ex02_선형분류(직원 이직여부 예측).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3bd56ff5-bd31-4649-b2db-7defa46e4f56",
   "metadata": {},
   "source": [
    "### 문제정의\n",
    "- hr 데이터를 통해 사내 이직률 확인\n",
    "- 직원의 이직 여부 판단 → 정보가 없는 직원의 이직률 하락\n",
    "- 이직률과 연관이 있는 사항들을 지속적으로 모니터링, 개선"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ac5e9ed-996d-4a82-8d24-2dcda4c2e0d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 경고창 warnings 제거 코드\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "f8fe3455-ab63-426d-bc35-4e674651bc0e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
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       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
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       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
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       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
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       "      <td>No</td>\n",
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       "      <td>Research &amp; Development</td>\n",
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       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
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       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
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       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1023</td>\n",
       "      <td>Sales</td>\n",
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       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
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       "      <td>8</td>\n",
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       "      <th>1469</th>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>628</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
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       "<p>1470 rows × 35 columns</p>\n",
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      ],
      "text/plain": [
       "      Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0      41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1      49        No  Travel_Frequently        279  Research & Development   \n",
       "2      37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3      33        No  Travel_Frequently       1392  Research & Development   \n",
       "4      27        No      Travel_Rarely        591  Research & Development   \n",
       "...   ...       ...                ...        ...                     ...   \n",
       "1465   36        No  Travel_Frequently        884  Research & Development   \n",
       "1466   39        No      Travel_Rarely        613  Research & Development   \n",
       "1467   27        No      Travel_Rarely        155  Research & Development   \n",
       "1468   49        No  Travel_Frequently       1023                   Sales   \n",
       "1469   34        No      Travel_Rarely        628  Research & Development   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
       "0                    1          2  Life Sciences              1   \n",
       "1                    8          1  Life Sciences              1   \n",
       "2                    2          2          Other              1   \n",
       "3                    3          4  Life Sciences              1   \n",
       "4                    2          1        Medical              1   \n",
       "...                ...        ...            ...            ...   \n",
       "1465                23          2        Medical              1   \n",
       "1466                 6          1        Medical              1   \n",
       "1467                 4          3  Life Sciences              1   \n",
       "1468                 2          3        Medical              1   \n",
       "1469                 8          3        Medical              1   \n",
       "\n",
       "      EmployeeNumber  ...  RelationshipSatisfaction StandardHours  \\\n",
       "0                  1  ...                         1            80   \n",
       "1                  2  ...                         4            80   \n",
       "2                  4  ...                         2            80   \n",
       "3                  5  ...                         3            80   \n",
       "4                  7  ...                         4            80   \n",
       "...              ...  ...                       ...           ...   \n",
       "1465            2061  ...                         3            80   \n",
       "1466            2062  ...                         1            80   \n",
       "1467            2064  ...                         2            80   \n",
       "1468            2065  ...                         4            80   \n",
       "1469            2068  ...                         1            80   \n",
       "\n",
       "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                    0                  8                      0   \n",
       "1                    1                 10                      3   \n",
       "2                    0                  7                      3   \n",
       "3                    0                  8                      3   \n",
       "4                    1                  6                      3   \n",
       "...                ...                ...                    ...   \n",
       "1465                 1                 17                      3   \n",
       "1466                 1                  9                      5   \n",
       "1467                 1                  6                      0   \n",
       "1468                 0                 17                      3   \n",
       "1469                 0                  6                      3   \n",
       "\n",
       "     WorkLifeBalance  YearsAtCompany YearsInCurrentRole  \\\n",
       "0                  1               6                  4   \n",
       "1                  3              10                  7   \n",
       "2                  3               0                  0   \n",
       "3                  3               8                  7   \n",
       "4                  3               2                  2   \n",
       "...              ...             ...                ...   \n",
       "1465               3               5                  2   \n",
       "1466               3               7                  7   \n",
       "1467               3               6                  2   \n",
       "1468               2               9                  6   \n",
       "1469               4               4                  3   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                           0                     5  \n",
       "1                           1                     7  \n",
       "2                           0                     0  \n",
       "3                           3                     0  \n",
       "4                           2                     2  \n",
       "...                       ...                   ...  \n",
       "1465                        0                     3  \n",
       "1466                        1                     7  \n",
       "1467                        0                     3  \n",
       "1468                        0                     8  \n",
       "1469                        1                     2  \n",
       "\n",
       "[1470 rows x 35 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# 데이터 불러오기\n",
    "data = pd.read_csv(\"data/job_transfer.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d935e94-f6fb-4f6c-95bb-f9474b2b9068",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Age                       1470 non-null   int64 \n",
      " 1   Attrition                 1470 non-null   object\n",
      " 2   BusinessTravel            1470 non-null   object\n",
      " 3   DailyRate                 1470 non-null   int64 \n",
      " 4   Department                1470 non-null   object\n",
      " 5   DistanceFromHome          1470 non-null   int64 \n",
      " 6   Education                 1470 non-null   int64 \n",
      " 7   EducationField            1470 non-null   object\n",
      " 8   EmployeeCount             1470 non-null   int64 \n",
      " 9   EmployeeNumber            1470 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
      " 11  Gender                    1470 non-null   object\n",
      " 12  HourlyRate                1470 non-null   int64 \n",
      " 13  JobInvolvement            1470 non-null   int64 \n",
      " 14  JobLevel                  1470 non-null   int64 \n",
      " 15  JobRole                   1470 non-null   object\n",
      " 16  JobSatisfaction           1470 non-null   int64 \n",
      " 17  MaritalStatus             1470 non-null   object\n",
      " 18  MonthlyIncome             1470 non-null   int64 \n",
      " 19  MonthlyRate               1470 non-null   int64 \n",
      " 20  NumCompaniesWorked        1470 non-null   int64 \n",
      " 21  Over18                    1470 non-null   object\n",
      " 22  OverTime                  1470 non-null   object\n",
      " 23  PercentSalaryHike         1470 non-null   int64 \n",
      " 24  PerformanceRating         1470 non-null   int64 \n",
      " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
      " 26  StandardHours             1470 non-null   int64 \n",
      " 27  StockOptionLevel          1470 non-null   int64 \n",
      " 28  TotalWorkingYears         1470 non-null   int64 \n",
      " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
      " 30  WorkLifeBalance           1470 non-null   int64 \n",
      " 31  YearsAtCompany            1470 non-null   int64 \n",
      " 32  YearsInCurrentRole        1470 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
      " 34  YearsWithCurrManager      1470 non-null   int64 \n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 402.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# 데이터 정보확인\n",
    "data.info()\n",
    "# 1. Dtype 2. Non-Null -> 결측치 여부 확인\n",
    "# 결측치 없음 확인! good!"
