'Location of herniation', 'ODI', '가족력', '간질성폐질환', '고혈압여부', '과거수술횟수',\n",
       "       '당뇨여부', '말초동맥질환여부', '빈혈여부', '성별', '스테로이드치료', '신부전여부', '신장', '심혈관질환',\n",
       "       '암발병여부', '연령', '우울증여부', '입원기간', '입원일자', '종양진행여부', '직업', '체중', '퇴원일자',\n",
       "       '헤모글로빈수치', '혈전합병증여부', '환자통증정도', '흡연여부', '통증기간(월)', '수술기법', '수술시간',\n",
       "       '수술실패여부', '수술일자', '재발여부', '혈액형'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1894 entries, 0 to 1893\n",
      "Data columns (total 51 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   환자ID                    1894 non-null   object \n",
      " 1   전방디스크높이(mm)             1894 non-null   float64\n",
      " 2   후방디스크높이(mm)             1894 non-null   float64\n",
      " 3   지방축적도                   1891 non-null   float64\n",
      " 4   Instability             1894 non-null   int64  \n",
      " 5   MF + ES                 1894 non-null   float64\n",
      " 6   Modic change            1894 non-null   int64  \n",
      " 7   PI                      1890 non-null   float64\n",
      " 8   PT                      1890 non-null   float64\n",
      " 9   Seg Angle(raw)          1893 non-null   float64\n",
      " 10  Vaccum disc             1894 non-null   int64  \n",
      " 11  골밀도                     998 non-null    float64\n",
      " 12  디스크단면적                  1893 non-null   float64\n",
      " 13  디스크위치                   1894 non-null   int64  \n",
      " 14  척추이동척도                  1894 non-null   object \n",
      " 15  척추전방위증                  1894 non-null   int64  \n",
      " 16  Large Lymphocyte        1894 non-null   float64\n",
      " 17  Location of herniation  1894 non-null   int64  \n",
      " 18  ODI                     462 non-null    float64\n",
      " 19  가족력                     1843 non-null   float64\n",
      " 20  간질성폐질환                  1894 non-null   int64  \n",
      " 21  고혈압여부                   1894 non-null   int64  \n",
      " 22  과거수술횟수                  1894 non-null   int64  \n",
      " 23  당뇨여부                    1894 non-null   int64  \n",
      " 24  말초동맥질환여부                1894 non-null   int64  \n",
      " 25  빈혈여부                    1894 non-null   int64  \n",
      " 26  성별                      1894 non-null   int64  \n",
      " 27  스테로이드치료                 1894 non-null   int64  \n",
      " 28  신부전여부                   1894 non-null   int64  \n",
      " 29  신장                      1894 non-null   int64  \n",
      " 30  심혈관질환                   1894 non-null   int64  \n",
      " 31  암발병여부                   1894 non-null   int64  \n",
      " 32  연령                      1894 non-null   int64  \n",
      " 33  우울증여부                   1894 non-null   int64  \n",
      " 34  입원기간                    1894 non-null   int64  \n",
      " 35  입원일자                    1894 non-null   int64  \n",
      " 36  종양진행여부                  1894 non-null   int64  \n",
      " 37  직업                      1479 non-null   object \n",
      " 38  체중                      1894 non-null   float64\n",
      " 39  퇴원일자                    1894 non-null   int64  \n",
      " 40  헤모글로빈수치                 1893 non-null   float64\n",
      " 41  혈전합병증여부                 1894 non-null   int64  \n",
      " 42  환자통증정도                  1894 non-null   int64  \n",
      " 43  흡연여부                    1894 non-null   int64  \n",
      " 44  통증기간(월)                 1890 non-null   float64\n",
      " 45  수술기법                    1813 non-null   object \n",
      " 46  수술시간                    1840 non-null   float64\n",
      " 47  수술실패여부                  1894 non-null   int64  \n",
      " 48  수술일자                    1894 non-null   int64  \n",
      " 49  재발여부                    1894 non-null   int64  \n",
      " 50  혈액형                     1894 non-null   object \n",
      "dtypes: float64(16), int64(30), object(5)\n",
      "memory usage: 769.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tiamo\\AppData\\Local\\Temp\\ipykernel_1276\\1150810927.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['비만여부'][i] = bmi(data['신장'][i], data['체중'][i])\n"
     ]
    }
   ],
   "source": [
    "# 비만 여부 35 미만 비만 35 이상 고도 비만 \n",
    "\n",
    "\n",
    "def bmi(a, b):\n",
    "    height = a / 100\n",
    "    weight = b\n",
    "    temp = (weight / (height ** 2))\n",
    "    if temp > 35: #초고도비만\n",
    "        return 2\n",
    "    elif temp > 25: #비만\n",
    "        return 1\n",
    "    elif temp < 18.5: #저체중\n",
    "        return -1\n",
    "    else :\n",
    "        return 0 #정상\n",
    "data['비만여부'] = 0\n",
    "for i in range(0, data.shape[0]):\n",
    "    data['비만여부'][i] = bmi(data['신장'][i], data['체중'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>환자ID</th>\n",
       "      <th>전방디스크높이(mm)</th>\n",
       "      <th>후방디스크높이(mm)</th>\n",
       "      <th>지방축적도</th>\n",
       "      <th>Instability</th>\n",
       "      <th>MF + ES</th>\n",
       "      <th>Modic change</th>\n",
       "      <th>PI</th>\n",
       "      <th>PT</th>\n",
       "      <th>Seg Angle(raw)</th>\n",
       "      <th>...</th>\n",
       "      <th>환자통증정도</th>\n",
       "      <th>흡연여부</th>\n",
       "      <th>통증기간(월)</th>\n",
       "      <th>수술기법</th>\n",
       "      <th>수술시간</th>\n",
       "      <th>수술실패여부</th>\n",
       "      <th>수술일자</th>\n",
       "      <th>재발여부</th>\n",
       "      <th>혈액형</th>\n",
       "      <th>비만여부</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1PT</td>\n",
       "      <td>16.1</td>\n",
       "      <td>12.3</td>\n",
       "      <td>282.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1824.6</td>\n",
       "      <td>3</td>\n",
       "      <td>51.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>14.4</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TELD</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190715</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2PT</td>\n",
       "      <td>13.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>177.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1737.5</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TELD</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190716</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3PT</td>\n",
       "      <td>13.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>256.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1188.5</td>\n",
       "      <td>0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TELD</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190731</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+B</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4PT</td>\n",
       "      <td>10.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>250.1</td>\n",
       "      <td>0</td>\n",
       "      <td>2534.5</td>\n",
       "      <td>0</td>\n",
       "      <td>49.2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>19.9</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>TELD</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190802</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+O</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5PT</td>\n",
       "      <td>17.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>232.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1840.6</td>\n",
       "      <td>0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>14.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TELD</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20190906</td>\n",
       "      <td>0</td>\n",
       "      <td>RH+A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  환자ID  전방디스크높이(mm)  후방디스크높이(mm)  지방축적도  Instability  MF + ES  Modic change  \\\n",
       "0  1PT         16.1         12.3  282.3            0   1824.6             3   \n",
       "1  2PT         13.7          6.4  177.3            0   1737.5             0   \n",
       "2  3PT         13.6          7.4  256.8            0   1188.5             0   \n",
       "3  4PT         10.6          7.3  250.1            0   2534.5             0   \n",
       "4  5PT         17.1          8.1  232.2            0   1840.6             0   \n",
       "\n",
       "     PI    PT  Seg Angle(raw)  ...  환자통증정도  흡연여부  통증기간(월)  수술기법  수술시간  수술실패여부  \\\n",
       "0  51.6  36.6            14.4  ...      10     0      1.0  TELD  68.0       0   \n",
       "1  40.8   7.2            17.8  ...      10     0      1.0  TELD  31.0       0   \n",
       "2  67.5  27.3            10.2  ...       7     0      1.0  TELD  78.0       0   \n",
       "3  49.2  18.7            19.9  ...       7     0      2.0  TELD  73.0       0   \n",
       "4  58.8  14.7             5.2  ...       7     0      1.0  TELD  29.0       0   \n",
       "\n",
       "       수술일자  재발여부   혈액형  비만여부  \n",
       "0  20190715     0  RH+A     0  \n",
       "1  20190716     0  RH+A     0  \n",
       "2  20190731     0  RH+B     0  \n",
       "3  20190802     0  RH+O     0  \n",
       "4  20190906     0  RH+A     0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='jobs_count', ylabel='직업'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jobs = data[['환자ID', '성별', '직업' , '비만여부', '지방축적도', '입원기간' , '고혈압여부' ,'당뇨여부', '과거수술횟수', '수술실패여부', '재발여부']]\n",
    "\n",
    "# 사무직 자영업 무직 순으로 많다 \n",
    "\n",
    "jobs_count = jobs.groupby(['직업'], as_index=False).agg(jobs_count = ('환자ID', 'count'))\n",
    "jobs_count2 = jobs_count.sort_values(by = 'jobs_count', ascending=False )\n",
    "sns.barplot(data = jobs_count2, x = 'jobs_count', y = '직업' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='비만여부', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = data , x = '비만여부' ) # 전체 수술환자 비만정도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='재발여부', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = data.sort_values(by = '비만여부'), x='재발여부', hue = '비만여부') # 비만정도에 따른 재발여부"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "수술환자 대부분은 정상이었고 재수술환자도 대부분 정상이었다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='지방축적도_1', ylabel='count'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Total_1(data):    \n",
    "    if data < 100:        \n",
    "        return '0~100'\n",
    "    elif data < 200:\n",
    "        return '100~200'\n",
    "    elif data < 300:\n",
    "        return '200~300'\n",
    "    elif data < 400:\n",
    "        return '300~400'\n",
    "    elif data < 500:\n",
    "        return '400~500'\n",
    "    else:\n",
    "        return '500이상'\n",
    "plt.rc('font', family='Malgun Gothic')    # 한글 폰트수정\n",
    "plt.figure(figsize = (16, 5))  # 피규어 사이즈 확장 \n",
    "data['지방축적도_1'] = data['지방축적도'].apply(Total_1)\n",
    "sns.countplot(data = data.sort_values(by = '비만여부'), x='지방축적도_1', hue = '비만여부', order=['0~100','100~200','200~300','300~400','400~500','500이상'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "지방축적도랑 비만여부는 상관관계가 없다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "1889    0\n",
       "1890    0\n",
       "1891    0\n",
       "1892    0\n",
       "1893    0\n",
       "Name: 재발여부, Length: 1894, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['재발여부']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='재발여부', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = data.sort_values(by = '수술기법'), x='재발여부', hue = '수술기법') # 크게차이 안남"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2340015b820>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5)) # 그래프 크기 확대\n",
    "sns.countplot(data = data.sort_values(by = '환자ID'), x='직업') # 수술환자의 직업 분류\n",
    "plt.legend(loc = 'upper right') # 범례 우측상단표기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x234001daca0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5)) # 그래프 크기 확대 \n",
    "sns.countplot(data = data.sort_values(by = '비만여부'), x='직업', hue = '비만여부') \n",
    "plt.legend(loc = 'upper right') # 범례 우측상단표기 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>재발여부</th>\n",
       "      <th>체중</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>1</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>1</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>1</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>1</td>\n",
       "      <td>136.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>378 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     재발여부     체중  count\n",
       "0       0   35.0      2\n",
       "1       0   38.0      1\n",
       "2       0   43.0      2\n",
       "3       0   44.0      4\n",
       "4       0   45.0      8\n",
       "..    ...    ...    ...\n",
       "373     1  103.0      1\n",
       "374     1  104.0      2\n",
       "375     1  106.0      1\n",
       "376     1  110.0      2\n",
       "377     1  136.1      1\n",
       "\n",
       "[378 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1 = data.groupby(['재발여부','체중'], as_index = False).agg(count = ('체중', 'count'))\n",
    "data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='체중', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data = data_1, x='체중', y= 'count', hue = '재발여부')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "체중 70 부터는 재발율이 높아진다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1 = data.groupby(['재발여부','신장'], as_index = False).agg(count = ('신장', 'count'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='신장', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data = data_1, x='신장', y= 'count', hue = '재발여부')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "신장 170이상부터 재발율이 높아진다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>전방디스크높이(mm)</th>\n",
       "      <th>후방디스크높이(mm)</th>\n",
       "      <th>지방축적도</th>\n",
       "      <th>Instability</th>\n",
       "      <th>MF + ES</th>\n",
       "      <th>Modic change</th>\n",
       "      <th>PI</th>\n",
       "      <th>PT</th>\n",
       "      <th>Seg Angle(raw)</th>\n",
       "      <th>Vaccum disc</th>\n",
       "      <th>...</th>\n",
       "      <th>헤모글로빈수치</th>\n",
       "      <th>혈전합병증여부</th>\n",
       "      <th>환자통증정도</th>\n",
       "      <th>흡연여부</th>\n",
       "      <th>통증기간(월)</th>\n",
       "      <th>수술시간</th>\n",
       "      <th>수술실패여부</th>\n",
       "      <th>수술일자</th>\n",
       "      <th>재발여부</th>\n",
       "      <th>비만여부</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>449.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>447.000000</td>\n",
       "      <td>447.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>4.490000e+02</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>449.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.360290</td>\n",
       "      <td>8.484744</td>\n",
       "      <td>248.583638</td>\n",
       "      <td>0.033408</td>\n",
       "      <td>2148.423229</td>\n",
       "      <td>0.195991</td>\n",
       "      <td>45.574497</td>\n",
       "      <td>16.577852</td>\n",
       "      <td>15.271804</td>\n",
       "      <td>0.031180</td>\n",
       "      <td>...</td>\n",
       "      <td>14.776526</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>6.712695</td>\n",
       "      <td>0.256125</td>\n",
       "      <td>5.663728</td>\n",
       "      <td>61.543379</td>\n",
       "      <td>0.055679</td>\n",
       "      <td>2.015853e+07</td>\n",
       "      <td>0.135857</td>\n",
       "      <td>0.271715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.897193</td>\n",
       "      <td>4.891643</td>\n",
       "      <td>435.952080</td>\n",
       "      <td>0.179899</td>\n",
       "      <td>477.134032</td>\n",
       "      <td>0.591519</td>\n",
       "      <td>9.699481</td>\n",
       "      <td>8.352402</td>\n",
       "      <td>11.278941</td>\n",
       "      <td>0.173999</td>\n",
       "      <td>...</td>\n",
       "      <td>1.550824</td>\n",
       "      <td>0.047193</td>\n",
       "      <td>2.264801</td>\n",
       "      <td>0.436978</td>\n",
       "      <td>14.192309</td>\n",
       "      <td>27.144864</td>\n",
       "      <td>0.229557</td>\n",
       "      <td>3.718350e+04</td>\n",
       "      <td>0.343019</td>\n",
       "      <td>0.492920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1021.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>-6.300000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.009033e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.600000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>119.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.900000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.012093e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.150000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>163.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2098.390000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.700000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.017031e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.300000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>250.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2423.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.200000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.019071e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.500000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>6089.430000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4292.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.200000</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.280000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.020073e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       전방디스크높이(mm)  후방디스크높이(mm)        지방축적도  Instability      MF + ES  \\\n",
       "count   449.000000   449.000000   448.000000   449.000000   449.000000   \n",
