Task_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание №1\n",
    "\n",
    "Кладиева Алина, 18ИАД.\n",
    "\n",
    "Датасет (https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset) содержит разного рода информацию о сотрудниках некоторой компании.\n",
    "\n",
    "Описание некоторых признаков:<br>\n",
    "<b>Education</b>:                1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor' <br>\n",
    "<b>EnvironmentSatisfaction</b>:  1 'Low' 2 'Medium' 3 'High' 4 'Very High' <br>\n",
    "<b>JobInvolvement</b>:           1 'Low' 2 'Medium' 3 'High' 4 'Very High' <br>\n",
    "<b>JobSatisfaction</b>:          1 'Low' 2 'Medium' 3 'High' 4 'Very High' <br>\n",
    "<b>PerformanceRating</b>:        1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding' <br>\n",
    "<b>RelationshipSatisfaction</b>: 1 'Low' 2 'Medium' 3 'High' 4 'Very High' <br>\n",
    "<b>WorkLifeBalance</b>:          1 'Bad' 2 'Good' 3 'Better' 4 'Best' <br>\n",
    "\n",
    "<b>Attrition</b> - изнурён ли сотрудник своей работой (Yes/No) <br>\n",
    "<b>OverTime</b> - Работает ли сотрудник во внерабочее время <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Чтение данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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",
       "      <th>Education</th>\n",
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       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
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       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
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       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
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       "      <th>3</th>\n",
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       "      <td>No</td>\n",
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       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
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       "      <td>Life Sciences</td>\n",
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       "      <td>24</td>\n",
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       "      <td>No</td>\n",
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       "      <td>15</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>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <th>13</th>\n",
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       "      <td>No</td>\n",
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       "      <td>1346</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>18</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>No</td>\n",
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       "      <td>5</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>22</td>\n",
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       "      <td>Non-Travel</td>\n",
       "      <td>1123</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
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       "      <td>80</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1219</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "      <th>19</th>\n",
       "      <td>38</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>371</td>\n",
       "      <td>Research &amp; Development</td>\n",
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       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>673</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1218</td>\n",
       "      <td>Sales</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>419</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>391</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</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>24</th>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>699</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>31</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1282</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>32</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1125</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>691</td>\n",
       "      <td>Sales</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>477</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>46</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>705</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>36</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>688</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2025</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>56</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>667</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2026</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>29</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1092</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2027</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>300</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2031</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>56</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>310</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>2032</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445</th>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>582</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2034</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1446</th>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>704</td>\n",
       "      <td>Sales</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>2035</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>36</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>301</td>\n",
       "      <td>Sales</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>2036</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>930</td>\n",
       "      <td>Sales</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2037</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>529</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>2038</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1146</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2040</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>38</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>345</td>\n",
       "      <td>Sales</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2041</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>878</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2044</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>36</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1120</td>\n",
       "      <td>Sales</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>2045</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>374</td>\n",
       "      <td>Sales</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2046</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>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1322</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2048</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</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>1456</th>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1199</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2049</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1194</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2051</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>287</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2052</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1378</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>2053</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>468</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2054</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>410</td>\n",
       "      <td>Sales</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>2055</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>722</td>\n",
       "      <td>Sales</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>2056</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>31</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>325</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2057</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1167</td>\n",
