data_engineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</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>Yes</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",
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       "      <td>No</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>4</td>\n",
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       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
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       "      <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>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>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "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",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber   \n",
       "0                 1          2  Life Sciences              1               1  \\\n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel   \n",
       "0  ...                         1            80                 0  \\\n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany   \n",
       "0                  8                      0               1               6  \\\n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \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",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('HR Employee Attrition.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "['Y']\n",
      "[80]\n"
     ]
    },
    {
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       "      <td>No</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Medical</td>\n",
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       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "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",
       "   DistanceFromHome  Education EducationField  EmployeeNumber   \n",
       "0                 1          2  Life Sciences               1  \\\n",
       "1                 8          1  Life Sciences               2   \n",
       "2                 2          2          Other               4   \n",
       "3                 3          4  Life Sciences               5   \n",
       "4                 2          1        Medical               7   \n",
       "\n",
       "   EnvironmentSatisfaction  ... PerformanceRating  RelationshipSatisfaction   \n",
       "0                        2  ...                 3                         1  \\\n",
       "1                        3  ...                 4                         4   \n",
       "2                        4  ...                 3                         2   \n",
       "3                        4  ...                 3                         3   \n",
       "4                        1  ...                 3                         4   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears TrainingTimesLastYear  WorkLifeBalance   \n",
       "0                 0                  8                     0                1  \\\n",
       "1                 1                 10                     3                3   \n",
       "2                 0                  7                     3                3   \n",
       "3                 0                  8                     3                3   \n",
       "4                 1                  6                     3                3   \n",
       "\n",
       "  YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion   \n",
       "0              6                   4                        0  \\\n",
       "1             10                   7                        1   \n",
       "2              0                   0                        0   \n",
       "3              8                   7                        3   \n",
       "4              2                   2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 고유값 1개인 데이터 삭제\n",
    "for col in data.columns:\n",
    "    if data[col].unique().size == 1:\n",
    "        print(data[col].unique())\n",
    "        data.drop(col, axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
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       "      <th>DailyRate</th>\n",
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       "      <th>TrainingTimesLastYear</th>\n",
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       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>High</td>\n",
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       "      <td>3</td>\n",
       "      <td>Better</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>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>Master</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>5</td>\n",
       "      <td>Very High</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>High</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Better</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>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>Below College</td>\n",
