개인미션_4주차_3일.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f312dac",
   "metadata": {},
   "source": [
    "- 16_Data.csv와 15_Data.csv 데이터를 결합해 아래와 같이 분석을 수행합니다. ( 두 데이터는 어느 한 정형외과에서 나온 척추 환자 디스크 수술 데이터)\n",
    "- 15_Data.csv : 수술 후에 측정한 환자의 건강 상태 / 수술 실패여부 / 재발여부 데이터\n",
    "- 16_Data.csv : 수술 이전에 환자로 부터 수집된 환자 신체정보 데이터\n",
    "\n",
    "1. 15_Data.csv데이터와 16_Data.csv데이터를 가져와 \"환자ID\"를 기준으로 병합하여, df1 으로 선언하시오.\n",
    "2. 환자 '성별'에 따라 '통증기간(월)', '헤모글로빈수치'의 평균의 차이가 있는지 검정 하시오.\n",
    "3. 환자의 '신장'과 '체중'을 이용해 'BMI'수치를 계산하시오.\n",
    "4. 환자의 '흡연여부'와 관련 있는 질병을 확인하고자 한다. '간질성폐질환', '고혈압여부', '당뇨여부', '말초동맥질환여부', '빈혈여부', '신부전여부','암발병여부','우울증여부', '종양진행여부', '혈전합병증여부' 항목들 중, 흡연 여부에 대해 유의미한 차이가 있는 질병을 가설 검정을 통해 찾으시오.\n",
    "5. 환자의 '직업'중 가장 많은 직업은 어떤 직업이며, 해당 직업 군의 남/여 성비를 확인하시오.\n",
    "6. 수술에 성공했으나, 재발한 환자들의 특성을 살펴보고자 한다. 해당 환자들의 평균연령과 평균 BMI, 남/여 성비의 비율과 직업군의 빈도수를 확인하시오.\n",
    "7. 통증기간과 환자의 통증정도의 상관성이 있는지 가설검정을 수행하여 확인하고, 상관계수를 확인하시오.\n",
    "8. 환자의 신장과 체중을 입력했을 때, BMI수치를 계산하고, 해당 연령대의 환자들의 성별에 따른 평균 헤모글로빈수치, 평균 환자통증정도를 시각화하여 출력하는 함수를 생성하시오.\n",
    "9. 과거에 수술했던 환자들의 특성을 파악하고자 한다. 해당 환자들의 직업군과 성별의 빈도수를 확인하고, 해당 환자들 중 BMI수치가 높은 상위 5명의 환자 명단을 출력하여 recurs_patient.xlsx로 저장하시오.\n",
    "10. 수술실패여부를 목표변수로 환자의 기본 특성을 입력했을 때, 해당 환자가 수술을 실패할 지/ 하지 않을지 분류모델을 만들고자 한다. 아래의 조건을 이용해 분류모델을 생성하시오.\n",
    "- X값에는 환자의 기본 정보(신체적 정보 + 직업)와 질병력이 포함되어야 합니다.\n",
    "- Y값은 \"수술실패여부\"\n",
    "- DecisionTree Classifier를 이용해 학습을 수행하세요.\n",
    "- 특성 공학 기법은 자유롭게 적용하여 모델을 구성해 주세요.\n",
    "- **단 학습 데이터의 과적합이 발생하면 안되며, 학습 성능 F1 Score 기준 40%이상 나와야 합니다.\n",
    "-** 학습한 모델은 model_medical.sav로 저장해 주세요."