       "      <td>Sales</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>2060</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2061</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>No</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>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>No</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>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>No</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>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>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",
       "      <td>1</td>\n",
       "      <td>2068</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 × 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",
       "5      32        No  Travel_Frequently       1005  Research & Development   \n",
       "6      59        No      Travel_Rarely       1324  Research & Development   \n",
       "7      30        No      Travel_Rarely       1358  Research & Development   \n",
       "8      38        No  Travel_Frequently        216  Research & Development   \n",
       "9      36        No      Travel_Rarely       1299  Research & Development   \n",
       "10     35        No      Travel_Rarely        809  Research & Development   \n",
       "11     29        No      Travel_Rarely        153  Research & Development   \n",
       "12     31        No      Travel_Rarely        670  Research & Development   \n",
       "13     34        No      Travel_Rarely       1346  Research & Development   \n",
       "14     28       Yes      Travel_Rarely        103  Research & Development   \n",
       "15     29        No      Travel_Rarely       1389  Research & Development   \n",
       "16     32        No      Travel_Rarely        334  Research & Development   \n",
       "17     22        No         Non-Travel       1123  Research & Development   \n",
       "18     53        No      Travel_Rarely       1219                   Sales   \n",
       "19     38        No      Travel_Rarely        371  Research & Development   \n",
       "20     24        No         Non-Travel        673  Research & Development   \n",
       "21     36       Yes      Travel_Rarely       1218                   Sales   \n",
       "22     34        No      Travel_Rarely        419  Research & Development   \n",
       "23     21        No      Travel_Rarely        391  Research & Development   \n",
       "24     34       Yes      Travel_Rarely        699  Research & Development   \n",
       "25     53        No      Travel_Rarely       1282  Research & Development   \n",
       "26     32       Yes  Travel_Frequently       1125  Research & Development   \n",
       "27     42        No      Travel_Rarely        691                   Sales   \n",
       "28     44        No      Travel_Rarely        477  Research & Development   \n",
       "29     46        No      Travel_Rarely        705                   Sales   \n",
       "...   ...       ...                ...        ...                     ...   \n",
       "1440   36        No  Travel_Frequently        688  Research & Development   \n",
       "1441   56        No         Non-Travel        667  Research & Development   \n",
       "1442   29       Yes      Travel_Rarely       1092  Research & Development   \n",
       "1443   42        No      Travel_Rarely        300  Research & Development   \n",
       "1444   56       Yes      Travel_Rarely        310  Research & Development   \n",
       "1445   41        No      Travel_Rarely        582  Research & Development   \n",
       "1446   34        No      Travel_Rarely        704                   Sales   \n",
       "1447   36        No         Non-Travel        301                   Sales   \n",
       "1448   41        No      Travel_Rarely        930                   Sales   \n",
       "1449   32        No      Travel_Rarely        529  Research & Development   \n",
       "1450   35        No      Travel_Rarely       1146         Human Resources   \n",
       "1451   38        No      Travel_Rarely        345                   Sales   \n",
       "1452   50       Yes  Travel_Frequently        878                   Sales   \n",
       "1453   36        No      Travel_Rarely       1120                   Sales   \n",
       "1454   45        No      Travel_Rarely        374                   Sales   \n",
       "1455   40        No      Travel_Rarely       1322  Research & Development   \n",
       "1456   35        No  Travel_Frequently       1199  Research & Development   \n",
       "1457   40        No      Travel_Rarely       1194  Research & Development   \n",
       "1458   35        No      Travel_Rarely        287  Research & Development   \n",
       "1459   29        No      Travel_Rarely       1378  Research & Development   \n",
       "1460   29        No      Travel_Rarely        468  Research & Development   \n",
       "1461   50       Yes      Travel_Rarely        410                   Sales   \n",
       "1462   39        No      Travel_Rarely        722                   Sales   \n",
       "1463   31        No         Non-Travel        325  Research & Development   \n",
       "1464   26        No      Travel_Rarely       1167                   Sales   \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",
       "5                    2          2     Life Sciences              1   \n",
       "6                    3          3           Medical              1   \n",
       "7                   24          1     Life Sciences              1   \n",
       "8                   23          3     Life Sciences              1   \n",
       "9                   27          3           Medical              1   \n",
       "10                  16          3           Medical              1   \n",
       "11                  15          2     Life Sciences              1   \n",
       "12                  26          1     Life Sciences              1   \n",
       "13                  19          2           Medical              1   \n",
       "14                  24          3     Life Sciences              1   \n",
       "15                  21          4     Life Sciences              1   \n",
       "16                   5          2     Life Sciences              1   \n",
       "17                  16          2           Medical              1   \n",
       "18                   2          4     Life Sciences              1   \n",
       "19                   2          3     Life Sciences              1   \n",
       "20                  11          2             Other              1   \n",
       "21                   9          4     Life Sciences              1   \n",
       "22                   7          4     Life Sciences              1   \n",
       "23                  15          2     Life Sciences              1   \n",
       "24                   6          1           Medical              1   \n",
       "25                   5          3             Other              1   \n",
       "26                  16          1     Life Sciences              1   \n",
       "27                   8          4         Marketing              1   \n",
       "28                   7          4           Medical              1   \n",
       "29                   2          4         Marketing              1   \n",
       "...                ...        ...               ...            ...   \n",
       "1440                 4          2     Life Sciences              1   \n",
       "1441                 1          4     Life Sciences              1   \n",
       "1442                 1          4           Medical              1   \n",
       "1443                 2          3     Life Sciences              1   \n",
       "1444                 7          2  Technical Degree              1   \n",
       "1445                28          4     Life Sciences              1   \n",
       "1446                28          3         Marketing              1   \n",
       "1447                15          4         Marketing              1   \n",
       "1448                 3          3     Life Sciences              1   \n",
       "1449                 2          3  Technical Degree              1   \n",
       "1450                26          4     Life Sciences              1   \n",
       "1451                10          2     Life Sciences              1   \n",
       "1452                 1          4     Life Sciences              1   \n",
       "1453                11          4         Marketing              1   \n",
       "1454                20          3     Life Sciences              1   \n",
       "1455                 2          4     Life Sciences              1   \n",