       "      <td>Medical</td>\n",
       "      <td>7</td>\n",
       "      <td>Low</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Very High</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Better</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>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>884</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>23</td>\n",
       "      <td>College</td>\n",
       "      <td>Medical</td>\n",
       "      <td>2061</td>\n",
       "      <td>High</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Better</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>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>613</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>Below College</td>\n",
       "      <td>Medical</td>\n",
       "      <td>2062</td>\n",
       "      <td>Very High</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Low</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>Better</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>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>155</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2064</td>\n",
       "      <td>Medium</td>\n",
       "      <td>...</td>\n",
       "      <td>Outstanding</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Better</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>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1023</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Medical</td>\n",
       "      <td>2065</td>\n",
       "      <td>Very High</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Very High</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Good</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>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>628</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Medical</td>\n",
       "      <td>2068</td>\n",
       "      <td>Medium</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Best</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 × 32 columns</p>\n",
       "</div>"
      ],
      "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  EmployeeNumber   \n",
       "0                    1        College  Life Sciences               1  \\\n",
       "1                    8  Below College  Life Sciences               2   \n",
       "2                    2        College          Other               4   \n",
       "3                    3         Master  Life Sciences               5   \n",
       "4                    2  Below College        Medical               7   \n",
       "...                ...            ...            ...             ...   \n",
       "1465                23        College        Medical            2061   \n",
       "1466                 6  Below College        Medical            2062   \n",
       "1467                 4       Bachelor  Life Sciences            2064   \n",
       "1468                 2       Bachelor        Medical            2065   \n",
       "1469                 8       Bachelor        Medical            2068   \n",
       "\n",
       "     EnvironmentSatisfaction  ... PerformanceRating  RelationshipSatisfaction   \n",
       "0                     Medium  ...         Excellent                       Low  \\\n",
       "1                       High  ...       Outstanding                 Very High   \n",
       "2                  Very High  ...         Excellent                    Medium   \n",
       "3                  Very High  ...         Excellent                      High   \n",
       "4                        Low  ...         Excellent                 Very High   \n",
       "...                      ...  ...               ...                       ...   \n",
       "1465                    High  ...         Excellent                      High   \n",
       "1466               Very High  ...         Excellent                       Low   \n",
       "1467                  Medium  ...       Outstanding                    Medium   \n",
       "1468               Very High  ...         Excellent                 Very High   \n",
       "1469                  Medium  ...         Excellent                       Low   \n",
       "\n",
       "     StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance   \n",
       "0                   0                 8                     0             Bad  \\\n",
       "1                   1                10                     3          Better   \n",
       "2                   0                 7                     3          Better   \n",
       "3                   0                 8                     3          Better   \n",
       "4                   1                 6                     3          Better   \n",
       "...               ...               ...                   ...             ...   \n",
       "1465                1                17                     3          Better   \n",