   ]
  },
  {
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "fdde1f8a-f636-4b5b-b92f-0634ab16b4ad",
   "metadata": {},
   "source": [
    "![image.png](attachment:abbadafd-bef8-4765-8986-17c34936cbe6.png)\n",
    "![image.png](attachment:ca664dba-af67-47c4-a696-490337367f0b.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c3610d-fe95-4928-92c0-3fc63a5785c9",
   "metadata": {},
   "source": [
    "## EDA(탐색적 데이터 분석)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f1dce9fb-affd-42c9-b844-3c435cfb77ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attrition\n",
       "No     1233\n",
       "Yes     237\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 직원들의 이직여부 확인\n",
    "data['Attrition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f583b9c-81bd-4c31-add9-569bc6b701f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16122448979591836"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 우리 회사의 이직률 확인\n",
    "237/(237+1233)\n",
    "# 약 16% 정도의 이직률"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "ac47f114-5d13-494b-bc6e-d19faee5fdcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 정답데이터 -> Attrition(이직여부) -> 숫자 데이터\n",
    "# 이직한 사람 -> 1, 이직하지 않은 사람 -> 0\n",
    "import numpy as np\n",
    "# np.where(조건, 조건에 부합하는 데이터 변경, 나머지 데이터 변경)\n",
    "data['Attrition']=np.where(data['Attrition']==\"Yes\", 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce01b12c-48a8-4e38-993a-f6631bb52c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "35    78\n",
       "34    77\n",
       "36    69\n",
       "31    69\n",
       "29    68\n",
       "32    61\n",
       "30    60\n",
       "33    58\n",
       "38    58\n",
       "40    57\n",
       "37    50\n",
       "27    48\n",
       "28    48\n",
       "42    46\n",
       "39    42\n",
       "45    41\n",
       "41    40\n",
       "26    39\n",
       "44    33\n",
       "46    33\n",
       "43    32\n",
       "50    30\n",
       "25    26\n",
       "24    26\n",
       "49    24\n",
       "47    24\n",
       "55    22\n",
       "51    19\n",
       "53    19\n",
       "48    19\n",
       "54    18\n",
       "52    18\n",
       "22    16\n",
       "56    14\n",
       "23    14\n",
       "58    14\n",
       "21    13\n",
       "20    11\n",
       "59    10\n",
       "19     9\n",
       "18     8\n",
       "60     5\n",
       "57     4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연령데이터 확인\n",
    "data['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "daf8b3c9-9967-4d05-9287-fc8b1cc9dc60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 연력데이터 3개의 카테고리로 묶어주기!\n",
    "# 30대 이하, 30-40, 40대 이상\n",
    "data['Age_gp'] = np.where(data['Age']<=30,'30 이하', np.where(data['Age']<=40, '30-40', '40대 이상'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a44acf8-b614-4775-a32d-38a1a06a257f",
   "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>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>Age_gp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>40대 이상</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>40대 이상</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>30 이하</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>1465</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>884</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2061</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>613</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2062</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>155</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2064</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>30 이하</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1023</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2065</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>40대 이상</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>628</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2068</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0      41          1      Travel_Rarely       1102                   Sales   \n",
       "1      49          0  Travel_Frequently        279  Research & Development   \n",
       "2      37          1      Travel_Rarely       1373  Research & Development   \n",
       "3      33          0  Travel_Frequently       1392  Research & Development   \n",
       "4      27          0      Travel_Rarely        591  Research & Development   \n",
       "...   ...        ...                ...        ...                     ...   \n",
       "1465   36          0  Travel_Frequently        884  Research & Development   \n",
       "1466   39          0      Travel_Rarely        613  Research & Development   \n",
       "1467   27          0      Travel_Rarely        155  Research & Development   \n",
       "1468   49          0  Travel_Frequently       1023                   Sales   \n",
       "1469   34          0      Travel_Rarely        628  Research & Development   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
       "0                    1          2  Life Sciences              1   \n",
       "1                    8          1  Life Sciences              1   \n",
       "2                    2          2          Other              1   \n",
       "3                    3          4  Life Sciences              1   \n",
       "4                    2          1        Medical              1   \n",
       "...                ...        ...            ...            ...   \n",
       "1465                23          2        Medical              1   \n",
       "1466                 6          1        Medical              1   \n",
       "1467                 4          3  Life Sciences              1   \n",
       "1468                 2          3        Medical              1   \n",
       "1469                 8          3        Medical              1   \n",
       "\n",
       "      EmployeeNumber  ...  StandardHours StockOptionLevel  TotalWorkingYears  \\\n",
       "0                  1  ...             80                0                  8   \n",
       "1                  2  ...             80                1                 10   \n",
       "2                  4  ...             80                0                  7   \n",
       "3                  5  ...             80                0                  8   \n",
       "4                  7  ...             80                1                  6   \n",
       "...              ...  ...            ...              ...                ...   \n",
       "1465            2061  ...             80                1                 17   \n",
       "1466            2062  ...             80                1                  9   \n",
       "1467            2064  ...             80                1                  6   \n",
       "1468            2065  ...             80                0                 17   \n",
       "1469            2068  ...             80                0                  6   \n",
       "\n",
       "      TrainingTimesLastYear  WorkLifeBalance YearsAtCompany  \\\n",
       "0                         0                1              6   \n",
       "1                         3                3             10   \n",
       "2                         3                3              0   \n",