   ]
  },
  {
   "cell_type": "raw",
   "id": "32e85b54",
   "metadata": {},
   "source": [
    "### 차집합으로 뽑는법 공부하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ed52ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False\n",
    "import pandas as pd\n",
    "a=pd.read_csv(r\"C:\\Users\\hanjw\\Desktop\\python\\실습파일\\15_Data.csv\")\n",
    "b=pd.read_csv(r\"C:\\Users\\hanjw\\Desktop\\python\\실습파일\\16_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9530ebbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "item = list(set(b.columns) - set(a.columns))\n",
    "item.append(\"환자ID\")\n",
    "\n",
    "df1=pd.merge(a,b[item],on='환자ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ff2b3b",
   "metadata": {},
   "source": [
    "2. 환자 '성별'에 따라 '통증기간(월)', '헤모글로빈수치'의 평균의 차이가 있는지 검정 하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1590f268",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전체에서 드롭나를 하지 말것. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5702b833",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=1973.5186287263045, pvalue=0.0)\n",
      "NormaltestResult(statistic=824.3852918801933, pvalue=9.705287099600454e-180)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RanksumsResult(statistic=-2.8632608163010476, pvalue=0.004193052551353649)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "xx=df1[df1['성별']==1]['통증기간(월)'].dropna()\n",
    "xy=df1[df1['성별']==2]['통증기간(월)'].dropna()\n",
    "print(stats.normaltest(xx))\n",
    "print(stats.normaltest(xy))# 정규성 안따름\n",
    "\n",
    "stats.ranksums(xx,xy)# 중앙값이 같지 않다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f18c5cc",
   "metadata": {},
   "source": [
    "3. 환자의 '신장'과 '체중'을 이용해 'BMI'수치를 계산하시오.(체중(kg) / [신장(m)]2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e457cce6",
   "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>Column 1</th>\n",
       "      <th>환자ID</th>\n",
       "      <th>수술기법</th>\n",
       "      <th>수술시간</th>\n",
       "      <th>수술실패여부</th>\n",
       "      <th>신장</th>\n",
       "      <th>연령</th>\n",
       "      <th>재발여부</th>\n",
       "      <th>체중</th>\n",
       "      <th>헤모글로빈수치</th>\n",
       "      <th>...</th>\n",
       "      <th>신부전여부</th>\n",
       "      <th>흡연여부</th>\n",
       "      <th>성별</th>\n",
       "      <th>고혈압여부</th>\n",
       "      <th>암발병여부</th>\n",
       "      <th>과거수술횟수</th>\n",
       "      <th>간질성폐질환</th>\n",
       "      <th>우울증여부</th>\n",
       "      <th>말초동맥질환여부</th>\n",
       "      <th>BMI수치</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>989</td>\n",
       "      <td>990PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>15.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.255019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1019</td>\n",
       "      <td>1020PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>13.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>21.719250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>971</td>\n",
       "      <td>972PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>16.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.453287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1051</td>\n",
       "      <td>1052PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>15.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.772097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1050</td>\n",
       "      <td>1051PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>24.526818</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>678</td>\n",
       "      <td>679PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>15.14</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.301095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>679</td>\n",
       "      <td>680PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>11.54</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>27.343750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>914</td>\n",
       "      <td>915PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>86.0</td>\n",
       "      <td>13.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27.450605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>380</td>\n",
       "      <td>381PT</td>\n",
       "      <td>IELD</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>74.2</td>\n",
       "      <td>16.97</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.507861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>180</td>\n",
       "      <td>181PT</td>\n",
       "      <td>TELD</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>80.4</td>\n",