       "mean     11.360290     8.484744   248.583638     0.033408  2148.423229   \n",
       "std       2.897193     4.891643   435.952080     0.179899   477.134032   \n",
       "min       1.500000     1.000000    53.100000     0.000000  1021.000000   \n",
       "25%       9.600000     7.100000   119.340000     0.000000  1801.000000   \n",
       "50%      11.150000     8.500000   163.380000     0.000000  2098.390000   \n",
       "75%      13.300000     9.600000   250.025000     0.000000  2423.100000   \n",
       "max      22.500000   101.000000  6089.430000     1.000000  4292.000000   \n",
       "\n",
       "       Modic change          PI          PT  Seg Angle(raw)  Vaccum disc  ...  \\\n",
       "count    449.000000  447.000000  447.000000      449.000000   449.000000  ...   \n",
       "mean       0.195991   45.574497   16.577852       15.271804     0.031180  ...   \n",
       "std        0.591519    9.699481    8.352402       11.278941     0.173999  ...   \n",
       "min        0.000000   11.900000   -6.300000       -3.400000     0.000000  ...   \n",
       "25%        0.000000   38.900000   10.750000        8.200000     0.000000  ...   \n",
       "50%        0.000000   44.700000   16.000000       14.000000     0.000000  ...   \n",
       "75%        0.000000   51.200000   21.600000       21.100000     0.000000  ...   \n",
       "max        3.000000   76.200000   45.500000      165.000000     1.000000  ...   \n",
       "\n",
       "          헤모글로빈수치     혈전합병증여부      환자통증정도        흡연여부     통증기간(월)        수술시간  \\\n",
       "count  449.000000  449.000000  449.000000  449.000000  448.000000  438.000000   \n",
       "mean    14.776526    0.002227    6.712695    0.256125    5.663728   61.543379   \n",
       "std      1.550824    0.047193    2.264801    0.436978   14.192309   27.144864   \n",
       "min      9.700000    0.000000    1.000000    0.000000    0.100000    5.000000   \n",
       "25%     13.710000    0.000000    7.000000    0.000000    1.000000   41.250000   \n",
       "50%     14.800000    0.000000    7.000000    0.000000    2.000000   60.000000   \n",
       "75%     15.900000    0.000000    8.000000    1.000000    4.000000   76.000000   \n",
       "max     19.280000    1.000000   10.000000    1.000000  180.000000  220.000000   \n",
       "\n",
       "           수술실패여부          수술일자        재발여부        비만여부  \n",
       "count  449.000000  4.490000e+02  449.000000  449.000000  \n",
       "mean     0.055679  2.015853e+07    0.135857    0.271715  \n",
       "std      0.229557  3.718350e+04    0.343019    0.492920  \n",
       "min      0.000000  2.009033e+07    0.000000   -1.000000  \n",
       "25%      0.000000  2.012093e+07    0.000000    0.000000  \n",
       "50%      0.000000  2.017031e+07    0.000000    0.000000  \n",
       "75%      0.000000  2.019071e+07    0.000000    1.000000  \n",
       "max      1.000000  2.020073e+07    1.000000    2.000000  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_3 = data[data['직업'] == '사무직']\n",
    "data_3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>전방디스크높이(mm)</th>\n",
       "      <th>후방디스크높이(mm)</th>\n",
       "      <th>지방축적도</th>\n",
       "      <th>Instability</th>\n",
       "      <th>MF + ES</th>\n",
       "      <th>Modic change</th>\n",
       "      <th>PI</th>\n",
       "      <th>PT</th>\n",
       "      <th>Seg Angle(raw)</th>\n",
       "      <th>Vaccum disc</th>\n",
       "      <th>...</th>\n",
       "      <th>헤모글로빈수치</th>\n",
       "      <th>혈전합병증여부</th>\n",
       "      <th>환자통증정도</th>\n",
       "      <th>흡연여부</th>\n",
       "      <th>통증기간(월)</th>\n",
       "      <th>수술시간</th>\n",
       "      <th>수술실패여부</th>\n",
       "      <th>수술일자</th>\n",
       "      <th>재발여부</th>\n",
       "      <th>비만여부</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1443.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1443.000000</td>\n",
       "      <td>1443.000000</td>\n",
       "      <td>1444.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1444.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1442.000000</td>\n",
       "      <td>1402.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1.445000e+03</td>\n",
       "      <td>1445.000000</td>\n",
       "      <td>1445.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.389578</td>\n",
       "      <td>8.318782</td>\n",
       "      <td>239.332613</td>\n",
       "      <td>0.042907</td>\n",
       "      <td>2080.263308</td>\n",
       "      <td>0.262976</td>\n",
       "      <td>46.909356</td>\n",
       "      <td>16.253015</td>\n",
       "      <td>15.292950</td>\n",
       "      <td>0.064360</td>\n",
       "      <td>...</td>\n",
       "      <td>14.520374</td>\n",
       "      <td>0.002076</td>\n",
       "      <td>6.937716</td>\n",
       "      <td>0.204844</td>\n",
       "      <td>5.946983</td>\n",
       "      <td>62.229672</td>\n",
       "      <td>0.062284</td>\n",
       "      <td>2.016076e+07</td>\n",
       "      <td>0.114879</td>\n",
       "      <td>0.213841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.839383</td>\n",
       "      <td>4.023137</td>\n",
       "      <td>448.174225</td>\n",
       "      <td>0.202717</td>\n",
       "      <td>470.486959</td>\n",
       "      <td>0.681202</td>\n",
       "      <td>25.449128</td>\n",
       "      <td>8.443958</td>\n",
       "      <td>10.150858</td>\n",
       "      <td>0.245478</td>\n",
       "      <td>...</td>\n",
       "      <td>1.546907</td>\n",
       "      <td>0.045533</td>\n",
       "      <td>2.011462</td>\n",
       "      <td>0.403728</td>\n",
       "      <td>18.088944</td>\n",
       "      <td>24.291856</td>\n",
       "      <td>0.241754</td>\n",
       "      <td>3.671459e+04</td>\n",
       "      <td>0.318986</td>\n",
       "      <td>0.462532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.270000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.970000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1016.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>-10.600000</td>\n",
       "      <td>-27.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.009012e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.540000</td>\n",
       "      <td>7.060000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1744.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.012112e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.300000</td>\n",
       "      <td>8.270000</td>\n",
       "      <td>170.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2035.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>14.650000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.550000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.019010e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.100000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>253.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2356.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>52.100000</td>\n",
       "      <td>21.350000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.019062e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.500000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>12113.470000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5569.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>559.000000</td>\n",
       "      <td>48.700000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.380000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.020081e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       전방디스크높이(mm)  후방디스크높이(mm)         지방축적도  Instability      MF + ES  \\\n",
       "count  1445.000000  1445.000000   1443.000000  1445.000000  1445.000000   \n",
       "mean     11.389578     8.318782    239.332613     0.042907  2080.263308   \n",
       "std       2.839383     4.023137    448.174225     0.202717   470.486959   \n",
       "min       1.270000     1.000000     25.970000     0.000000  1016.800000   \n",
       "25%       9.540000     7.060000    122.000000     0.000000  1744.200000   \n",
       "50%      11.300000     8.270000    170.200000     0.000000  2035.900000   \n",
       "75%      13.100000     9.500000    253.200000     0.000000  2356.300000   \n",
       "max      22.500000   101.000000  12113.470000     1.000000  5569.000000   \n",
       "\n",
       "       Modic change           PI           PT  Seg Angle(raw)  Vaccum disc  \\\n",
       "count   1445.000000  1443.000000  1443.000000     1444.000000  1445.000000   \n",
       "mean       0.262976    46.909356    16.253015       15.292950     0.064360   \n",
       "std        0.681202    25.449128     8.443958       10.150858     0.245478   \n",
       "min        0.000000    14.000000   -10.600000      -27.400000     0.000000   \n",
       "25%        0.000000    38.600000    10.300000        9.100000     0.000000   \n",
       "50%        0.000000    45.400000    15.600000       14.650000     0.000000   \n",
       "75%        0.000000    52.100000    21.350000       21.100000     0.000000   \n",
       "max        3.000000   559.000000    48.700000      165.000000     1.000000   \n",
       "\n",
       "       ...      헤모글로빈수치      혈전합병증여부       환자통증정도         흡연여부      통증기간(월)  \\\n",
       "count  ...  1444.000000  1445.000000  1445.000000  1445.000000  1442.000000   \n",
       "mean   ...    14.520374     0.002076     6.937716     0.204844     5.946983   \n",
       "std    ...     1.546907     0.045533     2.011462     0.403728    18.088944   \n",
       "min    ...     7.000000     0.000000     1.000000     0.000000     0.000000   \n",
       "25%    ...    13.400000     0.000000     7.000000     0.000000     1.000000   \n",
       "50%    ...    14.550000     0.000000     7.000000     0.000000     2.000000   \n",
       "75%    ...    15.600000     0.000000     8.000000     0.000000     4.000000   \n",
       "max    ...    19.380000     1.000000    10.000000     1.000000   360.000000   \n",
       "\n",
       "              수술시간       수술실패여부          수술일자         재발여부         비만여부  \n",
       "count  1402.000000  1445.000000  1.445000e+03  1445.000000  1445.000000  \n",
       "mean     62.229672     0.062284  2.016076e+07     0.114879     0.213841  \n",
       "std      24.291856     0.241754  3.671459e+04     0.318986     0.462532  \n",
       "min       6.000000     0.000000  2.009012e+07     0.000000    -1.000000  \n",
       "25%      45.000000     0.000000  2.012112e+07     0.000000     0.000000  \n",
       "50%      60.000000     0.000000  2.019010e+07     0.000000     0.000000  \n",
       "75%      78.000000     0.000000  2.019062e+07     0.000000     0.000000  \n",
       "max     190.000000     1.000000  2.020081e+07     1.000000     2.000000  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_4 = data.drop(index = data[data['직업'] == '사무직'].index)\n",
    "data_4.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='입원기간', ylabel='직업'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RsbL4+UlEvL3o2qn4KceubNl9AbBXqf9I4BXAGcBKYHnRNQ/YdxLSlyRJktqmVktrMvPjwMcBIuKjwLcGi4uIPYHnRMSjgL8Engs8AVhWCnsRzbe/3gPcFBF3RMSlNF8cde2knYQkSZLUBrWakR8QEY8BGsD9EXEM8NRS32zgApqz7UuB72XmycBnW4b5KXBIsc8s4NHA8cBZk52/JEmSNFG1mpEHiIj5wP8F3gbsDuwN7Fr07UKziD8jM38ZET8C/ndEvHeQoS4CzoqIZcD2wCmZuSUiOnAWUnf68Te2ct/G7PhxT/rGSR0/5mg1Gg36+/urTkOSpEeoTSEfzQr7JTQL+Ldk5q+BXwPfj4iNwPRimcxrI+I5EXFGsevtwLuLz18dGC8zt0XEmUBk5tbSoa4Dbhgihz6gD2DO7jPbdm5St7hvY3Lv+s4fd836NZ0/qCRJNVebQp7mrPkimsX8phFifwM80NL2AppLcP6j1HY8MJOHr50/CFgMnN06aGYupblchwX7zen8tKU0yWbuHEDnb+1dd17Q8WOOVqPRqDoFSZIGVZtCPjPvB/oj4gRaiu/MbF3/vhh4Q0tbAzh3kKFPiYglpe05wGUTzVeqo6e+cFolxz3jlRdXclxJkuqsNoX8GC0Ezs7MVaOIPS8zlw1sRMTBNH8RkCRJkrpWXQv590bEa1vaVmTmh0rbH42Iu1pirsjMf2hpc0ZekiRJtVO7Qj4zLwEuGSHmXAZfRjPmsSRJkqRuVLtCvlvsOW9/znjlVVWnIUmSpCmqli+EkiRJkqY6C3lJkiSphizkJUmSpBpyjfw43Xz3b3jBZa+qOg1JXeSbxy2vOgVJ0hQypWfkI+KwiLggIk6LiLlF2wERcWLFqUmSJEnDqt2MfEQcBJxZbgL+PjO/M0hsHzA9My8cpO9I4KXAe4AnAMuBFwDzgH0nIXVJkiSpbWpXyAMfBV6YmXcARMRuwBXAs8pBEbEIeGHzY1ydmb9qGedFNN/qeg9wU0TcERGX0nwh1LWTfRKSJEnSRNSxkF8NHBcRK4vtQ4s2ACLiVcAhwN3AEprLh86MiHnAdzLzU0XoT4u4X0fELODRwOHAQcDBHTgPSZIkadxqV8hn5lsi4jnA0UXTTzNzWSnkJuDSzLyv1PauiNgR2LvUdhFwVkQsA7YHTsnMLRExeclLGpMtl95FbthWdRqjdtKXT6o6hXFpNBr09/dXnYYkaYxqU8hHxBHA6aWmaTRn28vF93nAO4r4ocbpz8yrM3NbRJwJRGZuLYVcB9wwxL59QB/AzPk7jv9kJI1KbtgGd28dObBLrLl7TdUpSJKmkNoU8pm5AlgxsB0RxwCLMvP8ltBvlGKW0Pyy67Ihhj0emAmU+w8CFgNnD5LDUmApwJxFu+YYT0HSGMXs7ajTf9EW7NSoOoVxaTTqmbckTXW1KeQHRMRnMvM1bRzylKLgHzAHuKyN40sapxkvmVd1CmNy8XEXV52CJGkKqV0hD+xR/HsNcH0bxjuvPGMfEQfTnJGXJEmSulYdC/knlp5YU14Lf1Jm3tYS+weaa+mH44y8JEmSaqd2hXxm7jOG2BUj9F8CXDLhpCRJkqQOq10h3y32n/tYvnnc8qrTkCRJ0hS1XdUJSJIkSRo7C3lJkiSphizkJUmSpBpyjfw43Xz3Wo6+9CNVpyGpx1zxkv9ddQqSpJroiRn5iDiujWMtiIhntms8SZIkaTLUckY+IlZm5uGlprdSevZ7RNwMrGnZbZ/M3LcUcw5wILCoiL0XeDswl+YLoW6ahNQlSZKktqhlIQ9sP0L/LzPzmHJDRFxZ3s7MU4v25cCFmXlDsb24fWlKkiRJk6N2S2siYjrw9OLfiY71WGA+8K6ImDHh5CRJkqQOqeOM/NHA74Bjga8UbdtFxCXAtZm5FNgcEStb9ltf3oiIZwCnAycABwBfiYhTJjVzSbXywGWr4Z57O3rMky79WUePNx6NRoP+/v6q05CkKa9WhXwxa/5m4EhgWURcnZkbgW2ZecJAXGYeP4rh9geWZOYm4JqI+DlwPzALuGWI4/cBfQAz58+dyKlIqoN77iXXb+7oIdd0+HiSpPqqTSFfLKX5JPCxzLw1Is4AvhQRJ5ZijqA5yz5gDyCAtaW2czPzysxcHhHnRMSBgxxu2WA5FLP9SwHmLNo7J3RCkrrfLrOIDh9yr53ndfiIY9doNKpOQZJEjQp5YG/gG5l5BUBm3hgR7wMeHAjIzBXAioHtiFgCTM/MZYMNOPCF17Liy66L25i3pJra/rind/yYF/sceUnSKNWmkM/M3wK/bWn7AUBEp+fMJEmSpGrV7qk1HbABuK3qJCRJkqTh1GZGfjgtL4cqWwljW+KamauB1RNOSpIkSZpEPVHIDyUz144cNT77z21whWtZJUmSVBGX1kiSJEk1ZCEvSZIk1dCQS2si4tXAwpbmc4FTgYeeoZ6ZH56UzCRJkiQNabg18v8BrAHeB3wQIDO3RcR1wEeBdwL/AEzJQv5Xd93OMV/+VNVpSJoivv6y11edgiSpywy5tCYzf5KZ1wH3AN8uPpOZ3wbWZ+Z3ir6eEhELIuI5VechSZIkDWe4pTVzgOXAHcDKiDglM3/YscyGEBEXA49paX4i8JTMXFfE3Ezzrwll+2Tmvi1jvR84lObbYdcCfwPsCxwOfLf92UuSJEntMdzSmjcD78/MH0TELOCTwJKWmHzkbpMrM08CiIhnAP+emVsiYjnwQCnsl5l5THm/iLiyZfuJwP6Z+bxi+2+BE4H/nMz8JUmSpHYY7qk1e1IUtZl5L7Alml4D7BURJxUxVXk3sFPx+W6ab2Qdi98Bu0TECyPiEJoz899rX3qSJEnS5BluRv4yoD8izgOeB9yamRkR91F8+RU4e7ITHBARTwCOKjU9DnhzRNwL/BJ4W0RcnZk/BzZHxMqWIdaXNzJzc0S8AjgMWACcBtwL7DpZ5yCpu9x/+bXkhk1VpzEqJ112XdUpjFqj0aC/v7/qNCSp5w1ZyGfmtRExDXgH8BPgzKL98x3KrdUfgFWl7VWDxKwFyMzjhxsoInYAPgFspVm4N4BvARuBfxtmvz6gD2DW7tb7Ut3lhk3k+rH+Ma8aa2qSpySpc4abkSczVwArOpTLsDLzLuCuiDiQ5iMxpxVdDwBnZua/RcQREXF6abc9gKAo8AvnZuaVwF8DRMSzac70/yvNR2o+C/jxEDksBZYCzN1vYce/HyCpvWL2TiMHdYm9dt6l6hRGrdFoVJ2CJE0Jwxby3SYitgP+CTgqM/9YtO0JfBV4VusvHhGxBJiemcuGGO/jwMXATcCtwPnAn9NctiOpx+1w7KFVpzBqF/sceUlSi+EeP/kXwAtamm8BtvGnN77elpkXTU5qg6dF80k55SfUPDBE7GjMBW7JzJuK7V8UP5IkSVJXG25Gfg3Q+u2q9TTf5vr3xXZHF21m5taIeAfw+YgYaN4GvG0Cw34hIlp/GfhRZr5nAmNKkiRJk2q4L7veFhG7Z+ZPACLiccC6oq+yxydk5vXA9aMMX0lzFn+osVqfiy9JkiTVwkhr5M8HDo2I6cB5wBsnPaM2ysy1I0eNz6J5u/N116xKkiSpIsO9EApgx4g4CPgCcP5kFsaSJEmSRm+kQn4B8BZgNn96JOOQS1UkSZIkdcZIhfyvMvPVNJfUXBQRs4DvTX5akiRJkoYz0hr57wFk5m8i4sPAsZl52uSn1f1+ddddvOhLX646DUmSxuxrL39Z1SlIaoOR3ux6WunzamD1pGfUQRFxGHAczTe/XpiZd0fEAcBTMvNz1WYnSZIkDW2kpTW1FRErR+g/EngFcAbNx1QuL7rmAftObnaSJEnSxIy0tKaXvQg4LzPvAW6KiDsi4lJgDnBttalJkiRJw+vlQv62iFgF7EHzSTsDj858aWbeCfwUOAT4dfEl3kcDhwMHAQd3Pl1JkiRp9HqukI+ImcBc4D1F08tonufni+3ti8L9IuCsiFgGbA+ckplbIny6pqTq3fe1y8kNG6pOQz3qpMsvqzoFqXYajQb9/f1Vp/EwPVfIA48DXjpI+5tKn7+amT+OiDOByMytpb7rgBsGGzgi+oA+gFm7796mdCXpkXLDBnL9+qrTUI9a470l9YSeK+Qz8yfATyJiIfBO4IlF1y+Af8zM35TCjwdmAstKbQcBi4GzBxl7KbAUYO5++2WbU5ekh8Ts2VWnoB621847V52CVDuNRqPqFB6h5wr5ks8DbwNuKrafCXw+Ip6ZmeUi/JSIWFLangP4N0dJlZr5omOrTkE97GKfIy/1hJ4s5CNiGs0vuP5soGiPiJ8BCUwDHiyFn5eZy0r7HkxzRl6SJEnqWj1ZyGfm1oh4L/Dl0pdXAzgtMx9sCXdGXpIkSbXTk4U8QGZeywjPg8/MS4BLOpORJEmS1D49W8hPtkXz5vE11xhKkiSpIttVnYAkSZKksbOQlyRJkmrIQl6SJEmqIdfIj9Ov79rAS778rarTkNQhl77sL6tOQZKkh+mJGfmImB0Rbfl/2YhYEBHPbMdYkiRJ0mSp1Yx88aKn84E/o/lip3/OzE8BuwEnAt+KiJnA1wfZ/c+Ax2bmxmKsc4ADgUXAGuBe4O3AXJovhLppkDEkSZKkrlCrQh7oA27JzLcWRf0XIuJGYNNAQGbeBxzeumNEfLW8nZmnFu3LgQsz84Zie/FkJS9JkiS1S90K+ScCS+Ght7euAh4P/GggICJmAyuBDS377gtkuSEiHgvMB94VETdm5pbJS12SJElqn7oV8pcD746Ik4FdgRcDrwJ2LMVMA/4nM18+3EAR8QzgdOAE4ADgKxFxymQkLU0lmy7/HLlhfdVptN1Jl/1z1Sl0jUajQX9/f9VpSNKUV6tCPjNXFktqLqC5nOZvM/OPEbGwJfR5xWx9q3dl5uri8/7AkszcBFwTET8H7gdmAbcMdvyI6KO5vIdZu+8x0dORelJuWM+29XdWnUbbrem9300kSTVXq0IeIDOvAq4apv9uYH5EXJmZRwFExMrMPLwlbnlEnBMRBw4yzLIhxl5KsbRn3n6Pz8FipKkuZs/pjcdhtdhz51lVp9A1Go1G1SlIkqhRIR8RewNH0Fw6Mx3YHpgJ7ASM64HuA194bTnOYppPrZE0Djsde2LVKUyKi32OvCSpy9SmkAc2Ar8BtgIPAg8UP5spziMijqC57p1ie9Ugn8/NzCs7krEkSZI0SWpTyBdLZq4brG9gjXxmrgBWTPBQG4DbJjiGJEmSNKlqU8gPJzN/C7yhTWOtBlaPGChJkiRVqCcK+SrsN282l7pmVpIkSRXpxYdLSJIkST3PQl6SJEmqIQt5SZIkqYZ6eo18RCzPzFeVtj+dma8bYZ/tgT0z83fDxf3mrvt45Zf/q02ZStLofP5lj6s6BUlSl+i5GfmIeG5ErIyIlcCRA58j4gDgMS2xR0XEyaXtK4G9gPd1NmtJkiRpbHpuRj4zb4iI5wMvBhYBvwC+lpnbIqI1fDdgfmczlCRJkiau52bkC+cDuwBfBxrAR4r2aRGxKiKOLbYPA54YETOL7QOAz3YyUUmSJGk8erWQfxzw5cz8GfBF4ElF+9bMXJyZl0fEm4DvA2cByyJid+DfgSWVZCxJkiSNQc8trSmcDVweEQ/QPMf3ljsj4tHA/Mz8YLH9YeAR624kaf3lF7B1wx1Vp/GQky6bUXUKw2o0GvT391edhiRNCT1ZyGfmDcDiQbpOL/r/B/hgRMwAzgCeBySwE/BG4N2DjRsRfUAfwI6779X2vCV1n60b7mDb+j9WncZD1qyvOgNJUrfoyUI+IhYCFw3S9WRgz9L2e4D1wKHFl2EDOBl4J3Bm686ZuRRYCrDrfk/JNqctqQtNm71b1Sk8zJ47d/+MvCSpM3qykM/M3wKHt7YXj5cs2x64NTO3FftlRNwO7D3pSUqqhTnHvq3qFB7mYp8jL0kq9GQhPwbnAP0R8RpgKzCN5uMqB11aI0mSJHWLqVbIP+xLr5l5H9Bd022SJEnSKPTq4ycHlZk/rjoHSZIkqR2m2ox82zx23kw+71pVSZIkVWRKzchLkiRJvcJCXpIkSaohC3lJkiSphlwjP07r7n6QpV/pnrc9StJw+l76qKpTkCS1mTPyLSLiLyLinVXnIUmSJA2nZ2fkI+ILwK4tzU/IzL2L/mnA+cATgRnARZn5L8AOwC4dTFWSJEkas54t5DPz+Na2iPh6afN1wG8y861FUf/FiPh2xxKUJEmSJmCqLa3ZWvp8APANgMzcCqwEnlxFUpIkSdJY9dyMfEQcAZw+RPdTImIVcC6wGjgS+K+ICOAQ4D3AYzqRp6Rqrbr8w2y6Z13VaXTMt786reoUaq/RaNDf3191GpL0kJ4r5DNzBbBiYDsijgEWZubHy3ERsR1wbkR8meYa+Ysz85aIGLKQj4g+oA9g1933noz0JXXIpnvWsXH92qrT6JiN66vOQJLUbj1XyA+IiD0y8w9D9Wfmtoj4CHBvZm4ezZiZuRRYCrDPoqdmezKVVIWddplfdQodNWdnZ+QnqtFoVJ2CJD1MzxbywOeAw4H1wFB/P38z8G1g1UBDZn67aJPUwxYfe1rVKXSUz5GXpN7Ty4U8AJl5Q9U5SJIkSe3Wy4X8vsUXW1udlJm3lLbPj4i7W2J+nJlvn6zEJEmSpInq2UI+M/cbRczZwNkdSEeSJElqq54t5Cfb/LnTXXMqSZKkyky1F0JJkiRJPcFCXpIkSaohC3lJkiSphlwjP04b7nyQaz83dV7vLtXdoSdOrRdASZJ6nzPyLSJiQUQ8s+o8JEmSpOHUbkY+Iq7MzKNGiPkNcEtL872Z+YJSzDnAgcAiYA1wL/B2YC6wGLipbUlLkiRJbVa7Qn6UbsnMxcMFZOapABGxHLhw4A2wETHsfpIkSVI36NVCfruIWAnsDASwoWg/LjM3DQRFxGOB+cC7IuLGzNzS+VQlSZKksatjIf/0iFg1SPsbMvNXAJl5CEBELAGmZ+ay1uCIeAZwOnACcADwlYg4ZbKSlvRw/3rlh1i/sXNfGF921bSOHWsojUaD/v7+qtOQJPWI2hXymTkfBi/SI+LZwBtK4ds1m+PgUtunM/O7wP7AkmKG/pqI+DlwPzCLR66vHxi/D+gDeNRue7ftnKSpaP3Gddx5z9rOHfCezh1KkqROqF0hP5zMvBG4MSIWACcDTy66fgZ8PDPXlGKXR8Q5EXHgIEMtG2L8pcBSgMc/9qnZztylqWbOzp19HOSs2d0xIy9JUrv0VCFf8gXgNOD9xfazgS8CzykHDXzhtaz4suviSc1OEq8+6vSOHs/nyEuSek1tCvmIOILmmvbW9teWNs8FVgA7AKsz84Ei5kfA9hExwy+0SpIkqRfUppDPzBU0i/QRRcTpwGURUW4+fZRF/AbgtrFnKEmSJHVObQr5scjMq4CrxrnvamB1ezOSJEmS2qsnC/lOmL3rdNfcSpIkqTLbVZ2AJEmSpLGzkJckSZJqyEJekiRJqiHXyI/T/X/cws0f/0PVaUjSuOx/8h5VpyBJmiBn5FtExHFV5yBJkiSNpCdn5CPiScCFg3Q9AXh2Zv62FLsyMw8vxbwVuGxyM5QkSZImpicL+cz8ObC4tT0ilg0Svv1k5yNJkiS121RbWjMDeHBgIyKmA08v/pUkSZJqo2cL2IjYCzgtM0+OiEsz8yXAbGBzKexo4HfAscBXirbtIuIS4NrMXNrRpCUN6/985xzu2LSu6jR6woybplWdgirSaDTo7++vOg1JbdCzhTzNvzbMLD7PKv17N0BEzADeDBwJLIuIqzNzI7AtM08YbMCI6AP6APaat/fkZS5pUHdsWscfN62tOo3esKnqBCRJE9XLhfxgjsnMbcVSmk8CH8vMWyPiDOBLEXHicDsXM/RLAf7XYw7IyU9XUtluO82vOoWeMWOuM/JTVaPRqDoFSW3Sc4V8RBwBnA7sACyMiFXAnxX/EhEAlwDfyMwrADLzxoh4H6X185K6z98ddGrVKfQMnyMvSfXXc4V8Zq4AVoxjvx/AQ4W+JEmS1NWm2lNrJEmSpJ5gId+i5eVQkiRJUlfquaU1nbLDo2a4xlSSJEmVcUZekiRJqiELeUmSJKmGLOQlSZKkGnKN/Dht+cO9rP2Hn1WdhqQ2abzzyVWnIEnSmNR+Rj4idoqIw9o43nHtGkuSJEmaLLUp5CPiKRFxfUR8PyIOLtpWAvOAvxpinzMi4swh+s6PiFXFz00R8bGi662TcgKSJElSG9Vpac0HgJcD9wHfjIgVwMLBAiPi8cDrgI3Aloj4MPDJzPzvgZjMfHsp/iXAgslKXJIkSWq3OhXyMzLzjwARcQfwReDI1qCIeD2wPfDRUvxC4MSIeDAzPzLI2EcDH56sxCVJkqR2q1MhH6XPG4Bbgc0PC4g4gj8ts3llRHmXh2L+PTOvLG3/OTCrNFu/XUR8FfhuZva3L32pO53z/X/i9nvvrDqNyk378fZVp9A1Go0G/f3+z58kdbs6FfJZ+jwfOBHY62EBmSuAFRGxK831/y+neY6XANsy82HVSkTsA5wLnFBq3paZLx4sgYjoA/oAFszbcyLnInWN2++9k7Wb1lWdRvU2VZ2AJEljU6dCfk1EPAO4B7gf+DktM/IlfcDM0vbJNIv6pww0RMRLgTcBb2kt8IeSmUuBpQAHPPrJOUK4VAu7z9q16hS6wrS5zsgPaDQaVacgSRqFOhXy7wXOBnYG3pSZayLi7iFin13Elc0e+BARM4D9gRdn5lC/DEhTwqnPekvVKXQFnyMvSaqb2hTymXkP8LZRhs/MzMOHGWsLMNiXXiVJkqRaqE0hP0ZPiohVg7S/ITN/1elkJEmSpHardSFfmnV/bUv7Y9owpiRJktS1al3IV2nGHrNcUytJkqTKbFd1ApIkSZLGzkJekiRJqiELeUmSJKmGXCM/Tg/+cQN//Ng1VachSZoCHvXWw6pOQVIXcka+RUQsiIhnVp2HJEmSNJyenZGPiJuBNS3N+2TmvkX/OcCBwKIi7l7g7cBcYDFwU4dSlSRJksasZwt54JeZeUy5ISKuHPicmacWbcuBCzPzhmJ7cQdzlCRJksZlSi+tiYjHAvOBd0XEjKrzkSRJkkarl2fkN0fEypa29QMfIuIZwOnACcABwFci4pQO5idJtfLh7yxn3eb1Iweq7ab94DNVp6Au02g06O/vrzoNVaxnC/nMPH6EkP2BJZm5CbgmIn4O3A/MAm4ZbIeI6AP6APae96g2ZitJ3W/d5vWs3XRn1WlMTZuqTkBSN+q5Qj4ijqA50z5gDyCAtaW2czNzeUScExEHDjLMssHGzsylwFKApz7m8dmejCWpHubvOKfqFKasaXNnVZ2Cukyj0ag6BXWBnivkM3MFsGJgOyKWANMzc9kgsae2thVfdl08aQlKUk2ddtCrqk5hyvI58pIGM6W/7CpJkiTVlYX8I20Abqs6CUmSJGk4Pbe0ZhAraa6RH5XMXA2snrx0JEmSpInr+UI+M9eOHDV20x812zWLkiRJqoxLayRJkqQaikyfojgeEbEB+GXVefSA3YHbq06i5ryG7eF1bA+vY3t4HSfOa9geXsf2mOh13Ccz57c29vzSmkn0y8x8RtVJ1F1E/NDrODFew/bwOraH17E9vI4T5zVsD69je0zWdXRpjSRJklRDFvKSJElSDVnIj9/SqhPoEV7HifMatofXsT28ju3hdZw4r2F7eB3bY1Kuo192lSRJkmrIGXlJkiSphizkRyEiPhgR10XEdyLiyaX2nSNieURcHxFfjYhdqsyzmw1zDR8dEbdFxKri50lV5tntImJ+RHwoIj7Y0u69OAbDXEfvx1GKiLkRcUlxna6PiH1Lfd6PozTCdfR+HIWI2D4ivlZco+siYkGpz3txlEa4jt6LYxQRP4qIo0rbk3IvWsiPICKeC+yRmc8D3gicV+p+B/C1zDwEWAG8uYIUu94I13Au8PnMXFz8/LyKHGvko8D9wIyWdu/FsRnqOs7F+3G0dgTemZmLgY8A7y71eT+O3nDXcS7ej6PxIPDK4hp+EnhNqc97cfSGu45z8V4ctYh4OTCnpXlS7kUL+ZEdCSwHyMz/AHYt9R0KfLH4/GXgwM6mVhvDXcO5wF0V5FRLmXkScP0gXd6LYzDMdZyL9+OoZOZtmXlbsXkXsKnU7f04SiNcx7l4P44oM7dl5uZic3/gp6Vu78VRGuE6zsV7cVQiYjbwV8DnWrom5V60kB/Zo4B1pe0HI2Lguu2QmVuKz3cA8zqaWX0Mdw13BF5WLLk5PyJaZ0g1Ot6L7eH9OEbFn9/fDZxfavZ+HKMhrqP34yhFxCkRcTPwDODaUpf34hgMcx29F0fvAuBsYFtL+6TcixbyI1vPwy/2tswc+A9nW6kgncfDi1X9yZDXMDOvyswDgOcCG4C/qSC/XuC92Abej2MTEccAZwJ/U5pVBu/HMRnqOno/jl5mnpeZ+wMfB/6p1OW9OAZDXUfvxdGJiBOBWzLzB4N0T8q9aCE/shuAlwMUX+64tdT3feC44vPLgJWdTa02hryGETEdmn/So/kbqsbHe7ENvB9HLyL+DHhRZr4xM1uvlffjKA13Hb0fRyciZkdEFJu3ADuXur0XR2m46+i9OGqvBp4UEZfQrHveGxGPL/om5V70OfIjKH57+ifgKTR/C30jcDLwPmAX4F+AWcCvgLdk5v0Vpdq1RriGLwPeAmwFfgv0eQ2HFxGLgaMy870R8RG8F8dliOvo/ThKEfEe4LXAH4umW4Df4/04JiNcR+/HUYiIv6C5JOl+4F6a///yJrwXx2SE6+i9OEYR8QHgRuAvmcR70UJekiRJqiGX1kiSJEk1ZCEvSZIk1ZCFvCRJklRDFvKSJElSDVnIS5LGJSKeHhGvGyFmQUQ8ZxRj7R8RTxhF3L+MJUdJ6mXTq05AktTdImI34CKaz5WeCXwiMz8HzAb2KsW9n+ZryB8E1tJ8acy+wOHAd4uY04Ejil2mAZsz8/nA04uxf1HEncOfXmG+F/DRzPx/wPxJO1FJqhkLeUnSSE4BLsrMbxQvhlkVEZeVAyLiicD+mfm8YvtvgROB/yzHZeaHgA8VMc8E/mqwA2bmqaWxLwaua9/pSFJvcGmNJGkkd9B8EQzANmA9zZfGlP0O2CUiXhgRh9Ccmf/eCOMeA1w+XEBEPB3YOTN/UWo7ISL2H0P+ktSTLOQlSSM5H3haRHwQ+Ajw/szcUg7IzM3AK4AEFgCn0Sz4B/3/mYh4NHAQw7ymvJjl/yjNN0pKklq4tEaSNKyiaD9nkPZVNJfZ7AB8guas/a5AA/gWsBH4t9b9ImIe8P+A1+cgrxePiJk0Xw1/LPDqzPx9y3EvmeApSVJPsJCXJI0oIl4GzCy+5DrQdiDw3MzsB/66aHs2cBTwr8A7gWcBPy7tczDwfuCMzPztMIe8HTgiM7cOEyNJU5pLayRJozEDOC0iVg78ABcA2w8ERMTHaa6hvwm4leaSnC+0jPMcmrPs3x/qQJl5X2Z+FvjSIH1HTfREJKlXOCMvSRqt8zJz2cBGMbu+uNQ/F7glM28qtn9R/DykmL0frVnjylKSpggLeUnSaJ0SEUtK23OAy1pivhARD7S0/Sgz3zOeA0bEqkGa35WZq8czniT1khjke0aSJEmSupxr5CVJkqQaspCXJEmSashCXpIkSaohC3lJkiSphizkJUmSpBqykJckSZJqyEJekiRJqqH/DwUO8N6J3vVHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 5))\n",
    "sns.barplot(data = data, y = '직업', x = '입원기간')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='비만여부', ylabel='직업'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 5))\n",
    "sns.barplot(data = data, y = '직업', x = '비만여부')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='과거수술횟수', ylabel='직업'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 5))\n",
    "sns.barplot(data = data, y = '직업', x = '과거수술횟수')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 기타"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>전방디스크높이(mm)</th>\n",
       "      <th>후방디스크높이(mm)</th>\n",
       "      <th>비만여부</th>\n",
       "      <th>지방축적도</th>\n",
       "      <th>연령</th>\n",
       "      <th>체중</th>\n",
       "      <th>신장</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.1</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0</td>\n",
       "      <td>282.3</td>\n",
       "      <td>66</td>\n",
       "      <td>60.3</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0</td>\n",
       "      <td>177.3</td>\n",
       "      <td>47</td>\n",
       "      <td>71.7</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0</td>\n",
       "      <td>256.8</td>\n",
       "      <td>39</td>\n",
       "      <td>77.1</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0</td>\n",
       "      <td>250.1</td>\n",
       "      <td>40</td>\n",
       "      <td>74.2</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0</td>\n",
       "      <td>232.2</td>\n",
       "      <td>42</td>\n",
       "      <td>80.7</td>\n",
       "      <td>183</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>17.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1</td>\n",
       "      <td>237.5</td>\n",
       "      <td>59</td>\n",
       "      <td>64.0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>9.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>42</td>\n",
       "      <td>59.0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>13.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1</td>\n",
       "      <td>148.5</td>\n",
       "      <td>61</td>\n",
       "      <td>70.0</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>29</td>\n",
       "      <td>77.0</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>16.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>-1</td>\n",
       "      <td>251.4</td>\n",
       "      <td>30</td>\n",
       "      <td>49.0</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1894 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      전방디스크높이(mm)  후방디스크높이(mm)  비만여부  지방축적도  연령    체중   신장\n",
       "0            16.1         12.3     0  282.3  66  60.3  163\n",
       "1            13.7          6.4     0  177.3  47  71.7  171\n",
       "2            13.6          7.4     0  256.8  39  77.1  178\n",
       "3            10.6          7.3     0  250.1  40  74.2  174\n",
       "4            17.1          8.1     0  232.2  42  80.7  183\n",
       "...           ...          ...   ...    ...  ..   ...  ...\n",
       "1889         17.0         10.7     1  237.5  59  64.0  157\n",
       "1890          9.4          8.2     0  288.0  42  59.0  157\n",
       "1891         13.5          5.5     1  148.5  61  70.0  167\n",
       "1892         14.0         10.0     0   89.0  29  77.0  177\n",
       "1893         16.1          9.5    -1  251.4  30  49.0  168\n",
       "\n",
       "[1894 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2 = ['전방디스크높이(mm)','후방디스크높이(mm)','비만여부','지방축적도','연령','체중','신장']\n",
    "data_3 = data[data_2].fillna(method='backfill')\n",
    "data_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 모델링"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## decisiontree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "X = data_3\n",
    "Y = data['재발여부'].ravel()\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "    X,Y, test_size = 0.3, random_state = 0)\n",
    "\n",
    "model = DecisionTreeClassifier(max_depth = 1 )\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# depth에 따른 정확도\n",
    "train_score=[]\n",
    "test_score=[]\n",
    "for i in range(1,21):\n",
    "    \n",
    "    model=DecisionTreeClassifier(max_depth=i,random_state=4)\n",
    "    model.fit(X_train,Y_train)\n",
    "    train_score.append(model.score(X_train,Y_train))\n",
    "    test_score.append(model.score(X_test,Y_test))\n",
    "plt.figure()\n",
    "plt.title('score for depths')\n",
    "plt.plot(range(1,21),train_score)\n",
    "plt.plot(range(1,21),test_score)\n",
    "plt.xticks(range(1,21))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 정확도 : 0.8837735849056604\n",
      "테스트 정확도 : 0.8717047451669596\n"
     ]
    }
   ],
   "source": [
    "model_1=DecisionTreeClassifier(max_depth=1,random_state=4)\n",
    "model_1.fit(X_train, Y_train)\n",
    "train_accuracy = model_1.score(X_train, Y_train)\n",
    "test_accuracy = model_1.score(X_test, Y_test)\n",
    "print(f'훈련 정확도 : {train_accuracy}')\n",
    "print(f'테스트 정확도 : {test_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.22427564, 0.18909937, 0.01414848, 0.21353339, 0.12167197,\n",
       "       0.16359858, 0.07367257])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "importance = model.feature_importances_\n",
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n2EOBW+7vF9EJM5BkdGOZuWt5IZlHkMybZrdhg6MU+21sWMzr61IHWN9X8nOpCv02YPe+5XvjG7B9CnDKCN9/VkhabXtB0zmmI5k3v67lhWSeLW3OPMqmmJXA4QCS9gRu7ntsFXBoPf0S4BLgauAgSVtJeiDwJOB7M5Y4IiImNUqxLwO2lrQS+CBwrKQTJG0NfAA4QtJy4BnAJ2zfApwOXAF8CXin7XWbI3xERGxsyk0x9WaWowaGj60/3wIcPOQ5pwKn3u90s6s1m4WmIZk3v67lhWSeLa3NLNtNZ4iIiBmUM08jIgqTYo+IKEyKPSKiMCn2iIjCjHWx18faP0nSsyU9quk8o+hi5hJIWtR0htJ07Xcq6dCpl2qHsTwqRtIuwPHAo6hOnvod8HDgkcDpts9sMN5QXcw8SNIi2xc1nWNUkraxvbaevsz2AU1n6jpJL7T9RUlvBw7q0u908P9AfXHDObbvaDDWUONa7B8H3m/7hoFxAX8F3GX7042Em0AXMw9qezlK+rTtl/XN35tX0ldtP7+5dBuT9Bxgo0y23y/p5cCjgZN7L05tIOkyqnNf/hHY0/YBkh4NfBoQcKztFU1m7CfphcAbAFPlA/gxcAbwz/X4u21f2EzC4cay2GPzk/RlYG/gW8A9tl/UxnLsN5ivf76NL0r1prjHAK8Ank51uWyAHYFDgK8DT7N9dCMBh5D0VeATwE+A/10X+4eAc4Frgc/aPqjJjJPpXRtL0ueB1wF/AM63vbDhaBsY923sx0j6jqSrJX1D0tVNZ5pKVzLbPhj4D9uLgd71+Nu+FjGY72GSXinpVY2kmYLtn9Zrtz8AbrW9op4/FHiX7Y8DT2g05MbmA/vZXk71hvOVVO86rrJ9e5PBJiLpk32zx9U3G3qA7VvrS5O3zihXdyzZq4Cn9l16uAu6lNkDn3et/5B7Pt/WP+aagXXc9xa8dSS9Avg58GJJBwJrgXnAL+tF1k/03IaYDTPdw4YvqG188X8EgKTXAbb9nWoLaHuN9Ro78CPuW5vsii5m7ukV5T1s/AfdRr+0fVbLd0y/A9iX6trgWwBPBH4NPKR+vG1/4zcBl0t6GlVJngksB/aRtD3tfBHdW9KPgGfbfk899itJe9b7B/7QYLahxn2N/cPADyV9t55327ajDtHFzD2/tH1W0yEm8QhJvaN2BPyqyTAj+jnQ29n4e2Ab4ELgLfUVWX/eVLBJfJb79gdAddXYc6hehN7WSKLJfRtYDLxJ0hLbS4F/AD4ObA28vslww4x7sX8I+FPghqkWbJFOZK53nj5R0peoCgdavoZue7Lt0W1ck+y3D/AwYAvbZ0r6W6rbWh7TaKqNnWT7Lkk3Uv9Obf8EeG6jqSYn238ATpT0Jkkvsf05qvs6t9K4F/u3bX+n6RDT1InM9c7TQW0vx8m8r+kAE/gEcB3w4nr+EgDb/9hYoknY/lL9+SxJXXhHxMCRXB+mOuqo1cb6cEdJZ1Cd5PMN6rVJ2+9oNNQUupi5R9JC25c0nSOaIekA4K+5752b2PBdnKg2Lb52trNNRNL+1DtPB/U2K0o6xvbJs5lrKuO+xn5a0wE2QRczA9DmUpf0OOCZA8M3A1sBu9Tzt7bpRBRJRwM7DHvM9vvrZZbaXjKrwSb2deD7DN8kN1jybbEl1f+BySwGTt78UUY31sVue4WkbYAHN51lVF3ILOkJwLOnWs72GbMQZ1RbUP0BHwsspSqaOcDfU93qUVR/5G3yDaqdpe8A3g88i+r/xcV9yzyrgVxD2f6dpE9RHdUl4PHAD6kKvXdmp6mKshVsXzY4Jmnbepv7vUOzGGkkY13skj4IvIDqEKzef6o/azTUFDqS+R6q46lfT/UO426qk1B+BbRy/4Dt7wPfl/Rq22dI2tL2PZKOa9kL0L1sfx1A0t8Aa4D/BLZt0yn5g2y/WNITbX9X0qnAG22vlbSv7SubzjcVSXOBTwIv7Btu3TuNsS52qjPgntJ0iGlqfeb6ejY31CfMfKY+CmJ74MfD1oDaQtJZwKX17CmS3txknlFIeh7V2ZyvAp5CdUx42x1HdRmETwGWtBS4C2h1sUt6BvBe2nek0UbGvdh/IOnR9eFWXdGJzJJ2BZ4GnC/JVDugvgK0ttiBh9l+haS/Ay62fUfbzzAE3gM8z/bvASR9pnc9k4ZzDVUfhrlHfXXHhwMfoLpQ2WeaTTax+tDdh1O9gO5v+/uStgKeQ/WueW6D8YYa92I/G7iuPtnHVHvk23w8LXQn8xnAkbavhnuvQvluSX9l+1PNRpvQUyT9BPg/ts+px1r3NnvA2l6p1/4L2EHSIVSls8vwpzXmeuA3wDXAz6jyLZL0Fdu/bTLYRHqH7kp6CtX/4Q9R7Qjer17kvIaiTWjci30pMN/2r5sOMg1dyTynV+pQvfrUh2q+psFMU/kWcCDVGYZH2f6/VNut2+ynkl4GnA88FXiE7dvqNUqAExtLNoTtC+v9GL1NXp+StA/wBUkvsH1Xk/kmY/tbkl5KdebsDbbbem7D2B/HfgpwlO17ms4yqq5klvQxqh2lp9m+U9JjqV6UTrC9utl0w0m60PaB9fQ7gQt7OyjbStXNHt4GPAP4D6rf723NppqcpO09cHMKSfNsr2kq03RIelBb3130jHuxL6O6nvU36yHbfkVziabWlcyS5lCtnR8APIDqmPDTbF/baLAR1YeUru/IVTQjNjDuxb7b4Jjtm5rIMqouZu4iSY8E7rb9yykXjmiZtl3Sc1ZIOlHSDrZvGvyQtJ+klzSdcVAXMwNIeqKkoadkt42k3t2S3ka1Y2yvZhNFbJqxXGOXtDuwhOpEmu9Q3Rj6EVR3m/ka8C9u2S+mi5nh3pNnbvDATawl7dW2i5lJusj2IknnUV2S9U6q+11uXy/yOdsfaypfxKjGsth76h1PTwAeCPzC9o8bjjSlLmSuT/SZ15utP/cOzVxUL9PGe4heTHXyzMFUh+PdSXVz5UWNBouYprE+3LG+3sO3ms4xHV3IPOLO3Nac+SNpC6odvY8DjgcOo7rW9iJg1+aSRWyasSr2+pTgA6darndlvDboYmaA+szCfmtsf6Jvvk1vFU11e7Pexah6R8J04fZ9ERsZq2Knuk1Yq69HMUQXMwO8HHgz962Z/z3VTSFap943caakV1Odov8GqhOTLgUWNBgtYpOMVbHb/hnVacyd0cXMtd/Yvrw3I+mtky3cFravkPRa7jvjtDWbjCJGNVbFDiBpJ6rbnM2nequ9BXC27U82mWsyXczMxpswXF/h8fMTPN4G760/X1N/NvCXDWWJ2GRjd1SMpH8HPmz7q/X8HKo/6KtsX9BouAl0NPO1VJuRemu8v7PdymPth6mPvV+XE5Sii8ax2FfY3n9gbDfg9bb/rqFYk+pi5i6TtNj1TZcjumjsNsUAP6l3kv1bfcXBHamOXW7lXXJqncqs4fcP3YjrmwG30DHAvcVev0PaxvadjSWKmIZxXGN/ANXRGs8F1gN3AKe2+XZiXctc3/N0n6mWs/1vsxBnJJIOB46k2q6+JdXveR1wNHBmPX2i7fMbCxkxorEr9ojpqG+q8K/AD4Av2l7YcKSIKY3jpph7SdoB2NX2D5rOMqquZK5PzzfVETy927QJeJ/t5U3lmsyQk6o+D+xu+9v1462+Bn5Ez1he3RFA0oOp7mO4sD6csDe+Z3OpJtelzLZfUF9j5WvA0fX0R4Gdm002qVXAY6muHb8FsDf3vShBOw/RjNjI2BY71e2tTLUWeU7f+MmNpBlNFzNDRwqx3mfxI6qCv64evl7SvvULaSd+johx3hTTf0ahJH2pHntqM3FG0sXMnmC6rTzwcTLVDcS3o9qRGtF641zsuwJ/TnWEiW0vhnu3DbdVZzL3bWMX8BxJ1PPvnex5TZL0ZeDBwAuo/jbeZ/sXwPMbDRYxTeNc7KY6hG3YeFt1JrPtFzSdYbpsH9x0hoiZMM7F/gvgC1RrZ13ZrNHFzJ1QH3v/7KmWs93Kk8Ii+o1dsfdtIvhJ/3hHNmt0JnMH3QOsbTpExEwY6xOU6rW0hcA5tm+tx/aw/b1mk02si5m7QtL2tu8YHAN2sP3zhmJFTNs4H+4I1XXOL+oVJEAHCrKLmbvi3/tn6lI/DXhUM3EiNs1YF7vt223/EEBSJ25Y3MXMXSPpIZJeB1xAdX2Yq5vOFDEdY70ppp+ky2wf0HSO6ehi5jaTdAtwPfB4qnugHmr7usmfFdE+47jz9MfAd6mOJvkjqjMN1wNPbjLXZLqYuaOu7R2mWd9E/B8kfcP2BxvOFTEt47gp5gbbi+tjln9UT7+Qak2trbqYuYt+0ZuwfY3tlwFzhlwcLKLVxm6NnQ1P5pkzwXjbdDFz59j+6yFjSyU9qIk8EZtqHNfY+6+30pUXti5mLobt3zadIWI6xrHYJ1r7bbMuZo6Ihoxjsfev/W7dWIrp6WLmiGjIOBb7a/qm+2+mrMEFW6SLmSOiITmOvSZpoe1Lms4xHV3MHBGb3ziusUdEFG2si13SOyVtW8924ljlLmaOiNk1tsVeX+DpCbb/0BtqMs8oupg5ImbfWBa7qvu0nQS8v2+41Tsbupg5IpoxdsUu6fXAucAFtjtxSn4XM0dEc8au2Gtb0L2fvYuZI6IBY1cUtj8KvBhYLOkpTecZRRczR0Rzxq7YAVwdvP9W4Nims4yqi5kjohljWewAtn8PfFfSA+qh1h9h0sXMETH7cuZpTdIBti9rOsd0dDFzRGx+KfaIiMKM1SVgJT0TOHiq5Wy/ZxbijKSLmSOiWWNV7MBPgUubDjFNXcwcEQ0au00xkh5p++aBsecDP7f9vYZiTaqLmSOiOeN4VMwZQ8Z2BXaf7SDT0MXMEdGQcSz2YYcIrgW2m+0g09DFzBHRkHEs9mHbnu6h3b+LLmaOiIaM285TgIdJesXA2DOAa5oIM6IuZo6IhozjztNXTfDQCts3zmaWUXUxc0Q0Z+yKPSKidGO7jVbS4qYzTFcXM0fE7BvbYgeO6Z+RNEdS248yOaZ/piOZI2KWjdWmGEmHA0dSHWWyJbAeWAccDZxZT59o+/zGQg7oYuaIaNZYFftEJH0I+FfgB8AXbS9sONKUupg5ImbH2B3uKOntA0OfB3a3/e368XtmP9Xkupg5IpozjtvYVwGPBW6m+vn3ptq80dPGtzBdzBwRDRm7Yre9AvgRVVleVw9fL2lfSTvRwpLsYuaIaM7YbYqpeeDjZOBsqmuvHN1crEl1MXNENGDsdp5K+jLwYOBOqhe299m+pNlUk+ti5ohoztgVeykkLbJ9UdM5IqJ9xqrYJe0PPGLYY7bPqpc5xvbJs5lrKpIOsv2VgbHLbB/QVKaIaK9x23m6JbBV/fG/qDZr9OZ72nja/nMlPUbSNyRdIOkBDL9Ge0TEeO08tX1Zb1rSYbbPkLSN7bV9i7WxMAW8AfhbYAfgz8iRMBExgXFbY0fSWfXkZZIeApw7sEirClPS04FHA4+nOtzxm8Dj6sdeWX8c2FzCiGibsVpjrz2s/nwN1bVW3thgllHcQ3UyUv8Ljvoe6/8cETGWxb63pOuAXYB9bd8g6YHAU6kK88FNhhtk+1uSbgZ+DSwA5lJdH+b5ts9sNFxEtNLYbYoBrrP9ZOBFwImS9qHabv0CYCHwpSbDTeIjVCclHQlc0GyUiGizcVxjvwvA9ipJL6faxn6E7Xc3G2tSV9j+T+CZvQFJbdzJGxEtMFbHsQ8jaUfbv2k6x3RJWpizTyNimLEv9oiI0ozjNvaIiKKl2CMiCpNij4goTIo9IqIw/x+3OcnFz4vXvAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title('feature importance')\n",
    "plt.bar(range(len(importance)), importance)\n",
    "plt.xticks(range(len(importance)), X.columns, rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GradientBoosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(max_depth=1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "X = data_3\n",
    "Y = data['재발여부'].ravel()\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "    X,Y, test_size = 0.3, random_state = 0)\n",
    "\n",
    "model = GradientBoostingClassifier(max_depth = 1 )\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_score=[]\n",
    "test_score=[]\n",
    "for i in range(1,21):\n",
    "    \n",
    "    model=GradientBoostingClassifier(max_depth=i,random_state=4)\n",
    "    model.fit(X_train,Y_train)\n",
    "    train_score.append(model.score(X_train,Y_train))\n",
    "    test_score.append(model.score(X_test,Y_test))\n",
    "plt.figure()\n",
    "plt.title('score for depths')\n",
    "plt.plot(range(1,21),train_score)\n",
    "plt.plot(range(1,21),test_score)\n",
    "plt.xticks(range(1,21))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 정확도 : 0.9562264150943396\n",
      "테스트 정확도 : 0.8875219683655536\n"
     ]
    }
   ],
   "source": [
    "model_1=GradientBoostingClassifier(max_depth=4,random_state=4)\n",
    "model_1.fit(X_train, Y_train)\n",
    "train_accuracy = model_1.score(X_train, Y_train)\n",
    "test_accuracy = model_1.score(X_test, Y_test)\n",
    "print(f'훈련 정확도 : {train_accuracy}')\n",