       "3                         3                3              8   \n",
       "4                         3                3              2   \n",
       "...                     ...              ...            ...   \n",
       "1465                      3                3              5   \n",
       "1466                      5                3              7   \n",
       "1467                      0                3              6   \n",
       "1468                      3                2              9   \n",
       "1469                      3                4              4   \n",
       "\n",
       "      YearsInCurrentRole YearsSinceLastPromotion  YearsWithCurrManager  Age_gp  \n",
       "0                      4                       0                     5  40대 이상  \n",
       "1                      7                       1                     7  40대 이상  \n",
       "2                      0                       0                     0   30-40  \n",
       "3                      7                       3                     0   30-40  \n",
       "4                      2                       2                     2   30 이하  \n",
       "...                  ...                     ...                   ...     ...  \n",
       "1465                   2                       0                     3   30-40  \n",
       "1466                   7                       1                     7   30-40  \n",
       "1467                   2                       0                     3   30 이하  \n",
       "1468                   6                       0                     8  40대 이상  \n",
       "1469                   3                       1                     2   30-40  \n",
       "\n",
       "[1470 rows x 36 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3b0980e-946b-4bc0-95e6-9f8cdd25cfa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_gp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30 이하</th>\n",
       "      <td>100</td>\n",
       "      <td>386</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-40</th>\n",
       "      <td>85</td>\n",
       "      <td>619</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40대 이상</th>\n",
       "      <td>52</td>\n",
       "      <td>465</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sum  count  ratio\n",
       "Age_gp                   \n",
       "30 이하   100    386   25.9\n",
       "30-40    85    619   13.7\n",
       "40대 이상   52    465   11.2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연령대별 이직률 확인\n",
    "# 이직률 = 이직자 / 전체수 * 100\n",
    "# 30대 이하 이직률 = 30대 이하 이직자 (Attrition sum) / 30대 이하 사람수 (Attrition count) \n",
    "df_gb = data.groupby('Age_gp')['Attrition'].agg(['sum','count'])\n",
    "df_gb['ratio'] = round(df_gb['sum']/df_gb['count']*100, 1)\n",
    "df_gb\n",
    "# 연령대가 낮을수록 이직률이 높은 것을 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e537dceb-9417-46e8-805f-34c06782b257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>87</td>\n",
       "      <td>588</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>150</td>\n",
       "      <td>882</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sum  count  ratio\n",
       "Gender                   \n",
       "Female   87    588   14.8\n",
       "Male    150    882   17.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 성별에 따른 이직률 현황\n",
    "# 성별 컬럼: 'Gender'\n",
    "df_gender = data.groupby(by='Gender')['Attrition'].agg(['sum','count'])\n",
    "df_gender['ratio']=round(df_gender['sum']/df_gender['count']*100, 1)\n",
    "df_gender\n",
    "# 여성보다 남성의 이직률이 더 높음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a2a59d40-8edd-417c-85c6-12321c3ac6cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>12</td>\n",
       "      <td>63</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research &amp; Development</th>\n",
       "      <td>133</td>\n",
       "      <td>961</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>92</td>\n",
       "      <td>446</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        sum  count  ratio\n",
       "Department                               \n",
       "Human Resources          12     63   19.0\n",
       "Research & Development  133    961   13.8\n",
       "Sales                    92    446   20.6"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 부서별 이직률 현황\n",
    "# Department\n",
    "df_dp=data.groupby('Department')['Attrition'].agg(['sum','count'])\n",
    "df_dp['ratio']=round(df_dp['sum']/df_dp['count']*100,1)\n",
    "df_dp\n",
    "# 판매 부서의 이직률이 더 높다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9aaaae-3ce7-4ac1-85b8-17baead1c7ce",
   "metadata": {},
   "source": [
    "### 가설1: 업무만족도는 높으나 인간관계로 인한 이직률이 높을 것이다~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cf392270-752c-461e-b42d-71a9616d0ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1465</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RelationshipSatisfaction  JobSatisfaction\n",
       "0                            1                4\n",
       "1                            4                2\n",
       "2                            2                3\n",
       "3                            3                3\n",
       "4                            4                2\n",
       "...                        ...              ...\n",
       "1465                         3                4\n",
       "1466                         1                1\n",
       "1467                         2                2\n",
       "1468                         4                2\n",
       "1469                         1                3\n",
       "\n",
       "[1470 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인간관계 만족도와 업무만족도상의 상관관계 확인\n",
    "# 인간과계 만족도: RelationshipSatisfaction, 업무만족도: JobSatifaction)\n",
    "# 숫자가 클수록 만족!\n",
    "data[['RelationshipSatisfaction', 'JobSatisfaction']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f4ae1938-c4ac-4af2-8f08-e310533e549e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66</td>\n",
       "      <td>289</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "      <td>280</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73</td>\n",
       "      <td>442</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>459</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sum  count  ratio\n",
       "JobSatisfaction                   \n",
       "1                 66    289   22.8\n",
       "2                 46    280   16.4\n",
       "3                 73    442   16.5\n",
       "4                 52    459   11.3"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 업무 만족도별 이직률현황\n",
    "data['JobSatisfaction'].unique()\n",
    "df = data.groupby('JobSatisfaction')['Attrition'].agg(['sum','count'])\n",
    "df['ratio']=round(df['sum']/df['count']*100,1)\n",
    "df\n",
    "# 업무만족도가 낮을수록 이직률이 높다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3e2265dc-4b2d-4d33-ac76-7a52577c1b6d",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>276</td>\n",
       "      <td>20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>303</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71</td>\n",
       "      <td>459</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64</td>\n",