       "1466                1                 9                     5          Better   \n",
       "1467                1                 6                     0          Better   \n",
       "1468                0                17                     3            Good   \n",
       "1469                0                 6                     3            Best   \n",
       "\n",
       "     YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion   \n",
       "0                 6                   4                        0  \\\n",
       "1                10                   7                        1   \n",
       "2                 0                   0                        0   \n",
       "3                 8                   7                        3   \n",
       "4                 2                   2                        2   \n",
       "...             ...                 ...                      ...   \n",
       "1465              5                   2                        0   \n",
       "1466              7                   7                        1   \n",
       "1467              6                   2                        0   \n",
       "1468              9                   6                        0   \n",
       "1469              4                   3                        1   \n",
       "\n",
       "     YearsWithCurrManager  \n",
       "0                       5  \n",
       "1                       7  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       2  \n",
       "...                   ...  \n",
       "1465                    3  \n",
       "1466                    7  \n",
       "1467                    3  \n",
       "1468                    8  \n",
       "1469                    2  \n",
       "\n",
       "[1470 rows x 32 columns]"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change_dict = {1: 'Below College', 2: 'College', 3: 'Bachelor', 4: 'Master', 5: 'Doctor'}\n",
    "data.replace({'Education': change_dict}, inplace=True)\n",
    "data['Education'].unique()\n",
    "\n",
    "change_dict = {1: 'Low', 2: 'Medium', 3: 'High', 4: 'Very High'}\n",
    "data.replace({'EnvironmentSatisfaction': change_dict}, inplace=True)\n",
    "data['EnvironmentSatisfaction'].unique()\n",
    "\n",
    "change_dict = {1: 'Low', 2: 'Medium', 3: 'High', 4: 'Very High'}\n",
    "data.replace({'JobInvolvement': change_dict}, inplace=True)\n",
    "data['JobInvolvement'].unique()\n",
    "\n",
    "change_dict = {1: 'Low', 2: 'Medium', 3: 'High', 4: 'Very High'}\n",
    "data.replace({'JobSatisfaction': change_dict}, inplace=True)\n",
    "data['JobSatisfaction'].unique()\n",
    "\n",
    "change_dict = {1: 'Low', 2: 'Good', 3: 'Excellent', 4: 'Outstanding'}\n",
    "data.replace({'PerformanceRating': change_dict}, inplace=True)\n",
    "data['PerformanceRating'].unique()\n",
    "\n",
    "change_dict = {1: 'Low', 2: 'Medium', 3: 'High', 4: 'Very High'}\n",
    "data.replace({'RelationshipSatisfaction': change_dict}, inplace=True)\n",
    "data['RelationshipSatisfaction'].unique()\n",
    "\n",
    "change_dict = {1: 'Bad', 2: 'Good', 3: 'Better', 4: 'Best'}\n",
    "data.replace({'WorkLifeBalance': change_dict}, inplace=True)\n",
    "data['WorkLifeBalance'].unique()\n",
    "\n",
    "data = data.astype({'JobLevel': object, 'StockOptionLevel': object, 'NumCompaniesWorked': object})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1470"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['EmployeeNumber'].unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('EmployeeNumber', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 31)"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>VIF 확인</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cp = data.copy()\n",
    "data_cp = pd.get_dummies(data_cp, drop_first= True)\n",
    "data_cp = data_cp[['Attrition_Yes'] + [column for column in data_cp.columns if column != 'Attrition_Yes']]\n",
    "data_corr = data_cp.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "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>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13640.716455</td>\n",
       "      <td>Department_Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13522.448575</td>\n",
       "      <td>Department_Research &amp; Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3238.702786</td>\n",
       "      <td>EducationField_Life Sciences</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3212.315104</td>\n",
       "      <td>MonthlyIncome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2882.516493</td>\n",
       "      <td>EducationField_Medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.447858</td>\n",
       "      <td>DailyRate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.379119</td>\n",
       "      <td>MonthlyRate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.349891</td>\n",
       "      <td>HourlyRate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.328611</td>\n",