    "print(f'테스트 정확도 : {test_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.20998112, 0.19507439, 0.0101609 , 0.21247607, 0.12655726,\n",
       "       0.16426253, 0.08148772])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "importance = model.feature_importances_\n",
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title('feature importance')\n",
    "plt.bar(range(len(importance)), importance)\n",
    "plt.xticks(range(len(importance)), X.columns, rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "std_scale = StandardScaler()\n",
    "std_scale.fit(x_train)\n",
    "x_train_std = std_scale.transform(x_train)\n",
    "x_test_std = std_scale.transform(x_test)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf_rf = RandomForestClassifier(max_depth = 10, random_state = 0)\n",
    "clf_rf.fit(x_train_std, y_train)\n",
    "pred_rf =clf_rf.predict(x_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_score=[]\n",
    "test_score=[]\n",
    "for i in range(1,21):\n",
    "    model=RandomForestClassifier(max_depth=i,random_state=4)\n",
    "    model.fit(x_train,y_train)\n",
    "    train_score.append(model.score(x_train,y_train))\n",
    "    test_score.append(model.score(x_test,y_test))\n",
    "plt.figure()\n",
    "plt.title('score for depths')\n",
    "plt.plot(range(1,21),train_score)\n",
    "plt.plot(range(1,21),test_score)\n",
    "plt.xticks(range(1,21))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 정확도는 : 0.8838028169014085, 테스트 정확도는 0.879746835443038 입니다.\n"
     ]
    }
   ],
   "source": [
    "model_3=RandomForestClassifier(max_depth=3,random_state=4)\n",
    "model_3.fit(x_train,y_train)\n",
    "train_accuracy_3 = model_3.score(x_train, y_train)\n",
    "test_accuracy_3 = model_3.score(x_test, y_test)\n",
    "print(f'훈련 정확도는 : {train_accuracy_3}, 테스트 정확도는 {test_accuracy_3} 입니다.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>재발여부</th>\n",
       "      <th>전방디스크높이(mm)</th>\n",
       "      <th>후방디스크높이(mm)</th>\n",
       "      <th>비만여부</th>\n",
       "      <th>지방축적도</th>\n",
       "      <th>연령</th>\n",
       "      <th>체중</th>\n",
       "      <th>신장</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0</td>\n",
       "      <td>282.3</td>\n",
       "      <td>66</td>\n",
       "      <td>60.3</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0</td>\n",
       "      <td>177.3</td>\n",
       "      <td>47</td>\n",
       "      <td>71.7</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0</td>\n",
       "      <td>256.8</td>\n",
       "      <td>39</td>\n",
       "      <td>77.1</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0</td>\n",
       "      <td>250.1</td>\n",
       "      <td>40</td>\n",
       "      <td>74.2</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0</td>\n",
       "      <td>232.2</td>\n",
       "      <td>42</td>\n",
       "      <td>80.7</td>\n",
       "      <td>183</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1</td>\n",
       "      <td>237.5</td>\n",
       "      <td>59</td>\n",
       "      <td>64.0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>42</td>\n",
       "      <td>59.0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1</td>\n",
       "      <td>148.5</td>\n",
       "      <td>61</td>\n",
       "      <td>70.0</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>29</td>\n",
       "      <td>77.0</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>-1</td>\n",
       "      <td>251.4</td>\n",
       "      <td>30</td>\n",
       "      <td>49.0</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1894 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      재발여부  전방디스크높이(mm)  후방디스크높이(mm)  비만여부  지방축적도  연령    체중   신장\n",
       "0        0         16.1         12.3     0  282.3  66  60.3  163\n",
       "1        0         13.7          6.4     0  177.3  47  71.7  171\n",
       "2        0         13.6          7.4     0  256.8  39  77.1  178\n",
       "3        0         10.6          7.3     0  250.1  40  74.2  174\n",
       "4        0         17.1          8.1     0  232.2  42  80.7  183\n",
       "...    ...          ...          ...   ...    ...  ..   ...  ...\n",
       "1889     0         17.0         10.7     1  237.5  59  64.0  157\n",
       "1890     0          9.4          8.2     0  288.0  42  59.0  157\n",
       "1891     0         13.5          5.5     1  148.5  61  70.0  167\n",
       "1892     0         14.0         10.0     0   89.0  29  77.0  177\n",
       "1893     0         16.1          9.5    -1  251.4  30  49.0  168\n",
       "\n",
       "[1894 rows x 8 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_4 = ['재발여부','전방디스크높이(mm)','후방디스크높이(mm)','비만여부','지방축적도','연령','체중','신장']\n",
    "data_5 = data[data_4].fillna(method='backfill')\n",
    "data_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 스케일 변환\n",
    "scaler = MinMaxScaler()\n",
    "data_5_std = scaler.fit_transform(data_5)\n",
    "\n",
    "# 군집\n",
    "kmeans = KMeans(n_clusters = 2)\n",
    "kmeans.fit(data_5_std)\n",
    "\n",
    "# 생성 된 클러스터를 데이터 프레임에 넣습니다.\n",
    "clusters = kmeans.labels_\n",
    "data_5.copy().loc[:, 'cluster'] = clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2) #2차원으로 변환하라는 코딩\n",
    "pca.fit(data_5_std)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.118938</td>\n",
       "      <td>0.372937</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X         Y  cluster\n",
       "0 -0.118938  0.372937        0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df = pd.DataFrame(pca.transform(data_5_std), columns = ['X','Y'])\n",
    "pca_df.loc[:,'cluster'] = clusters\n",
    "pca_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='X', ylabel='Y'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.scatterplot(data = pca_df, x = 'X', y = 'Y', hue = 'cluster')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 직업"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>전방디스크높이(mm)</th>\n",
       "      <th>후방디스크높이(mm)</th>\n",
       "      <th>비만여부</th>\n",
       "      <th>지방축적도</th>\n",
       "      <th>연령</th>\n",
       "      <th>체중</th>\n",
       "      <th>신장</th>\n",
       "      <th>직업_0</th>\n",
       "      <th>직업_건설업</th>\n",
       "      <th>직업_공무원</th>\n",
       "      <th>...</th>\n",
       "      <th>직업_사무직</th>\n",
       "      <th>직업_사업가</th>\n",
       "      <th>직업_예술가</th>\n",
       "      <th>직업_운동선수</th>\n",
       "      <th>직업_운수업</th>\n",
       "      <th>직업_의료직</th>\n",
       "      <th>직업_자영업</th>\n",
       "      <th>직업_주부</th>\n",
       "      <th>직업_특수전문직</th>\n",
       "      <th>직업_학생</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.1</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0</td>\n",
       "      <td>282.3</td>\n",
       "      <td>66</td>\n",
       "      <td>60.3</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0</td>\n",
       "      <td>177.3</td>\n",
       "      <td>47</td>\n",
       "      <td>71.7</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0</td>\n",
       "      <td>256.8</td>\n",
       "      <td>39</td>\n",
       "      <td>77.1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0</td>\n",
       "      <td>250.1</td>\n",
       "      <td>40</td>\n",
       "      <td>74.2</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0</td>\n",
       "      <td>232.2</td>\n",
       "      <td>42</td>\n",
       "      <td>80.7</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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>1889</th>\n",
       "      <td>17.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1</td>\n",
       "      <td>237.5</td>\n",
       "      <td>59</td>\n",
       "      <td>64.0</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>9.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>42</td>\n",
       "      <td>59.0</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>13.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1</td>\n",
       "      <td>148.5</td>\n",
       "      <td>61</td>\n",
       "      <td>70.0</td>\n",
       "      <td>167</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>29</td>\n",
       "      <td>77.0</td>\n",
       "      <td>177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>16.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>-1</td>\n",
       "      <td>251.4</td>\n",
       "      <td>30</td>\n",
       "      <td>49.0</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1894 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      전방디스크높이(mm)  후방디스크높이(mm)  비만여부  지방축적도  연령    체중   신장  직업_0  직업_건설업  \\\n",
       "0            16.1         12.3     0  282.3  66  60.3  163     0       0   \n",
       "1            13.7          6.4     0  177.3  47  71.7  171     0       0   \n",
       "2            13.6          7.4     0  256.8  39  77.1  178     0       0   \n",
       "3            10.6          7.3     0  250.1  40  74.2  174     0       0   \n",
       "4            17.1          8.1     0  232.2  42  80.7  183     0       0   \n",
       "...           ...          ...   ...    ...  ..   ...  ...   ...     ...   \n",
       "1889         17.0         10.7     1  237.5  59  64.0  157     0       0   \n",
       "1890          9.4          8.2     0  288.0  42  59.0  157     0       0   \n",
       "1891         13.5          5.5     1  148.5  61  70.0  167     0       0   \n",
       "1892         14.0         10.0     0   89.0  29  77.0  177     0       0   \n",
       "1893         16.1          9.5    -1  251.4  30  49.0  168     0       0   \n",
       "\n",
       "      직업_공무원  ...  직업_사무직  직업_사업가  직업_예술가  직업_운동선수  직업_운수업  직업_의료직  직업_자영업  \\\n",
       "0          0  ...       0       0       0        0       0       0       1   \n",
       "1          0  ...       0       0       0        1       0       0       0   \n",
       "2          0  ...       0       0       0        0       0       0       0   \n",
       "3          0  ...       0       0       0        0       0       0       0   \n",
       "4          0  ...       0       0       0        0       0       0       0   \n",
       "...      ...  ...     ...     ...     ...      ...     ...     ...     ...   \n",
       "1889       0  ...       0       0       0        0       0       0       0   \n",
       "1890       0  ...       1       0       0        0       0       0       0   \n",
       "1891       0  ...       0       0       0        0       0       0       0   \n",
       "1892       0  ...       1       0       0        0       0       0       0   \n",
       "1893       0  ...       1       0       0        0       0       0       0   \n",
       "\n",
       "      직업_주부  직업_특수전문직  직업_학생  \n",
       "0         0         0      0  \n",
       "1         0         0      0  \n",
       "2         0         1      0  \n",
       "3         1         0      0  \n",
       "4         0         1      0  \n",
       "...     ...       ...    ...  \n",
       "1889      0         0      0  \n",
       "1890      0         0      0  \n",
       "1891      0         0      0  \n",
       "1892      0         0      0  \n",
       "1893      0         0      0  \n",
       "\n",
       "[1894 rows x 25 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_6 = ['전방디스크높이(mm)','후방디스크높이(mm)','비만여부','지방축적도','연령','체중','신장','직업']\n",
    "data_7 = data[data_6].fillna(0)\n",
    "data_7 = pd.get_dummies(data_7)\n",
    "data_7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "X = data_7\n",
    "Y = data['재발여부'].ravel()\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "    X,Y, test_size = 0.3, random_state = 0)\n",
    "\n",
    "model = DecisionTreeClassifier(max_depth = 1 )\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# depth에 따른 정확도\n",
    "train_score=[]\n",
    "test_score=[]\n",
    "for i in range(1,21):\n",
    "    \n",
    "    model=DecisionTreeClassifier(max_depth=i,random_state=4)\n",
    "    model.fit(X_train,Y_train)\n",
    "    train_score.append(model.score(X_train,Y_train))\n",
    "    test_score.append(model.score(X_test,Y_test))\n",
    "plt.figure()\n",
    "plt.title('score for depths')\n",
    "plt.plot(range(1,21),train_score)\n",
    "plt.plot(range(1,21),test_score)\n",
    "plt.xticks(range(1,21))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 정확도 : 0.8837735849056604\n",
      "테스트 정확도 : 0.8717047451669596\n"
     ]
    }
   ],
   "source": [
    "model_1=DecisionTreeClassifier(max_depth=1,random_state=4)\n",
    "model_1.fit(X_train, Y_train)\n",
    "train_accuracy = model_1.score(X_train, Y_train)\n",
    "test_accuracy = model_1.score(X_test, Y_test)\n",
    "print(f'훈련 정확도 : {train_accuracy}')\n",
    "print(f'테스트 정확도 : {test_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1fFVvSHJf4HjgXVV1OkBVfa6qtgW2B64AnnfLqixJkiRJWshiEryzgb0A2oFRLl7VwkluA7wN2K+qzh8rXwbQJoeX3dIKS5IkSZIWttpBVoCTgN2TnE1z523/JEcBh1bV7xZY/gHAHwEnJxmVPRnYJcmBwPU0ffb2W8O6S5IkSZLGrDbBa++4HTBRfPDEMq8de/wV4K4L/KkT2h9JkiRJUgec6FySJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgbCBE+SJEmSBsIET5IkSZIGwgRPkiRJkgZiUQleksOTnJXk3CRbT7x2vyQfTbLrWNkTk5yd5LwkT2vLNkxyQpIvJPl4kttPd1UkSZIkab6tNsFLsj2wWVXtAOwPvHnstXsAhwBXjpXdFngF8DhgR+CQJOsDBwGfqqpHA6cCB0xxPSRJkiRp7i3mDt7OwAkAVXUBsMnohar6UVU9B7hobPmHA5+vqmuq6irgPOAPaZK9j7TLfAx4xBrXXpIkSZJ0g8UkeHcGLh17fl2SVb1vcvnLgI2B9arq2okySZIkSdKULCbBu5wbJ2PLq2r5zVh+Y5qEb/lYYjgqkyRJkiRNyWISvLOBvQCSbAVcvJrlvwLsmmSdJBsA9we+Q9NUc492mT2B025RjSVJkiRJC1pMgncSsG6Ss4G3AAcnOSrJugstXFW/BP4ZOAc4GTisqq4D3gTsl+RM4MHAe9a8+pIkSZKkkWWrW6Btjjk54uXBE8u8duL5scCxE2W/BHa7RbWUJEmSJK2WE51LkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJAmOBJkiRJ0kCY4EmSJEnSQJjgSZIkSdJALFvMQkkOBx7dLr9fVf1nW74hcCywBfArYB9gG+ANY2/fBngM8GvgPOB7bfkLqurba74KkiRJkiRYxB28JNsDm1XVDsD+wJvHXj4I+FRVPRo4FTigqs6pqsdU1WOAZwGnVdX5wEbAiaPXTO4kSZIkaboW00RzZ+AEgKq6ANhk7LUdgY+0jz8GPGLiva8B3tg+3ojmLp4kSZIkqQOLSfDuDFw69vy6JKP3rVdV17aPLwM2Hi2UZDNg86r6Zlu0AbBnknOTHJNknTWsuyRJkiRpzGISvMsZS9yA5VW1fPR4LNnbmBsngvsC7xk9qarPVdW2wPbAFcDzbmmlJUmSJEk3tZgE72xgL4AkWwEXj712HrBH+3hP4LSx1/YATh49SbIMoE0OL7vlVZYkSZIkLWQxCd5JwLpJzgbeAhyc5Kgk6wJvAvZLcibwYNo7dkk2AX5XVVeP/Z2nJDknyVnAg4Djp7gekiRJkjT3VjtNQnvH7YCJ4oPb378EdlvgPb+imRphvOwE2sFaJEmSJEnT50TnkiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQi0rwkhye5Kwk5ybZeqx8wyQnJPlCko8nuX1bfnySLyY5M8nRq1pWkiRJkjQdq03wkmwPbFZVOwD7A28ee/kg4FNV9WjgVOCAtnwjYLeqekxVvXI1y0qSJEmSpmAxd/B2Bk4AqKoLgE3GXtsR+Ej7+GPAI9rHtwN+M/F3VrasJEmSJGkKFpPg3Rm4dOz5dUlG71uvqq5tH18GbNw+LuDMJKe0dwBXtawkSZIkaQqWLWKZy7lxMra8qpaPHidZq32+MW0iWFW7ACS5G3ASsM3KlpUkSZIkTcdi7uCdDewFkGQr4OKx184D9mgf7wmc1i43Shx/DVy7qmUlSZIkSdOxmATvJGDdJGcDbwEOTnJUknWBNwH7JTkTeDDwnvY9n23LPgO8ui1b2bKSJEmSpClYbRPNtknl5IiXB7e/fwnstsB7HrdA2YLLSpIkSZKmw4nOJUmSJGkgTPAkSZIkaSBM8CRJkiRpIEzwJEmSJGkgTPAkSZIkaSBM8CRJkiRpIEzwJEmSJGkgTPAkSZIkaSBM8CRJkiRpIEzwJEmSJGkgTPAkSZIkaSBM8CRJkiRpIJb1XQFJS8eWh5zU6d+/6MjHd/r3JUmS5p138CRJkiRpIEzwJEmSJGkgTPAkSZIkaSBM8CRJkiRpIEzwJEmSJGkgTPAkSZIkaSBM8CRJkiRpIEzwJEmSJGkgTPAkSZIkaSBM8CRJkiRpIEzwJEmSJGkgTPAkSZIkaSBM8CRJkiRpIEzwJEmSJGkgTPAkSZIkaSBM8CRJkiRpIEzwJEmSJGkgTPAkSZIkaSBM8CRJkiRpIEzwJEmSJGkgFpXgJTk8yVlJzk2y9Vj5hklOSPKFJB9Pcvu2/KgkZyb5WpJd27K7JbmkLT8zyVbdrJIkSZIkzafVJnhJtgc2q6odgP2BN4+9fBDwqap6NHAqcEBb/pGqegywG/CGtmwj4MSqekz78+3prIIkSZIkCRZ3B29n4ASAqroA2GTstR2Bj7SPPwY8ol3ua23Zb4D/bR9vBPx6jWorSZIkSVqpxSR4dwYuHXt+XZLR+9arqmvbx5cBG48WSrIe8A7giLZoA2DPtpnnMUnWWbOqS5IkSZLGLSbBu5yxxA1YXlXLR4/Hkr2NaRPBJPcFjgfeVVWnA1TV56pqW2B74ArgeVOovyRJkiSptZgE72xgL4B2YJSLx147D9ijfbwncFqS2wBvA/arqvNHCyZZBtAmh5etedUlSZIkSeOWLWKZk4Ddk5xNc+dt/yRHAYcCbwLel+QlwIXAgcC2wB8BJycZ/Y0nA7skORC4HrgI2G+K6yFJkiRJc2+1CV57x+2AieKD29+/pBkpc9xXgLsu8KdOaH8kSZIkSR1wonNJkiRJGggTPEmSJEkaCBM8SZIkSRoIEzxJkiRJGggTPEmSJEkaCBM8SZIkSRoIEzxJkiRJGggTPEmSJEkaCBM8SZIkSRoIEzxJkiRJGggTPEmSJEkaCBM8SZIkSRoIEzxJkiRJGggTPEmSJEkaiGV9V0CSNH+2POSkzmNcdOTjO48hSfPOz/Olxzt4kiRJkjQQ3sGTJEmSbsW8i6ZxJniSlgS/nCRpGPw8l/plE01JkiRJGggTPEmSJEkaCBM8SZIkSRoIEzxJkiRJGggTPEmSJEkaCEfRlCRJnXJURUmaHe/gSZIkSdJAeAdP0tzz7oIkSRoKEzytUtcnvp70SpIkSdNjE01JkiRJGggTPEmSJEkaCBM8SZIkSRoI++BJkuaKg+pIkobMO3iSJEmSNBDewZMkaQ5451KS5sOi7uAlOTzJWUnOTbL1WPmGSU5I8oUkH09y+7b8iUnOTnJekqetallJkiRJ0nSs9g5eku2BzapqhyT3B94M7N6+fBDwqar6YJIDgQOS/B3wCuBP2r9/TpJPLLQscNT0V0mSpKXJu2jScHl8a6lYzB28nYETAKrqAmCTsdd2BD7SPv4Y8Ajg4cDnq+qaqroKOA/4w5UsK0mSJEmaklTVqhdI/hF4Z5vckeQc4NFVtTzJF6vqkW35OsBpwD8Ad6yqv2vL39iWv3Fy2araYSLWpcCPprmCkiRJkjRA96iqO00WLmaQlcuBjceeL6+q5aPHSdZqn28MXNouf5+x5UflCy17IwtVUJIkSZK0OItponk2sBdAkq2Ai8deOw/Yo328J82duq8AuyZZJ8kGwP2B76xkWUmSJEnSlCymieZawLtoErUrgP2BFwKHArcH3gfcBrgQOLCqrknyPOC5wG+B11XVGUk2XWjZTtZKkiRJkubQahM8SZIkSdKtw6LmwZMkSZIkLX0meFPU9ju8f5KHJbnbvMReapLs3HcdutbnOibZY/VLdRZ73STbJNkhyVZtE3JJkiS1PDmagiSbJfkn4JPAXwJ/Brw+yalJnjnU2AvUpc+kY72xp4f0VY8uJfnT9vcr6XcdXzL+JMn6STbsOmiSpwJfBp4P7AQcCJyeZIdVvlFrLMnb+q6DNAtJ/nneYie5bfv75X3EV3/a8wkN0GKmSdDqvQk4oqouHC9MEuBZSZ5WVScOMPakQ4BTZhEoyYlV9bSxos8AO45e7jDuI4DHTpZX1RFJngHcHTimowGEXpbk1DbGqD53B06kWeeDq+qsDuKOkssDgQKuSfIZ4IfAe4G3A5XkdVX1uS7itw4CHlVV/zdWrw2AT7Pifz9VSY4C/ohmvdP+3h341NjzVNWuHcTei2ZQq5FRh+m1aKar2TnJZ6pqtw5ibwbce/QUeECSR04uV1VfnHbsNv4HgU1ZcSzfsP2raud2mdOraqr/9yRPpLmAMf7//hZwPfCg9vl3q+qAacYdi9/n/vZEelr3nmNP7uv3nNW+3mfsCQcmORnYBXhrx7HmeT/vbb3b+H889vTyqvousCtwdBfxxuI+kf62+e8Df98+vTPwizbeC4F3AmsD+1fVf087dt8cZEW3SHuCvw3wTeD6qnpCkjOq6ibJT0fxbxRr/HkXJ35jce4G3AvYm+aD+hXtSxsBj6e5w/SgqnpRB7HPAN4D/Bh4TVXtmOQdwEeB/wA+0tUXwwJ1Wauqlif5N5oRc68GPlFVj+sw5heAHavqurGytYFzq+rhXcVt45xQVc+YKDulTbJOraqduozfxtuAJrl/CfD2qvpeV7GTPJTmxGNVqqpeP+3YizXjz5vOPlNWEq/3/W0s7kzXfdax+9zXl8pxluQDwLOBz81435rL/byv9U5yGM3/+X3Adm28mX2OLlCfWX+u3rBtk7yHJvH7NXBkVe01q3rMinfwpijJS4HnAVex4mrzH6/yTbfS2FW1W3uw7N7eVYIVdxhmYTLWXZLsQ4d37wCq6ifAT5I8BLjH6I5ZkncDf1NVlyTp6i7mlsD2VfW8JIe16/tY4GVVdV1z07Y7Sd5XVc9unx6a5KPAbarqsvb1TuPTXFk+I8lpwKXAJjRXnI/tMmh7lf1uSR5QVd/qMtYCsX8feA1wKs3d8T0nFunkmKuqrwJfXaA+2wLXVdV/dhF3gXjPBLYFvlpVH5l4uZN1T7JPVb23fbwr8L0u4qwifp/7W2/r3lfsle3rQ489kmQX4MvtBbtZxp3X/by39a6q1yV5VPt7pudtPW/zz9Cs53XtnWqAO7THH0luN6u6zJJ98KbrOcADq+qPq+qhs0rueoxdE783T7LP2M8sD5oCrmt/OpVkb+BnwJOT7JLkMcCdgP9pF1neUeia+NvXc+MP564/qLcASPJcmgsIMznJH6mqT9AkdGcDvwK+Djyxqo5v6/WgacdsE6x/prlzelBm3K+VpgnJEcADaLb/bavqgKqaadIBkGQb4G3AL2cU7y3A5jTb/q5JXjeLuMC+bfy7AC8CfkrHF45GlsD+tm9bj5mve5+xk3w1yYeSPKttFTAzPcc+kaZ52jvHyt499tNJ/6x53c+XwHrDTc/b7tf+r9/T/t6so7j7Qj/Hd1XtVlW7t90ZDgZeTTNP9w2LzKIes+YdvOn6AbA+cO2cxR4ZJVnj/WZm5X+q6oMASf6y41ivBs4CzqG5SHI/mtv8d6Rp393VhZMfAV9oE5mqqg8keRjw8CTfoPsPy22S/AD4fFU9ry37RZKtgCtpmml2qu1/d/pKXn4r0++L91zguVX1U+D4JO8HPjBZrSnHHJeq+q/2bukfAM9I8kDgk1X1Dx3GJcldafon3APYi6ZP3FOq6lddxh2zbVWNmkD/V5LJO3hduWuSvwZ2AP6iqq5JMqvPsr73tz7Xvc/YVwIvoGlmf1aSl1bV1+Yg9qHAYUm2rKqLxspGuuhLDvO7n/e93gv5IfA3rDh/6OoCXp/HNwBJ/gA4DNiPZtyAdWnOmdedZT1mxQRvut4JfD/Jf7XPa4bti/uMPXJDkjUDW4w1hQxNYjUrP6NJ8KC5CrQe8DmaK3Jnt6935SOs6PcH8BbgQzRJ5V91GBfgfJr+Ii9OckhVHUnzxfBumg/I53ccf3W6SHBv8gXUXmU/Dtg6TTv+rTqIO7JeknVoLiJ8D/hsVR2Q5M+SPA3o6morwBtpEry7AXcAXjDD5A5gnYnn6ybZhObqLzRNlrtwLU0T4Otptu/FzO4uVt/7W5/r3ut2b/ft96XpV/zPSY6rqs8OOXbbj3d/mu+R57dlP+06LvO7n/e63m2s5MbTC11TVZd0FXNMb8d3e3FwU5rztT2r6rdpRqx9L3AF8LFZ1GPmqsqfKf3QnABvTXPCvx6w3lBj04xa+RPgZJrBNQBO7/t/0NbjjI7//qnAU9ufF9P0gQN4OXAMsHFHcXdvf+/d9Tqubru2671n3//rifpNff+jGeHuZODPab6En96Wbzb+0+E67QF8Efh/7fNj29/PoJkW5S9ntG03Bd4BvHGG/8+/AD5Mc9X7A+1+vz6w3eino7hntL/XBT5IM7DNU2e0zn3vb32ue++xx56vD5wE3H3IscdiHg1sDJwyo3hzuZ8vgfX+VPvzSeD1bdlMztt6Pr7XA+5Kc97yD8D6bfkjgcfMog59/DiK5hQleX9VPWveYo/V4YzqaTSmiXo8rqpO6/Dv700zauWT26LTquq8ruKtpA6druMi4gfYqKp+3VcdJnW1/yW5M/Ao4NtV9Z1p//2bWZfXVL8jV+4PrFNVfzejeH8A3B/4Zk1MBdNhzKdW1Yfbx3cBtq6qz88idhuzt/2tz3XvOfZzqupfJsq2Af64qo4bauyxeNsBFwLvrxmNXDnH+/mS+T5p6zOT87a+P1fH6vEQ4GlV1XWLp96Z4E1RkvfSXCX4Ku2t+Kp69dBjj9Wh16RjyJLsSDO88eiADTdu7jEaOfUvOoq/A+0gK5NqRd/Hl1bVMV3E19KRZFmNTVUhaRiS3LHaUZE1H5JsWlUzGThrqUiSmoPkxz5403X8nMYGYFbJXTsS1UMnii+m6bMz6pN0WXUw6XaSFwELjg5aVUe0yxxZVYdMOfSXge+ycAfsyWSvC2tz0z5Rk3anaaI6Ve0d0y0nio8EXsXYeo+2/9AleWVVdTox7Wq8jI4nxp3U5zovdIdlhrFvT9OE6JN9xG/r0Oe273tfn5m2r+1Bfa5vH8ldkttW1VVJXl5VnU+yvoj6DH6fS3KbavqhzdWxneRvquoN85DcgQneVFXVWUnWoxmQYLCx22ZTD1tEnd7bURXWokk2DqY50Q/NvvzXNEMQhyYh6cJXadpzv5pm+Po/ptnmp44tM/UpKqrq/9oRt9anWb/7At+nSXCKFUne6ibNvaXxbzJyZZL1q2p85MyuOkxfQDOk8qHA4W19lic5i2bkzJfRDN8/9QQvyUto+raO+zea5rnLq2r/JO+pqj+fduw2/niH+yur6sfArswgwUozzyI0I+P+H3AZcO4M4z+YpkP+1cATJ2Mm+dOq+nQHcX+/qr6f5OFV9WWa/n7/kuRewJ1m1Rw7zfQr/wU8EPhke2Fro2rnbuooZp/7Wy+xkxwF/BE3/Rz91NjzVNWuHdYhwNuBrZK8mWZaFKrq/CRPrqp/7SDmUTT97m5U3ISt/dplOvtsG3NgmrnJdqH5PO9cktH35O+q6rQkf15V72nLOtvnkvwhTV9mWLFv/RZ4KfAvNOcx+1dH8+MleVVVvQnYI8llzOjYbmPfd/S4mml+bhQ7yS4dXZR/ILABzfZ+UpLRucx/0oxMPfLtqrpy2vH7ZII3RWnmbdqJZjj70cH7ZwOMfT3N8MnPp7lzeC3wWJqRLDufG62qvgt8N8m+VfXeJGtX1fVJDu0wqRzF/jJAkhfQjAj13zQdds9a5RunE/vJSe5XzbD5xwIvrGao4e2q6tyu449LsjHwPuBPx6vYRayqOr+N+RLgnKq6vi0/J8nlVXVukt90ERs4AbgN8Dqa4ZUBngn8KzC6S/t7HcWGZjCjE2mO6W2BnZndyIKvAt5E8+W4GU1fuHNnGP9E4P00F1QW2sYvA6ae4AH/SDPdxutpt3ebbB4N/CTJI2bUFPk1wNPb+I8CXgtcnORrHfaB7HN/6yV2VR0MkOSEqnrG2Eu7JTmlqnbOikmhu/IQmoHStqJJut5F0+Xi3sALaT5vpu0dNOeAdwIOoNnfJ3X52TayLc0onrObZb1JJP+BZm62BwHPTvJpOp4buqq+0zYP3Amg3b/2SPI+mvOp3wB/T3fnbo+l+Uzfnub7a5bb/DSa0SqfRNMiZzL2y2lGI5+27WimsArN4DI70ZyrXEoz2MvonPEXNFOWDIYJ3nRtX1XbDj12O9jBhUl2AT5cVb9LsiHww4Xu9HQhyQeBUQfdf2pP/meivbK+Jc3k8tsCZ84qNs1drL1pTnwryZHA72hOvGeiPdl9A81Vx1nEuwNNonUZcFqSv6oZzRNVVb9o63BFVf1o7CrkL+hujqhxP6iqV7Z1GE0LMqvmJT8fXTBpE4y1aE4MZtVC4YdV9do2/sMXeH2WJyfPpTkJ/j5wBh00RV6N/WlGFf0JzYlSVwlen/tbb7HTTO58tyQP6OruyWo8j+YE9+nt89NoTkyho/282ukQklwN/Kb9fNtmdEFttFgXsUfac4gvty0yugw16SdV9bdJRhcoQ5P0zWrY/gNoLpSP3Kmqvt2+tl7HsR8IXFpVV8x4m/93VR2U5P4reb2r/fxdSZ5VVe8HSLJWVS1vH/+wehy0rGsmeNP1vSR3b5uWDDp2ks1prnx9Is1klVsAn2Xlk1BP212qau8krwJOraorZ/hh9XqafjG/BUjy4fEPja4keTnwh0leSXN1903AMaORqbqW5DNt3C2BHarqu22/kUfQfDhPNveZlgOAw6rqq0luAxwLTI4Y2/WJ4LvbJGdnZjCh+5iF1ut+Sd7Nijv1R1c3o7GNx34oKyaDvX0HsVYXnySj/fyHo7suHdus3c5r0ez336+qSnL9at43LfcD/hb4DrD56LM9SZcD3CyV/W1msdumr+8A3kMzl+nnq2py8unOJDmNZvqTK9qmmu+kmfNyVv6Xpgnyg2ju2j91FkGTnEgzIu+Tx8rePbbId2bQR2u8D/c+bR26Poe5O3AfmgtFv01y15XVqQMPounOcENz44lt/t9V9caOYtfE7/Xbz/TLquqAjmKO7JPkX2mm3dkwybvbi5eD7otngjddJwDfSjPZeNG0ZX/kQGO/l6at+Ffghj4Erxu/UtKxbZP8GPj7qvpQWzarg/WaUXLX+ilwuySPpzkR6Wry6Qtovoy/DlzSxtk5yWerqqsmijeoqt0AkmxL879+B83gL9u3i3y8o9Cb0/RFou0Yfm27v+0D3DVNX7HNO4pNkk1pmio9myaxnBxeedZfEj+kmWB+dEXj0q4DVtXbR4/bq+59GPUFGiVYXW/3y2jumL8WuIqmqepVM4g78kPgKJq5EK/JitFLB7+/zTj2c4Hntne0jk/T13kywetym7+W5rPlxPb5m4A9O4x3gzRD9t8e+BOavuOdjMK8EocChyXZsqouGisbmUULiRskuScdN9FsXVlVL29jfofm+L62bQV1Dd2el/8AuIimX+9X2rK+tvnVNJ/powvjnRxj7ff3ejQXLv4OOIXmRsR7mW0rkJkzwZuuI4Etq5+5wWYde9kouYMmm0wzVUPXHbJHvknTKfvFSQ6oqv9H0x9uFn6S5GnAJ2g+KLeoqsvbu1nQUaflqvpcmn6Ho6ap72+brn0qyU5V9bsu4i5Qj28meQrwEeDCDq/4jXwCODrN4AM7ABe3+9vVtIOu0DQZ7crfAtsAR7XNkUflSfIa4F4dxl7W3rUcP/G4pqou6TDmTbRXP0cD/Gwzq7Bjj6uakfYCbJzktqx+VNdbat32xLeq6qft//tLwFOTnAtc0VFcANIM5hKak63R//mLwNOSfLXj+H3ub33FvsmJZZK1aSai3jrJe2j6xnUTvOlL/JA0A3AUK/7nJFmXbk9Cj6IZFfoBwMvbljC3pWkuGjr8bKuq76WZV/MtNP3Pbmg22pODmM0J//jF2OVtzL+l6R/2O5o+wF25nKa59z/SJngz3ObrJNmEsc/tqrpqBnEPpbljekfgK21z4JlePOiLCd50fZkbH7xDjv29JC8Gjm9PvO5Nc+XxqBnFv7aqrgXemuSwNCPePW9GsV9AcxfnQ8C3afpPULMZSv1G61hVX06y16ySu7G41ybZZ0Z3Dk9vT7gOohmI4DVt+YmrfOP04j8TIMkRSXalacr1v8CLaPqjddExfOSnNAMZhaZJD8zuDs61owcTzajOWHjxqVsozh1pmrBBMzVKF35Cc8L1zfZ50Qx88E6aAQK67u/7V23MT7KiaeLbaa4+7wW8uMPYfe5vfcU+Fjg2yUdo+r19uppBuw5hxUBK0572ZtIHgL9sH19D0yw3NHf1OrtoW+0ImW2/r8PSTLB+FCv6t3c69VGbUP4mzaBds7wzPWoC+8s0A+hsUFUvhpk00TyjvVh5Bk0//hdW1aVJvk3TZPVHHcZO+9397ST3Ybbb/CKaz9WftM8Lbmiqu0lXdamql6QZofdLwPOSfJwV322XdxFzqXCi8ylKchLNFa9vtEVVVXsPMXaSZTR363akGWXwYppk7z+6irmKuqxHM2T9tatdWFoDSY6aUf+vVdXhjKp67DzF73Odkxw7w4tHk7E3A55fVa/rI35bhz63/Uxit3dsH0UzVHoX/QsXU4cdgNf2fGwfRdPtocskYzLmdsCFwPurHV2yT7PY59oLhVsCn5xla4wkm1fVz5I8gGbqm+OGfmy3sU6tqp3alkcPBN5ZVT+fRew+meBNUZJ7TJbN6oOyz9h9S/J7NHf0/qfvukhdS7JpVf1yKcRP8qBZXNRZ2TrPIn6Sjarqf7uMsYrYATasqismymey3dtYN9r2fcYeunlb33FJ7lg9TLS+QD3m5n+w0Lr2/ZneUazbTX6GLrDMzD7XZsUEbwqSHA0cvtAOlGR74M5V9bGhxW5j3I9miOWZtp1P8tiqOiPJX9HcPfyfmtEUDZqtJA8Fdpso/jFN/4Ut2+eXVNVxs6zXrCV5HM3Icl01TbzZkpxeVTvOS/wkr6zuR/ZbTD162+6ziJ3kbVX1si5jLMXYfUvynBl1NVgo9pI4tmYpyd9W1cymeFqMvj/T+zLE9bYP3nT8E/D2NMNn/yfNre8tgD+gaffb1ZxFfceGZuLMC2n6T9wgydZV1eWk56+iacO+HfDuJmT+Fdiwff1jVdVlZ2XNzk+ByYnkL6cZ7nnUdK2TgSeSvJWbTv8QmibQf9Euc2JVPa2j+HvSdLz/H5qBHn5BM9n1vWgGOprlcOoL6XsUsk7jJ3lyVY1PMr0rHQ2idDP1ud2nHrttinrvsb//gCQ3GQW6qr44sNh/RtOXd5VX2qtq52nHbuP/flV9v+3D/mWa79N/aT9f7lRV53URdyz+FsAv2u4VNzq2kuxSVVPv3zyxzUf78vj2H32+T32b97mv3QxdHN8v5KaDc422837tMu8Z9QntSd/fZVNngjcF1Uz8/dwk69MkVhsAn6+qHw4xdppJxu80etqW/RU3/lB8J03/vA6rkYezYiAEaJoydfJFqP5U1SVtc47zAdJMNn5p+9pk4jdtR7Pic/JPaKZjmJwG5M4dxn8jzQS8m4wKkuxFMyDD1Uk+VrOZlmRl+m4C0nX8FwLjCd5SOQnoc7t3EfvuwHj/q3Mmno/idnHi21vsqvpkkj6bJf4jzff062nm+EySB9N87v0kySOq6pguAif5C5qJ3a9uP9Mmj62X08EAVlX1SZoBjBaq02OBr62uOd8amNzXzmJ2+/lidXF8nwh8qn38jzQjeU76vQ7i3hx9f5dNnQneFFXV1dw44Rhk7EUO3tLJiVCStWgGd/l9miHynwg8jubLqbO50NS7Y4Ad28F93szCXxBTN96vsz0RW3+B5shdfjH8rKqObeO/mOa42g94PE0T1c9x04RzlvpOeLqOP/n3l8pJwKDu4FXVV4Gv3iRQM+fmdV22BukzdutxNNO/XAD8EnhI+/siVoyiem7HdRj3XOAA4Ps0rWSO6SjOM2mSm8e2vyePrc728bZv637A/WgGOhl173gkTfP/ThK8qvpqkouq6kbzOaaZq+0OVTWrqZ5WpYvj+9Ikd66qXyT5bVX9aIFWXn1/tvb9XTZ1JnhroL3StdpJf6vqiCHFbuO/cqLo0qp6z3joLuK2f/fq9nexYrjb6zuMqf5t0I629nLgmKr6eTKbz+O26e8ZwKeZ/QWcapv1PJxmrqqzGBsxtm2a3Zs+R/ybVfwk7x497DrWYvW53Wc48t02NM2wZzISdV+xq+p1SV5LM6n7r2mahH98Bq0Txm3W7udrAXcFvl9V1fHny3XVzEn2beDJwG3az1poLuB1+X1+DE0CfRzw8iRrV9WpNOcW63cYF5opMW5oadQ2U/0Xmu+23nV4fH+I5m7xO5I8jGZ9n9pRrJut7++yLpjgrZmfMdsra0slNsAzaOaDGp30/DXN/GCdqmZUoA8k2ZemWcmBNBOcf57myqeGaQua//XtWDEVyKxOuDehOfl6O80+PutBTtYD7kKz7nDj9e7kJCjJ3qwYwGbkSJq+rzfE7PACUq/xJxw6qhbw3i4D9bnefW/zJHcF1gbuQTPf36bAU6rqV13EW0KxDwQeRDO35jo0/Wz/Osk1bb+4WbiMZj9/LXAVTVePq+g2yRp9jm3Yxvot8GxoJsDu+ALeNqPBTdr+YX8LjBK8dbsMDKyb5I40TRKfBPwx8IKu+1P3fXzTrPe9WXGxct+O4tzIEljv3pjgrYFq5i+Z2RwmSyV263+r6gujJ0leMesKVNU5bTv+UbOGJXOFXVN3YVXt3Xb+Py7Js2gGEZqF31XVp5N8Fvi7JOvS3M37+/b1P+wwdqrqx8A/tv1soZmc92E0Ayp1NVHrBTSD2xxK0xSa9mr7WTR9Al9Gc4ejqy/FvuOP1HiT3BncNe5zvfve5m+kSbLuBtyB5qS38wRrCcR+VlU9YrwgyTto7i51neCtm2YOwKqqn7b795eApyY5l46aKrZ+nWbuvz+l6Ze1T5vYrZNmbtu1Ooy9TpK0F4w3pvkshaZFUNcJ3n1p7iBuQbO/vWhGg2X1fXzfG3gLzffl86vq/9p977k0525bdhS37/XujQneGkqyCc2Xw5Y0zQTXAk6oqvcNOTY3vbJXSTYE/m0lr0/bG9rfXx+L98yOY6o/XwKoqh8kOQL4s6p69Yxip419XZIX0HTQ/wpwSPv6ISt74xRMDgZQwCuAd7X1OrCLoGMD2rwEOKeqrm/Lz0lyeVWdm+Q3XcTuO36Sz7BihL1rV7P4VPW53kvgf37DCHptn6TXJHlCVf11VzGXQmzg50n+FPhs+xlzW5qT3q/NIPZPaO5ejZqeF82Fq3fS3F3qcgj/l9LcMTyzqi4cu3jydzQDV3WZXL4b+GiSz9Os5wva8uvo/rz4P6vq2QBJNgZemWYC7gOq6nddBe37+KaZ4udJ7f59eNvH9ThWtEQ7p4ugS2C9e2OCt+aOA95ZVWcAtINAvCHJn7WjNQ019sZJTmbFXbP/q6oruemIUJ0Y9U+oqne1bdivq6pfzCK2Zm88mauqr7MisZ+F0VQMoyt/hwK71gym4aiqty9Q9jOaPiudSXIH4ASaplunJfmrqprFCWfv8atqcs7FG1Wty9h9rnff//Nx1UyA/OIk+yd5YVV1Pd1Pn7H3oelztk/bOuBy4OSqOrHjuFTVMyaKUlXX0AxA0nXsn3HjwbJGF9I6H0Crqt6d5Ms0I4/vXSsGPVlOt3cOAf59rB6/Bl6V5AnAwbR3mLqwBI7vr0DT/BZ4WZK/BTbuuq/pEljv/lSVP2vwA5y1QNk9gDcNOfZS+QF277sO/vgDPGgGMW5LM/dd57Fp7ko+tH18G+D9Y6+dMv67o3XtNf4q6rVpl//vPtd7CW/zZWOPOz/OlkrsiXrMJDawUY/ruGlf6z0Wb+2h/r+X6vE9g205l+tdVZ1fqZgHP06yb9r2BUk2omnr+5mhxk7y+0n2Xt1Pl3UY89KJui1rmwBIs/TWrgNU1VVVdd2MYm8O/Fcb97fAtWk8B7hrkn3odlqSvuMvqJq7O9Dd/7vP9V6q23x8n+/8OFsqsSfMKnbnd+5WZuzYGjfr//f4qKFD+38vieN7dH6WZFajhi6J9e6DCd6a249mhLtPJPk4Tfvx99XYACQDjL0WzWhfq/vpRJK9kpya5BRg7fbxZ5Lch6Y99ylJ9ugqvrSAQc1LBnwCODrJPdOMWHtxNZc6r6ZpRnQNK/rBdqHv+KvT1f+7z/Ve6tschnec9R47yRZJRt/Xu068ttqpmDo2yG3eU+ylcnwfmOT+LGKarylZKus9c/bBW0PtFYEj5yl2VX0X+O6s447F/yjw0cnyNCOPPQ/4Hs0oh5+YcdU0v/qcg3Hqsavq9CRrAwcB5wOvacs77xe0FOIvQif/7z7X+1awzWFgx1nfsdOMQv104Ooke3HTxOLlwOe6iL1Ig9vmfcVeQsf3tjSjac4kgV5C6z1zJnhTlOR2wOY1myFve42d5FSaD6G1aDomQ3PAvrGqzpxB/MmJ1v8NuE+tGDGp1wmgNXeGdrWXaib+PbWLv31riL8anf2/+1zvJb7NYYDHWc+xn0kzMNpj29+TiUXfUw8NcZv3Frvv47u9I/zlagYrm1ncvte7LyZ4U9KO1HMX4E+S/LLaOXSSbFVV3x5a7KraqY1xOE2n1e8m2ZNmeONZOA/YGzgLuDuwDSsSTej36pvmTFU9dh5jzyu3eT/m9TjrMPZ17cn2t2lG5r1Nkn9tX9ufnr9HB7rNl3TsriQ5EVinqp48VvbusUW+U1VHz75mw2WCNz0fAV5Ic+XlQ8DObfkxY4+HGBt6+BKoqrOSPJwm0bscWB+4IMl2NB1qTfC0xtrBgracKD4SeBVj+1hVTX2S1J5jPxSYnC7gxzQXUUZ1uqSqjpt27D7j97nN2/i9bfeeY8/rcdbr/saKO0UbAlcBvwWe3ca8qqu7LPO6zef5M51mAMDDkmxZVReNlY1c00XQJbDevXGQlekZ/yRMkpPTTJj7wIHHrpU8noWa+DmGprPsZ2kmUJXW1AU0A/fs2P4+t6qW09w53mPs99Bi/7T9++M/3wD2HXv+7yt57605fp/bHPrd7n3GntfjrO/97ddJdqAZsO0cuGGest8lWY/uzhHndZvP7Wd6231of5ppC0ZlPx37WWgU1Wno+7usN97Bm57NgScBVwJVVbvDDX3VBhd7rA9egEe0V/qKGY1G1Cawd6DpN7CMpu/fz2n6EkhTMdan8yXAOaNhtKvqnCSXV9W5SX4zwNiXJNl0rA73BS5tX+t0Yto+4/e5zds4vW33nmPP63HW6/5GM83Qa4Ezq+rCsTt2f0fT3eKKLoLO6zaf58/0Ns6VSX6TZGNmdENgKax3X7yDNz0FLDRH1Sx24pnHrqqdqmrn9vdOY89nMT0EVbVbVT2yjfvYqjptFnE1X5LcIcnJNM2XTkvykHmI3Tqmrccy4M3AekOPvwS2OfS73XuJPa/HWd/7W1X9rKr2r6oTRlVqy/evqidV1ZO6iDuv27zv/zf9f6ZDM7r5usx2EJtjoPf1njnv4E3Pz4FP0dxRSnsQh9k0k+wz9swl+QPgYatbrqreO4PqaNgOAA6rqq8muQ1wLPCsiWW6upDSZ2yADdo+rS8Hjqmqn3fVJ2cJxe97m0O/272v2PN6nC2F/W3cUyYLkjyoqv5jynHmdZv3/f/u+zOdqjoXIMnTZxi29/XugwneGhprqvjj8fIZN5OceeyeXU9HHXKlCZvTDNpDVf02ybVpvhn2Ae6aZJ92maHFBtgCOBC4HU2fBZjtVdc+4ve9zaHf7d5X7Hk9zpbC/naDlfSDeitNn7Fpmtdt3vf/u+/P9HH70uxbs7CU1ntmTPDWULXTBcANd5agmTh05EVDjN2ntq/Az6vqyvHyJBsCt6uqn/VUNQ3PJ4Cjk7wZ2AG4uKoqydXA4e0yXfU77TM2wIVVtXeSewHHJXkW8KUO4y2F+H1vc+h3u/cVe16Ps6Wwv61OFyfC87rN+/5/9/bZkuQ+wEU0I7beFtgFeGuSrUbLVHdTivX9XdaLVDma/LSkmWz8LlX1/XmK3Yckp1TVzmPPNwSOB95aVV/pr2YamiQ7AU8AzgeOrxl+aPYc+4iqenX7+MHAfarqxKHH73Obt/F72+49x57X46zX/W11kpxeVdO+gze323xeP9PbFmVrA38D3BN4TlXtnORHwMfaxV5VVVNvndX3d1lfTPA6kmTnqjpl3mLPyijBS3JHmhFE9wb+qqq+3nPVJEkahCRn1AAn3tZsrSLBO3W8NZqmxyaa3TkE6CvJ6jP2rPxRkjOB+wJXA3tU1bf6rZIkScNhcifdOpngraEkP6TpNBuaqxI/AJYDDxhy7CXgP0ZXfdpb7n+T5KtV9Zae66UBSfJQYLeJ4h/THGdbts8vqarjhhR7XrnN+zGvx1nPsfceizFyJPAqxkZyrKojOog9r9vczxfNjAnemrtwbNTKk8cenzHw2H37+ehB2yzzaUkOSfLKqjq6x3ppWH4KTE6GejnwNuB17fNOJgPuOfa8cpv3Y16Psz5jX9DGP5R2gI+qWp7kLJrRDV/W1mPqCR7zu83n+fNlHZommnfouyLzwgRvzY13Yly2kvIhxu5VVT17gbIjk9y+j/pomKrqkiSbVtX5AEnuC1zavjb5RT2Y2PPKbd6PeT3Oeo49ivkS4Jyqur4tPyfJ5VV1bpLfdBR7Xrf5PH++/KT9/Qzg/42V/7CHusyFtfquwACMDyE864S5z9hLUlV18oWkuXYMQJJlwJuB9eYk9rw6BtzmPTgG5vI46yV2kjskORm4CjgtyUNmEXfMMW095mabL4HYvamqZ7c/+1TVl2jPX6tqv56rNlgmeGtuZXfRhh5bmhcbJNkO+DBwTFX9fHVvGEjseeU278e8Hmd9xT4AOKxtDbM78NIFlumyNdA8bvO+Yy8lT58sSPKgPioyVCZ4a278Ltq6cxRbmhdbAAcCtwO+0ZZ1MfnvUos9r9zm/ZjX46yv2JvTDNJGVf0WuDaN5wB3TbJPu0xX5nGb9x17yaiqyxYofuvMKzJgJnhr7s/HHn9w7PEsDtg+Y0vz4sKq2hvYHzguyW2AL81B7HnlNu/HvB5nfcX+BHB0knsm2Re4uJ10+2qaQVeuAd7QYfx53OZ9x17qPHedIic670iSx1XVafMWWxqaJEdU1avbxw8G7lNVJw499rxym/djXo+znmPvBDwBOB84vmZ4QjjH29zPl5VIcnpV7dh3PYbCBK8jJniSJEnS6iU5o6oe23c9hsImmlOU5LAk67dPXzkvsSVJkqRbyuRuuhx5cUqSbAj8QVVdPSqah9iSJOnWJclDgd0min8MLAe2bJ9fUlXHzbJeGp4ke7Ninxo5EngVYyO1VtURM6zW4HkHbwqShGY+k/GdcyZtX/uMLUmSbpV+Cpw18fMNYN+x5//eU900LBcA5wI7tr/PrarlNPvYHmO/NUXewVtDSZ4P7AQcV1UXzEtsSZJ061RVlyTZtKrOB0hyX+DS9rWzeq2cBmVsH3sJcE5VXd+Wn5Pk8qo6N8lveq3kAHkHbzrWor9t2WdsSZJ063QMQJJlNC2B1uu1NhqkJHdIcjJwFXBakof0Xad5YGKwhqrqH4AnA7sn2XZeYkuSpFu1DZJsB3wYOKaqft53hTRIBwCHVdWzgd2Bly6wjF2LpswEbwrauWNeARw8T7ElSdKt1hbAgcDtaPrfgYO0afo2B/4LoKp+C1ybxnOAuybZp11GU2SCNyXtTvtfSW7TFs3sQ7LP2JIk6VbpwqraG9gfOK49h/hSz3XS8HwCODrJPZPsC1zc3py4GjgcuAZ4Q4/1GyQnOu9Ikh2r6vR5iy1Jkpa+JEdU1avbxw8G7lNVJ/ZcLQ1Qkp2AJwDnA8eXyUfnTPAkSZIkaSCcJmENrGSi0JuoqtcPKbYkSZKkpckEb838BPj8HMaWJEmSVmklNyR+DCwHtmyfX1JVx82yXkNngrcGqurnSZZV1cXj5UkeC/ysqr4zxNiSJEnSIvwUOGui7HLgbcDr2udXzLRGc8AEb829F9hxomxz4LZA10lWn7ElSZKklaqqS5JsWlXnAyS5L3Bp+9pk4qcpcZqENbfQlATX0CRZQ44tSZIkrc4xAEmWAW8G1uu1NnPABG/NLTQM6fXMZtv2GVuSJElanQ2SbAd8GDimqn7ed4WGziaaa+4uSfaeKHsw8PWBx5YkSZJWZwvgQOB2wDfasoVaoWlKTPDW3FHAOhNl5wNfHHhsSZIkaXUurKq9k9wLOC7Js4Av9V2pIXOic0mSJEmdSHJEVb26ffxg4D5VdWLP1Ro0E7wpSbJ7VZ08b7ElSZIkLR0OxjE9Lx1/kmRZklmNZtlnbEmSJElLhHfw1kCSvYD9aUazXBtYDlwHvAj4QPv46Kr6xJBiS5IkSVqaTPA6kOQdwHHA94BPV9Xj5iG2JEmSpH45iuYaSvLKiaJ/o+k8en77+vVDjC1JkiRp6bEP3po7D7g3cDHN9tyGprnkSJe3SPuMLUmSJGmJMcFbQ1V1FvADmmTrW23xBUm2S7IJHSZZfcaWJEmStPTYRHM6auLnGOAE4LY0g54MNbYkSZKkJcRBVtZQks8AdwCuokmY31hVpw09tiRJkqSlxwRvYJLsXFWn9F0PSZIkSbNngrcGkuwAbLHQa1X1wXaZl1bVMR3F37WqPjtRdnpV7dhFPEmSJElLm4OsrJm1gXXan5fRNJMcPR/ZvcP4j0xyryRfTfLJJLcB0mE8SZIkSUuYg6ysgao6ffQ4yROr6r1J1quqa8YW6zLhCnAg8HLgdsCf4ciZkiRJ0tzyDt4aSvLB9uHpSe4IfHRikU4SriR/BNwduC/NNAnfAH6/fW2f9meXLmJLkiRJWpq8g7fm7tL+/jrwAeCFM4p7Pc2k5uMJZMZeG/8tSZIkaQ6Y4K25bZJ8C9gM2K6qLkyyAfBAmoTrDl0ErapvJrkY+DXwEGBj4HvAY6vqA13ElCRJkrS02URzzX2rqh4APAE4OsnDafrD7QQ8Dji54/jvopncfH/gkx3HkiRJkrSEeQdvzf0OoKrOS/IMmj54+1XV62YQ+5yq+m/goaOCJI6iKUmSJM0p58GbsiQbVdX/9hj/cVV1Wl/xJUmSJPXHBE+SJEmSBsI+eJIkSZI0ECZ4kiRJkjQQJniSJEmSNBAmeJIkSZI0ECZ4kiRJkjQQ/x+gIskSrPSQGQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.title('feature importance')\n",
    "plt.bar(range(len(importance)), importance)\n",
    "plt.xticks(range(len(importance)), X.columns, rotation = 90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>재발여부_x</th>\n",
       "      <th>연령</th>\n",
       "      <th>정상</th>\n",
       "      <th>재발여부_y</th>\n",
       "      <th>재발</th>\n",
       "      <th>total</th>\n",
       "      <th>A_percent</th>\n",
       "      <th>B_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>92.3</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>100.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.0</td>\n",
       "      <td>79</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>71.4</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.0</td>\n",
       "      <td>84</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.0</td>\n",
       "      <td>86</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.0</td>\n",
       "      <td>82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    재발여부_x  연령    정상  재발여부_y   재발  total  A_percent  B_percent\n",
       "0      0.0  15   1.0     1.0  0.0    1.0      100.0        0.0\n",
       "1      0.0  16   5.0     1.0  0.0    5.0      100.0        0.0\n",
       "2      0.0  17  12.0     1.0  1.0   13.0       92.3        7.7\n",
       "3      0.0  18  18.0     1.0  2.0   20.0       90.0       10.0\n",
       "4      0.0  19  29.0     1.0  0.0   29.0      100.0        0.0\n",
       "..     ...  ..   ...     ...  ...    ...        ...        ...\n",
       "63     0.0  79   5.0     1.0  2.0    7.0       71.4       28.6\n",
       "64     0.0  80   1.0     1.0  0.0    1.0      100.0        0.0\n",
       "65     0.0  84   2.0     1.0  0.0    2.0      100.0        0.0\n",
       "66     0.0  86   1.0     1.0  0.0    1.0      100.0        0.0\n",
       "67     0.0  82   0.0     1.0  1.0    1.0        0.0      100.0\n",
       "\n",
       "[68 rows x 8 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0 = data[data['재발여부'] == 0]\n",
    "df_0 = df0.groupby(['재발여부','연령'], as_index = False).agg(정상 = ('재발여부', 'count'))\n",
    "df1 = data[data['재발여부'] == 1]\n",
    "df_1 = df1.groupby(['재발여부','연령'], as_index = False).agg(재발 = ('재발여부', 'count'))\n",
    "df = df_0.merge(df_1, on='연령', how='outer' )\n",
    "df[['재발여부_x','정상','재발']] = df[['재발여부_x','정상','재발']].fillna(0)\n",
    "df['재발여부_y'] = df['재발여부_y'].fillna(1)\n",
    "df['total'] = df['정상']+df['재발']\n",
    "df['A_percent'] = round(df['정상'] / df['total'] * 100, 1)\n",
    "df['B_percent'] = round(100 - df['A_percent'], 1)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "# 피규어 크기\n",
    "fig.set_size_inches(25, 10)\n",
    "# 그래프 뒤에 그리드 그리기\n",
    "ax.set_axisbelow(True)\n",
    "# y 축만 그리드 그리기\n",
    "ax.grid(True, axis='y')\n",
    "# bar 그리기\n",
    "ax.bar(df['연령'], df['정상'], label = '정상', color = '#0599e8')\n",
    "ax.bar(df['연령'], df['재발'], bottom = df['정상'], label = '재발', color = '#fc4e03')\n",
    "# 라벨 설정\n",
    "ax.set_xlabel('year', fontsize = 25)\n",
    "ax.set_ylabel('revenue', fontsize = 25)\n",
    "# tick 설정\n",
    "ax.tick_params(axis = 'x', labelsize = 20)\n",
    "ax.tick_params(axis = 'y', labelsize = 20)\n",
    "# 범례 표시\n",
    "ax.legend(fontsize = 25, loc = 'upper right')\n",
    "\n",
    "# 텍스트 지정\n",
    "for i in range(len(df['연령'])):\n",
    "    ax.text(df['연령'][i], df['정상'][i] / 2, str(df['A_percent'][i]) + '%', fontsize = 10, horizontalalignment='center', color = 'w')\n",