       "      <td>432</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          sum  count  ratio\n",
       "RelationshipSatisfaction                   \n",
       "1                          57    276   20.7\n",
       "2                          45    303   14.9\n",
       "3                          71    459   15.5\n",
       "4                          64    432   14.8"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인간관계 만족도별 이직률현황\n",
    "df = data.groupby('RelationshipSatisfaction')['Attrition'].agg(['sum','count'])\n",
    "df['ratio']=round(df['sum']/df['count']*100,1)                                                                           \n",
    "df\n",
    "# 업무만족도가 낮을수록 이직률이 높다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "220e864f-eddd-4a62-bba6-efa6291a599b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>57</td>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>91</td>\n",
       "      <td>24.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>87</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>28.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>57</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>81</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>89</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>85</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>94</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>133</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>130</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">4</th>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>84</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>95</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>154</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>126</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          sum  count  ratio\n",
       "JobSatisfaction RelationshipSatisfaction                   \n",
       "1               1                          16     54   29.6\n",
       "                2                          15     57   26.3\n",
       "                3                          22     91   24.2\n",
       "                4                          13     87   14.9\n",
       "2               1                          15     53   28.3\n",
       "                2                           8     57   14.0\n",
       "                3                          13     81   16.0\n",
       "                4                          10     89   11.2\n",
       "3               1                          16     85   18.8\n",
       "                2                          11     94   11.7\n",
       "                3                          19    133   14.3\n",
       "                4                          27    130   20.8\n",
       "4               1                          10     84   11.9\n",
       "                2                          11     95   11.6\n",
       "                3                          17    154   11.0\n",
       "                4                          14    126   11.1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인간관계, 업무만족도 두 컬럼의 관계 확인\n",
    "df = data.groupby(['JobSatisfaction','RelationshipSatisfaction'])['Attrition'].agg(['sum','count'])\n",
    "df['ratio']=round(df['sum']/df['count']*100,1)\n",
    "df\n",
    "# 결과해석\n",
    "# 업무만족도가 높은 직원은 인간관계에 따른 이직률 변동이 적다(영향을 덜 받음)\n",
    "# 업무만족도가 낮은 직원은 이직률에 인간관계 영향력이 큰 것 확인\n",
    "\n",
    "# 우리의가설: 업무만족도는 높으나 인간관계로 인한 이직률이 높을 것이다\n",
    "# 우리의 가설이 잘못 설정됨 -> 가설 수정 -> 결과 확인\n",
    "# 업무만족도가 이직률에 미치는 영향이 더 높구나! -> 직원들의 업무 만족도 향상을 위해 힘쓸 것!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78bde5be-49b3-411a-b5b9-c261d0f62d55",
   "metadata": {},
   "source": [
    "### 가설2) 근속년수 대비 같은 업무를 한 비중이 높다면 이직률이 높을 것이다?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "87a8c25e-517e-4104-a251-f7bba78da1d3",
   "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>YearsInCurrentRole</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>Attrition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      YearsInCurrentRole  YearsAtCompany  Attrition\n",
       "0                      4               6          1\n",
       "1                      7              10          0\n",
       "2                      0               0          1\n",
       "3                      7               8          0\n",
       "4                      2               2          0\n",
       "...                  ...             ...        ...\n",
       "1465                   2               5          0\n",
       "1466                   7               7          0\n",
       "1467                   2               6          0\n",
       "1468                   6               9          0\n",
       "1469                   3               4          0\n",
       "\n",
       "[1470 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YearsInCurrentRole: 현재 역할에서 근무한 기간\n",
    "# YearsAtCompany : 현재까지 근무한 기간\n",
    "data[['YearsInCurrentRole','YearsAtCompany','Attrition']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "752df8fd-9a78-4dd3-8bed-6c8a93a5aa25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.666667\n",
       "1       0.700000\n",
       "2       0.000000\n",
       "3       0.875000\n",
       "4       1.000000\n",
       "          ...   \n",
       "1465    0.400000\n",
       "1466    1.000000\n",
       "1467    0.333333\n",
       "1468    0.666667\n",
       "1469    0.750000\n",
       "Name: Role_Company, Length: 1470, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 근속년수 대비 한가지 업종에서 일한 비중\n",
    "data['Role_Company']=data['YearsInCurrentRole']/data['YearsAtCompany']\n",
    "data['Role_Company'].fillna(0, inplace=True)\n",
    "data['Role_Company']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "744f35ff-80a0-47ab-ad58-6ec41a5da0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "68ea44e5-acc8-4e81-9511-8b8d0b939919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Role_Company', ylabel='Density'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Role_Company 분포 확인\n",
    "# distplot : 데이터의 분포를 확인하는데 주로 사용됨\n",
    "# 히스토그램과 커널밀도추정을 함께 그려서 데이터의 분포를 확인\n",
    "sns.distplot(data['Role_Company'])"
   ]
  },
  {
   "attachments": {
    "70c1b29c-28de-49b9-9823-d94fb47e0305.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "964b4c93-490b-4d1b-9c29-a7ea598d2a9d",
   "metadata": {},
   "source": [
    "![image.png](attachment:70c1b29c-28de-49b9-9823-d94fb47e0305.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "7f73b54c-ec34-48c1-b624-d7da7bd68aa6",
   "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>Role_Company</th>\n",
       "      <th>Role_Company_gp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.6초과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.6초과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3이하</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.6초과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.6초과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.3-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.6초과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.3-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.6초과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.6초과</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Role_Company Role_Company_gp\n",