       "      <td>Gender_Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.318633</td>\n",
       "      <td>TrainingTimesLastYear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      VIF Factor                           features\n",
       "17  13640.716455                   Department_Sales\n",
       "16  13522.448575  Department_Research & Development\n",
       "22   3238.702786       EducationField_Life Sciences\n",
       "5    3212.315104                      MonthlyIncome\n",
       "24   2882.516493             EducationField_Medical\n",
       "..           ...                                ...\n",
       "2       1.447858                          DailyRate\n",
       "6       1.379119                        MonthlyRate\n",
       "4       1.349891                         HourlyRate\n",
       "30      1.328611                        Gender_Male\n",
       "9       1.318633              TrainingTimesLastYear\n",
       "\n",
       "[71 rows x 2 columns]"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "# 피처마다의 VIF 계수를 출력합니다.\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(data_corr.values, i) for i in range(data_corr.shape[1])]\n",
    "vif[\"features\"] = data_corr.columns\n",
    "vif.sort_values(by='VIF Factor', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VIF가 과도하게 높은 Department컬럼 삭제\n",
    "data.drop('Department', axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>가설검정 결과 전처리</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    3\n",
       "2    2\n",
       "3    1\n",
       "4    1\n",
       "Name: Age_cut, dtype: object"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 나이 컬럼 범주화 (연령대별)\n",
    "data['Age_cut'] = pd.cut(data['Age'], 5, labels=[0, 1, 2, 3, 4]).astype(object)\n",
    "data['Age_cut'].head()\n",
    "# data.drop('Age', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 집 근처 거리가 22 이상인 경우 1, 아닌 경우 0으로 변경\n",
    "round(data[data.DistanceFromHome >= 22]['DistanceFromHome'].count() / len(data),2)\n",
    "data['FarFromHome'] = np.where(data.DistanceFromHome >= 22 , 1, 0)\n",
    "data['FarFromHome'] = data['FarFromHome'].astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 월 급여가 중앙값보다 낮은 경우 1, 아닌 경우 0으로 변경\n",
    "data[data['Attrition'] == 'Yes']['MonthlyIncome'].median()\n",
    "data['LowMonthlyIncome'] = np.where(data.MonthlyIncome <= 3202 , 1, 0)\n",
    "data['LowMonthlyIncome'] = data['LowMonthlyIncome'].astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 초기 경력자여부(2년이하) 컬럼 생성\n",
    "data['LowWorkingYears'] = np.where(data.TotalWorkingYears <= 2 , 1, 0)\n",
    "data['LowWorkingYears'] = data['LowWorkingYears'].astype(object)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>데이터 분석 중 이상 컬럼 전처리</h1>\n",
    "<h3> NumCompaniesWorked</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 일한 회사 수 0 -> 1로 변경\n",
    "data.NumCompaniesWorked.replace(0, 1, inplace = True)\n",
    "data['NumCompaniesWorked'].head()\n",
    "sns.countplot(data=data, x = 'NumCompaniesWorked')\n",
    "data.NumCompaniesWorked = data.NumCompaniesWorked.astype(object)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>기타 데이터 전처리</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 나이 컬럼 범주화 (연령대별)\n",
    "data['Age_cut'] = pd.cut(data['Age'], 5, labels=[0, 1, 2, 3, 4]).astype(object)\n",
    "data['Age_cut'].head()\n",
    "data.drop('Age', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    6\n",
       "2    6\n",
       "3    7\n",
       "4    4\n",
       "Name: TotalSatisfaction, dtype: object"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# '만족도'와 관련된 컬럼 한 개로 통합\n",
    "point = {'Low' : 0, 'Medium' : 1, 'High' : 2 , 'Very High' : 3}\n",
    "EnvironmentSatisfaction = data['EnvironmentSatisfaction'].map(point)\n",
    "RelationshipSatisfaction = data['RelationshipSatisfaction'].map(point)\n",
    "JobSatisfaction = data['JobSatisfaction'].map(point)\n",
    "data['TotalSatisfaction'] = (EnvironmentSatisfaction + RelationshipSatisfaction + JobSatisfaction).astype(object) \n",
    "data['TotalSatisfaction'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['EnvironmentSatisfaction', 'RelationshipSatisfaction', 'JobSatisfaction'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Total Satisfaction')\n",
    "sns.countplot(data = data, x = 'TotalSatisfaction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>이상치 제거</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 아웃라이어 확인 함수 정의\n",
    "def outlier(data, col):\n",
    "    q1 = np.percentile(data[col], 25) \n",
    "    q3 = np.percentile(data[col], 75)\n",
    "    IQR = q3 - q1\n",
    "    outlier_step = 1.5 * IQR\n",
    "    return data[(data[col] < q1 - outlier_step) | (data[col] > q3 + outlier_step)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MonthlyIncome의 아웃라이어 갯수 114개\n",
      "전체 데이터의 0.08% 차지\n",
      "Attrition\n",
      "No     109\n",