       "1456                18          4     Life Sciences              1   \n",
       "1457                 2          4           Medical              1   \n",
       "1458                 1          4     Life Sciences              1   \n",
       "1459                13          2             Other              1   \n",
       "1460                28          4           Medical              1   \n",
       "1461                28          3         Marketing              1   \n",
       "1462                24          1         Marketing              1   \n",
       "1463                 5          3           Medical              1   \n",
       "1464                 5          3             Other              1   \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  \\\n",
       "0                  1          ...                                  1   \n",
       "1                  2          ...                                  4   \n",
       "2                  4          ...                                  2   \n",
       "3                  5          ...                                  3   \n",
       "4                  7          ...                                  4   \n",
       "5                  8          ...                                  3   \n",
       "6                 10          ...                                  1   \n",
       "7                 11          ...                                  2   \n",
       "8                 12          ...                                  2   \n",
       "9                 13          ...                                  2   \n",
       "10                14          ...                                  3   \n",
       "11                15          ...                                  4   \n",
       "12                16          ...                                  4   \n",
       "13                18          ...                                  3   \n",
       "14                19          ...                                  2   \n",
       "15                20          ...                                  3   \n",
       "16                21          ...                                  4   \n",
       "17                22          ...                                  2   \n",
       "18                23          ...                                  3   \n",
       "19                24          ...                                  3   \n",
       "20                26          ...                                  4   \n",
       "21                27          ...                                  2   \n",
       "22                28          ...                                  3   \n",
       "23                30          ...                                  4   \n",
       "24                31          ...                                  3   \n",
       "25                32          ...                                  4   \n",
       "26                33          ...                                  2   \n",
       "27                35          ...                                  4   \n",
       "28                36          ...                                  4   \n",
       "29                38          ...                                  4   \n",
       "...              ...          ...                                ...   \n",
       "1440            2025          ...                                  2   \n",
       "1441            2026          ...                                  1   \n",
       "1442            2027          ...                                  2   \n",
       "1443            2031          ...                                  1   \n",
       "1444            2032          ...                                  4   \n",
       "1445            2034          ...                                  3   \n",
       "1446            2035          ...                                  4   \n",
       "1447            2036          ...                                  1   \n",
       "1448            2037          ...                                  3   \n",
       "1449            2038          ...                                  4   \n",
       "1450            2040          ...                                  3   \n",
       "1451            2041          ...                                  3   \n",
       "1452            2044          ...                                  4   \n",
       "1453            2045          ...                                  1   \n",
       "1454            2046          ...                                  3   \n",
       "1455            2048          ...                                  4   \n",
       "1456            2049          ...                                  4   \n",
       "1457            2051          ...                                  2   \n",
       "1458            2052          ...                                  4   \n",
       "1459            2053          ...                                  1   \n",
       "1460            2054          ...                                  2   \n",
       "1461            2055          ...                                  2   \n",
       "1462            2056          ...                                  1   \n",
       "1463            2057          ...                                  2   \n",
       "1464            2060          ...                                  4   \n",
       "1465            2061          ...                                  3   \n",
       "1466            2062          ...                                  1   \n",
       "1467            2064          ...                                  2   \n",
       "1468            2065          ...                                  4   \n",
       "1469            2068          ...                                  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",
       "5               80                 0                  8   \n",
       "6               80                 3                 12   \n",
       "7               80                 1                  1   \n",
       "8               80                 0                 10   \n",
       "9               80                 2                 17   \n",
       "10              80                 1                  6   \n",
       "11              80                 0                 10   \n",
       "12              80                 1                  5   \n",
       "13              80                 1                  3   \n",
       "14              80                 0                  6   \n",
       "15              80                 1                 10   \n",
       "16              80                 2                  7   \n",
       "17              80                 2                  1   \n",
       "18              80                 0                 31   \n",
       "19              80                 0                  6   \n",
       "20              80                 1                  5   \n",
       "21              80                 0                 10   \n",
       "22              80                 0                 13   \n",
       "23              80                 0                  0   \n",
       "24              80                 0                  8   \n",
       "25              80                 1                 26   \n",
       "26              80                 0                 10   \n",
       "27              80                 1                 10   \n",
       "28              80                 1                 24   \n",
       "29              80                 0                 22   \n",
       "...            ...               ...                ...   \n",
       "1440            80                 3                 18   \n",
       "1441            80                 1                 13   \n",
       "1442            80                 3                  4   \n",
       "1443            80                 0                 24   \n",
       "1444            80                 1                 14   \n",
       "1445            80                 1                 21   \n",
       "1446            80                 2                  8   \n",
       "1447            80                 1                 15   \n",
       "1448            80                 1                 14   \n",
       "1449            80                 0                  4   \n",