       "0         0.666667           0.6초과\n",
       "1         0.700000           0.6초과\n",
       "2         0.000000           0.3이하\n",
       "3         0.875000           0.6초과\n",
       "4         1.000000           0.6초과\n",
       "...            ...             ...\n",
       "1465      0.400000         0.3-0.6\n",
       "1466      1.000000           0.6초과\n",
       "1467      0.333333         0.3-0.6\n",
       "1468      0.666667           0.6초과\n",
       "1469      0.750000           0.6초과\n",
       "\n",
       "[1470 rows x 2 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 카테고리화~\n",
    "# np.where()\n",
    "# '0.3이하', '0.3-0.6', '0.6 초과'\n",
    "# Role_Company_gp\n",
    "data['Role_Company_gp'] = np.where(data['Role_Company']<=0.3, '0.3이하', np.where(data['Role_Company']<=0.6, '0.3-0.6', '0.6초과'))\n",
    "data[['Role_Company','Role_Company_gp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d1378e8d-c015-47ea-b300-bff511b0fd80",
   "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>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role_Company_gp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.3이하</th>\n",
       "      <td>89</td>\n",
       "      <td>323</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3-0.6</th>\n",
       "      <td>38</td>\n",
       "      <td>321</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6초과</th>\n",
       "      <td>110</td>\n",
       "      <td>826</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sum  count  ratio\n",
       "Role_Company_gp                   \n",
       "0.3이하             89    323   27.6\n",
       "0.3-0.6           38    321   11.8\n",
       "0.6초과            110    826   13.3"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 근속년수 대비 같은일을 오래 한 사람들의 이직률 파악\n",
    "df = data.groupby('Role_Company_gp')['Attrition'].agg(['sum','count'])\n",
    "df['ratio']=round(df['sum']/df['count']*100,1)\n",
    "df.loc[['0.3이하','0.3-0.6','0.6초과']]\n",
    "# 근속 년수 대비 같은 일을 짧게 한 사람일 수록 이직률이 높은 것!\n",
    "# 가설 수정 -> 근속년수 대비 한 업무를 길게 할 수 있도록 권장"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81dd284e-b7b6-4abb-9db0-77b64db61e38",
   "metadata": {},
   "source": [
    "### 가설3) 야근을 많이 해도 급여인상률이 높다면 이직률이 낮을 것이다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "8ef666c6-dcfa-45f9-bd93-b64dfd17bcd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "1465    0\n",
       "1466    0\n",
       "1467    0\n",
       "1468    0\n",
       "1469    0\n",
       "Name: OverTime, Length: 1470, dtype: int32"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['OverTime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "cbb8f1ab-ef98-4388-ab0f-93be2d1a656c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>110</td>\n",
       "      <td>1054</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>127</td>\n",
       "      <td>416</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sum  count  ratio\n",
       "OverTime                   \n",
       "No        110   1054   10.4\n",
       "Yes       127    416   30.5"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 야근여부에 따른 이직률 현황\n",
    "# OverTime\n",
    "df_overtime = data.groupby('OverTime')['Attrition'].agg(['sum','count'])\n",
    "df_overtime['ratio']=round(df_overtime['sum']/df_overtime['count']*100,1)\n",
    "df_overtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "15a11e52-2488-4040-890b-48205aa9aa1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>41</td>\n",
       "      <td>210</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33</td>\n",
       "      <td>198</td>\n",
       "      <td>16.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>34</td>\n",
       "      <td>209</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24</td>\n",
       "      <td>201</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18</td>\n",
       "      <td>101</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14</td>\n",
       "      <td>82</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13</td>\n",
       "      <td>89</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>76</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7</td>\n",
       "      <td>55</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sum  count  ratio\n",
       "PercentSalaryHike                   \n",
       "11                  41    210   19.5\n",
       "12                  33    198   16.7\n",
       "13                  34    209   16.3\n",
       "14                  24    201   11.9\n",
       "15                  18    101   17.8\n",
       "16                  14     78   17.9\n",
       "17                  14     82   17.1\n",
       "18                  13     89   14.6\n",
       "19                   9     76   11.8\n",
       "20                   7     55   12.7\n",
       "21                   5     48   10.4\n",
       "22                  12     56   21.4\n",
       "23                   6     28   21.4\n",
       "24                   6     21   28.6\n",
       "25                   1     18    5.6"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연봉인상률에 따른 이직률 현황\n",
    "# PercentSalaryHike\n",
    "df_p=data.groupby('PercentSalaryHike')['Attrition'].agg(['sum','count'])\n",
    "df_p['ratio'] = round(df_p['sum']/df_p['count']*100,1)\n",
    "df_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "4f4f9fac-2d27-4a03-a1bd-e91699bad33c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <th>ratio</th>\n",
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       "    <tr>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th rowspan=\"15\" valign=\"top\">No</th>\n",
       "      <th>11</th>\n",
       "      <td>19</td>\n",
       "      <td>149</td>\n",
       "      <td>12.8</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>139</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>144</td>\n",
       "      <td>9.7</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>145</td>\n",
       "      <td>5.5</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12</td>\n",
       "      <td>80</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>8.3</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>5.6</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>14.6</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">Yes</th>\n",
       "      <th>11</th>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "      <td>36.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>55.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>55.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            sum  count  ratio\n",
       "OverTime PercentSalaryHike                   \n",
       "No       11                  19    149   12.8\n",
       "         12                  18    139   12.9\n",
       "         13                  14    144    9.7\n",
       "         14                   8    145    5.5\n",
       "         15                  12     80   15.0\n",
       "         16                   8     55   14.5\n",
       "         17                   9     66   13.6\n",
       "         18                   5     60    8.3\n",
       "         19                   3     55    5.5\n",
       "         20                   4     40   10.0\n",