    "    ax.text(df['연령'][i], (df['정상'][i] + df['정상'][i] + df['재발'][i])/ 2, str(df['B_percent'][i]) + '%', fontsize = 10, horizontalalignment='center', color = 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='연령대', ylabel='count'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def Total_1(data):    \n",
    "    if data < 20:        \n",
    "        return '10대'\n",
    "    elif data < 30:\n",
    "        return '20대'\n",
    "    elif data < 40:\n",
    "        return '30대'\n",
    "    elif data < 50:\n",
    "        return '40대'\n",
    "    elif data < 60:\n",
    "        return '50대'\n",
    "    elif data < 70:\n",
    "        return '60대'\n",
    "    elif data < 80:\n",
    "        return '70대'\n",
    "    else:\n",
    "        return '80대'\n",
    "plt.rc('font', family='Malgun Gothic')    # 한글 폰트수정\n",
    "plt.figure(figsize = (16, 5))  # 피규어 사이즈 확장 \n",
    "data['연령대'] = data['연령'].apply(Total_1)\n",
    "sns.countplot(data = data.sort_values(by = '연령대'), x='연령대', order=['10대','20대','30대','40대','50대','60대','70대','80대'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='연령', ylabel='count'>"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25,10))\n",
    "sns.countplot(data = data , x = '연령', hue = '재발여부') # 전체 수술환자 비만정도\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='비만여부', ylabel='count'>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qniEELihYxSVCQUgRM/tBZHIgR2szs/nAK+GTq9MJvsIXd28ys33ArcBfAaf0WHf78XBFNqYjghTVmsjPff1v35ddwOHw51PdvT3yWqe7HzWzDnoHoSPHEeHTAe675CkIKeLuO4//bGa5FjliZv8V/pwlfGo1WNWj/4u3mtl4d98VPsB3jpndAvwp8Ose+7y6YL9ACVMQUsTMro9O9nzd3a8zs5Hufrjnaz08AfwwvJieC/wP0AJURvY1E/jHyDpjw33+PjKvrkfATlp6xCIlwovfsyKzPnD3l83sRuCYu68Jl3vJ3Wfksb1q4NvATndfHc67Aci6+5M5lv8OMCzXa0OBjggp4e4NBd7e68DrhdzmyUxBKD2nRJ7GjPoHdy/Nb+lIAZ0aCQBmNpLg/XCo2LUUg4Iggh6xEAEUBBFAQRABFAQRQEEQARQEEUBBEAHg/wEIL48FDVvfmQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = data , x = '비만여부', hue = '재발여부') # 전체 수술환자 비만정도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='직업', ylabel='count'>"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "sns.countplot(data = data.sort_values(by = '재발여부'), x='직업', hue = '재발여부') # 비만정도에 따른 재발여부"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1894 entries, 0 to 1893\n",
      "Data columns (total 65 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   전방디스크높이(mm)             1894 non-null   float64\n",
      " 1   후방디스크높이(mm)             1894 non-null   float64\n",
      " 2   지방축적도                   1891 non-null   float64\n",
      " 3   Instability             1894 non-null   int64  \n",
      " 4   MF + ES                 1894 non-null   float64\n",
      " 5   Modic change            1894 non-null   int64  \n",
      " 6   PI                      1890 non-null   float64\n",
      " 7   PT                      1890 non-null   float64\n",
      " 8   Seg Angle(raw)          1893 non-null   float64\n",
      " 9   Vaccum disc             1894 non-null   int64  \n",
      " 10  디스크단면적                  1893 non-null   float64\n",
      " 11  디스크위치                   1894 non-null   int64  \n",
      " 12  척추전방위증                  1894 non-null   int64  \n",
      " 13  Large Lymphocyte        1894 non-null   float64\n",
      " 14  Location of herniation  1894 non-null   int64  \n",
      " 15  가족력                     1843 non-null   float64\n",
      " 16  간질성폐질환                  1894 non-null   int64  \n",
      " 17  고혈압여부                   1894 non-null   int64  \n",
      " 18  과거수술횟수                  1894 non-null   int64  \n",
      " 19  당뇨여부                    1894 non-null   int64  \n",
      " 20  말초동맥질환여부                1894 non-null   int64  \n",
      " 21  빈혈여부                    1894 non-null   int64  \n",
      " 22  성별                      1894 non-null   int64  \n",
      " 23  스테로이드치료                 1894 non-null   int64  \n",
      " 24  신부전여부                   1894 non-null   int64  \n",
      " 25  신장                      1894 non-null   int64  \n",
      " 26  심혈관질환                   1894 non-null   int64  \n",
      " 27  암발병여부                   1894 non-null   int64  \n",
      " 28  연령                      1894 non-null   int64  \n",
      " 29  우울증여부                   1894 non-null   int64  \n",
      " 30  입원기간                    1894 non-null   int64  \n",
      " 31  종양진행여부                  1894 non-null   int64  \n",
      " 32  체중                      1894 non-null   float64\n",
      " 33  헤모글로빈수치                 1893 non-null   float64\n",
      " 34  혈전합병증여부                 1894 non-null   int64  \n",
      " 35  환자통증정도                  1894 non-null   int64  \n",
      " 36  흡연여부                    1894 non-null   int64  \n",
      " 37  통증기간(월)                 1890 non-null   float64\n",
      " 38  수술시간                    1840 non-null   float64\n",
      " 39  수술실패여부                  1894 non-null   int64  \n",
      " 40  비만여부                    1894 non-null   int64  \n",
      " 41  척추이동척도_Down             1894 non-null   uint8  \n",
      " 42  척추이동척도_Extremely down   1894 non-null   uint8  \n",
      " 43  척추이동척도_Extremely up     1894 non-null   uint8  \n",
      " 44  척추이동척도_Middle           1894 non-null   uint8  \n",
      " 45  척추이동척도_Up               1894 non-null   uint8  \n",
      " 46  직업_건설업                  1894 non-null   uint8  \n",
      " 47  직업_공무원                  1894 non-null   uint8  \n",
      " 48  직업_교사                   1894 non-null   uint8  \n",
      " 49  직업_군인                   1894 non-null   uint8  \n",
      " 50  직업_노동직                  1894 non-null   uint8  \n",
      " 51  직업_농업                   1894 non-null   uint8  \n",
      " 52  직업_무직                   1894 non-null   uint8  \n",
      " 53  직업_사무직                  1894 non-null   uint8  \n",
      " 54  직업_사업가                  1894 non-null   uint8  \n",
      " 55  직업_예술가                  1894 non-null   uint8  \n",
      " 56  직업_운동선수                 1894 non-null   uint8  \n",
      " 57  직업_운수업                  1894 non-null   uint8  \n",
      " 58  직업_의료직                  1894 non-null   uint8  \n",
      " 59  직업_자영업                  1894 non-null   uint8  \n",
      " 60  직업_주부                   1894 non-null   uint8  \n",
      " 61  직업_특수전문직                1894 non-null   uint8  \n",
      " 62  직업_학생                   1894 non-null   uint8  \n",
      " 63  수술기법_IELD               1894 non-null   uint8  \n",
      " 64  수술기법_TELD               1894 non-null   uint8  \n",
      "dtypes: float64(14), int64(27), uint8(24)\n",
      "memory usage: 730.4 KB\n"
     ]
    }
   ],
   "source": [
    "data_11 = data.drop(['혈액형','환자ID','수술일자','입원일자','퇴원일자','ODI','골밀도','연령대','지방축적도_1','재발여부'], axis = 1)\n",
    "data_11 = pd.get_dummies(data_11)\n",
    "data_11.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.value_counts of       전방디스크높이(mm)  후방디스크높이(mm)  지방축적도  Instability  MF + ES  Modic change  \\\n",
       "0            16.1         12.3  282.3            0   1824.6             3   \n",
       "1            13.7          6.4  177.3            0   1737.5             0   \n",
       "2            13.6          7.4  256.8            0   1188.5             0   \n",
       "3            10.6          7.3  250.1            0   2534.5             0   \n",
       "4            17.1          8.1  232.2            0   1840.6             0   \n",
       "...           ...          ...    ...          ...      ...           ...   \n",
       "1889         17.0         10.7  237.5            0   2795.7             2   \n",
       "1890          9.4          8.2  288.0            0   1473.0             0   \n",
       "1891         13.5          5.5  148.5            0   3864.1             0   \n",
       "1892         14.0         10.0   89.0            0   2481.8             2   \n",
       "1893         16.1          9.5  251.4            0   1796.1             0   \n",
       "\n",
       "        PI    PT  Seg Angle(raw)  Vaccum disc  ...  직업_예술가  직업_운동선수  직업_운수업  \\\n",
       "0     51.6  36.6            14.4            0  ...       0        0       0   \n",
       "1     40.8   7.2            17.8            0  ...       0        1       0   \n",
       "2     67.5  27.3            10.2            0  ...       0        0       0   \n",
       "3     49.2  18.7            19.9            0  ...       0        0       0   \n",
       "4     58.8  14.7             5.2            0  ...       0        0       0   \n",
       "...    ...   ...             ...          ...  ...     ...      ...     ...   \n",
       "1889  59.5  23.0            21.8            0  ...       0        0       0   \n",
       "1890  47.7  20.2             5.0            0  ...       0        0       0   \n",
       "1891  44.6  15.0            17.4            1  ...       0        0       0   \n",
       "1892  32.2  11.1            17.7            0  ...       0        0       0   \n",
       "1893  38.9   6.8            27.8            0  ...       0        0       0   \n",
       "\n",
       "      직업_의료직  직업_자영업  직업_주부  직업_특수전문직  직업_학생  수술기법_IELD  수술기법_TELD  \n",
       "0          0       1      0         0      0          0          1  \n",
       "1          0       0      0         0      0          0          1  \n",
       "2          0       0      0         1      0          0          1  \n",
       "3          0       0      1         0      0          0          1  \n",
       "4          0       0      0         1      0          0          1  \n",
       "...      ...     ...    ...       ...    ...        ...        ...  \n",
       "1889       0       0      0         0      0          0          0  \n",
       "1890       0       0      0         0      0          0          0  \n",
       "1891       0       0      0         0      0          1          0  \n",
       "1892       0       0      0         0      0          0          0  \n",
       "1893       0       0      0         0      0          0          0  \n",
       "\n",
       "[1894 rows x 65 columns]>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_11.value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_12 = data_11.fillna(data_11.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>전방디스크높이(mm)</th>\n",
       "      <th>후방디스크높이(mm)</th>\n",
       "      <th>지방축적도</th>\n",
       "      <th>Instability</th>\n",
       "      <th>MF + ES</th>\n",
       "      <th>Modic change</th>\n",
       "      <th>PI</th>\n",
       "      <th>PT</th>\n",
       "      <th>Seg Angle(raw)</th>\n",
       "      <th>Vaccum disc</th>\n",
       "      <th>...</th>\n",
       "      <th>직업_예술가</th>\n",
       "      <th>직업_운동선수</th>\n",
       "      <th>직업_운수업</th>\n",
       "      <th>직업_의료직</th>\n",
       "      <th>직업_자영업</th>\n",
       "      <th>직업_주부</th>\n",
       "      <th>직업_특수전문직</th>\n",
       "      <th>직업_학생</th>\n",
       "      <th>수술기법_IELD</th>\n",
       "      <th>수술기법_TELD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.1</td>\n",
       "      <td>12.3</td>\n",
       "      <td>282.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1824.6</td>\n",
       "      <td>3</td>\n",
       "      <td>51.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>177.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1737.5</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>256.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1188.5</td>\n",
       "      <td>0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>250.1</td>\n",
       "      <td>0</td>\n",
       "      <td>2534.5</td>\n",
       "      <td>0</td>\n",
       "      <td>49.2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>19.9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>232.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1840.6</td>\n",
       "      <td>0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>14.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1889</th>\n",
       "      <td>17.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>237.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2795.7</td>\n",
       "      <td>2</td>\n",
       "      <td>59.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>9.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>288.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1473.0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.7</td>\n",
       "      <td>20.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>13.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>148.5</td>\n",
       "      <td>0</td>\n",
       "      <td>3864.1</td>\n",
       "      <td>0</td>\n",
       "      <td>44.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2481.8</td>\n",
       "      <td>2</td>\n",
       "      <td>32.2</td>\n",
       "      <td>11.1</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>16.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>251.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1796.1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1894 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      전방디스크높이(mm)  후방디스크높이(mm)  지방축적도  Instability  MF + ES  Modic change  \\\n",
       "0            16.1         12.3  282.3            0   1824.6             3   \n",
       "1            13.7          6.4  177.3            0   1737.5             0   \n",
       "2            13.6          7.4  256.8            0   1188.5             0   \n",
       "3            10.6          7.3  250.1            0   2534.5             0   \n",
       "4            17.1          8.1  232.2            0   1840.6             0   \n",
       "...           ...          ...    ...          ...      ...           ...   \n",
       "1889         17.0         10.7  237.5            0   2795.7             2   \n",
       "1890          9.4          8.2  288.0            0   1473.0             0   \n",
       "1891         13.5          5.5  148.5            0   3864.1             0   \n",
       "1892         14.0         10.0   89.0            0   2481.8             2   \n",
       "1893         16.1          9.5  251.4            0   1796.1             0   \n",
       "\n",
       "        PI    PT  Seg Angle(raw)  Vaccum disc  ...  직업_예술가  직업_운동선수  직업_운수업  \\\n",
       "0     51.6  36.6            14.4            0  ...       0        0       0   \n",
       "1     40.8   7.2            17.8            0  ...       0        1       0   \n",
       "2     67.5  27.3            10.2            0  ...       0        0       0   \n",
       "3     49.2  18.7            19.9            0  ...       0        0       0   \n",
       "4     58.8  14.7             5.2            0  ...       0        0       0   \n",
       "...    ...   ...             ...          ...  ...     ...      ...     ...   \n",
       "1889  59.5  23.0            21.8            0  ...       0        0       0   \n",
       "1890  47.7  20.2             5.0            0  ...       0        0       0   \n",
       "1891  44.6  15.0            17.4            1  ...       0        0       0   \n",
       "1892  32.2  11.1            17.7            0  ...       0        0       0   \n",
       "1893  38.9   6.8            27.8            0  ...       0        0       0   \n",
       "\n",
       "      직업_의료직  직업_자영업  직업_주부  직업_특수전문직  직업_학생  수술기법_IELD  수술기법_TELD  \n",
       "0          0       1      0         0      0          0          1  \n",
       "1          0       0      0         0      0          0          1  \n",
       "2          0       0      0         1      0          0          1  \n",
       "3          0       0      1         0      0          0          1  \n",
       "4          0       0      0         1      0          0          1  \n",
       "...      ...     ...    ...       ...    ...        ...        ...  \n",
       "1889       0       0      0         0      0          0          0  \n",
       "1890       0       0      0         0      0          0          0  \n",
       "1891       0       0      0         0      0          1          0  \n",
       "1892       0       0      0         0      0          0          0  \n",
       "1893       0       0      0         0      0          0          0  \n",
       "\n",
       "[1894 rows x 65 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1894 entries, 0 to 1893\n",
      "Data columns (total 65 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   전방디스크높이(mm)             1894 non-null   float64\n",
      " 1   후방디스크높이(mm)             1894 non-null   float64\n",
      " 2   지방축적도                   1894 non-null   float64\n",
      " 3   Instability             1894 non-null   int64  \n",
      " 4   MF + ES                 1894 non-null   float64\n",
      " 5   Modic change            1894 non-null   int64  \n",
      " 6   PI                      1894 non-null   float64\n",
      " 7   PT                      1894 non-null   float64\n",
      " 8   Seg Angle(raw)          1894 non-null   float64\n",
      " 9   Vaccum disc             1894 non-null   int64  \n",
      " 10  디스크단면적                  1894 non-null   float64\n",
      " 11  디스크위치                   1894 non-null   int64  \n",
      " 12  척추전방위증                  1894 non-null   int64  \n",
      " 13  Large Lymphocyte        1894 non-null   float64\n",
      " 14  Location of herniation  1894 non-null   int64  \n",
      " 15  가족력                     1894 non-null   float64\n",
      " 16  간질성폐질환                  1894 non-null   int64  \n",
      " 17  고혈압여부                   1894 non-null   int64  \n",
      " 18  과거수술횟수                  1894 non-null   int64  \n",