       "1450            80                 0                  9   \n",
       "1451            80                 1                 10   \n",
       "1452            80                 2                 12   \n",
       "1453            80                 1                  8   \n",
       "1454            80                 0                  8   \n",
       "1455            80                 0                  8   \n",
       "1456            80                 2                 10   \n",
       "1457            80                 3                 20   \n",
       "1458            80                 1                  4   \n",
       "1459            80                 1                 10   \n",
       "1460            80                 0                  5   \n",
       "1461            80                 1                 20   \n",
       "1462            80                 1                 21   \n",
       "1463            80                 0                 10   \n",
       "1464            80                 0                  5   \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",
       "5                         2               2               7   \n",
       "6                         3               2               1   \n",
       "7                         2               3               1   \n",
       "8                         2               3               9   \n",
       "9                         3               2               7   \n",
       "10                        5               3               5   \n",
       "11                        3               3               9   \n",
       "12                        1               2               5   \n",
       "13                        2               3               2   \n",
       "14                        4               3               4   \n",
       "15                        1               3              10   \n",
       "16                        5               2               6   \n",
       "17                        2               2               1   \n",
       "18                        3               3              25   \n",
       "19                        3               3               3   \n",
       "20                        5               2               4   \n",
       "21                        4               3               5   \n",
       "22                        4               3              12   \n",
       "23                        6               3               0   \n",
       "24                        2               3               4   \n",
       "25                        3               2              14   \n",
       "26                        5               3              10   \n",
       "27                        2               3               9   \n",
       "28                        4               3              22   \n",
       "29                        2               2               2   \n",
       "...                     ...             ...             ...   \n",
       "1440                      3               3               4   \n",
       "1441                      2               2              13   \n",
       "1442                      3               4               2   \n",
       "1443                      2               2              22   \n",
       "1444                      4               1              10   \n",
       "1445                      3               3              20   \n",
       "1446                      2               3               8   \n",
       "1447                      4               2              15   \n",
       "1448                      5               3               5   \n",
       "1449                      4               3               4   \n",
       "1450                      2               3               9   \n",
       "1451                      1               3              10   \n",
       "1452                      3               3               6   \n",
       "1453                      2               2               6   \n",
       "1454                      3               3               5   \n",
       "1455                      2               3               2   \n",
       "1456                      2               4              10   \n",
       "1457                      2               3               5   \n",
       "1458                      5               3               4   \n",
       "1459                      2               3               4   \n",
       "1460                      3               1               5   \n",
       "1461                      3               3               3   \n",
       "1462                      2               2              20   \n",
       "1463                      2               3               9   \n",
       "1464                      2               3               4   \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",
       "5                     7                        3                     6  \n",
       "6                     0                        0                     0  \n",
       "7                     0                        0                     0  \n",
       "8                     7                        1                     8  \n",
       "9                     7                        7                     7  \n",
       "10                    4                        0                     3  \n",
       "11                    5                        0                     8  \n",
       "12                    2                        4                     3  \n",
       "13                    2                        1                     2  \n",
       "14                    2                        0                     3  \n",
       "15                    9                        8                     8  \n",
       "16                    2                        0                     5  \n",
       "17                    0                        0                     0  \n",
       "18                    8                        3                     7  \n",
       "19                    2                        1                     2  \n",
       "20                    2                        1                     3  \n",
       "21                    3                        0                     3  \n",
       "22                    6                        2                    11  \n",
       "23                    0                        0                     0  \n",
       "24                    2                        1                     3  \n",
       "25                   13                        4                     8  \n",
       "26                    2                        6                     7  \n",
       "27                    7                        4                     2  \n",
       "28                    6                        5                    17  \n",
       "29                    2                        2                     1  \n",
       "...                 ...                      ...                   ...  \n",
       "1440                  2                        0                     2  \n",
       "1441                 12                        1                     9  \n",
       "1442                  2                        2                     2  \n",
       "1443                  6                        4                    14  \n",
       "1444                  9                        9                     8  \n",
       "1445                  7                        0                    10  \n",
       "1446                  7                        1                     7  \n",
       "1447                 12                       11                    11  \n",
       "1448                  4                        0                     4  \n",
       "1449                  2                        1                     2  \n",
       "1450                  0                        1                     7  \n",
       "1451                  7                        1                     9  \n",
       "1452                  3                        0                     1  \n",
       "1453                  3                        0                     0  \n",
       "1454                  3                        0                     1  \n",
       "1455                  2                        2                     2  \n",