      "Yes      5\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MonthlyIncome의 아웃라이어 확인\n",
    "monthlyincom_outlier = outlier(data, 'MonthlyIncome')\n",
    "print(f'MonthlyIncome의 아웃라이어 갯수 {monthlyincom_outlier.shape[0]}개')\n",
    "print(f'전체 데이터의 {round(monthlyincom_outlier.shape[0] / data.shape[0],2)}% 차지')\n",
    "print(monthlyincom_outlier.Attrition.value_counts())\n",
    "sns.countplot(data = monthlyincom_outlier, x = 'Attrition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MonthlyIncome의 아웃라이어가 많고, VIF가 높음\n",
    "# 컬럼 삭제 결정\n",
    "data.drop('MonthlyIncome', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TotalWorkingYears의 아웃라이어 갯수 63개\n",
      "전체 데이터의 0.04% 차지\n",
      "Attrition\n",
      "No     58\n",
      "Yes     5\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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MzEyf0HK73YqPj/f7fAAAIHD5FTJbt26ttQFCQkLUpk0bSVJSUpL27Nmjl156SUOGDFFVVZXKysp8rsqUlpbK6XRe8nx2u112u73W5gMAAIHrqu6ROXnypHbs2KEdO3bo5MmTtTJQdXW1KisrlZSUpIYNGyo3N9e7r6ioSEeOHFFycnKtvBYAADCbX1dkysvLNW7cOK1atUrV1dWSpKCgID3wwANasGCBrrvuuis6T2Zmpvr3768WLVrozJkzWr16tbZt26YtW7bI4XBo9OjRysjIUHR0tCIjIzVu3DglJydf8SeWAADAtc2vKzIZGRnKy8vTxo0bVVZWprKyMr3zzjvKy8vTxIkTr/g8J06c0AMPPKD27durb9++2rNnj7Zs2aI//vGPkqS5c+fqT3/6k9LS0tSrVy85nU6tW7fOn5EBAMA1yK/vkWnSpInefvtt9e7d22d969atGjx4cK29zVQb+B4ZwDp8jwwAf9Xp98j88MMPio2NvWC9adOm+uGHH/w5JQAAQI35FTLJycmaPn26KioqvGv//e9/NWPGDG7EBQAA9cavm33nzZunO+64Q82bN1eXLl0kSfv375fdbtcHH3xQqwMCAABcil8h06lTJx06dEivvfaavvzyS0nSsGHDNHz4cIWFhdXqgAAAAJfiV8hkZWUpNjZWY8aM8Vlfvny5Tp48qSlTptTKcAAAAJfj1z0yS5cuVUJCwgXriYmJWrJkyVUPBQAAcCX8CpmSkhI1a9bsgvWYmBgdP378qocCAAC4En6FTHx8vPLz8y9Yz8/PV1xc3FUPBQAAcCX8ukdmzJgxGj9+vM6dO6c+ffpIknJzczV58uQafbMvAADA1fArZJ544gl9//33euSRR1RVVSVJCg0N1ZQpU5SZmVmrAwIAAFyKXyFjs9n07LPPaurUqTp48KDCwsLUtm1b2e322p4PAADgkvwKmZ+Fh4frlltuqa1ZAAAAasSvm30BAAACASEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMZWnIZGVl6ZZbblFERISaNm2qAQMGqKioyOeYiooKpaenq3HjxgoPD1daWppKS0stmhgAAAQSS0MmLy9P6enp2rlzpz788EOdO3dOt99+u8rLy73HTJgwQRs3btSaNWuUl5enY8eOaeDAgRZODQAAAkWwlS++efNmn8c5OTlq2rSpCgsL1atXL7lcLmVnZ2v16tXq06ePJGnFihXq0KGDdu7cqZ49e15wzsrKSlVWVnofu93uuv0hAACAZQLqHhmXyyVJio6OliQVFhbq3LlzSk1N9R6TkJCgFi1aqKCg4KLnyMrKksPh8G7x8fF1PzgAALBEwIRMdXW1xo8fr5SUFN10002SpJKSEoWEhCgqKsrn2NjYWJWUlFz0PJmZmXK5XN7t6NGjdT06AACwiKVvLf1Senq6Dhw4oB07dlzVeex2u+x2ey1NBQAAAllAXJF59NFH9d5772nr1q1q3ry5d93pdKqqqkplZWU+x5eWlsrpdNbzlAAAINBYGjIej0ePPvqo1q9fr48++kitW7f22Z+UlKSGDRsqNzfXu1ZUVKQjR44oOTm5vscFAAABxtK3ltLT07V69Wq98847ioiI8N734nA4FBYWJofDodGjRysjI0PR0dGKjIzUuHHjlJycfNFPLAEAgN8WS0Nm8eLFkqTevXv7rK9YsUIjR46UJM2dO1cNGjRQWlqaKisr1a9fPy1atKieJwUAAIHI0pDxeDy/ekxoaKgWLlyohQsX1sNEAADAJAFxsy8AAIA/CBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxLA2Z7du36+6771ZcXJxsNps2bNjgs9/j8WjatGlq1qyZwsLClJqaqkOHDlkzLAAACDiWhkx5ebm6dOmihQsXXnT/c889p/nz52vJkiXatWuXGjVqpH79+qmioqKeJwUAAIEo2MoX79+/v/r373/RfR6PR/PmzdOTTz6pe+65R5K0atUqxcbGasOGDRo6dGh9jgoAAAJQwN4jU1xcrJKSEqWmpnrXHA6HevTooYKCgks+r7KyUm6322cDAADXpoANmZKSEklSbGysz3psbKx338VkZWXJ4XB4t/j4+DqdEwAAWCdgQ8ZfmZmZcrlc3u3o0aNWjwQAAOpIwIaM0+mUJJWWlvqsl5aWevddjN1uV2RkpM8GAACuTQEbMq1bt5bT6VRubq53ze12a9euXUpOTrZwMgAAECgs/dTS2bNn9fXXX3sfFxcXa9++fYqOjlaLFi00fvx4PfPMM2rbtq1at26tqVOnKi4uTgMGDLBuaAAAEDAsDZm9e/fqtttu8z7OyMiQJI0YMUI5OTmaPHmyysvL9dBDD6msrEy33nqrNm/erNDQUKtGBgAAAcTm8Xg8Vg9Rl9xutxwOh1wuV53eL5P0xKo6OzdgqsLnH7B6BACGutLf3wF7jwwAAMCvIWQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgrGCrBwCAQHdkZierRwACTotpn1s9giSuyAAAAIMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFhGhMzChQvVqlUrhYaGqkePHtq9e7fVIwEAgAAQ8CHz5ptvKiMjQ9OnT9cnn3yiLl26qF+/fjpx4oTVowEAAIsFfMi8+OKLGjNmjEaNGqWOHTtqyZIluu6667R8+XKrRwMAABYLtnqAy6mqqlJhYaEyMzO9aw0aNFBqaqoKCgou+pzKykpVVlZ6H7tcLkmS2+2u01nPV/63Ts8PmKiu/97VlzMV560eAQg4df33++fzezyeyx4X0CHz3Xff6fz584qNjfVZj42N1ZdffnnR52RlZWnGjBkXrMfHx9fJjAAuzbHg/1k9AoC6kuWol5c5c+aMHI5Lv1ZAh4w/MjMzlZGR4X1cXV2tU6dOqXHjxrLZbBZOhvrgdrsVHx+vo0ePKjIy0upxANQi/n7/tng8Hp05c0ZxcXGXPS6gQ6ZJkyYKCgpSaWmpz3ppaamcTudFn2O322W3233WoqKi6mpEBKjIyEj+Rwdco/j7/dtxuSsxPwvom31DQkKUlJSk3Nxc71p1dbVyc3OVnJxs4WQAACAQBPQVGUnKyMjQiBEjdPPNN+v3v/+95s2bp/Lyco0aNcrq0QAAgMUCPmSGDBmikydPatq0aSopKVHXrl21efPmC24ABqSf3lqcPn36BW8vAjAff79xMTbPr32uCQAAIEAF9D0yAAAAl0PIAAAAYxEyAADAWIQMAAAwFiED44wcOVI2m02zZ8/2Wd+wYQPf3gwYyOPxKDU1Vf369btg36JFixQVFaVvv/3WgslgAkIGRgoNDdWzzz6r06dPWz0KgKtks9m0YsUK7dq1S0uXLvWuFxcXa/LkyVqwYIGaN29u4YQIZIQMjJSamiqn06msrKxLHrN27VolJibKbrerVatWmjNnTj1OCKAm4uPj9dJLL2nSpEkqLi6Wx+PR6NGjdfvtt6tbt27q37+/wsPDFRsbqz//+c/67rvvvM99++231alTJ4WFhalx48ZKTU1VeXm5hT8N6hMhAyMFBQVp1qxZWrBgwUUvORcWFmrw4MEaOnSoPv/8cz311FOaOnWqcnJy6n9YAFdkxIgR6tu3rx588EG9/PLLOnDggJYuXao+ffqoW7du2rt3rzZv3qzS0lINHjxYknT8+HENGzZMDz74oA4ePKht27Zp4MCB4ivSfjv4QjwYZ+TIkSorK9OGDRuUnJysjh07Kjs7Wxs2bNC9994rj8ej4cOH6+TJk/rggw+8z5s8ebLef/99/fOf/7RwegCXc+LECSUmJurUqVNau3atDhw4oI8//lhbtmzxHvPtt98qPj5eRUVFOnv2rJKSkvTNN9+oZcuWFk4Oq3BFBkZ79tlntXLlSh08eNBn/eDBg0pJSfFZS0lJ0aFDh3T+/Pn6HBFADTRt2lQPP/ywOnTooAEDBmj//v3aunWrwsPDvVtCQoIk6fDhw+rSpYv69u2rTp06adCgQVq2bBn3zv3GEDIwWq9evdSvXz9lZmZaPQqAWhIcHKzg4J/+KcCzZ8/q7rvv1r59+3y2Q4cOqVevXgoKCtKHH36oTZs2qWPHjlqwYIHat2+v4uJii38K1JeA/0cjgV8ze/Zsde3aVe3bt/eudejQQfn5+T7H5efnq127dgoKCqrvEQH4qXv37lq7dq1atWrljZv/ZbPZlJKSopSUFE2bNk0tW7bU+vXrlZGRUc/TwgpckYHxOnXqpOHDh2v+/PnetYkTJyo3N1dPP/20vvrqK61cuVIvv/yyJk2aZOGkAGoqPT1dp06d0rBhw7Rnzx4dPnxYW7Zs0ahRo3T+/Hnt2rVLs2bN0t69e3XkyBGtW7dOJ0+eVIcOHaweHfWEkME1YebMmaqurvY+7t69u9566y298cYbuummmzRt2jTNnDlTI0eOtG5IADUWFxen/Px8nT9/Xrfffrs6deqk8ePHKyoqSg0aNFBkZKS2b9+uO++8U+3atdOTTz6pOXPmqH///laPjnrCp5YAAICxuCIDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhA8BIvXv31vjx4y97TE5OjqKiouplHgDWIGQA1JmCggIFBQXprrvu8ll/6qmn1LVr1wuOt9ls2rBhwxWde926dXr66ae9j1u1aqV58+b5HDNkyBB99dVXNR0bgEEIGQB1Jjs7W+PGjdP27dt17NixWjlnVVWVJCk6OloRERGXPTYsLExNmzatldcFEJgIGQB14uzZs3rzzTc1duxY3XXXXcrJyZH009s9M2bM0P79+2Wz2WSz2ZSTk6NWrVpJku69917ZbDbv45+v3rz66qtq3bq1QkNDJfm+tdS7d2/9+9//1oQJE7zn/Pm1/vetpcWLF+vGG29USEiI2rdvr7/97W8++202m1599VXde++9uu6669S2bVu9++67dfJnBODqETIA6sRbb72lhIQEtW/fXvfff7+WL18uj8ejIUOGaOLEiUpMTNTx48d1/PhxDRkyRHv27JEkrVixQsePH/c+lqSvv/5aa9eu1bp167Rv374LXmvdunVq3ry5Zs6c6T3nxaxfv16PP/64Jk6cqAMHDujhhx/WqFGjtHXrVp/jZsyYocGDB+uzzz7TnXfeqeHDh+vUqVO194cDoNYQMgDqRHZ2tu6//35J0h133CGXy6W8vDyFhYUpPDxcwcHBcjqdcjqdCgsLU0xMjCQpKipKTqfT+1j66e2kVatWqVu3burcufMFrxUdHa2goCBFRER4z3kxL7zwgkaOHKlHHnlE7dq1U0ZGhgYOHKgXXnjB57iRI0dq2LBhatOmjWbNmqWzZ89q9+7dtfVHA6AWETIAal1RUZF2796tYcOGSZKCg4M1ZMgQZWdn+3W+li1b+oSNvw4ePKiUlBSftZSUFB08eNBn7Zex1KhRI0VGRurEiRNX/foAal+w1QMAuPZkZ2frxx9/VFxcnHfN4/HIbrfr5ZdfrvH5GjVqVJvj/aqGDRv6PLbZbKqurq7XGQBcGa7IAKhVP/74o1atWqU5c+Zo37593m3//v2Ki4vT66+/rpCQEJ0/f/6C5zZs2PCi61fiUuf8pQ4dOig/P99nLT8/Xx07dvTrNQFYjysyAGrVe++9p9OnT2v06NFyOBw++9LS0pSdna0JEyaouLhY+/btU/PmzRURESG73a5WrVopNzdXKSkpstvtuv7666/4dVu1aqXt27dr6NChstvtatKkyQXHPPHEExo8eLC6deum1NRUbdy4UevWrdPf//73q/65AViDKzIAalV2drZSU1MviBjpp5DZu3evEhMTdccdd+i2225TTEyMXn/9dUnSnDlz9OGHHyo+Pl7dunWr0evOnDlT33zzjW688cZL3k8zYMAAvfTSS3rhhReUmJiopUuXasWKFerdu3eNf04AgcHm8Xg8Vg8BAADgD67IAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMNb/B/qPz0NgKH6iAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TotalWorkingYears의 아웃라이어 확인\n",
    "workingyear_outlier = outlier(data, 'TotalWorkingYears')\n",
    "print(f'TotalWorkingYears의 아웃라이어 갯수 {workingyear_outlier.shape[0]}개')\n",
    "print(f'전체 데이터의 {round(workingyear_outlier.shape[0] / data.shape[0],2)}% 차지')\n",
    "print(workingyear_outlier.Attrition.value_counts())\n",
    "sns.countplot(data = workingyear_outlier, x = 'Attrition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 아웃라이어가 많진 않으나, VIF가 높음\n",
    "# 컬럼 삭제\n",
    "data.drop('TotalWorkingYears', axis=1, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TrainingTimesLastYear의 아웃라이어 갯수 238개\n",
      "전체 데이터의 0.16% 차지\n",
      "Attrition\n",
      "No     203\n",
      "Yes     35\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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SUpKu93z0jZ6dHjx4sA4cONDUYwEAAIO0qu/ZAQAAuFXEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjNSp2RowYoYqKiqu2O51OjRgx4nZnAgAAaDKNip19+/bpypUrV22/fPmyPvjgg9seCgAAoKnc0j8XcezYMc9//+///q/nH+aUpLq6Ou3YsUP/8A//0HTTAQAA3KZbip1BgwbJYrHIYrE0eLsqJCREq1atarLhAAAAbtctxU5JSYncbrfuvPNOHTp0SBEREZ59wcHBioyMVGBgYJMPCQAA0Fi3FDvdunWTJNXX1zfLMAAAAE3tlmLn+06ePKm9e/fq/PnzV8XP4sWLb3swAACAptCo2Hnttdc0e/ZsderUSdHR0bJYLJ59FouF2AEAAH6jUbHz29/+Vv/xH/+hBQsWNPU8AAAATapR37Pz9ddf68EHH2zqWQAAAJpco2LnwQcf1Pvvv9/UswAAADS5Rt3Guuuuu7Ro0SIdOHBAcXFxatOmjdf+xx57rEmGAwAAuF0Wt9vtvtUXde/e/dontFj0f//3f7c1VEtzOp2y2+2qrKyUzWbz9TgAWpkz2XG+HgHwS10Xf9Ks57/Zn9+NurJTUlLS6MEAAABaUqOe2QEAAGgtGnVlZ8aMGdfdv3bt2kYNAwAA0NQaFTtff/2113pNTY2OHz+uioqKBv+BUAAAAF9pVOxs3br1qm319fWaPXu2evTocdtDAQAANJUme2YnICBAmZmZeuGFF5rqlAAAALetSR9QPn36tGpra5vylAAAALelUbexMjMzvdbdbrdKS0v17rvvatq0aU0yGAAAQFNoVOz8z//8j9d6QECAIiIitGLFihv+phYAAEBLalTs7N27t6nnAAAAaBaNip3vXLhwQcXFxZKkXr16KSIiokmGAgAAaCqNekC5urpaM2bMUOfOnTVs2DANGzZMMTExmjlzpi5dutTUMwIAADRao2InMzNT+fn52rZtmyoqKlRRUaF33nlH+fn5evzxx2/6PAUFBRo/frxiYmJksViUl5fntd/tdmvx4sXq3LmzQkJClJycrJMnT3odc/HiRaWlpclmsyksLEwzZ85UVVVVYz4WAAAwUKNiZ/PmzcrNzVVKSopsNptsNpvGjh2r1157TX/84x9v+jzV1dUaOHCgVq9e3eD+Z599Vi+++KLWrFmjgwcPqn379ho9erQuX77sOSYtLU2ffvqpdu7cqe3bt6ugoECPPPJIYz4WAAAwUKOe2bl06ZKioqKu2h4ZGXlLt7FSUlKUkpLS4D63262VK1dq4cKF+td//VdJ0htvvKGoqCjl5eVpypQpOnHihHbs2KHDhw/rnnvukSStWrVKY8eO1fLlyxUTE9PguV0ul1wul2fd6XTe9MwAAKB1adSVncTERD311FNeV1j+/ve/a+nSpUpMTGySwUpKSlRWVqbk5GTPNrvdroSEBBUWFkqSCgsLFRYW5gkdSUpOTlZAQIAOHjx4zXPn5OTIbrd7FofD0SQzAwAA/9OoKzsrV67UmDFj1KVLFw0cOFCS9PHHH8tqter9999vksHKysok6aorSFFRUZ59ZWVlioyM9NofFBSk8PBwzzENycrK8vpiRKfTSfAAAGCoRsVOXFycTp48qQ0bNuizzz6TJE2dOlVpaWkKCQlp0gGbg9VqldVq9fUYAACgBTQqdnJychQVFaVZs2Z5bV+7dq0uXLigBQsW3PZg0dHRkqTy8nJ17tzZs728vFyDBg3yHHP+/Hmv19XW1urixYue1wMAgB+3Rj2z8+qrr6p3795Xbe/Xr5/WrFlz20NJUvfu3RUdHa3du3d7tjmdTh08eNDzXFBiYqIqKip09OhRzzF79uxRfX29EhISmmQOAADQujXqyk5ZWZnX1ZbvREREqLS09KbPU1VVpVOnTnnWS0pKVFRUpPDwcHXt2lXz5s3Tb3/7W919993q3r27Fi1apJiYGE2YMEGS1KdPH40ZM0azZs3SmjVrVFNTozlz5mjKlCnX/E0sAADw49Ko2HE4HNq/f7+6d+/utX3//v23FBlHjhzR8OHDPevfPTQ8bdo0rV+/XvPnz1d1dbUeeeQRVVRU6P7779eOHTvUtm1bz2s2bNigOXPmaOTIkQoICFBqaqpefPHFxnwsAABgoEbFzqxZszRv3jzV1NRoxIgRkqTdu3dr/vz5t/QNyklJSXK73dfcb7FYlJ2drezs7GseEx4ero0bN9788AAA4EelUbHz5JNP6quvvtKvf/1rXblyRZLUtm1bLViwQFlZWU06IAAAwO1oVOxYLBY988wzWrRokU6cOKGQkBDdfffd/Do3AADwO42Kne906NBB9957b1PNAgAA0OQa9avnAAAArQWxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwmt/HTmxsrCwWy1VLenq6JCkpKemqfb/61a98PDUAAPAXQb4e4EYOHz6suro6z/rx48f1z//8z3rwwQc922bNmqXs7GzPert27Vp0RgAA4L/8PnYiIiK81pctW6YePXron/7pnzzb2rVrp+jo6JYeDQAAtAJ+fxvr+65cuaL//u//1owZM2SxWDzbN2zYoE6dOql///7KysrSpUuXrnsel8slp9PptQAAADP5/ZWd78vLy1NFRYWmT5/u2fbzn/9c3bp1U0xMjI4dO6YFCxaouLhYW7ZsueZ5cnJytHTp0haYGAAA+JrF7Xa7fT3EzRo9erSCg4O1bdu2ax6zZ88ejRw5UqdOnVKPHj0aPMblcsnlcnnWnU6nHA6HKisrZbPZmnxuAGY7kx3n6xEAv9R18SfNen6n0ym73X7Dn9+t5srOF198oV27dl33io0kJSQkSNJ1Y8dqtcpqtTb5jAAAwP+0mmd21q1bp8jISI0bN+66xxUVFUmSOnfu3AJTAQAAf9cqruzU19dr3bp1mjZtmoKC/v/Ip0+f1saNGzV27Fh17NhRx44dU0ZGhoYNG6YBAwb4cGIAAOAvWkXs7Nq1S2fOnNGMGTO8tgcHB2vXrl1auXKlqqur5XA4lJqaqoULF/poUgAA4G9aReyMGjVKDT1H7XA4lJ+f74OJAABAa9FqntkBAABoDGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNH8OnaWLFkii8XitfTu3duz//Lly0pPT1fHjh3VoUMHpaamqry83IcTAwAAf+PXsSNJ/fr1U2lpqWf5y1/+4tmXkZGhbdu26e2331Z+fr7OnTuniRMn+nBaAADgb4J8PcCNBAUFKTo6+qrtlZWVys3N1caNGzVixAhJ0rp169SnTx8dOHBA9913X0uPCgAA/JDfX9k5efKkYmJidOeddyotLU1nzpyRJB09elQ1NTVKTk72HNu7d2917dpVhYWF1z2ny+WS0+n0WgAAgJn8OnYSEhK0fv167dixQ6+88opKSkr0j//4j/rmm29UVlam4OBghYWFeb0mKipKZWVl1z1vTk6O7Ha7Z3E4HM34KQAAgC/59W2slJQUz38PGDBACQkJ6tatm9566y2FhIQ0+rxZWVnKzMz0rDudToIHAABD+fWVnR8KCwtTz549derUKUVHR+vKlSuqqKjwOqa8vLzBZ3y+z2q1ymazeS0AAMBMrSp2qqqqdPr0aXXu3FlDhgxRmzZttHv3bs/+4uJinTlzRomJiT6cEgAA+BO/vo31xBNPaPz48erWrZvOnTunp556SoGBgZo6darsdrtmzpypzMxMhYeHy2azae7cuUpMTOQ3sQAAgIdfx87Zs2c1depUffXVV4qIiND999+vAwcOKCIiQpL0wgsvKCAgQKmpqXK5XBo9erRefvllH08NAAD8icXtdrt9PYSvOZ1O2e12VVZW8vwOgFt2JjvO1yMAfqnr4k+a9fw3+/O7VT2zAwAAcKuIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYLcjXA/xYDHnyDV+PAPilo8/90tcjADAcV3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDR/Dp2cnJydO+99yo0NFSRkZGaMGGCiouLvY5JSkqSxWLxWn71q1/5aGIAAOBv/Dp28vPzlZ6ergMHDmjnzp2qqanRqFGjVF1d7XXcrFmzVFpa6lmeffZZH00MAAD8TZCvB7ieHTt2eK2vX79ekZGROnr0qIYNG+bZ3q5dO0VHR9/0eV0ul1wul2fd6XTe/rAAAMAv+fWVnR+qrKyUJIWHh3tt37Bhgzp16qT+/fsrKytLly5duu55cnJyZLfbPYvD4Wi2mQEAgG/59ZWd76uvr9e8efM0dOhQ9e/f37P95z//ubp166aYmBgdO3ZMCxYsUHFxsbZs2XLNc2VlZSkzM9Oz7nQ6CR4AAAzVamInPT1dx48f11/+8hev7Y888ojnv+Pi4tS5c2eNHDlSp0+fVo8ePRo8l9VqldVqbdZ5AQCAf2gVt7HmzJmj7du3a+/everSpct1j01ISJAknTp1qiVGAwAAfs6vr+y43W7NnTtXW7du1b59+9S9e/cbvqaoqEiS1Llz52aeDgAAtAZ+HTvp6enauHGj3nnnHYWGhqqsrEySZLfbFRISotOnT2vjxo0aO3asOnbsqGPHjikjI0PDhg3TgAEDfDw9AADwB34dO6+88oqkb7848PvWrVun6dOnKzg4WLt27dLKlStVXV0th8Oh1NRULVy40AfTAgAAf+TXseN2u6+73+FwKD8/v4WmAQAArVGreEAZAACgsYgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYzJnZWr16t2NhYtW3bVgkJCTp06JCvRwIAAH7AiNj5wx/+oMzMTD311FP66KOPNHDgQI0ePVrnz5/39WgAAMDHjIid559/XrNmzdLDDz+svn37as2aNWrXrp3Wrl3r69EAAICPBfl6gNt15coVHT16VFlZWZ5tAQEBSk5OVmFhYYOvcblccrlcnvXKykpJktPpbLY561x/b7ZzA61Zc/69aynfXK7z9QiAX2ruv9/fnd/tdl/3uFYfO3/7299UV1enqKgor+1RUVH67LPPGnxNTk6Oli5detV2h8PRLDMCuDb7ql/5egQAzSXH3iJv880338huv/Z7tfrYaYysrCxlZmZ61uvr63Xx4kV17NhRFovFh5OhJTidTjkcDn355Zey2Wy+HgdAE+Lv94+L2+3WN998o5iYmOse1+pjp1OnTgoMDFR5ebnX9vLyckVHRzf4GqvVKqvV6rUtLCysuUaEn7LZbPzPEDAUf79/PK53Rec7rf4B5eDgYA0ZMkS7d+/2bKuvr9fu3buVmJjow8kAAIA/aPVXdiQpMzNT06ZN0z333KOf/OQnWrlypaqrq/Xwww/7ejQAAOBjRsTO5MmTdeHCBS1evFhlZWUaNGiQduzYcdVDy4D07W3Mp5566qpbmQBaP/5+oyEW941+XwsAAKAVa/XP7AAAAFwPsQMAAIxG7AAAAKMROwAAwGjEDozhdruVnJys0aNHX7Xv5ZdfVlhYmM6ePeuDyQA0penTp8tisWjZsmVe2/Py8vgWfDSI2IExLBaL1q1bp4MHD+rVV1/1bC8pKdH8+fO1atUqdenSxYcTAmgqbdu21TPPPKOvv/7a16OgFSB2YBSHw6Hf/e53euKJJ1RSUiK3262ZM2dq1KhRio+PV0pKijp06KCoqCj94he/0N/+9jfPa//4xz8qLi5OISEh6tixo5KTk1VdXe3DTwPgWpKTkxUdHa2cnJxrHrN582b169dPVqtVsbGxWrFiRQtOCH9C7MA406ZN08iRIzVjxgy99NJLOn78uF599VWNGDFC8fHxOnLkiHbs2KHy8nJNmjRJklRaWqqpU6dqxowZOnHihPbt26eJEyeKr6EC/FNgYKCefvpprVq1qsHb00ePHtWkSZM0ZcoUffLJJ1qyZIkWLVqk9evXt/yw8Dm+VBBGOn/+vPr166eLFy9q8+bNOn78uD744AO99957nmPOnj0rh8Oh4uJiVVVVaciQIfr888/VrVs3H04O4EamT5+uiooK5eXlKTExUX379lVubq7y8vL0wAMPyO12Ky0tTRcuXND777/ved38+fP17rvv6tNPP/Xh9PAFruzASJGRkXr00UfVp08fTZgwQR9//LH27t2rDh06eJbevXtLkk6fPq2BAwdq5MiRiouL04MPPqjXXnuNZwGAVuCZZ57R66+/rhMnTnhtP3HihIYOHeq1bejQoTp58qTq6upackT4AWIHxgoKClJQ0Lf//FtVVZXGjx+voqIir+XkyZMaNmyYAgMDtXPnTv35z39W3759tWrVKvXq1UslJSU+/hQArmfYsGEaPXq0srKyfD0K/JgR/xAocCODBw/W5s2bFRsb6wmgH7JYLBo6dKiGDh2qxYsXq1u3btq6dasyMzNbeFoAt2LZsmUaNGiQevXq5dnWp08f7d+/3+u4/fv3q2fPngoMDGzpEeFjXNnBj0J6erouXryoqVOn6vDhwzp9+rTee+89Pfzww6qrq9PBgwf19NNP68iRIzpz5oy2bNmiCxcuqE+fPr4eHcANxMXFKS0tTS+++KJn2+OPP67du3frN7/5jf7617/q9ddf10svvaQnnnjCh5PCV4gd/CjExMRo//79qqur06hRoxQXF6d58+YpLCxMAQEBstlsKigo0NixY9WzZ08tXLhQK1asUEpKiq9HB3ATsrOzVV9f71kfPHiw3nrrLW3atEn9+/fX4sWLlZ2drenTp/tuSPgMv40FAACMxpUdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQDGSkpK0rx58657zPr16xUWFtYi8wDwDWIHgE8VFhYqMDBQ48aN89q+ZMkSDRo06KrjLRaL8vLyburcW7Zs0W9+8xvPemxsrFauXOl1zOTJk/XXv/71VscG0IoQOwB8Kjc3V3PnzlVBQYHOnTvXJOe8cuWKJCk8PFyhoaHXPTYkJESRkZFN8r4A/BOxA8Bnqqqq9Ic//EGzZ8/WuHHjtH79eknf3lpaunSpPv74Y1ksFlksFq1fv16xsbGSpAceeEAWi8Wz/t1VoN///vfq3r272rZtK8n7NlZSUpK++OILZWRkeM753Xv98DbWK6+8oh49eig4OFi9evXSf/3Xf3ntt1gs+v3vf68HHnhA7dq10913360//elPzfJnBOD2ETsAfOatt95S79691atXLz300ENau3at3G63Jk+erMcff1z9+vVTaWmpSktLNXnyZB0+fFiStG7dOpWWlnrWJenUqVPavHmztmzZoqKioqvea8uWLerSpYuys7M952zI1q1b9e///u96/PHHdfz4cT366KN6+OGHtXfvXq/jli5dqkmTJunYsWMaO3as0tLSdPHixab7wwHQZIgdAD6Tm5urhx56SJI0ZswYVVZWKj8/XyEhIerQoYOCgoIUHR2t6OhohYSEKCIiQpIUFham6Ohoz7r07a2rN954Q/Hx8RowYMBV7xUeHq7AwECFhoZ6ztmQ5cuXa/r06fr1r3+tnj17KjMzUxMnTtTy5cu9jps+fbqmTp2qu+66S08//bSqqqp06NChpvqjAdCEiB0APlFcXKxDhw5p6tSpkqSgoCBNnjxZubm5jTpft27dvOKnsU6cOKGhQ4d6bRs6dKhOnDjhte37QdW+fXvZbDadP3/+tt8fQNML8vUAAH6ccnNzVVtbq5iYGM82t9stq9Wql1566ZbP1759+6Yc74batGnjtW6xWFRfX9+iMwC4OVzZAdDiamtr9cYbb2jFihUqKiryLB9//LFiYmL05ptvKjg4WHV1dVe9tk2bNg1uvxnXOuf39enTR/v37/fatn//fvXt27dR7wnA97iyA6DFbd++XV9//bVmzpwpu93utS81NVW5ubnKyMhQSUmJioqK1KVLF4WGhspqtSo2Nla7d+/W0KFDZbVadccdd9z0+8bGxqqgoEBTpkyR1WpVp06drjrmySef1KRJkxQfH6/k5GRt27ZNW7Zs0a5du277cwPwDa7sAGhxubm5Sk5Ovip0pG9j58iRI+rXr5/GjBmj4cOHKyIiQm+++aYkacWKFdq5c6ccDofi4+Nv6X2zs7P1+eefq0ePHtd8vmfChAn63e9+p+XLl6tfv3569dVXtW7dOiUlJd3y5wTgHyxut9vt6yEAAACaC1d2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGO3/AYJmwjjsiGgjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traininigtimes_outlier = outlier(data, 'TrainingTimesLastYear')\n",
    "print(f'TrainingTimesLastYear의 아웃라이어 갯수 {traininigtimes_outlier.shape[0]}개')\n",
    "print(f'전체 데이터의 {round(traininigtimes_outlier.shape[0] / data.shape[0],2)}% 차지')\n",
    "print(traininigtimes_outlier.Attrition.value_counts())\n",
    "sns.countplot(data = traininigtimes_outlier, x='Attrition')\n",
    "plt.show()\n",
    "# VIF계수 낮음, 아웃라이어가 전체 데이터 많은양 차이\n",
    "# 컬럼과 아웃라이어 둘 다 유지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YearsAtCompany의 아웃라이어 갯수 104개\n",
      "전체 데이터의 0.07% 차지\n",
      "Attrition\n",
      "No     94\n",
      "Yes    10\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 아웃라이어가 전체데이터 0.07% 차지 (많은 편)\n",
    "# 해당 컬럼이 퇴사에 영향을 미칠 것으로 예상됨\n",
    "# 아웃라이어만 제거\n",
    "yearsatcompany_outlier = outlier(data, 'YearsAtCompany')\n",
    "print(f'YearsAtCompany의 아웃라이어 갯수 {yearsatcompany_outlier.shape[0]}개')\n",
    "print(f'전체 데이터의 {round(yearsatcompany_outlier.shape[0] / data.shape[0],2)}% 차지')\n",
    "print(yearsatcompany_outlier.Attrition.value_counts())\n",
    "sns.countplot(data = yearsatcompany_outlier, x='Attrition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YearsInCurrentRole의 아웃라이어 갯수 21개\n",
      "전체 데이터의 0.01% 차지\n",
      "Attrition\n",
      "No     19\n",
      "Yes     2\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VIF계수가 낮고 아웃라이어 수 적음\n",
    "# 아웃라이어만 삭제\n",
    "yearsincurrentrole_outlier = outlier(data, 'YearsInCurrentRole')\n",
    "print(f'YearsInCurrentRole의 아웃라이어 갯수 {yearsincurrentrole_outlier.shape[0]}개')\n",
    "print(f'전체 데이터의 {round(yearsincurrentrole_outlier.shape[0] / data.shape[0],2)}% 차지')\n",
    "print(yearsincurrentrole_outlier.Attrition.value_counts())\n",
    "sns.countplot(data = yearsincurrentrole_outlier, x='Attrition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YearsSinceLastPromotion의 아웃라이어 갯수 107개\n",
      "전체 데이터의 0.07% 차지\n",
      "Attrition\n",
      "No     94\n",
      "Yes    13\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 아웃라이어가 많고, VIF높음\n",
    "# 유지\n",
    "yearssincelastpromotion_outlier = outlier(data, 'YearsSinceLastPromotion')\n",
    "print(f'YearsSinceLastPromotion의 아웃라이어 갯수 {yearssincelastpromotion_outlier.shape[0]}개')\n",
    "print(f'전체 데이터의 {round(yearssincelastpromotion_outlier.shape[0] / data.shape[0],2)}% 차지')\n",
    "print(yearssincelastpromotion_outlier.Attrition.value_counts())\n",
    "sns.countplot(data = yearssincelastpromotion_outlier, x='Attrition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YearsWithCurrManager의 아웃라이어 갯수 14개\n",
      "전체 데이터의 0.01% 차지\n",
      "Attrition\n",
      "No    14\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VIF 낮음\n",
    "# 아웃라이어가 적긴하지만, 모두 NO값을 가져 의미가 있을 것으로 예상됨\n",
    "# 유지\n",
    "yearswithcurrmanager_outlier = outlier(data, 'YearsWithCurrManager')\n",
    "print(f'YearsWithCurrManager의 아웃라이어 갯수 {yearswithcurrmanager_outlier.shape[0]}개')\n",
    "print(f'전체 데이터의 {round(yearswithcurrmanager_outlier.shape[0] / data.shape[0],2)}% 차지')\n",
    "print(yearswithcurrmanager_outlier.Attrition.value_counts())\n",
    "sns.countplot(data = yearswithcurrmanager_outlier, x='Attrition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_df = pd.concat([yearsatcompany_outlier, yearsincurrentrole_outlier]).drop_duplicates()\n",
    "data.drop(outlier_df.index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1361, 29)"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('HR_Analytics_preprocessing.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "database",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}