      " 19  당뇨여부                    1894 non-null   int64  \n",
      " 20  말초동맥질환여부                1894 non-null   int64  \n",
      " 21  빈혈여부                    1894 non-null   int64  \n",
      " 22  성별                      1894 non-null   int64  \n",
      " 23  스테로이드치료                 1894 non-null   int64  \n",
      " 24  신부전여부                   1894 non-null   int64  \n",
      " 25  신장                      1894 non-null   int64  \n",
      " 26  심혈관질환                   1894 non-null   int64  \n",
      " 27  암발병여부                   1894 non-null   int64  \n",
      " 28  연령                      1894 non-null   int64  \n",
      " 29  우울증여부                   1894 non-null   int64  \n",
      " 30  입원기간                    1894 non-null   int64  \n",
      " 31  종양진행여부                  1894 non-null   int64  \n",
      " 32  체중                      1894 non-null   float64\n",
      " 33  헤모글로빈수치                 1894 non-null   float64\n",
      " 34  혈전합병증여부                 1894 non-null   int64  \n",
      " 35  환자통증정도                  1894 non-null   int64  \n",
      " 36  흡연여부                    1894 non-null   int64  \n",
      " 37  통증기간(월)                 1894 non-null   float64\n",
      " 38  수술시간                    1894 non-null   float64\n",
      " 39  수술실패여부                  1894 non-null   int64  \n",
      " 40  비만여부                    1894 non-null   int64  \n",
      " 41  척추이동척도_Down             1894 non-null   uint8  \n",
      " 42  척추이동척도_Extremely down   1894 non-null   uint8  \n",
      " 43  척추이동척도_Extremely up     1894 non-null   uint8  \n",
      " 44  척추이동척도_Middle           1894 non-null   uint8  \n",
      " 45  척추이동척도_Up               1894 non-null   uint8  \n",
      " 46  직업_건설업                  1894 non-null   uint8  \n",
      " 47  직업_공무원                  1894 non-null   uint8  \n",
      " 48  직업_교사                   1894 non-null   uint8  \n",
      " 49  직업_군인                   1894 non-null   uint8  \n",
      " 50  직업_노동직                  1894 non-null   uint8  \n",
      " 51  직업_농업                   1894 non-null   uint8  \n",
      " 52  직업_무직                   1894 non-null   uint8  \n",
      " 53  직업_사무직                  1894 non-null   uint8  \n",
      " 54  직업_사업가                  1894 non-null   uint8  \n",
      " 55  직업_예술가                  1894 non-null   uint8  \n",
      " 56  직업_운동선수                 1894 non-null   uint8  \n",
      " 57  직업_운수업                  1894 non-null   uint8  \n",
      " 58  직업_의료직                  1894 non-null   uint8  \n",
      " 59  직업_자영업                  1894 non-null   uint8  \n",
      " 60  직업_주부                   1894 non-null   uint8  \n",
      " 61  직업_특수전문직                1894 non-null   uint8  \n",
      " 62  직업_학생                   1894 non-null   uint8  \n",
      " 63  수술기법_IELD               1894 non-null   uint8  \n",
      " 64  수술기법_TELD               1894 non-null   uint8  \n",
      "dtypes: float64(14), int64(27), uint8(24)\n",
      "memory usage: 730.4 KB\n"
     ]
    }
   ],
   "source": [
    "data_12.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler\n",
    "X = data_12\n",
    "Y = data['재발여부'].ravel()\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "    X,Y, test_size = 0.3, random_state = 0)\n",
    "\n",
    "model = DecisionTreeClassifier(max_depth = 1 )\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# depth에 따른 정확도\n",
    "train_score=[]\n",
    "test_score=[]\n",
    "for i in range(1,21):\n",
    "    \n",
    "    model=DecisionTreeClassifier(max_depth=i)\n",
    "    model.fit(X_train,Y_train)\n",
    "    train_score.append(model.score(X_train,Y_train))\n",
    "    test_score.append(model.score(X_test,Y_test))\n",
    "plt.figure()\n",
    "plt.title('score for depths')\n",
    "plt.plot(range(1,21),train_score)\n",
    "plt.plot(range(1,21),test_score)\n",
    "plt.xticks(range(1,21))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 정확도 : 0.9343396226415094\n",
      "테스트 정확도 : 0.8840070298769771\n"
     ]
    }
   ],
   "source": [
    "model_1=DecisionTreeClassifier(max_depth=5)\n",
    "model_1.fit(X_train, Y_train)\n",
    "train_accuracy = model_1.score(X_train, Y_train)\n",
    "test_accuracy = model_1.score(X_test, Y_test)\n",
    "print(f'훈련 정확도 : {train_accuracy}')\n",
    "print(f'테스트 정확도 : {test_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01505388, 0.06803619, 0.02804252, 0.        , 0.        ,\n",
       "       0.        , 0.06264636, 0.        , 0.01144593, 0.        ,\n",
       "       0.06400252, 0.        , 0.        , 0.        , 0.02336877,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.09616234,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.03840874, 0.        ,\n",
       "       0.01511691, 0.        , 0.2171416 , 0.01471619, 0.        ,\n",
       "       0.        , 0.3303863 , 0.        , 0.        , 0.        ,\n",
       "       0.01547174, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance = model_1.feature_importances_\n",
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.title('feature importance')\n",
    "plt.bar(range(len(importance)), importance)\n",
    "plt.xticks(range(len(importance)), X.columns, rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "흡연여부,체중,당뇨여부"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(max_depth=5)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler\n",
    "model = GradientBoostingClassifier(max_depth = 5 )\n",
    "model.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# depth에 따른 정확도\n",
    "train_score=[]\n",
    "test_score=[]\n",
    "for i in range(1,21):\n",
    "    \n",
    "    model=GradientBoostingClassifier(max_depth=i)\n",
    "    model.fit(X_train,Y_train)\n",
    "    train_score.append(model.score(X_train,Y_train))\n",
    "    test_score.append(model.score(X_test,Y_test))\n",
    "plt.figure()\n",
    "plt.title('score for depths')\n",
    "plt.plot(range(1,21),train_score)\n",
    "plt.plot(range(1,21),test_score)\n",
    "plt.xticks(range(1,21))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 정확도 : 1.0\n",
      "테스트 정확도 : 0.9103690685413005\n"
     ]
    }
   ],
   "source": [
    "model_1=GradientBoostingClassifier(max_depth=5)\n",
    "model_1.fit(X_train, Y_train)\n",
    "train_accuracy = model_1.score(X_train, Y_train)\n",
    "test_accuracy = model_1.score(X_test, Y_test)\n",
    "print(f'훈련 정확도 : {train_accuracy}')\n",
    "print(f'테스트 정확도 : {test_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.041, 0.049, 0.040, 0.002, 0.041, 0.003, 0.045, 0.035, 0.033,\n",
       "       0.002, 0.125, 0.004, 0.000, 0.054, 0.017, 0.001, 0.005, 0.002,\n",
       "       0.004, 0.048, 0.001, 0.001, 0.000, 0.002, 0.000, 0.009, 0.000,\n",
       "       0.000, 0.027, 0.001, 0.009, 0.000, 0.132, 0.020, 0.000, 0.014,\n",
       "       0.143, 0.012, 0.016, 0.000, 0.005, 0.002, 0.000, 0.000, 0.000,\n",
       "       0.002, 0.001, 0.001, 0.000, 0.001, 0.000, 0.000, 0.001, 0.004,\n",
       "       0.000, 0.000, 0.002, 0.000, 0.003, 0.001, 0.007, 0.000, 0.000,\n",
       "       0.016, 0.013])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance = model_1.feature_importances_\n",
    "np.set_printoptions(formatter={'float_kind': lambda x: \"{0:0.3f}\".format(x)}) # 소수3째자리까지 표기\n",
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.title('feature importance')\n",
    "plt.bar(range(len(importance)), importance)\n",
    "plt.xticks(range(len(importance)), X.columns, rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "흡연여부,체중,디스크면적,당뇨여부"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=5)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model = RandomForestClassifier(max_depth = 5 )\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ygEnunu3u24GvgLZ7r9TdX3D3DHfPSEvTDXyl/Fi2fhtXvzqZ9DrV+ccVGdRM0Y3IJLFEE+zjgQsAzKwTsLxwgbtvdveB7n4yUBcYDvwAdDaz2mZWGcgI54mUeznbd3HlK5PZmVfAywN70bB2tXiXJPIz0QT7GCDFzMYDjwG3m9lQM0sxsxPCI1++Ada5+7iwl/4A8BkwDnjV3deU2isQKSM78wr43b+nsCR7K89fnkHb9NrxLklkn4r8DBkOu1y71+zbw38/B47eR5vRBEfJiCQFd+ePo2cwYUE2jw/oRp82DeNdksh+6SwKkSj89cuFvJW5nBtPPJzzj2wW73JEDkjBLlKE975f+dPld2856fB4lyNSJAW7yAFkLlnPbW9/T+9WDRh6QVfd0k7KBQW7yH4sWbeVa17LpGlqDZ6/XJcKkPJDwS6yDxu27uS3r0wG4J8De1G/VkqcKxKJns6sENlLbl4+g/81hRUbtvP6Nb+gVaNa8S5JJCYKdpEI7s4do2bw7eL1PHNJDzJaNSi6kUiC0VCMSISnPp3Pu1NX8PtT2/Orbk3iXY5IsSjYRUKjpizn6c/mM+DIZvzu+DbxLkek2BTsIsA3C7O5453pHN2mIQ+e10WHNUq5pmCXCm/B2i0MHp5Jy4a1+Nuvj9Rt7aTc0x4sFVr2llyufGUyKVUq8c+BvahXo2q8SxI5aDoqRiqsHbvyuea1TNZs2sGbg/vQvEHNeJckUiIU7FIhFRQ4//fW90xdtpG/XtqT7s1T412SSInRUIxUSMM+nseYGau48/QOnN7lZ3duFCnXFOxS4Yz4dil/+3Ihl/2iBdf0PSze5YiUOAW7VCjj52fxx9Ez6dcujXt/1VmHNUpSUrBLhTFv9WZ+96/vODy9Ns9e2oMqlbX7S3LSni0VwtpNO7jylcnUSKnMywN7Uae6DmuU5KWjYiTpbduZx9WvZbJh207eGtyHJqk14l2SSKlSj12SWn6Bc9Mb05i5Ioe/XNKDI5rWi3dJIqVOwS5J7aH/zuGT2Wv401mdOLFj43iXI1ImFOyStF77ZgkvfbWY3x7TioHHtI53OSJlJqpgN7P7zWysmU0ws84R81PNbGS47AMzq79XuyfN7JGSLlqkKJ/PXcOf35vFSR0b88czO8W7HJEyVWSwm1lfoLG79wMGA8MiFt8BvB4uGw3cEtGuBXBSiVYrEoWZK3K4/vWpdGpSl2cu6U7lSjpWXSqWaHrspwAjANx9JhB5r7AuwBfh9PtAr4hlDwFDS6BGkaitytnOVa9OJrVGVV76TS9qpujAL6l4ogn2dCAr4nGemRW2mw70D6dPJDx80syuAqYAy/e3UjMbZGaZZpaZlZW1v6eJRG1Lbh5XvpLJ1tx8Xv5tLxrXrR7vkkTiIppgzwEix84L3L0gnH4I6GtmnwCtgSVm1g44D3j6QCt19xfcPcPdM9LS0opRushuefkFXP/6d/ywZjPPXdaTDofUjXdJInETzefU8cAFwHgz60REL9zdNwMDAcxsKDAcuJTgDeN1gt7+IWY2yd3fLdnSRQLuzp/fn8WX87J46Lwu9GunjoJUbNEE+xjgDDMbD2wGBochfjdwLPAAYMA77j4OGFfY0MyOB05TqEtpeumrxfxr4lIG9zuMS3/RIt7liMSduXu8ayAjI8MzMzPjXYaUQx/OXM21/57C6UccwrOX9KSSjoCRCsTMprh7xt7zdciAlEvuzkez1nDzm1Pp1iyVJy7srlAXCSnYpdyZv2Yz930wm/Hz19HhkDr84zcZVK9aOd5liSQMBbuUGznbdvHkpz8wfOKP1EqpzD1nd+LXR7Wkqq6rLrIHBbskvPwCZ8S3S3n843nkbN/FJb1b8H+ntKdBrZR4lyaSkBTsktAmLsrmz+/NYu7qzfRu3YB7zu5E5ya69K7IgSjYJSEt37CNh/87lzEzVtE0tQbPXdqTM7oconuUikRBwS4JZfvOfP42diHPj12IGdxyUjsGHXcYNVL05ahItBTskhDcnQ+mr+Lh/85hZc4Ozup6KHee0ZGmuo2dSMwU7BJ3M1fkcN/7s/l2yXo6HVqXpy7uQe/WDYpuKCL7pGCXuNm4bSdDP5zHG5OXUr9mCg/378KFGc11/XSRg6Rgl7iYs2oTg4ZnsmrjDn57dGtuOulw6tWoGu+yRJKCgl3K3Pvfr+QPI6dTt0YV3hrSh54t6hfdSESipmCXMpOXX8Cwj+bx/LhFZLSsz19/3ZP0OroZhkhJU7BLmdiwdSc3jJjKVwvWcflRLbn7rE6kVNGlAERKg4JdSt3slcF4+tpNuTx6flcu7NU83iWJJDUFu5Sq/0xbwe2jppNaI4W3hvShe/PUeJckkvQU7FIq8vILGPrhXF4cv5jerRrw3GU9SatTLd5liVQICnYpceu37uSGEd8xYUE2v+nTkrvO1Hi6SFlSsEuJmrUyh0GvTSFrSy7DLujKgAyNp4uUNQW7lJjC8fT6NVN4e3Afumk8XSQuFOxy0PLyC3j4f3N56avF9G7dgOcu1Xi6SDwp2OWgZG/J5frXp/LNomwGHt2Ku87sqFvVicSZgl2Kbf6azQz852SytuTy2IBuXHBks3iXJCJAVF0rM7vfzMaa2QQz6xwxP9XMRobLPjCz+uH8oWb2pZllmtlppVW8xM+2nXkM+dcUcvMKGDmkj0JdJIEUGexm1hdo7O79gMHAsIjFdwCvh8tGA7eE89929+OB04EHSrJgSQz3vT+bReu28vTF3enaLDXe5YhIhGh67KcAIwDcfSYQeQeELsAX4fT7QK/weZnhvE3AxpIoVBLHmOmreGPyMob0a8MxbRvFuxwR2Us0wZ4OZEU8zjOzwnbTgf7h9IlEjNmbWTXgGeChfa3UzAaFQzWZWVlZ+3qKJKDlG7ZxxzvT6dY8lVtPbhfvckRkH6IJ9hwg8oLZBe5eEE4/BPQ1s0+A1sASADNrB7wEPOfun+9rpe7+grtnuHtGWlpaceuXMpSXX8BNb0zDHZ65uLuOfhFJUNH8ZY4HLgAws07A8sIF7r7Z3Qe6+8lAXWC4mdUAngAGufv0UqhZ4uSZzxcw5ccNPHjeEbRsWCve5YjIfkQT7GOAFDMbDzwG3B4e9ZJiZieY2ddm9g2wzt3HEYy79wT+Gx4Z86WZ6c7E5dykRdk8+/l8+vdsyjndm8a7HBE5AHP3eNdARkaGZ2ZmFv1EiYuN23Zy+tPjqValEh/c2Jfa1XT6g0giMLMp7p6x93z9hcoBuTu3j5rOui25vHPtMQp1kXJA337JAf170lI+mrWG35/ani7N6sW7HBGJgoJd9uuHNZu5/4PZ9D28EVcfe1i8yxGRKCnYZZ927MrnhtenUqd6FR6/sBuVKlm8SxKRKGnAVPbpwTFzmLdmM6/8thfpdarHuxwRiYF67PIzH89azfCJP3L1sa05vn16vMsRkRgp2GUPq3K284dR0+ncpC6/P619vMsRkWJQsMtP8gucW96cxs68Av5ySQ+qVakc75JEpBg0xi4/+duXC5i4aD3DLujKYWm1412OiBSTeuwCwJQfN/Dkp/M5u1sT3TRDpJxTsAs523dx44ipHFqvOg+edwRmOrRRpDzTUEwF5+7c9e4MVm/awdtD+lC3etV4lyQiB0k99gru7SnL+WD6Km49uR09W9QvuoGIJDwFewU2f81m7vnPLPoc1pAh/drEuxwRKSEK9gpqyo/rufD5b6iZUpknL+pOZV0yQCRpKNgroP/OWMUlL06iXo2qjLz2aA6pp0sGiCQTfXlagbg7/xi/mIf+N4cezVN58YoMGtauFu+yRKSEKdgriPwC5973Z/HaNz9y+hGH8ORF3aleVWeWiiQjBXsFsG1nHjeOmMqnc9ZyTd/W3Hl6R12GVySJKdiT3NrNO7j61UxmrsjhvnM6c0WfVvEuSURKmYI9iS1Yu5mB/5xM9padvHB5Bid1ahzvkkSkDCjYk9TERdkMei2TlCqVeXPwUXRtlhrvkkSkjCjYk9DoqSv4/cjvadmwFv8c2IvmDWrGuyQRKUNRHcduZveb2Vgzm2BmnSPmp5rZyHDZB2ZWP5x/rpmNN7NJZnZRaRUve3J3nvtiATe/OY2eLeozasjRCnWRCqjIYDezvkBjd+8HDAaGRSy+A3g9XDYauMXMagG3AScBJwB3mJnOgCllu/ILuPOdGQz7aB7ndG/Ca1f1pl5NXdBLpCKKpsd+CjACwN1nAg0ilnUBvgin3wd6AUcBn7l7rrtvBSYBHfZeqZkNMrNMM8vMyso6iJcgW3LzuOrVTN6YvIzrf9mWpy7qrrsfiVRg0QR7OhCZvHlmVthuOtA/nD6RYMx+7+dnAz+7bKC7v+DuGe6ekZaWFnPhElids4MBf/+GCQvW8Uj/Ltx2antdT12kgosm2HPYM5gL3L0gnH4I6GtmnwCtgSX7eH599gx6KSGzV27ivL9OYGn2Vl4e2IuLe7eId0kikgCiCfbxwAUAZtYJWF64wN03u/tAdz8ZqAsMB74FTjOzqmZWEzgCmFvilVdw732/kv5/m4A7vD3kaPq106ceEQlEc7jjGOAMMxsPbAYGm9lQ4G7gWOABwIB33H0cgJm9AnwFbAfucfe8Uqi9QsrLL+DRj+bxwrhF9GpVn+cu60l6HX03LSK7mbvHuwYyMjI8MzMz3mUkvA1bd3LDiKl8tWAdlx/VkrvP6kRKFV15WaSiMrMp7p6x93ydoFROzFqZw+DhU1i7KZdHz+/Khb2ax7skEUlQCvZy4D/TVnD7qOmk1kjhrSF96N48Nd4liUgCU7AnsLz8Ah7531z+8dVierdqwHOX9SStjm6MISIHpmBPUNlbcrlhxFS+XpjNwKNbcdeZHalaWePpIlI0BXsCmrkiGE/P2pLLsAu6MiBD4+mSZPJyYd182LoWWh4LVVLiXVFSUbAnmHenLueOUTNoUCuFkUP66HK7Ur7l74LshbB2NmTNhbVzgn+zF4LnB89JbQHH/QG6XQyVdX2jkqBgTxB5+QU89N+5vDxhMb9oHYynN9KNpqW8yM+DDYt3B/fa2bB2LmQvgIJdwXOsEtRvDekdodM5kNYhCPKvnoT3rofxj0O/26HLAKisaDoY+u0lgOwtuVz3+ndMXLRe4+kSnR2bYN5/YXkm1G8ZhGVaR6jbBErzWkEFBbBxSRDaP/XC58K6HyA/d/fzUltCeidof1pQV3pHaHQ4VK3x83V2/BX88CF88RCMHgLjH4N+d8AR/aFSgl3MrqAAcpbt/vSx8Ueo1zx4rekdoF4LqBT/v12doBRnM5bnMHh4Jtlbd/LQeV04/8hm8S5JElXuZvjhI5j5Diz4NAjSKjUgb/vu51SrFwRMWocgTAsDv3Z6bIHvHgTY2jl79sKzfthze3WbhdvpsDvA09pDSq3YX587zB0DXz4Ma2ZCo/Zw/O3Q6byyD0t32LQyfO1zgjevwn93bd39vOr1YEfO7sdVawWvP73jnv8HdZuWyhvu/k5QUrDHya78AoZ/8yNDP5xLw1opPH95Bl2a1Yt3WZJodm4NwnzWuzD/Y8jbAXUOhU7nQufzoFkv2L4hDJ3IEJ4D29fvXk+N+ruDNzJ0ajYMAqwwtAqDLGse7Nyyu32dQ8M2nXaHeFp7qF635F9zQQHMeS8I+Ky5wTaPvwM6nF3yAe8OW9b8fAgpay7kbtr9vFrpwetO77T7d5fWAWqkwvaNwe9r7/+DLWt2t69WNyLwI94I6xxyUIGvYE8g4+dncd/7s5m/dgv92qXxxIXdaKjxdCm0azvM/wRmvROE+q5tQbB0OicYnmh+VNEB5w5b1u7V2wyncyN6mFWqB28WhWql/Tx80jsEbwxlrSA/eEP78hHIng+Nu4QBf2bxwnDruojgjfi9bN+w+zk1GkS8eRW+kXWEmg32v9792bZ+z20VTm/L3v2c6qkw4J/Q5oTY14+CPSH8mL2V+z+Yw6dz1tCyYU3+eGYnTuqYruunS3D434LPgjCf97+gt1yzYRDmnc+DlseUzHizO2xetTvgNq2EBq13h3mthge/jZJWkA8zRwUBv34hHNoNjv9/0O7UfQf8tvV7HoFT+Fq3rdv9nOr19nrzCn9qpZXudxQAW7L27N0ffQM0bFOsVSnY42hLbh7PfbGAl8Yvpmpl4/oTDufKY1vpLkfl3bb1wZdnB/M3tGUNzP5PMLacuynoGXc8Gzr3h1Z9dXRIpPw8mPEWjB0KG5ZAk55w9PWQu2XPYZQtq3e3SamzewjkpyGoTgc9BJIoFOxxUFDgvDt1BUM/nMvazbn079mU20/rQOO6usxuubJ94757gFvXlsz6q9WDjmcFYX5YPx3LXZT8XfD9GzDuUdi4NJhXtWYQ4IW98MKx8HrNkiLA90dXdyxj05Zt5M/vzWLaso10a57K85cfSY8WcRinlOjlbgm+BPvpML6wB7h55e7nVK0ZBMbhJwf/NjgMKh3En1FKTWj+C6ii71iiVrkq9Lwcul4EyycHh3imtkyIwwwThYK9hK3dtIOhH85j1HfLSatTjccHdOO8Hk2pVCl5ew3lUkE+LPkKFn62+0vFnKW7l1epDo3aQeu+e36ZmCDHKQvBZQhaHRPvKhKSgr2E5Obl8/JXS3j28/nsyneG9GvD9Se0pXY1/YoTRkE+LJ0YfEE5+z+wNQsqp0DDw6F5bzjyit1fpNVvlXgnx4hESalzkNydz+as5YExs1mSvY2TOjbmj2d2pFWjYpygISWvoACWfxscNjdrdPDFWpUa0O6UYEz78FOC4RCRJFKug/3rheuYt3pzXGv4Yl4W437Iom16bV69srduKp0I3GHFlOAMzdmjYdMKqFwtGBfvfB60Ow2q1Y53lSKlplwH+/9mrGb4xB/jWkOd6lX401mduLxPS13fJZ7cYeXU3T3znKXBMEubE+GkPwdhXhpnSYokoHJ9uOO2nXnszCsohYqiVyOlso5Hjxd3WD0jGDOf9W5wbHOlKsFZfJ3Pg/ZnBKd8iySppDzcsWZKFWrq+vwVi3twFEthmGcvAKscHP/d97bgdPPinP4tkkSiCnYzux84Lnz+IHefFc5PAZ4HWgI7gEvcPcfMfgncC1QDnnX34aVRvFQgWfOCIJ/5DqybF1zbu9Wx0Oe64LKvtRrFu0KRhFFksJtZX6Cxu/czsyOAYcAZ4eLTgBXu/lszuxq4GnicINTPBrYBmWb2L0+EMR8pX7IXBkE+611YOwuw4Jopva8JrqFSOz3eFYokpGh67KcAIwDcfaaZRX7O3QwUnk7ZCCg8RW8bUA+oBGxRqEvU1i8OvwB9F1ZPD+Y1PwpOfzTomdc9NL71iZQD0QR7OpAV8TjPzCq5ewHwFXC3mc0G8oGjw+c8AWQCu4A/7WulZjYIGATQokWL4lUvyWHj0uBIllnvBEe2QHCd8VMfCq47Xq9pPKsTKXeiCfYcdvfKAQrCUAd4CHjM3f9rZt2BF8zsJuAmgnH3XcCrZjbZ3adHrtTdXwBegOComIN7GVIuzf8Uxj4SXO8DoEkPOPl+6HxucINjESmWaIJ9PHABMN7MOgHLI5a1BAqvkbkWaE4wJJPn7tsBzGwD0AzYI9ilAtuxCT6+C757DRq0gRPvCQ5PbNA63pWJJIVogn0McIaZjScYUx9sZkOBu8Ofv5pZJaAq8Ht3n21mk83sa8CBacCHpVK9lD+LxsJ/rgvOBj32Fjj+Tl3ZUKSElesTlKQc2bkVPrkHJr8IDdvCuX+H5r3iXZVIuZaUJyhJObF0Irw7JDgz9KjfwQl368JbIqVIwS6lZ9d2+PwB+Oa54MvQgWN0/WyRMqBgl9KxfAqMHgLrfoCMq+Dk+3RFRZEyomCXkpWXG9xs+KsnoU4TuPzd4KJcIlJmFOxSclZND8bS186C7r+G0x6C6vXiXZVIhaNgl4OXvyvooY8dCjUbwiVvQvvT4l2VSIWlYJeDs3ZO0EtfNQ26DAiu6aLL5orEVfkO9rHDYObIeFdRsa1fBNXqwIWvBVdcFJG4K9/BXjsd0trHu4qKrfVxcNwfoLbu9SqSKMp3sB/5m+BHRER+orsvi4gkGQW7iEiSUbCLiCQZBbuISJJRsIuIJBkFu4hIklGwi4gkGQW7iEiSSYhb45lZFvBjMZs3AtYdxObVXu3VXu0PRjxraOnuPz/t293L9Q+QqfZqr/ZqH4/2iVLD3j8aihERSTIKdhGRJJMMwf6C2qu92qt9nNonSg17SIgvT0VEpOQkQ49dREQiKNhFRJJMuQ52M0szswfN7P5itE01szfM7EszG2dmrWNsn2Jm74ftx5pZ01hriFjXd2YW892fzWxGuP0vzezSYrTvHb72CWb2hxjbXh+x7S/NLObjcM3s1vB3N8HMehSj/aNh+2/MrHuUbfbYZ8ysvZl9FtYwLNb24byTzGyamVUvxvYvDn9/mWZ2ZzHaX2Zmn4b70C3FqT+cf46ZTSzG9i83s9nha/i4GO0rmdlT4f/hBDNrGG17M6u91z64yMxujHH7zczsQzMbb2bPFKP+rmb2uZl9bWZPR9H+Z7kT6z4YjXId7MDjQC5QtRhtawK3uvvxwFDgthjb5wEXhe1fBIp1KyczuwCoV5y2wBp3Pz78eT3G7VYF/gSc4+7HuPujsbR392cLtw38BXgkxu2nAr8Cjif43d0XY/vTgOru3g+4kmBfiMbe+8xTwFXufgzQysx+EUt7MzsX+CWws5jbXxD+DnsD55hZUfcY3Lv9e+5+EpABXGlmFmN7zKwycEUx608F7gz3hVOK0X4wMN3d+4T7YXa07d19S8Q+eAIwH3g5xu3fCjzm7n2BBmZ2ZIztnwR+4+5HAw3N7IQi2u8rd54itn2wSOU62N39CmBcMduudPeV4cMNwNYY2xe4+7bw4eHAjFhrMLM6wOXAv2NtGyooZjuA0wnO9h0R9hZ6FmclZlYJuA54Nsam+QT7XwrBmXdZMbbvAnwB4O5zCAKmSJH7jJlVIXhzWBIuHgX0ibZ9+Hi0u98FbNt/qwO2zwz/LQCyKeINYh/tN4eTjYGlXsTREPv5m7meKPfBfbRPJfj7ico+2p8BtA97r8OKemM6wN/8xcAYd98SY/vNBIFeCahDEa9lH+1ruvuycPp9oFcR7ffOnVxi3AejUa6DvSRYMIRS+K4Za9vfm9l8gt7S58XY/DPAAxQjoM2sFtAm/IN4y8yax7iKw4EGwFnAVcBzsdYQOgf4xN13xNIoDKRxwBzgPYKeTyymA+da4HCCnk5RvdW9pRGEaaFsoH6M6ygRZvY7YLy758TYLs3Mvga+A54vxnaPAPq4+zuxtg1VAR4NhzIGFaN9b2Ckux8H1AD6F7OOa4CXitHueeAxgv0wx90Xxdg+18w6hfveL4nyPtIRufM4pbAPVuhgN7OzCIYjrol4F42auw9z98MJeqsxBaOZXUbQw5oc63bDbW919zbhH8SLRD8UUSgP+Njd88LeQkExghGCYZCY/6DM7EyCj7NtgA7AM+HwUFTc/SOCj95fAtcCE4vqre7DRvbs6dcn9k8OB8XM6pjZ34G17h7TcBaAu2eFwwBtgevMrG0M264OPA3cFOt2I7Z/j7sfBZwKDDCzzjGuYnXE38AYoFOsNYRDFzPcPaZP3aGXgWPcvT0wOXyDjcUQ4AmC3nousKSoBpG5A6ynFPbBChvsZtYVONvdB0cxrrev9nUignApUDvGVVwKdDKzN4ALgDvMrH0M268c8bA4O8I3BMMxmFljYFeswRh+0VXd3dcWY/stCb4jcGATwcfgIr98jOTuD4Rj7J8CRX7xt4/224FqtvuL7/7AZ7Gu5yA9Czzh7iOL09jM6kHwRk/we4zld3giQQ/z6XA/bGtmd8W4/cIe6naCYY1Y31yXmlmXcPp4gk9isboUeLsY7QAOJQhXgFVAq1gau/tcdz8NGBC2fe9Az987d0prH4zqY0OSOg3oa2Zfho+XhuNn0eoAPGVmuQQ79fWxbNzdzyycNrM/E/Q458WwirZm9jLBmOxOgl5rLNv/1szmmdkEgt77rbG0Dx1H8AZRHK8AL5vZWKAa8HzEeHGRwjeV/wAG/ECMrz/CrcDI8P/xvXC8viydBbSM+LB0n7vHMqz3qJl1JPg9vOvuM6Nt6O5jCHrJAJjZRHd/MIZtAzxsZr0JsuRdd58dY/vbgBfC1/89RQTjfhwNxHRUV4Q/Ah+b2S6C70kGxtLYzG4Dzgsf3hfFPvyz3KEU9kGdeSoikmQq7FCMiEiyUrCLiCQZBbuISJJRsIuIJBkFu4hIklGwi4gkGQW7iEiS+f9/YPmEWT7+ZwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# depth에 따른 정확도\n",