       "1456                  2                        0                     2  \n",
       "1457                  3                        0                     2  \n",
       "1458                  3                        1                     1  \n",
       "1459                  3                        0                     3  \n",
       "1460                  4                        0                     4  \n",
       "1461                  2                        2                     0  \n",
       "1462                  9                        9                     6  \n",
       "1463                  4                        1                     7  \n",
       "1464                  2                        0                     0  \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 35 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(\"WA_Fn-UseC_-HR-Employee-Attrition.csv\", sep=\",\")\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1470, 35)\n",
      "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
      "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
      "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
      "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
      "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
      "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
      "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
      "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
      "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
      "       'YearsWithCurrManager'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(raw_data.shape)\n",
    "print(raw_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Удалим несколько колонок с избыточной информацией для упрощения\n",
    "data = raw_data.drop([\"Over18\", \"EmployeeCount\", \"YearsWithCurrManager\", \"TrainingTimesLastYear\", \"HourlyRate\", \"DailyRate\", \"EmployeeNumber\", \"StockOptionLevel\", \"StandardHours\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1470, 26)\n",
      "Index(['Age', 'Attrition', 'BusinessTravel', 'Department', 'DistanceFromHome',\n",
      "       'Education', 'EducationField', 'EnvironmentSatisfaction', 'Gender',\n",
      "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
      "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
      "       'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
      "       'RelationshipSatisfaction', 'TotalWorkingYears', 'WorkLifeBalance',\n",
      "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>...</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</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>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</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>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</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>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel              Department  DistanceFromHome  \\\n",
       "0   41       Yes      Travel_Rarely                   Sales                 1   \n",
       "1   49        No  Travel_Frequently  Research & Development                 8   \n",
       "2   37       Yes      Travel_Rarely  Research & Development                 2   \n",
       "3   33        No  Travel_Frequently  Research & Development                 3   \n",
       "4   27        No      Travel_Rarely  Research & Development                 2   \n",
       "\n",
       "   Education EducationField  EnvironmentSatisfaction  Gender  JobInvolvement  \\\n",
       "0          2  Life Sciences                        2  Female               3   \n",
       "1          1  Life Sciences                        3    Male               2   \n",
       "2          2          Other                        4    Male               2   \n",
       "3          4  Life Sciences                        4  Female               3   \n",
       "4          1        Medical                        1    Male               3   \n",
       "\n",
       "            ...             NumCompaniesWorked OverTime  PercentSalaryHike  \\\n",
       "0           ...                              8      Yes                 11   \n",
       "1           ...                              1       No                 23   \n",
       "2           ...                              6      Yes                 15   \n",
       "3           ...                              1      Yes                 11   \n",
       "4           ...                              9       No                 12   \n",
       "\n",
       "  PerformanceRating  RelationshipSatisfaction  TotalWorkingYears  \\\n",
       "0                 3                         1                  8   \n",
       "1                 4                         4                 10   \n",
       "2                 3                         2                  7   \n",
       "3                 3                         3                  8   \n",
       "4                 3                         4                  6   \n",
       "\n",
       "   WorkLifeBalance YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \n",
       "0                1              6                   4                        0  \n",
       "1                3             10                   7                        1  \n",
       "2                3              0                   0                        0  \n",
       "3                3              8                   7                        3  \n",
       "4                3              2                   2                        2  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>...</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>36</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Attrition     BusinessTravel              Department  \\\n",
       "1465   36        No  Travel_Frequently  Research & Development   \n",
       "1466   39        No      Travel_Rarely  Research & Development   \n",
       "1467   27        No      Travel_Rarely  Research & Development   \n",
       "1468   49        No  Travel_Frequently                   Sales   \n",
       "1469   34        No      Travel_Rarely  Research & Development   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EnvironmentSatisfaction  \\\n",
       "1465                23          2        Medical                        3   \n",
       "1466                 6          1        Medical                        4   \n",
       "1467                 4          3  Life Sciences                        2   \n",
       "1468                 2          3        Medical                        4   \n",
       "1469                 8          3        Medical                        2   \n",
       "\n",
       "     Gender  JobInvolvement           ...             NumCompaniesWorked  \\\n",
       "1465   Male               4           ...                              4   \n",
       "1466   Male               2           ...                              4   \n",
       "1467   Male               4           ...                              1   \n",
       "1468   Male               2           ...                              2   \n",
       "1469   Male               4           ...                              2   \n",
       "\n",
       "     OverTime  PercentSalaryHike PerformanceRating  RelationshipSatisfaction  \\\n",
       "1465       No                 17                 3                         3   \n",
       "1466       No                 15                 3                         1   \n",
       "1467      Yes                 20                 4                         2   \n",
       "1468       No                 14                 3                         4   \n",
       "1469       No                 12                 3                         1   \n",
       "\n",
       "      TotalWorkingYears  WorkLifeBalance YearsAtCompany  YearsInCurrentRole  \\\n",
       "1465                 17                3              5                   2   \n",
       "1466                  9                3              7                   7   \n",
       "1467                  6                3              6                   2   \n",
       "1468                 17                2              9                   6   \n",
       "1469                  6                4              4                   3   \n",
       "\n",
       "      YearsSinceLastPromotion  \n",
       "1465                        0  \n",