       "         21                   2     36    5.6\n",
       "         22                   6     41   14.6\n",
       "         23                   1     19    5.3\n",
       "         24                   1     12    8.3\n",
       "         25                   0     13    0.0\n",
       "Yes      11                  22     61   36.1\n",
       "         12                  15     59   25.4\n",
       "         13                  20     65   30.8\n",
       "         14                  16     56   28.6\n",
       "         15                   6     21   28.6\n",
       "         16                   6     23   26.1\n",
       "         17                   5     16   31.2\n",
       "         18                   8     29   27.6\n",
       "         19                   6     21   28.6\n",
       "         20                   3     15   20.0\n",
       "         21                   3     12   25.0\n",
       "         22                   6     15   40.0\n",
       "         23                   5      9   55.6\n",
       "         24                   5      9   55.6\n",
       "         25                   1      5   20.0"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 야근과 연봉인상률의 상관관계에 따른 이직률\n",
    "df=data.groupby(['OverTime','PercentSalaryHike'])['Attrition'].agg(['sum','count'])\n",
    "df['ratio'] = round(df['sum']/df['count']*100,1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "1bc0e442-8212-48fa-aed3-9c181af3ea4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.440000000000001"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 야근 No 직원들의 평균 이직률 확인\n",
    "temp = df.reset_index()\n",
    "temp[temp['OverTime']==\"No\"]['ratio'].mean()\n",
    "# 야근을 하지 않는 직원의 연봉인상률"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "3452f8f8-b947-49f2-996d-c1750072c628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.94666666666667"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 야근 Yes 직원들의 평균 이직률 확인\n",
    "temp[temp['OverTime']==\"Yes\"]['ratio'].mean()\n",
    "# 결과 -> 야근을 할수록 이직률이 높은 것을 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "c048f23a-47c3-4a0b-be3f-07d5122a7e1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHRERMXfu3CH777zzzrj22msjImLFihWxd+/eWLx4cezcuTNmzpwZmzZtitra2rIMDABUn2EFSZZlx3xOLpeLYrEYxWJxpDMBAGOMz7IBAJITJABAcoIEAEhOkAAAyQkSACA5QQIAJCdIAIDkBAkAkJwgAQCSEyQAQHKCBABITpAAAMkJEgAgOUECACQnSACA5AQJAJCcIAEAkhMkAEByggQASE6QAADJCRIAIDlBAgAkJ0gAgOQECQCQnCABAJITJABAcoIEAEhOkAAAyQkSACA5QQIAJCdIAIDkBAkAkJwgAQCSEyQAQHKCBABITpAAAMkJEgAgOUECACQnSACA5AQJAJCcIAEAkhMkAEByggQASE6QAADJCRIAIDlBAgAkJ0gAgOQECQCQnCABAJITJABAcoIEAEhOkAAAyQkSACC5YQfJgw8+GAsWLIiGhobI5XJxzz33DDmeZVkUi8VoaGiImpqamDt3buzYsaNc8wIAVWjYQbJ79+644IILoq2t7YjH165dG62trdHW1hadnZ1RKBRi3rx5MTAwMOphAYDqNG64XzB//vyYP3/+EY9lWRbr1q2LVatWxcKFCyMiYv369VFfXx8bNmyI6667bnTTAgBVqax/Q9LV1RU9PT3R3Nxc2pfP52POnDmxZcuWI37N4OBg9Pf3D9kAgLGlrEHS09MTERH19fVD9tfX15eOHWr16tVRV1dX2qZNm1bOkQCAk0BF3mWTy+WGPM6y7LB9B61cuTL6+vpKW3d3dyVGAgBOYMP+G5KXUigUIuKFOyVTpkwp7e/t7T3srslB+Xw+8vl8OccAAE4yZb1D0tjYGIVCIdrb20v79u3bFx0dHTFr1qxyngoAqCLDvkOya9eu+POf/1x63NXVFdu3b4/JkyfH6aefHsuWLYuWlpZoamqKpqamaGlpiYkTJ8aiRYvKOjgAUD2GHSQPP/xwXHbZZaXHy5cvj4iIa665Jr7//e/HihUrYu/evbF48eLYuXNnzJw5MzZt2hS1tbXlmxoAqCrDDpK5c+dGlmVHPZ7L5aJYLEaxWBzNXADAGOKzbACA5AQJAJCcIAEAkhMkAEByggQASE6QAADJCRIAIDlBAgAkJ0gAgOQECQCQnCABAJITJABAcoIEAEhOkAAAyQkSACA5QQIAJCdIAIDkBAkAkJwgAQCSEyQAQHKCBABITpAAAMkJEgAgOUECACQnSACA5AQJAJCcIAEAkhMkAEByggQASE6QAADJCRIAIDlBAgAkJ0gAgOQECQCQnCABAJITJABAcoIEAEhOkAAAyQkSACA5QQIAJCdIAIDkBAkAkJwgAQCSEyQAQHKCBABITpAAAMkJEgAgOUECACQnSACA5AQJAJCcIAEAkhMkAEByFQuS22+/PRobG2PChAlx4YUXxkMPPVSpUwEAJ7mKBMmPfvSjWLZsWaxatSp+//vfx9vf/vaYP39+/P3vf6/E6QCAk1xFgqS1tTU+8YlPxCc/+ck4++yzY926dTFt2rS44447KnE6AOAkN67cL7hv37545JFH4uabbx6yv7m5ObZs2XLY8wcHB2NwcLD0uK+vLyIi+vv7yz1aREQcGNxTkdc96GhzV/q8Kc9tzcfvvCnPbc3H77wpz23N6c+b+tyjfc0sy0b2AlmZ/fOf/8wiIvvNb34zZP9tt92WnXnmmYc9/9Zbb80iwmaz2Ww2WxVs3d3dI+qHst8hOSiXyw15nGXZYfsiIlauXBnLly8vPT5w4ED897//jVNPPbX0/P7+/pg2bVp0d3fHpEmTKjXyCcWarbkajbX1RlizNVevQ9ecZVkMDAxEQ0PDiF6v7EHyute9Ll75yldGT0/PkP29vb1RX19/2PPz+Xzk8/kh+17zmtcc8bUnTZo0Zr7RB1nz2DDW1jzW1hthzWPFWF9zXV3diF+n7H/UOn78+Ljwwgujvb19yP729vaYNWtWuU8HAFSBivzKZvny5XH11VfHRRddFJdcckl85zvfib///e9x/fXXV+J0AMBJriJBcuWVV8Z//vOf+PKXvxzPPvtszJgxI372s5/F9OnTR/R6+Xw+br311sN+tVPNrHlsGGtrHmvrjbDmscKaRy+XZSN9fw4AQHn4LBsAIDlBAgAkJ0gAgOQECQCQ3AkTJA8++GAsWLAgGhoaIpfLxT333DPkeJZlUSwWo6GhIWpqamLu3LmxY8eONMOWyUutef/+/fH5z38+zjvvvDjllFOioaEhPv7xj8czzzyTbuAyONb3+cWuu+66yOVysW7duuM2XyW8nDU//vjj8d73vjfq6uqitrY23va2t53Un459rDXv2rUrli5dGlOnTo2ampo4++yzT/oP31y9enVcfPHFUVtbG6eddlq8//3vjyeeeGLIc6rpOnas9VbjNezlfI9frBquYS93zeW4hp0wQbJ79+644IILoq2t7YjH165dG62trdHW1hadnZ1RKBRi3rx5MTAwcJwnLZ+XWvOePXti27Ztccstt8S2bdvi7rvvjieffDLe+973Jpi0fI71fT7onnvuid/+9rcj/ieITyTHWvNf/vKXmD17drz5zW+OBx54IP7whz/ELbfcEhMmTDjOk5bPsdZ84403xsaNG+OHP/xhPP7443HjjTfGDTfcEPfee+9xnrR8Ojo6YsmSJbF169Zob2+P5557Lpqbm2P37t2l51TTdexY663Ga9jL+R4fVC3XsJez5rJdw0b0CTgVFhHZj3/849LjAwcOZIVCIVuzZk1p3//+97+srq4u+/a3v51gwvI7dM1H8rvf/S6LiOzpp58+PkNV2NHW/I9//CN7wxvekP3xj3/Mpk+fnn3jG9847rNVypHWfOWVV2Yf+9jH0gx0HBxpzeeee2725S9/eci+t771rdkXv/jF4zhZZfX29mYRkXV0dGRZVv3XsUPXeyTVdg072pqr+Rp2pDWX6xp2wtwheSldXV3R09MTzc3NpX35fD7mzJkTW7ZsSTjZ8dXX1xe5XO6on/VTDQ4cOBBXX3113HTTTXHuueemHqfiDhw4EPfff3+ceeaZ8a53vStOO+20mDlz5kv+KqsazJ49O+6777745z//GVmWxebNm+PJJ5+Md73rXalHK5u+vr6IiJg8eXJEVP917ND1Hu051XQNO9Kaq/0aduiay3kNOymC5OAH9R364Xz19fWHfYhftfrf//4XN998cyxatKiqP7jpq1/9aowbNy4+/elPpx7luOjt7Y1du3bFmjVr4t3vfnds2rQpPvCBD8TChQujo6Mj9XgV861vfSvOOeecmDp1aowfPz7e/e53x+233x6zZ89OPVpZZFkWy5cvj9mzZ8eMGTMiorqvY0da76Gq7Rp2tDVX8zXsSGsu5zWsIv90fKXkcrkhj7MsO2xfNdq/f39cddVVceDAgbj99ttTj1MxjzzySHzzm9+Mbdu2jYnva8QLP11ERLzvfe+LG2+8MSIi3vKWt8SWLVvi29/+dsyZMyfleBXzrW99K7Zu3Rr33XdfTJ8+PR588MFYvHhxTJkyJS6//PLU443a0qVL49FHH41f//rXhx2rxuvYS603ojqvYUdac7Vfw4605nJew06KOySFQiEi4rCfInp7ew/7aaPa7N+/Pz784Q9HV1dXtLe3V8VPFkfz0EMPRW9vb5x++ukxbty4GDduXDz99NPx2c9+Ns4444zU41XE6173uhg3blycc845Q/afffbZJ/W7bF7K3r174wtf+EK0trbGggUL4vzzz4+lS5fGlVdeGV/72tdSjzdqN9xwQ9x3332xefPmmDp1aml/tV7Hjrbeg6rxGna0NVfzNexoay7nNeykCJLGxsYoFArR3t5e2rdv377o6OiIWbNmJZyssg7+h/zUU0/FL3/5yzj11FNTj1RRV199dTz66KOxffv20tbQ0BA33XRT/OIXv0g9XkWMHz8+Lr744sPeRvfkk0+O+MMoT3T79++P/fv3xyteMfTy88pXvrL009bJKMuyWLp0adx9993xq1/9KhobG4ccr7br2LHWG1F917Bjrbkar2HHWnM5r2EnzK9sdu3aFX/+859Lj7u6umL79u0xefLkOP3002PZsmXR0tISTU1N0dTUFC0tLTFx4sRYtGhRwqlH56XW3NDQEB/60Idi27Zt8dOf/jSef/750k9WkydPjvHjx6cae1SO9X0+9IL1qle9KgqFQpx11lnHe9SyOdaab7rpprjyyivj0ksvjcsuuyw2btwYP/nJT+KBBx5IN/QoHWvNc+bMiZtuuilqampi+vTp0dHRET/4wQ+itbU14dSjs2TJktiwYUPce++9UVtbW/rvta6uLmpqaiKXy1XVdexY633uueeq7hp2rDWfeuqpVXcNO9aaI6J817BRv0+nTDZv3pxFxGHbNddck2XZC2+Zu/XWW7NCoZDl8/ns0ksvzR577LG0Q4/SS625q6vriMciItu8eXPq0UfsWN/nQ1XDW+Zezpq/973vZW9605uyCRMmZBdccEF2zz33pBu4DI615meffTa79tprs4aGhmzChAnZWWedlX3961/PDhw4kHbwUTjaf6933nln6TnVdB071nqr8Rr2cr7HhzrZr2Evd83luIbl/t8JAQCSOSn+hgQAqG6CBABITpAAAMkJEgAgOUECACQnSACA5AQJAJCcIAEAkhMkAEByggQASE6QAADJCRIAILn/A7q/2FMkMsbIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 야근을 Yes하는 직원의 데이터 분석\n",
    "over_yes = temp[temp['OverTime']==\"Yes\"]\n",
    "over_yes\n",
    "# 시각화(막대그래프, 연봉인상률(X), 이직률(y))\n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(over_yes['PercentSalaryHike'], over_yes['ratio'])\n",
    "plt.show()\n",
    "# 연봉인상률 20%까지는 이직률이 조금은 감소하는 듯 싶으나\n",
    "# 21% 이상부터는 이직률이 증가(핵심인재의 이탈)\n",