    "train_score=[]\n",
    "test_score=[]\n",
    "for i in range(1,21):\n",
    "    \n",
    "    model=RandomForestClassifier(max_depth=i,random_state=20)\n",
    "    model.fit(X_train,Y_train)\n",
    "    train_score.append(model.score(X_train,Y_train))\n",
    "    test_score.append(model.score(X_test,Y_test))\n",
    "plt.figure()\n",
    "plt.title('score for depths')\n",
    "plt.plot(range(1,21),train_score)\n",
    "plt.plot(range(1,21),test_score)\n",
    "plt.xticks(range(1,21))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 정확도 : 0.8981132075471698\n",
      "테스트 정확도 : 0.8717047451669596\n"
     ]
    }
   ],
   "source": [
    "model_1=RandomForestClassifier(max_depth=5,random_state=20)\n",
    "model_1.fit(X_train, Y_train)\n",
    "train_accuracy = model_1.score(X_train, Y_train)\n",
    "test_accuracy = model_1.score(X_test, Y_test)\n",
    "print(f'훈련 정확도 : {train_accuracy}')\n",
    "print(f'테스트 정확도 : {test_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.041, 0.038, 0.036, 0.002, 0.053, 0.002, 0.034, 0.029, 0.028,\n",
       "       0.002, 0.067, 0.007, 0.000, 0.031, 0.010, 0.003, 0.001, 0.005,\n",
       "       0.010, 0.071, 0.005, 0.000, 0.002, 0.003, 0.002, 0.045, 0.002,\n",
       "       0.002, 0.023, 0.002, 0.019, 0.000, 0.156, 0.032, 0.002, 0.013,\n",
       "       0.071, 0.023, 0.023, 0.001, 0.031, 0.004, 0.001, 0.000, 0.005,\n",
       "       0.005, 0.003, 0.003, 0.001, 0.002, 0.000, 0.001, 0.001, 0.005,\n",
       "       0.000, 0.000, 0.002, 0.000, 0.008, 0.002, 0.001, 0.000, 0.003,\n",
       "       0.010, 0.011])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance = model_1.feature_importances_\n",
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.title('feature importance')\n",
    "plt.bar(range(len(importance)), importance)\n",
    "plt.xticks(range(len(importance)), X.columns, rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='수술기법', ylabel='count'>"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#수술기법\n",
    "\n",
    "sns.set_palette('pastel')\n",
    "sns.countplot(data = data , x = '수술기법' ) # 전체 수술환자 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='수술기법', ylabel='count'>"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_palette('pastel')\n",
    "sns.countplot(data = data.sort_values(by = '재발여부'), x='수술기법', hue = '재발여부') # 재발여부"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='체중범위', ylabel='count'>"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " # min 35, max 136.1\n",
    "def Total_1(data):    \n",
    "    if  data < 50:\n",
    "        return '50이하'\n",
    "    elif data < 60:\n",
    "        return '50~60'\n",
    "    elif data < 70:\n",
    "        return '60~70'\n",
    "    elif data < 80:\n",
    "        return '70~80'\n",
    "    elif data < 90:\n",
    "        return '80~90'\n",
    "    elif data < 100:\n",
    "        return '90~100'\n",
    "    else:\n",
    "        return '100이상'\n",
    "plt.rc('font', family='Malgun Gothic')    # 한글 폰트수정\n",
    "plt.figure(figsize = (15, 5))  # 피규어 사이즈 확장 \n",
    "sns.set_palette('pastel')\n",
    "data['체중범위'] = data['체중'].apply(Total_1)\n",
    "sns.countplot(data = data.sort_values(by = '재발여부'), x='체중범위', \n",
    "              order=['50이하','50~60','60~70','70~80','80~90',\n",
    "                     '90~100','100이상'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='체중범위', ylabel='count'>"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " # min 35, max 136.1\n",
    "def Total_1(data):    \n",
    "    if data < 50:\n",
    "        return '50이하'\n",
    "    elif data < 60:\n",
    "        return '50~60'\n",
    "    elif data < 70:\n",
    "        return '60~70'\n",
    "    elif data < 80:\n",
    "        return '70~80'\n",
    "    elif data < 90:\n",
    "        return '80~90'\n",
    "    elif data < 100:\n",
    "        return '90~100'\n",
    "    else:\n",
    "        return '100이상'\n",
    "plt.rc('font', family='Malgun Gothic')    # 한글 폰트수정\n",
    "plt.figure(figsize = (15, 5))  # 피규어 사이즈 확장 \n",
    "sns.set_palette('pastel')\n",
    "data['체중범위'] = data['체중'].apply(Total_1)\n",
    "sns.countplot(data = data.sort_values(by = '재발여부'), x='체중범위',hue = '재발여부',  \n",
    "              order=['50이하','50~60','60~70','70~80','80~90',\n",
    "                     '90~100','100이상'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,  50., 100., 150.]),\n",
       " [Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')])"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=data, x= '체중', y = '재발여부')\n",
    "plt.xticks(rotation=45)  # x라벨이 겹쳐서 글자 회전"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='디스크단면적범위', ylabel='count'>"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    " # min 1145.8, max 3214.2\n",
    "def Total_1(data):    \n",
    "    if data < 1200:        \n",
    "        return '1100~1200'\n",
    "    elif data < 1400:\n",
    "        return '1200~1400'\n",
    "    elif data < 1600:\n",
    "        return '1400~1600'\n",
    "    elif data < 1800:\n",
    "        return '1600~1800'\n",
    "    elif data < 2000:\n",
    "        return '1800~2000'\n",
    "    elif data < 2200:\n",
    "        return '2000~2200'\n",
    "    elif data < 2400:\n",
    "        return '2200~2400'\n",
    "    elif data < 2600:\n",
    "        return '2400~2600'\n",
    "    elif data < 2800:\n",
    "        return '2600~2800'\n",
    "    elif data < 3000:\n",
    "        return '2800~3000'\n",
    "    else:\n",
    "        return '3000이상'\n",
    "plt.rc('font', family='Malgun Gothic')    # 한글 폰트수정\n",
    "plt.figure(figsize = (15, 5))  # 피규어 사이즈 확장 \n",
    "sns.set_palette('pastel')\n",
    "data['디스크단면적범위'] = data['디스크단면적'].apply(Total_1)\n",
    "sns.countplot(data = data.sort_values(by = '재발여부'), x='디스크단면적범위', \n",
    "              order=['1100~1200','1200~1400','1400~1600','1600~1800','1800~2000','2000~2200',\n",
    "                     '2200~2400','2400~2600','2600~2800','2800~3000','3000이상'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='디스크단면적범위', ylabel='count'>"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lOX14/8gkm6eoBwAAAGBvTTUT5ZQkx1XVlcP2jUlurqoNwzn/oruvq6rrk5xaVZsyC1bOnKgeAAAAgL0ySYjS3a9L8roR47YnOWuKGgAAAAAWk0VcAQAAAEYQogAAAACMIEQBAAAAGGGyRxwDADC9jZ/astwlLOj0DYcsdwkAsKjMRAEAAAAYQYgCAAAAMIIQBQAAAGAEIQoAAADACEIUAAAAgBGEKAAAAAAjCFEAAAAARhCiAAAAAIwgRAEAAAAYQYgCAAAAMIIQBQAAAGAEIQoAAADACEIUAAAAgBFGhShVdd5O238wTTkAAAAAK9PaXb1ZVT+W5N8l+fdV9Q/D7nVJTk/yuxPXBgAAALBi7DJESfLtJHcl6SR3D/vuSPLMKYsCAAAAWGl2GaJ0981J/qyqvtDdn12imgAAAABWnAeaibLDuqramOSgzNZR6e4+abqyAAAAAFaWsSHK25K8IMmXMru1BwAAAGBVGRui/GN3/+2klQAAAACsYGNDlGur6twkV2eYidLdl09WFQAAAMAKMzZEuX34/WeG3zuJEAUAAABYNUaFKN39+1MXAgAAALCSjQpRqur/y04Lyno6DwAAALCajL2d55Q5r49IctoEtQAAAACsWGvGDOruu+f8ujbJ901cFwAAAMCKMvZ2nqfP2XxMkp+cpBoAAACAFWrs7TzHDr93kq1JXjxNOQAAAAAr0+in81TVwUkem+Qr3f0v05YFAAAAsLKMWhOlqv5Dkg8meX6S91fVqZNWBQAAALDCjL2d52VJTuju71TV/kkuS/KRhQZX1YFJ3prk0ZkFNS9Msn+SP0myLsnHu/vsYew5SY4fajmju7+4Zz8KAAAAwHTGhih3dvd3kmQIUh5oBstDk/xGd99UVacleUWSH0ny4u6+oareV1XHZBasHNrdJ1TVUUnOT2KWCwAAALDijA1Rvl5Vz0vyoST/Z5Jv7Gpwd980Z/OWJHcnWdfdNwz7LslssdpHJLl4+My1w7orAAAAACvOqDVRkvzXJEcmeXeSo5O8fsyHquoxmc1CeUNmT/XZYWuSg5I8KsmWOfu3jZjlAgAAALDkxs5E+a3uPn7HRlVdnuTpu/pAVf1skmcm+eUkdyQ5cM7bB2UWnnzf8HqH7d29fWRNAAAAAEtm7KyP2ml7/S4HVz0pyTO7+8zu3trddyY5YJiZkiTPTnJFkk1JTh8+c2SSzaMrBwAAAFhCY2eibKyqdyfZmOSUzMKPXTklyXFVdeWwfWOS3xiOc3eSS7v7S1X15SSnVtWmJLclOXN3fwAAAACApTAqROnuN1bVcUk2JPlwd3/wAca/Lsnr5nnr2J3GbU9y1shaAQAAAJbN2Jko6e5NeeAZKAAAAAD7JE/CAQAAABhBiAIAAAAwghAFAAAAYAQhCgAAAMAIQhQAAACAEYQoAAAAACMIUQAAAABGEKIAAAAAjCBEAQAAABhBiAIAAAAwghAFAAAAYAQhCgAAAMAIQhQAAACAEYQoAAAAACMIUQAAAABGEKIAAAAAjCBEAQAAABhBiAIAAAAwghAFAAAAYAQhCgAAAMAIQhQAAACAEYQoAAAAACMIUQAAAABGEKIAAAAAjCBEAQAAABhBiAIAAAAwghAFAAAAYAQhCgAAAMAIQhQAAACAEYQoAAAAACMIUQAAAABGEKIAAAAAjDBJiFJVh1TVuVV1zrD9/Kq6rqqurKrL54w7p6quqqqPVdUTp6gFAAAAYDGsnei4b0jy1SQPHbYPTPLq7v7AjgFVdVySQ7v7hKo6Ksn5SU6dqB4AAACAvTLJTJTufkGSq+fsOjDJLTsNe3qSi4fx1yY5eIpaAAAAABbDUq2JsjbJ66pqU1WdMex7VJItc8ZsqyprtAAAAAAr0pKEFt392u7+mSTPSPLzw/on305y0Jxh27t7+1LUAwAAALC7liREqaoda6/cmeS2JJ1kU5LTh/ePTLJ5KWoBAAAA2BNTLSy7s/9UVRuG8/1Fd19XVdcnObWqNmUWrJy5RLUAAAAA7LbJQpTuvjLJlcPrs+d5f3uSs6Y6PwAAAMBispArAAAAwAhCFAAAAIARhCgAAAAAIwhRAAAAAEYQogAAAACMIEQBAAAAGEGIAgAAADDC2uUuANh7Gz+1ZblLWNDpGw5Z7hIAAAAWhZkoAAAAACMIUQAAAABGEKIAAAAAjCBEAQAAABhBiAIAAAAwghAFAAAAYAQhCgAAAMAIQhQAAACAEYQoAAAAACMIUQAAAABGEKIAAAAAjCBEAQAAABhBiAIAAAAwghAFAAAAYAQhCgAAAMAIQhQAAACAEYQoAAAAACMIUQAAAABGEKIAAAAAjCBEAQAAABhBiAIAAAAwghAFAAAAYAQhCgAAAMAIQhQAAACAEYQoAAAAACMIUQAAAABGmCREqapDqurcqjpn2H58VV1RVR+rqvPnjDunqq4a9j9xiloAAAAAFsNUM1HekOTuJPsN2xcmeXF3PznJ4VV1TFUdl+TQ7j4hyZlJzp/3SAAAAAArwCQhSne/IMnVSVJVa5Os6+4bhrcvSXJskqcnuXgYf22Sg6eoBQAAAGAxLMWaKIck2Tpne2uSg5I8KsmWOfu3VZU1WgAAAIAVae0SnOPWJAfO2T4os/Dk+4bXO2zv7u1LUA8AACyKjZ/a8sCDlsnpGw5Z7hIA9jmTz/zo7juTHFBVjxl2PTvJFUk2JTk9SarqyCSbp64FAAAAYE8txUyUJPmNJBur6u4kl3b3l6rqy0lOrapNSW7LbHFZAAAAgBVpshClu69McuXw+prMFpOd+/72JGdNdX4AAACAxWQhVwAAAIARhCgAAAAAIwhRAAAAAEYQogAAAACMIEQBAAAAGEGIAgAAADCCEAUAAABgBCEKAAAAwAhCFAAAAIARhCgAAAAAIwhRAAAAAEYQogAAAACMIEQBAAAAGEGIAgAAADDC2uUuAABgCrde9qblLmFBB57y8uUuAQDYA2aiAAAAAIwgRAEAAAAYQYgCAAAAMIIQBQAAAGAEIQoAAADACEIUAAAAgBGEKAAAAAAjCFEAAAAARhCiAAAAAIwgRAEAAAAYQYgCAAAAMIIQBQAAAGAEIQoAAADACEIUAAAAgBGEKAAAAAAjCFEAAAAARhCiAAAAAIwgRAEAAAAYQYgCAAAAMMLapTxZVX0hydZh86Ikn0nyJ0nWJfl4d5+9lPUAAAAAjLWkIUqSm7v75B0bVfWXSV7c3TdU1fuq6pju/uQS1wQAAADwgJb6dp7tO15U1dok67r7hmHXJUmOXeJ6AAAAAEZZshClqv63JD9aVVdX1X9L8gP53q09GV4ftFT1AAAAAOyOJbudp7tvT/KjSVJVT0tyQZID5ww5KMmWpaoHAAAAYHcs5UyUh8zZ3JKkkxxQVY8Z9j07yRVLVQ8AAADA7ljKhWUfV1X/Ocl3hl9nJXlEko1VdXeSS7v7S0tYDwBM6tbL3rTcJSzowFNevtwlAAA86Czl7TxfTvLknXb/QywmCwAAADwILPXTeQAAAAAelIQoAAAAACMIUQAAAABGEKIAAAAAjCBEAQAAABhBiAIAAAAwghAFAAAAYIS1y10AAAD7plsve9NylzCvA095+XKXAMCDlJkoAAAAACMIUQAAAABGEKIAAAAAjCBEAQAAABhBiAIAAAAwghAFAAAAYAQhCgAAAMAIQhQAAACAEdYudwHsGzZ+astyl7Cg0zccstwlAAAAsA8wEwUAAABgBCEKAAAAwAhu5wHYh9162ZuWu4R5HXjKy5e7BAAA2G1mogAAAACMYCYKAADwoLVSH3Dg4QawbzITBQAAAGAEIQoAAADACG7nAQAAWGQrdXH3xALvsDfMRAEAAAAYwUwUAAAAeBCysPLSMxMFAAAAYAQzUYBJrdT7gd0LDAAA7C4hCvs8/xEPAADTcDsJq43beQAAAABGEKIAAAAAjCBEAQAAABhhRayJUlXnJDk+s3rO6O4vLnNJAADwoGZdOIDFt+whSlUdl+TQ7j6hqo5Kcn6SU3f3OBY0AgAAAKa07CFKkqcnuThJuvvaqjp4mesBAAAA9tBKnQmX7P1suJUQojwqydxpJNuqak13b1+ughbTvvyHBwAAYCXy32FMpbp7eQuoel2SD3b3pmH76u4+fs77W5L8r+WqDwAAAFh1fri777c+x0qYibIpyelJNlXVkUk2z31zvqIBAAAAltpKeMTxh5PsX1Wbkrw+ySuXuR4AAABgIlX1mKo6esS445aint2x7CFKd2/v7rO6+7juPrW7/3Epz19Vh1TVucNjlnfsO7mqPldV6+bsO6eqrqqqj1XVE4d9j6+qK4Z95+/F+Z5TVVdW1aer6tWLfc6VbOd+TN2L+fo/573/q6o+sdjnXMkW6sdUvVjgz/+aqrqwqv5mONYjFvOcK908fwcm7ccC38FhVXVZVW2qqj+as3+f/w7m6f9kvaiqA6vqvcP/xl1dVY/d1XH29f7P148pe7GL/q/ma/B838Fk/VjoO5jz/qq6Du+qH1P0Yhd/B1bldXiBP/+T9WIX/V+V1+AF+j/lNXj/qvrgcL6rquoxuzrOvt7/pVRVD62qdw/f8/ur6pHDW0ckOWWe8R/aaddrJy9yd3X3qv6V5F1JfjfJecP2v09ybpJPJVk37DsuyUXD66OSfGR4/ZdJDh9evy/JMbt7vmHf0cPva5J8Iskhi3nOlfxrnv5P2ov5+j/sf0iSS5J8YrG/85X8a4E/j5P1YoHznZXk/95p3Kro/3w9mbofC3wHFyQ5eXj97iQ/vVq+g3n6P1kvkvxgkh8cXp+W5I8XOs5q6P98/ZiyF7vo/2q+Bs/3HUzWj4W+g2F71V2Hd/FncpJe7OJ8q/I6vMCf/8l6sYv+r8pr8AL9n/IavCbJQ4fXz0vyHxc6zmro/wI92j/JB5NcmeSqJI9J8vgkVyT5WJLz54w9ZxjzsSRPHPYtNPbVO/5eJTkpyVuH1ycmec1ONRyW5O922vc/l7s3O/9a9pkoy627X5Dk6jnb7+/u305yx5xh93kMc5KDq2ptZiHLDcOYS5Icu7vnG/Z9evh9e5KtSb6zmOdcyebp/6S9mK//g5cl+S9ztldl/weT9WKB852a5PFDOn1+VdVinnOlm6cnk/Zjge/gtuGYa5KsT3LLYp5zJZunH5P1ortv6u6bhs1bkty+i+Ps8/2fpx93Z8JezNf/Yf9qvgbfrydT9mOh72Cw6q7Du+jHJL3YxflW5XV4gX5M1otd9H9VXoMX6MeU1+Dt3b3jv++OSPKF1XwNXsC2JL/Q3Scm+dMkL0xyYZIXd/eTkxxeVcfU7PaaQ7v7hCRnJtkxK+d+Y4f9/zbJnyVJd380yX1mIe7k15P8XVX9/Jx9DxlmED1r73/ExbHqQ5SR7vcY5iSHZvaPix22JjkoSarqucM0r1dW1dqqevuYk1TVS5Ns6u5v7+459zV704vd7X9VHZXk2O7+73N2r8r+L0Yv9uDP/4YkG3v2VK7vS/Ls3T3nPmav+7EH38HbMluT6ktJvt3d/7C759yH7HUvHqj/wxTiV2T2j41DFjjOqun/nH68IYvQi93s/9z9q/YaPF9PprwO73y+1X4dntuPpbgOz/N9r+rr8E79mPwaPE//V/U1eKd+THoNrqqzq+orSY5O8tG4Bt/HfEFTFidk6u7+7pxTzX2dJBm+q99JclN3Py/JMVX1qiHI/G53n9jdly7Wz7q3hCjjfDv3/UuyPcm3khw4Z99B+d5ftqcmOT7JP2c2penLuzp4Va2vqrcm+UZ3n7eH59wnLFIvRve/ZuvevDHJr+301qrr/yL2Yrf+/Cf55+6+Znj94SRH7sE59yWL0Y/d/Q7+c5Ind/fjk1wz/MfTav0OFqMXC/a/qn42s9uHfnn4f+BuXeA4q6L/c/uRhX++Kfu/6q/BO/dk6uvwPOdb1dfhef4OTHodnu/vQFbxdXiefkx6DV6g/6v2GjxPPya9Bnf3+d19RJI3Z3b70K0LHGdV9H8+OwVNn83ihEz/WlUHD8ffL/NnEIck+UJ3X5Ak3f2KJB/q7k7ytb39uRabEGWcHY9hTg2PYe7uO5McMKSnySypviJJuvsl3f3d7v6z7j6hux9o4aE3J7mguzfu6Tn3IXvdi93s/1Mze9T3G6vqvUkeV1W/vbvn3EcsSi/24M//jVX148PrE5P83e6ecx+z1/3Yg+/gBzL7x0GS/FOSw3f3nPuQve7FQv2vqicleWZ3n9ndW4exCx1nn+//zv1YrF7sTv8Hq/YavEBPJrsOL3C+VXsdnqcfk16Hd/F3YFVehxfox2TX4F30f1Vegxfox5TX4PXDrIYkuTHJ96/ma/BCdgqaLsjihEwXJXl9VR2e5A8zzGLZ6bz/1N3vr6ofq6qLq+p/JLmgqv4is1uLVpS1y13Ag8SHk5xas8cw35bZvV9J8htJNlbV3Uku7e4v7eHxfzbJD3/v73X+YAnOuVItaS+6+8PD8ZMkVfWJ7j63Zvdirqr+L2MvXpHkouE7/3ySS5PUxOdcyZajH69JcnlV3ZPZelAvSvLNic+5Uk3Zi1OSHFdVVw7bN/ZsTZb7HaeqvrxI51zJ7tePTNuLhfq/mq/B830Hp2W6fiz0HazW6/B8/Thhx5sT9GKh/q/W6/B8f/6n7MVC/V+t1+D5+j9lL56Q2S1zdye5M7O1h+Y9ziq5Bt9PVa1P8q/d3Zl9Hw/JEBx199czC45+P8njMguZNs0NmapqvrHp7iuHv1NnJfmb7n7/Lsp4R2aL0H55qOnRST5QVSd0910T/Nh7pGY9AgAAAFajqvo/MlubZm7Q9MgkfzTsu7S7LxiC3T/O7MlFtyU5s7v/cfj8fcY+wPlOTPKU7v7DOfuuSfKs7v6nYXt9ZjN+/l133z7fcZaDEAUAAABYMguEKD+d2S0/+2c2C6yTvK67/8dy1LgQIQoAAADACBaWBQAAABhBiAIAAAAwghAFAAAAYAQhCgAwiar6garasMTnvGB4JOIDjTu2qn51xLgnVNWPjRj36Kp60ohxS94TAGDxrF3uAgCAB7eqelGSVya5ec7uFyT5oSQnJ/nUTuPPSpLufss8x/rfk7xjntMc0d0/PGfcO5P8aGaPUkySr3f3C5McnDn/vqmq1yQ5Pcmtw65HdvdRSQ5I8rA54y7P7GkAT0ryd0n+ubufk+RnkmxL8vdzfta7uvu9w/b/7O6TkzwhyVOGz6aq1ib5k6HGtUn+W3f/8bB9v54AAA8OQhQAYDGc293vnrujqn5o50FVtSbJM5J0Vb2tu7fPfb+7/zGzkGHnz102zzmf292bR9T269195XCcL1TVe5M8KslH55z36VW1f5J/SHJSktdU1ZVJHp3Z4xbn+t2q+pXh9Y9X1XOSHJnkO3PGPD/J9d19RlVVkvdV1V+OqBUAWMHczgMALImqemiSNyf5f5O8Pclbqmr9BKd6aVWdsMB7Nw8zTP5gnvd+NbPZI6/q7j/o7hOTnDfPuD/o7hOH9z+X5F+T3LnTmDVJtiRJd3eSrfHvLgB40DMTBQCYXFX9bmazNf6f7v7bYd+NSS6qqr/v7tcO+34tyS8muSez22i2Da9/ZJ7DPqSq9svs3zPrkvzLsP9vMptRMp9Dd56JUlXrkrwqyZbufn1VPbeq3pHkjAWO8dtV9ZLh9ZO6+0NV9a+Z3c6zw58neVNVnZTZbUJ/291fHbNeCwCwcglRAICl8Ibuvn3uju7+QpLnDjNUdux7Y5I37vzheW7nuSbJG5J8N7Og5c4krxne+9s5t/ncmuTVVfWyYfufuvs5VXVivhd6bE9yeXd/fKjh4qr6UHffU1X/OJxjR33vTPLOeX6+f0qyIxzaL7P1Vl6TZEfIc0BV/URmYQ8A8CAlRAEAFsNPV9XtSR6S2aKt/5zvLfqanQOUubr7jhHH/+7cje7+46r675nNHtm2Y/9s+ZH7jHtzVb0tSXX3dzKPYf/Hq+rtSR43z7HOnTu+qk7OLCDZNmf3miTvGl7/UJJfG95/QmYzaT6b5K4MC9QCAA9ONbtNFwBgz1TVjyT5t0k6s1kdtyf5amZPyjk5yaYkr36Aw7xu+PxvL/D+jyf5QpLzuvuy4bzvTPKaB1pctqqel2TtMItktwxP49k2d9HcYSHZdXOPV1VPSXJid//hrj4/jDu5u39vd2sBAJafmSgAwF7p7n/IPGuQDIFBuvuKJFeMPNxfLWJpAACLSogCADyYva+q7t5p37ndvXMY86phVshcX+julz/A8b+ZnW4lGpw9zHDZ4eFJPvCA1QIAD2pu5wEAAAAYYc1yFwAAAADwYCBEAQAAABhBiAIAAAAwghAFAAAAYAQhCgAAAMAIQhQAAACAEYQoAAAAACP8/10T8DiyMrO/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " # min 1145.8, max 3214.2\n",
    "def Total_1(data):    \n",
    "    if data < 1200:        \n",
    "        return '1100~1200'\n",
    "    elif data < 1400:\n",
    "        return '1200~1400'\n",
    "    elif data < 1600:\n",
    "        return '1400~1600'\n",
    "    elif data < 1800:\n",
    "        return '1600~1800'\n",
    "    elif data < 2000:\n",
    "        return '1800~2000'\n",
    "    elif data < 2200:\n",
    "        return '2000~2200'\n",
    "    elif data < 2400:\n",
    "        return '2200~2400'\n",
    "    elif data < 2600:\n",
    "        return '2400~2600'\n",
    "    elif data < 2800:\n",
    "        return '2600~2800'\n",
    "    elif data < 3000:\n",
    "        return '2800~3000'\n",
    "    else:\n",
    "        return '3000이상'\n",
    "plt.rc('font', family='Malgun Gothic')    # 한글 폰트수정\n",
    "plt.figure(figsize = (15, 5))  # 피규어 사이즈 확장 \n",
    "data['디스크단면적범위'] = data['디스크단면적'].apply(Total_1)\n",
    "sns.countplot(data = data.sort_values(by = '재발여부'), x='디스크단면적범위', hue = '재발여부', \n",
    "              order=['1100~1200','1200~1400','1400~1600','1600~1800','1800~2000','2000~2200',\n",
    "                     '2200~2400','2400~2600','2600~2800','2800~3000','3000이상'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1000., 2000., 3000., 4000.]),\n",
       " [Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')])"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=data, x= '디스크단면적', y = '재발여부')\n",
    "plt.xticks(rotation=45)  # x라벨이 겹쳐서 글자 회전"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Total_1(data):    \n",
    "    if data < 1200:        \n",
    "        return '1100~1200'\n",
    "    elif data < 1400:\n",
    "        return '1200~1400'\n",
    "    elif data < 1600:\n",
    "        return '1400~1600'\n",
    "    elif data < 1800:\n",
    "        return '1600~1800'\n",
    "    elif data < 2000:\n",
    "        return '1800~2000'\n",
    "    elif data < 2200:\n",
    "        return '2000~2200'\n",
    "    elif data < 2400:\n",
    "        return '2200~2400'\n",
    "    elif data < 2600:\n",
    "        return '2400~2600'\n",
    "    elif data < 2800:\n",
    "        return '2600~2800'\n",
    "    elif data < 3000:\n",
    "        return '2800~3000'\n",
    "    else:\n",
    "        return '3000이상'\n",
    "plt.rc('font', family='Malgun Gothic')    # 한글 폰트수정\n",
    "\n",
    "data['디스크단면적범위'] = data['디스크단면적'].apply(Total_1)\n",
    "\n",
    "sns.lineplot(data=data, x= '디스크단면적범위', y = '재발여부')\n",
    "plt.xticks(rotation=45)  # x라벨이 겹쳐서 글자 회전"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('data.csv', encoding = 'cp949')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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