       "1466                        1  \n",
       "1467                        0  \n",
       "1468                        0  \n",
       "1469                        1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sales', 'Research & Development', 'Human Resources'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Уникальные значения в колонке средствами Pandas\n",
    "data[\"Department\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['Human Resources', 'Life Sciences', 'Marketing', 'Medical', 'Other',\n",
       "        'Technical Degree'], dtype=object),\n",
       " array([ 27, 606, 159, 464,  82, 132]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Уикальные значения в колонке и их число средствам Numpy\n",
    "np.unique(data[\"EducationField\"], return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.at[1469, \"Age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Research & Development'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Department\"][1469]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Department</th>\n",
       "      <th>Age</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sales</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>49</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Department  Age  TotalWorkingYears\n",
       "0                   Sales   41                  8\n",
       "1  Research & Development   49                 10\n",
       "2  Research & Development   37                  7\n",
       "3  Research & Development   33                  8\n",
       "4  Research & Development   27                  6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"Department\", \"Age\", \"TotalWorkingYears\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.923810</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>2.728571</td>\n",
       "      <td>6502.931293</td>\n",
       "      <td>14313.103401</td>\n",
       "      <td>2.693197</td>\n",
       "      <td>15.209524</td>\n",
       "      <td>3.153741</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.135373</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>1.102846</td>\n",
       "      <td>4707.956783</td>\n",
       "      <td>7117.786044</td>\n",
       "      <td>2.498009</td>\n",
       "      <td>3.659938</td>\n",
       "      <td>0.360824</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2911.000000</td>\n",
       "      <td>8047.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4919.000000</td>\n",
       "      <td>14235.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8379.000000</td>\n",
       "      <td>20461.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19999.000000</td>\n",
       "      <td>26999.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  DistanceFromHome    Education  EnvironmentSatisfaction  \\\n",
       "count  1470.000000       1470.000000  1470.000000              1470.000000   \n",
       "mean     36.923810          9.192517     2.912925                 2.721769   \n",
       "std       9.135373          8.106864     1.024165                 1.093082   \n",
       "min      18.000000          1.000000     1.000000                 1.000000   \n",
       "25%      30.000000          2.000000     2.000000                 2.000000   \n",
       "50%      36.000000          7.000000     3.000000                 3.000000   \n",
       "75%      43.000000         14.000000     4.000000                 4.000000   \n",
       "max      60.000000         29.000000     5.000000                 4.000000   \n",
       "\n",
       "       JobInvolvement     JobLevel  JobSatisfaction  MonthlyIncome  \\\n",
       "count     1470.000000  1470.000000      1470.000000    1470.000000   \n",
       "mean         2.729932     2.063946         2.728571    6502.931293   \n",
       "std          0.711561     1.106940         1.102846    4707.956783   \n",
       "min          1.000000     1.000000         1.000000    1009.000000   \n",
       "25%          2.000000     1.000000         2.000000    2911.000000   \n",
       "50%          3.000000     2.000000         3.000000    4919.000000   \n",
       "75%          3.000000     3.000000         4.000000    8379.000000   \n",
       "max          4.000000     5.000000         4.000000   19999.000000   \n",
       "\n",
       "        MonthlyRate  NumCompaniesWorked  PercentSalaryHike  PerformanceRating  \\\n",
       "count   1470.000000         1470.000000        1470.000000        1470.000000   \n",
       "mean   14313.103401            2.693197          15.209524           3.153741   \n",
       "std     7117.786044            2.498009           3.659938           0.360824   \n",
       "min     2094.000000            0.000000          11.000000           3.000000   \n",
       "25%     8047.000000            1.000000          12.000000           3.000000   \n",
       "50%    14235.500000            2.000000          14.000000           3.000000   \n",
       "75%    20461.500000            4.000000          18.000000           3.000000   \n",
       "max    26999.000000            9.000000          25.000000           4.000000   \n",
       "\n",
       "       RelationshipSatisfaction  TotalWorkingYears  WorkLifeBalance  \\\n",
       "count               1470.000000        1470.000000      1470.000000   \n",
       "mean                   2.712245          11.279592         2.761224   \n",
       "std                    1.081209           7.780782         0.706476   \n",
       "min                    1.000000           0.000000         1.000000   \n",
       "25%                    2.000000           6.000000         2.000000   \n",
       "50%                    3.000000          10.000000         3.000000   \n",
       "75%                    4.000000          15.000000         3.000000   \n",
       "max                    4.000000          40.000000         4.000000   \n",
       "\n",
       "       YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \n",
       "count     1470.000000         1470.000000              1470.000000  \n",
       "mean         7.008163            4.229252                 2.187755  \n",
       "std          6.126525            3.623137                 3.222430  \n",
       "min          0.000000            0.000000                 0.000000  \n",
       "25%          3.000000            2.000000                 0.000000  \n",
       "50%          5.000000            3.000000                 1.000000  \n",
       "75%          9.000000            7.000000                 3.000000  \n",
       "max         40.000000           18.000000                15.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Можно видеть, например, что средний возраст сотрудников - около 37 лет (mean Age); \n",
    "# а максимальный срок работы в компании - 40 лет (max YearsAtCompany) \n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>OverTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1233</td>\n",
       "      <td>1043</td>\n",
       "      <td>961</td>\n",
       "      <td>606</td>\n",
       "      <td>882</td>\n",
       "      <td>326</td>\n",
       "      <td>673</td>\n",
       "      <td>1054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Attrition BusinessTravel              Department EducationField Gender  \\\n",
       "count       1470           1470                    1470           1470   1470   \n",
       "unique         2              3                       3              6      2   \n",
       "top           No  Travel_Rarely  Research & Development  Life Sciences   Male   \n",
       "freq        1233           1043                     961            606    882   \n",
       "\n",
       "                JobRole MaritalStatus OverTime  \n",
       "count              1470          1470     1470  \n",
       "unique                9             3        2  \n",
       "top     Sales Executive       Married       No  \n",
       "freq                326           673     1054  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include = ['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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j9bp7njy/px+FEEKIU2YairlC7qyXceYMQ3FZgrNjkcBNCCGEEDOj3gl53PAo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i9Bhsm7r3M4rNGiT0oey9FYZo4ITr+CnQmqaGfkU+U05RrUscjZ3zeujB5jcupEKaUuykT2uAFY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\nMS4qXl7v8JkzPbqNgLRQvLeVAnBmLmG+GT7ld+44juM4zieNK9w+RKUNW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5L0JDCKlBD8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\nvVxYGAQZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb87da30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Не рисую scatter matrix для всех признаков, т.к. она получается слишком огромная\n",