    "# 연봉인상률이 높은 직원은 야근시간을 줄이는 것이 핵심인재의 이탈을 방지하는 요소 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e5c57c-6d15-4397-b0d6-803aa743ac8e",
   "metadata": {},
   "source": [
    "### 모델선택 및 하이퍼 파라미터 조절\n",
    "- 데이터 타입별로 구분(숫자/문자)\n",
    "- 문자형태의 데이터는 인코딩 (숫자형태로 변경)\n",
    "- 데이터 정리 (불필요한 컬럼 삭제)\n",
    "- X,y 분리\n",
    "- train, test 데이터 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "bcaf29dc-3fbc-4e70-8741-526eba94a2df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Age                       1470 non-null   int64 \n",
      " 1   Attrition                 1470 non-null   int32 \n",
      " 2   BusinessTravel            1470 non-null   object\n",
      " 3   DailyRate                 1470 non-null   int64 \n",
      " 4   Department                1470 non-null   object\n",
      " 5   DistanceFromHome          1470 non-null   int64 \n",
      " 6   Education                 1470 non-null   int64 \n",
      " 7   EducationField            1470 non-null   object\n",
      " 8   EmployeeCount             1470 non-null   int64 \n",
      " 9   EmployeeNumber            1470 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
      " 11  Gender                    1470 non-null   object\n",
      " 12  HourlyRate                1470 non-null   int64 \n",
      " 13  JobInvolvement            1470 non-null   int64 \n",
      " 14  JobLevel                  1470 non-null   int64 \n",
      " 15  JobRole                   1470 non-null   object\n",
      " 16  JobSatisfaction           1470 non-null   int64 \n",
      " 17  MaritalStatus             1470 non-null   object\n",
      " 18  MonthlyIncome             1470 non-null   int64 \n",
      " 19  MonthlyRate               1470 non-null   int64 \n",
      " 20  NumCompaniesWorked        1470 non-null   int64 \n",
      " 21  Over18                    1470 non-null   object\n",
      " 22  OverTime                  1470 non-null   object\n",
      " 23  PercentSalaryHike         1470 non-null   int64 \n",
      " 24  PerformanceRating         1470 non-null   int64 \n",
      " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
      " 26  StandardHours             1470 non-null   int64 \n",
      " 27  StockOptionLevel          1470 non-null   int64 \n",
      " 28  TotalWorkingYears         1470 non-null   int64 \n",
      " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
      " 30  WorkLifeBalance           1470 non-null   int64 \n",
      " 31  YearsAtCompany            1470 non-null   int64 \n",
      " 32  YearsInCurrentRole        1470 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
      " 34  YearsWithCurrManager      1470 non-null   int64 \n",
      "dtypes: int32(1), int64(26), object(8)\n",
      "memory usage: 396.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "343a47b7-cdc4-457e-9435-4c8017dec370",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64 False\n",
      "int32 False\n",
      "object True\n",
      "int64 False\n",
      "object True\n",
      "int64 False\n",
      "int64 False\n",
      "object True\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "object True\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "object True\n",
      "int64 False\n",
      "object True\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "object True\n",
      "object True\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n",
      "int64 False\n"
     ]
    }
   ],
   "source": [
    "# 데이터 타입에 따른 컬럼 분리\n",
    "for i in data.columns:\n",
    "    print(data[i].dtype, data[i].dtype== 'O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "acdc8059-8496-4ba8-8deb-ba3681c71d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 숫자 타입의 컬럼과 문자타입의 컬럼을 각각 리스트에 저장\n",
    "numeric_list = []\n",
    "categorical_list = []\n",
    "\n",
    "for i in data.columns:\n",
    "    if data[i].dtype =='O':\n",
    "        categorical_list.append(i)\n",
    "    else:\n",
    "        numeric_list.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "8c765277-d45f-40fd-bc03-0b89a7ed0ea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole', 'MaritalStatus', 'Over18', 'OverTime']\n"
     ]
    }
   ],
   "source": [
    "print(categorical_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "13e1cb6e-b757-4ea3-a2ce-3e1c86f45066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'Attrition',\n",
       " 'DailyRate',\n",
       " 'DistanceFromHome',\n",
       " 'Education',\n",
       " 'EmployeeCount',\n",
       " 'EmployeeNumber',\n",
       " 'EnvironmentSatisfaction',\n",
       " 'HourlyRate',\n",
       " 'JobInvolvement',\n",
       " 'JobLevel',\n",
       " 'JobSatisfaction',\n",
       " 'MonthlyIncome',\n",
       " 'MonthlyRate',\n",
       " 'NumCompaniesWorked',\n",
       " 'PercentSalaryHike',\n",
       " 'PerformanceRating',\n",
       " 'RelationshipSatisfaction',\n",
       " 'StandardHours',\n",
       " 'StockOptionLevel',\n",
       " 'TotalWorkingYears',\n",
       " 'TrainingTimesLastYear',\n",
       " 'WorkLifeBalance',\n",
       " 'YearsAtCompany',\n",
       " 'YearsInCurrentRole',\n",
       " 'YearsSinceLastPromotion',\n",
       " 'YearsWithCurrManager']"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "63a7c4b0-1d15-4b5e-ba77-5a65a05d1b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델링을 위한 데이터 분리\n",
    "# 문제(X), 정답(y)\n",
    "# 가설 확인을 위해 만들 컬럼들은 삭제\n",
    "X = data.drop(['Attrition'], axis=1)\n",
    "y = data['Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "1925c8c9-273e-4c94-b8c7-15e20090e58c",
   "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>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>1465</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>884</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2061</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>613</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2062</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2064</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>1023</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2065</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>628</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2068</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  BusinessTravel  DailyRate              Department  \\\n",
       "0      41               1       1102                   Sales   \n",
       "1      49               2        279  Research & Development   \n",
       "2      37               1       1373  Research & Development   \n",
       "3      33               2       1392  Research & Development   \n",
       "4      27               1        591  Research & Development   \n",
       "...   ...             ...        ...                     ...   \n",
       "1465   36               2        884  Research & Development   \n",
       "1466   39               1        613  Research & Development   \n",
       "1467   27               1        155  Research & Development   \n",
       "1468   49               2       1023                   Sales   \n",
       "1469   34               1        628  Research & Development   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
       "0                    1          2  Life Sciences              1   \n",
       "1                    8          1  Life Sciences              1   \n",