       "      <td>16.71</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.272431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1894 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Column 1    환자ID  수술기법  수술시간  수술실패여부   신장  연령  재발여부    체중  헤모글로빈수치  ...  \\\n",
       "0          989   990PT  TELD  50.0       0  171  37     0  68.0    15.10  ...   \n",
       "1         1019  1020PT  TELD  55.0       0  162  27     0  57.0    13.10  ...   \n",
       "2          971   972PT  TELD  55.0       0  170  35     0  62.0    16.50  ...   \n",
       "3         1051  1052PT  TELD  30.0       0  174  34     0  75.0    15.10  ...   \n",
       "4         1050  1051PT  TELD  45.0       0  147  62     0  53.0    11.40  ...   \n",
       "...        ...     ...   ...   ...     ...  ...  ..   ...   ...      ...  ...   \n",
       "1889       678   679PT  TELD  51.0       0  177  47     0  73.0    15.14  ...   \n",
       "1890       679   680PT  TELD  69.0       0  160  39     0  70.0    11.54  ...   \n",
       "1891       914   915PT  TELD  79.0       0  177  38     1  86.0    13.17  ...   \n",
       "1892       380   381PT  IELD  35.0       0  174  26     0  74.2    16.97  ...   \n",
       "1893       180   181PT  TELD  50.0       0  182  22     0  80.4    16.71  ...   \n",
       "\n",
       "      신부전여부  흡연여부 성별 고혈압여부 암발병여부 과거수술횟수  간질성폐질환 우울증여부  말초동맥질환여부      BMI수치  \n",
       "0         0     0  1     0     0      1       0     0         0  23.255019  \n",
       "1         0     0  2     0     0      0       0     0         0  21.719250  \n",
       "2         0     0  1     0     0      0       0     0         0  21.453287  \n",
       "3         0     1  1     0     0      0       0     0         0  24.772097  \n",
       "4         0     0  2     1     0      0       0     0         0  24.526818  \n",
       "...     ...   ... ..   ...   ...    ...     ...   ...       ...        ...  \n",
       "1889      1     1  1     0     0      0       0     0         0  23.301095  \n",
       "1890      0     0  2     0     0      0       0     0         0  27.343750  \n",
       "1891      0     1  1     0     0      0       0     1         0  27.450605  \n",
       "1892      0     0  1     0     0      0       0     0         0  24.507861  \n",
       "1893      0     0  1     0     0      0       0     0         0  24.272431  \n",
       "\n",
       "[1894 rows x 38 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['BMI수치']=df1['체중']/((df1['신장']/100)**2)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25bcb266",
   "metadata": {},
   "source": [
    "4. 환자의 '흡연여부'와 관련 있는 질병을 확인하고자 한다. '간질성폐질환', '고혈압여부', '당뇨여부', '말초동맥질환여부', '빈혈여부', '신부전여부','암발병여부','우울증여부', '종양진행여부', '혈전합병증여부' 항목들 중, 흡연 여부에 대해 유의미한 차이가 있는 질병을 가설 검정을 통해 찾으시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5c19ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1= pd.crosstab(index= df1['흡연여부'], columns= df1['간질성폐질환'])\n",
    "a, p, n, table=stats.chi2_contingency(p1)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "414aea27",
   "metadata": {},
   "outputs": [],
   "source": [
    "box= [ '간질성폐질환', '고혈압여부', '당뇨여부', '말초동맥질환여부', '빈혈여부', '신부전여부','암발병여부',\n",
    "      '우울증여부', '종양진행여부', '혈전합병증여부' ]\n",
    "box2=[]\n",
    "for i in box:\n",
    "    p1= pd.crosstab(index= df1['흡연여부'], columns= df1[i])\n",
    "    a, p, n, table=stats.chi2_contingency(p1)\n",
    "    if p<0.05:\n",
    "        box2.append(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2489ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['빈혈여부']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box2 # 흡연 여부에 대해 유의미한 차이가 있는 질병"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cbe9179",
   "metadata": {},
   "source": [
    "5. 환자의 '직업'중 가장 많은 직업은 어떤 직업이며, 해당 직업 군의 남/여 성비를 확인하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "127a2f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "사무직    449\n",
       "Name: 직업, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['직업'].value_counts().sort_values(ascending= False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c8ab6a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=df1[df1['직업']=='사무직']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc4b5663",
   "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>col_0</th>\n",
       "      <th>성별</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성별</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.641425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.358575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0        성별\n",
       "성별             \n",
       "1      0.641425\n",
       "2      0.358575"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=a['성별'], columns= '성별', normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d71189b",
   "metadata": {},
   "source": [