    "\n",
    "# По графикам на главной диагонали видно, что доход большинства сотрудников - 2500-5000 у.е. в месяц; \n",
    "# и большая часть сотрудников работает в компании 5-10 лет\n",
    "\n",
    "# По графикам вне главной диагонали видно, что ежемесячный доход, как правило, растёт вместе с общим числом \n",
    "# отработанных сотрудником лет\n",
    "from pandas.plotting import scatter_matrix\n",
    "scatter_matrix(data[[\"MonthlyIncome\", \"TotalWorkingYears\"]], alpha = .2, figsize = (10, 10))\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001686</td>\n",
       "      <td>0.208034</td>\n",
       "      <td>0.010146</td>\n",
       "      <td>0.029820</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>-0.004892</td>\n",
       "      <td>0.497855</td>\n",
       "      <td>0.028051</td>\n",
       "      <td>0.299635</td>\n",
       "      <td>0.003634</td>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.053535</td>\n",
       "      <td>0.680381</td>\n",
       "      <td>-0.021490</td>\n",
       "      <td>0.311309</td>\n",
       "      <td>0.212901</td>\n",
       "      <td>0.216513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <td>-0.001686</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021042</td>\n",
       "      <td>-0.016075</td>\n",
       "      <td>0.008783</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>-0.003669</td>\n",
       "      <td>-0.017014</td>\n",
       "      <td>0.027473</td>\n",
       "      <td>-0.029251</td>\n",
       "      <td>0.040235</td>\n",
       "      <td>0.027110</td>\n",
       "      <td>0.006557</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>-0.026556</td>\n",
       "      <td>0.009508</td>\n",
       "      <td>0.018845</td>\n",
       "      <td>0.010029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>0.208034</td>\n",
       "      <td>0.021042</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027128</td>\n",
       "      <td>0.042438</td>\n",
       "      <td>0.101589</td>\n",
       "      <td>-0.011296</td>\n",
       "      <td>0.094961</td>\n",
       "      <td>-0.026084</td>\n",
       "      <td>0.126317</td>\n",
       "      <td>-0.011111</td>\n",
       "      <td>-0.024539</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>0.148280</td>\n",
       "      <td>0.009819</td>\n",
       "      <td>0.069114</td>\n",
       "      <td>0.060236</td>\n",
       "      <td>0.054254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <td>0.010146</td>\n",
       "      <td>-0.016075</td>\n",
       "      <td>-0.027128</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008278</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>-0.006784</td>\n",
       "      <td>-0.006259</td>\n",
       "      <td>0.037600</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>-0.031701</td>\n",
       "      <td>-0.029548</td>\n",
       "      <td>0.007665</td>\n",
       "      <td>-0.002693</td>\n",
       "      <td>0.027627</td>\n",
       "      <td>0.001458</td>\n",
       "      <td>0.018007</td>\n",
       "      <td>0.016194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobInvolvement</th>\n",
       "      <td>0.029820</td>\n",
       "      <td>0.008783</td>\n",
       "      <td>0.042438</td>\n",
       "      <td>-0.008278</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012630</td>\n",
       "      <td>-0.021476</td>\n",
       "      <td>-0.015271</td>\n",
       "      <td>-0.016322</td>\n",
       "      <td>0.015012</td>\n",
       "      <td>-0.017205</td>\n",
       "      <td>-0.029071</td>\n",
       "      <td>0.034297</td>\n",
       "      <td>-0.005533</td>\n",
       "      <td>-0.014617</td>\n",
       "      <td>-0.021355</td>\n",
       "      <td>0.008717</td>\n",
       "      <td>-0.024184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>0.101589</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>-0.012630</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001944</td>\n",
       "      <td>0.950300</td>\n",
       "      <td>0.039563</td>\n",
       "      <td>0.142501</td>\n",
       "      <td>-0.034730</td>\n",
       "      <td>-0.021222</td>\n",
       "      <td>0.021642</td>\n",
       "      <td>0.782208</td>\n",
       "      <td>0.037818</td>\n",
       "      <td>0.534739</td>\n",
       "      <td>0.389447</td>\n",
       "      <td>0.353885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <td>-0.004892</td>\n",
       "      <td>-0.003669</td>\n",
       "      <td>-0.011296</td>\n",
       "      <td>-0.006784</td>\n",
       "      <td>-0.021476</td>\n",
       "      <td>-0.001944</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007157</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>-0.055699</td>\n",
       "      <td>0.020002</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>-0.012454</td>\n",
       "      <td>-0.020185</td>\n",
       "      <td>-0.019459</td>\n",
       "      <td>-0.003803</td>\n",
       "      <td>-0.002305</td>\n",
       "      <td>-0.018214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>0.497855</td>\n",
       "      <td>-0.017014</td>\n",
       "      <td>0.094961</td>\n",
       "      <td>-0.006259</td>\n",
       "      <td>-0.015271</td>\n",
       "      <td>0.950300</td>\n",
       "      <td>-0.007157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034814</td>\n",
       "      <td>0.149515</td>\n",
       "      <td>-0.027269</td>\n",
       "      <td>-0.017120</td>\n",
       "      <td>0.025873</td>\n",
       "      <td>0.772893</td>\n",
       "      <td>0.030683</td>\n",
       "      <td>0.514285</td>\n",
       "      <td>0.363818</td>\n",
       "      <td>0.344978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyRate</th>\n",
       "      <td>0.028051</td>\n",
       "      <td>0.027473</td>\n",
       "      <td>-0.026084</td>\n",
       "      <td>0.037600</td>\n",
       "      <td>-0.016322</td>\n",
       "      <td>0.039563</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>0.034814</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017521</td>\n",
       "      <td>-0.006429</td>\n",
       "      <td>-0.009811</td>\n",
       "      <td>-0.004085</td>\n",
       "      <td>0.026442</td>\n",
       "      <td>0.007963</td>\n",
       "      <td>-0.023655</td>\n",
       "      <td>-0.012815</td>\n",
       "      <td>0.001567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <td>0.299635</td>\n",
       "      <td>-0.029251</td>\n",
       "      <td>0.126317</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.015012</td>\n",
       "      <td>0.142501</td>\n",
       "      <td>-0.055699</td>\n",
       "      <td>0.149515</td>\n",
       "      <td>0.017521</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010238</td>\n",
       "      <td>-0.014095</td>\n",
       "      <td>0.052733</td>\n",
       "      <td>0.237639</td>\n",
       "      <td>-0.008366</td>\n",
       "      <td>-0.118421</td>\n",
       "      <td>-0.090754</td>\n",
       "      <td>-0.036814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <td>0.003634</td>\n",
       "      <td>0.040235</td>\n",
       "      <td>-0.011111</td>\n",
       "      <td>-0.031701</td>\n",
       "      <td>-0.017205</td>\n",
       "      <td>-0.034730</td>\n",
       "      <td>0.020002</td>\n",
       "      <td>-0.027269</td>\n",
       "      <td>-0.006429</td>\n",
       "      <td>-0.010238</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.773550</td>\n",
       "      <td>-0.040490</td>\n",
       "      <td>-0.020608</td>\n",
       "      <td>-0.003280</td>\n",
       "      <td>-0.035991</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>-0.022154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PerformanceRating</th>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.027110</td>\n",
       "      <td>-0.024539</td>\n",
       "      <td>-0.029548</td>\n",
       "      <td>-0.029071</td>\n",
       "      <td>-0.021222</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>-0.017120</td>\n",
       "      <td>-0.009811</td>\n",
       "      <td>-0.014095</td>\n",