       "2                    2          2          Other              1   \n",
       "3                    3          4  Life Sciences              1   \n",
       "4                    2          1        Medical              1   \n",
       "...                ...        ...            ...            ...   \n",
       "1465                23          2        Medical              1   \n",
       "1466                 6          1        Medical              1   \n",
       "1467                 4          3  Life Sciences              1   \n",
       "1468                 2          3        Medical              1   \n",
       "1469                 8          3        Medical              1   \n",
       "\n",
       "      EmployeeNumber  EnvironmentSatisfaction  ... RelationshipSatisfaction  \\\n",
       "0                  1                        2  ...                        1   \n",
       "1                  2                        3  ...                        4   \n",
       "2                  4                        4  ...                        2   \n",
       "3                  5                        4  ...                        3   \n",
       "4                  7                        1  ...                        4   \n",
       "...              ...                      ...  ...                      ...   \n",
       "1465            2061                        3  ...                        3   \n",
       "1466            2062                        4  ...                        1   \n",
       "1467            2064                        2  ...                        2   \n",
       "1468            2065                        4  ...                        4   \n",
       "1469            2068                        2  ...                        1   \n",
       "\n",
       "      StandardHours  StockOptionLevel  TotalWorkingYears  \\\n",
       "0                80                 0                  8   \n",
       "1                80                 1                 10   \n",
       "2                80                 0                  7   \n",
       "3                80                 0                  8   \n",
       "4                80                 1                  6   \n",
       "...             ...               ...                ...   \n",
       "1465             80                 1                 17   \n",
       "1466             80                 1                  9   \n",
       "1467             80                 1                  6   \n",
       "1468             80                 0                 17   \n",
       "1469             80                 0                  6   \n",
       "\n",
       "     TrainingTimesLastYear  WorkLifeBalance YearsAtCompany  \\\n",
       "0                        0                1              6   \n",
       "1                        3                3             10   \n",
       "2                        3                3              0   \n",
       "3                        3                3              8   \n",
       "4                        3                3              2   \n",
       "...                    ...              ...            ...   \n",
       "1465                     3                3              5   \n",
       "1466                     5                3              7   \n",
       "1467                     0                3              6   \n",
       "1468                     3                2              9   \n",
       "1469                     3                4              4   \n",
       "\n",
       "      YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                      4                        0                     5  \n",
       "1                      7                        1                     7  \n",
       "2                      0                        0                     0  \n",
       "3                      7                        3                     0  \n",
       "4                      2                        2                     2  \n",
       "...                  ...                      ...                   ...  \n",
       "1465                   2                        0                     3  \n",
       "1466                   7                        1                     7  \n",
       "1467                   2                        0                     3  \n",
       "1468                   6                        0                     8  \n",
       "1469                   3                        1                     2  \n",
       "\n",
       "[1470 rows x 34 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 레이블 인코딩\n",
    "# BusinessTravel : 출장빈도 ->  Non-Travel, Travel_Rarely, Travel_Frequently\n",
    "# 출장이 많을 수록 이직률에 영향을 많이 미치지 않을까?\n",
    "# 출장이 잦은 데이터에는 높은 숫자를 매칭\n",
    "\n",
    "# 딕셔너리를 통해서 각 데이터에 숫자 매칭\n",
    "BT_dict = {'Non-Travel':0, 'Travel_Rarely':1, 'Travel_Frequently':2}\n",
    "\n",
    "# map(): 값을 맵핑해주는 함수 같은 키값을 밸류로 변경\n",
    "X['BusinessTravel']=X['BusinessTravel'].map(BT_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "6fed1da9-6187-457a-8d64-f7ee6dae5397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       2\n",
       "2       1\n",
       "3       2\n",
       "4       1\n",
       "       ..\n",
       "1465    2\n",
       "1466    1\n",
       "1467    1\n",
       "1468    2\n",
       "1469    1\n",
       "Name: BusinessTravel, Length: 1470, dtype: int64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['BusinessTravel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "0304af6c-4d97-4079-a1e8-91aab05a530c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 나머지 컬럼은 원핫인코딩 진행!\n",
    "X_one_hot = pd.get_dummies(X, dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "32fda3c7-2da9-40f2-9857-05921aefa42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 분리 도구 불러오기\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_one_hot, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "ce5e73d7-cbfa-41ad-b996-759a58d6e330",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1029, 53)\n",
      "(1029,)\n",
      "(441, 53)\n",
      "(441,)\n"
     ]
    }
   ],
   "source": [
    "# 데이터 크기확인\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a25eb80-9ae1-4abc-b963-3019a9caa54d",
   "metadata": {},
   "source": [
    "### 모델링\n",
    "- 모델 객체 생성\n",
    "- 모델 학습\n",
    "- 모델 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "16b8b26d-7b75-4e40-802a-56b5e2894f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 객체 생성 LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logi_model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "1539eb4d-debd-4273-bb87-2d66c4258435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모델 학습\n",
    "# 모델명.fit(학습용문제, 학습용정답)\n",
    "logi_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "d36447be-6dd4-41e6-ad1b-a5be838860a6",
   "metadata": {},
   "outputs": [
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      ]
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     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 평가\n",
    "# 모델명.score(테스트용 문제, 테스트용 정답)\n",
    "logi_model.score(X_test, y_test)\n",
    "# 정확도 accuracy"
   ]
  },
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