    "6.수술에 성공했으나, 재발한 환자들의 특성을 살펴보고자 한다. 해당 환자들의 평균연령과 평균 BMI, 남/여 성비의 비율과 직업군의 빈도수를 확인하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7c758a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "평균연령: 43.72558139534884\n",
      "평균BMI: 25.349856224344197\n"
     ]
    }
   ],
   "source": [
    "new =df1[(df1['수술실패여부']==0) & (df1['재발여부']==1)]\n",
    "print(f\"평균연령: {new['연령'].mean()}\") # 평균연령\n",
    "print(f\"평균BMI: {new['BMI수치'].mean()}\") # 평균BMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bba264f0",
   "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>col_0</th>\n",
       "      <th>성별</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성별</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.67907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.32093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0       성별\n",
       "성별            \n",
       "1      0.67907\n",
       "2      0.32093"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=new['성별'],columns= \"성별\", normalize= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "442b7a2f",
   "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>col_0</th>\n",
       "      <th>직업</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>직업</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>건설업</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>공무원</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>교사</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>군인</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>노동직</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>농업</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>무직</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사무직</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사업가</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>운동선수</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>운수업</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>의료직</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>자영업</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>주부</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>특수전문직</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학생</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0  직업\n",
       "직업       \n",
       "건설업     4\n",
       "공무원     8\n",
       "교사      6\n",
       "군인      3\n",
       "노동직     5\n",
       "농업      4\n",
       "무직     19\n",
       "사무직    57\n",
       "사업가     5\n",
       "운동선수    3\n",
       "운수업     3\n",
       "의료직     6\n",
       "자영업    27\n",
       "주부      9\n",
       "특수전문직   1\n",
       "학생      7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=new['직업'],columns= \"직업\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4a7ef5",
   "metadata": {},
   "source": [
    "7. 통증기간과 환자의 통증정도의 상관성이 있는지 가설검정을 수행하여 확인하고, 상관계수를 확인하시오.7. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00f431e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SignificanceResult(statistic=-0.010676647938840402, pvalue=0.6427450957438059)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample=df1[['통증기간(월)','환자통증정도']].dropna()\n",
    "A = sample['통증기간(월)']\n",
    "B = sample['환자통증정도']\n",
    "stats.normaltest(A)\n",
    "stats.normaltest(B) #정규성을 따르지 않으므로 스피어만\n",
    "\n",
    "stats.spearmanr(A,B)\n",
    "\n",
    "#통증기간과 환자의 통증정도의 상관성이 없다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375921e7",
   "metadata": {},
   "source": [
    "8. 환자의 신장과 체중을 입력했을 때, BMI수치를 계산하고, 해당 연령대의 환자들의 성별에 따른 평균 헤모글로빈수치, 평균 환자통증정도를 시각화하여 출력하는 함수를 생성하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "be9491c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def age (x):\n",
    "    if x//10<1:\n",
    "        return \"어린이\"\n",
    "    else:\n",
    "        return f\"{x//10}0대\"\n",
    "    \n",
    "df1['연령대']=df1['연령'].apply(age)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec7df98f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import koreanize_matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import koreanize_matplotlib\n",
    "df1['연령대']=df1['연령'].apply(age)\n",
    "\n",
    "\n",
    "new= df1.pivot_table(index= \"성별\", values= [\"헤모글로빈수치\",'환자통증정도'], aggfunc=\"mean\").reset_index()\n",
    "M=pd.melt(new, id_vars=\"성별\")\n",
    "sns.barplot(data=M, x= \"성별\", y=\"value\", hue = 'variable')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2758080e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import koreanize_matplotlib\n",
    "\n",
    "\n",
    "def func (x, y, z):\n",
    "    bmi=y / ((x/100)**2)\n",
    "    if x//10<1:\n",
    "        a= \"어린이\"\n",
    "    else:\n",
    "        a= f\"{z//10}0대\"\n",
    "    Age=df1[df1['연령대']==a]\n",
    "    new= Age.pivot_table(index= \"성별\", values= [\"헤모글로빈수치\",'환자통증정도'], aggfunc=\"mean\")\n",
    "    M=pd.melt(new, id_vars=\"성별\")\n",
    "    sns.barplot(data=M, x= \"성별\", y=\"value\", hue = 'variable')\n",
    "    \n",
    "func( 160, 45, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8ef6a70b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def func (x, y, z):\n",
    "    bmi=y / ((x/100)**2)\n",
    "    if z//10<1:\n",
    "        a= \"어린이\"\n",
    "    else:\n",
    "        a= f\"{z//10}0대\"\n",
    "    Age=df1[df1['연령대']==a]\n",
    "    new2= Age.pivot_table(index= \"성별\", values= [\"헤모글로빈수치\",'환자통증정도'], aggfunc=\"mean\").reset_index()\n",
    "    M=pd.melt(new, id_vars=\"성별\")\n",
    "    sns.barplot(data=M, x= \"성별\", y=\"value\", hue = 'variable')\n",
    "    \n",
    "\n",
    "func(160,48,25)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb9a843f",
   "metadata": {},
   "source": [
    "9. 과거에 수술했던 환자들의 특성을 파악하고자 한다. 해당 환자들의 직업군과 성별의 빈도수를 확인하고, 해당 환자들 중 BMI수치가 높은 상위 5명의 환자 명단을 출력하여 recurs_patient.xlsx로 저장하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "bcbcd29a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "사무직      93\n",
      "자영업      47\n",
      "무직       40\n",
      "주부       20\n",
      "공무원      19\n",
      "학생       15\n",
      "운수업      11\n",
      "군인       11\n",
      "사업가       9\n",
      "농업        8\n",
      "노동직       7\n",
      "의료직       7\n",
      "특수전문직     5\n",
      "교사        5\n",
      "건설업       3\n",
      "운동선수      2\n",
      "예술가       1\n",
      "Name: 직업, dtype: int64\n",
      "1    254\n",
      "2    141\n",
      "Name: 성별, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df1[df1['과거수술횟수']>=1]['직업'].value_counts())\n",
    "print(df1[df1['과거수술횟수']>=1]['성별'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "eee5bc53",
   "metadata": {},
   "outputs": [],
   "source": [
    "a= df1[df1['과거수술횟수']>=1]\n",
    "inx= a[\"BMI수치\"].sort_values(ascending= False).head().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "46f02de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([831, 759, 786, 233, 462], dtype='int64')"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "df2c32de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.iloc[inx,:].to_excel(\"recurs_patient.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a846b8e6",
   "metadata": {},
   "source": [
    "10. 수술실패여부를 목표변수로 환자의 기본 특성을 입력했을 때, 해당 환자가 수술을 실패할 지/ 하지 않을지 분류모델을 만들고자 한다. 아래의 조건을 이용해 분류모델을 생성하시오.\n",
    "\n",
    "X값에는 환자의 기본 정보(신체적 정보 + 직업)와 질병력이 포함되어야 합니다.\n",
    "Y값은 \"수술실패여부\"\n",
    "DecisionTree Classifier를 이용해 학습을 수행하세요.\n",
    "특성 공학 기법은 자유롭게 적용하여 모델을 구성해 주세요.\n",
    "**단 학습 데이터의 과적합이 발생하면 안되며, 학습 성능 F1 Score 기준 40%이상 나와야 합니다.\n",
    "** 학습한 모델은 model_medical.sav로 저장해 주세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "df58d6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Column 1', '환자ID', '수술기법', '수술시간', '수술실패여부', '신장', '연령', '재발여부', '체중',\n",
       "       '헤모글로빈수치', '환자통증정도', '통증기간(월)', '혈액형', '수술일', '입원일', '퇴원일', '고혈압여부',\n",
       "       '빈혈여부', '흡연여부', '직업', '신부전여부', '종양진행여부', '당뇨여부', '성별', '과거수술횟수', '입원기간',\n",
       "       '심혈관질환', '말초동맥질환여부', 'ODI', 'Large Lymphocyte',\n",
       "       'Location of herniation', '가족력', '스테로이드치료', '우울증여부', '혈전합병증여부', '암발병여부',\n",
       "       '간질성폐질환', 'BMI수치'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5b586671",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.42      0.55       341\n",
      "           1       0.63      0.92      0.75       371\n",
      "\n",
      "    accuracy                           0.68       712\n",
      "   macro avg       0.73      0.67      0.65       712\n",
      "weighted avg       0.72      0.68      0.66       712\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "sampling_model = RandomOverSampler()\n",
    "\n",
    "X= df1[['신장', '연령', '재발여부', '체중','헤모글로빈수치','혈액형','고혈압여부', '심혈관질환', '말초동맥질환여부','가족력',\n",
    "    '스테로이드치료', '우울증여부', '혈전합병증여부', '암발병여부','간질성폐질환', 'BMI수치']]\n",
    "Y= df1['수술실패여부']\n",
    "\n",
    "X = pd.get_dummies(X)\n",
    "\n",
    "X_resample, Y_resample = sampling_model.fit_resample(X, Y)\n",
    "\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(X_resample, Y_resample, test_size = 0.2)\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth= 5)\n",
    "dt.fit(train_x, train_y)\n",
    "pred = dt.predict(test_x)\n",
    "report = classification_report(test_y, pred)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7b14613c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6830639494026705"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "312f3ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6783707865168539"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2b27500",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}