       "      <td>0.773550</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031351</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>0.002572</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.034986</td>\n",
       "      <td>0.017896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <td>0.053535</td>\n",
       "      <td>0.006557</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>0.007665</td>\n",
       "      <td>0.034297</td>\n",
       "      <td>0.021642</td>\n",
       "      <td>-0.012454</td>\n",
       "      <td>0.025873</td>\n",
       "      <td>-0.004085</td>\n",
       "      <td>0.052733</td>\n",
       "      <td>-0.040490</td>\n",
       "      <td>-0.031351</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024054</td>\n",
       "      <td>0.019604</td>\n",
       "      <td>0.019367</td>\n",
       "      <td>-0.015123</td>\n",
       "      <td>0.033493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <td>0.680381</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.148280</td>\n",
       "      <td>-0.002693</td>\n",
       "      <td>-0.005533</td>\n",
       "      <td>0.782208</td>\n",
       "      <td>-0.020185</td>\n",
       "      <td>0.772893</td>\n",
       "      <td>0.026442</td>\n",
       "      <td>0.237639</td>\n",
       "      <td>-0.020608</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>0.024054</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.460365</td>\n",
       "      <td>0.404858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <td>-0.021490</td>\n",
       "      <td>-0.026556</td>\n",
       "      <td>0.009819</td>\n",
       "      <td>0.027627</td>\n",
       "      <td>-0.014617</td>\n",
       "      <td>0.037818</td>\n",
       "      <td>-0.019459</td>\n",
       "      <td>0.030683</td>\n",
       "      <td>0.007963</td>\n",
       "      <td>-0.008366</td>\n",
       "      <td>-0.003280</td>\n",
       "      <td>0.002572</td>\n",
       "      <td>0.019604</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012089</td>\n",
       "      <td>0.049856</td>\n",
       "      <td>0.008941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <td>0.311309</td>\n",
       "      <td>0.009508</td>\n",
       "      <td>0.069114</td>\n",
       "      <td>0.001458</td>\n",
       "      <td>-0.021355</td>\n",
       "      <td>0.534739</td>\n",
       "      <td>-0.003803</td>\n",
       "      <td>0.514285</td>\n",
       "      <td>-0.023655</td>\n",
       "      <td>-0.118421</td>\n",
       "      <td>-0.035991</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.019367</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.012089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758754</td>\n",
       "      <td>0.618409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <td>0.212901</td>\n",
       "      <td>0.018845</td>\n",
       "      <td>0.060236</td>\n",
       "      <td>0.018007</td>\n",
       "      <td>0.008717</td>\n",
       "      <td>0.389447</td>\n",
       "      <td>-0.002305</td>\n",
       "      <td>0.363818</td>\n",
       "      <td>-0.012815</td>\n",
       "      <td>-0.090754</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>0.034986</td>\n",
       "      <td>-0.015123</td>\n",
       "      <td>0.460365</td>\n",
       "      <td>0.049856</td>\n",
       "      <td>0.758754</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.548056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <td>0.216513</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.054254</td>\n",
       "      <td>0.016194</td>\n",
       "      <td>-0.024184</td>\n",
       "      <td>0.353885</td>\n",
       "      <td>-0.018214</td>\n",
       "      <td>0.344978</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>-0.036814</td>\n",
       "      <td>-0.022154</td>\n",
       "      <td>0.017896</td>\n",
       "      <td>0.033493</td>\n",
       "      <td>0.404858</td>\n",
       "      <td>0.008941</td>\n",
       "      <td>0.618409</td>\n",
       "      <td>0.548056</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Age  DistanceFromHome  Education  \\\n",
       "Age                       1.000000         -0.001686   0.208034   \n",
       "DistanceFromHome         -0.001686          1.000000   0.021042   \n",
       "Education                 0.208034          0.021042   1.000000   \n",
       "EnvironmentSatisfaction   0.010146         -0.016075  -0.027128   \n",
       "JobInvolvement            0.029820          0.008783   0.042438   \n",
       "JobLevel                  0.509604          0.005303   0.101589   \n",
       "JobSatisfaction          -0.004892         -0.003669  -0.011296   \n",
       "MonthlyIncome             0.497855         -0.017014   0.094961   \n",
       "MonthlyRate               0.028051          0.027473  -0.026084   \n",
       "NumCompaniesWorked        0.299635         -0.029251   0.126317   \n",
       "PercentSalaryHike         0.003634          0.040235  -0.011111   \n",
       "PerformanceRating         0.001904          0.027110  -0.024539   \n",
       "RelationshipSatisfaction  0.053535          0.006557  -0.009118   \n",
       "TotalWorkingYears         0.680381          0.004628   0.148280   \n",
       "WorkLifeBalance          -0.021490         -0.026556   0.009819   \n",
       "YearsAtCompany            0.311309          0.009508   0.069114   \n",
       "YearsInCurrentRole        0.212901          0.018845   0.060236   \n",
       "YearsSinceLastPromotion   0.216513          0.010029   0.054254   \n",
       "\n",
       "                          EnvironmentSatisfaction  JobInvolvement  JobLevel  \\\n",
       "Age                                      0.010146        0.029820  0.509604   \n",
       "DistanceFromHome                        -0.016075        0.008783  0.005303   \n",
       "Education                               -0.027128        0.042438  0.101589   \n",
       "EnvironmentSatisfaction                  1.000000       -0.008278  0.001212   \n",
       "JobInvolvement                          -0.008278        1.000000 -0.012630   \n",
       "JobLevel                                 0.001212       -0.012630  1.000000   \n",
       "JobSatisfaction                         -0.006784       -0.021476 -0.001944   \n",
       "MonthlyIncome                           -0.006259       -0.015271  0.950300   \n",
       "MonthlyRate                              0.037600       -0.016322  0.039563   \n",
       "NumCompaniesWorked                       0.012594        0.015012  0.142501   \n",
       "PercentSalaryHike                       -0.031701       -0.017205 -0.034730   \n",
       "PerformanceRating                       -0.029548       -0.029071 -0.021222   \n",
       "RelationshipSatisfaction                 0.007665        0.034297  0.021642   \n",
       "TotalWorkingYears                       -0.002693       -0.005533  0.782208   \n",
       "WorkLifeBalance                          0.027627       -0.014617  0.037818   \n",
       "YearsAtCompany                           0.001458       -0.021355  0.534739   \n",
       "YearsInCurrentRole                       0.018007        0.008717  0.389447   \n",
       "YearsSinceLastPromotion                  0.016194       -0.024184  0.353885   \n",
       "\n",
       "                          JobSatisfaction  MonthlyIncome  MonthlyRate  \\\n",
       "Age                             -0.004892       0.497855     0.028051   \n",
       "DistanceFromHome                -0.003669      -0.017014     0.027473   \n",
       "Education                       -0.011296       0.094961    -0.026084   \n",
       "EnvironmentSatisfaction         -0.006784      -0.006259     0.037600   \n",
       "JobInvolvement                  -0.021476      -0.015271    -0.016322   \n",
       "JobLevel                        -0.001944       0.950300     0.039563   \n",
       "JobSatisfaction                  1.000000      -0.007157     0.000644   \n",
       "MonthlyIncome                   -0.007157       1.000000     0.034814   \n",
       "MonthlyRate                      0.000644       0.034814     1.000000   \n",
       "NumCompaniesWorked              -0.055699       0.149515     0.017521   \n",
       "PercentSalaryHike                0.020002      -0.027269    -0.006429   \n",
       "PerformanceRating                0.002297      -0.017120    -0.009811   \n",
       "RelationshipSatisfaction        -0.012454       0.025873    -0.004085   \n",
       "TotalWorkingYears               -0.020185       0.772893     0.026442   \n",
       "WorkLifeBalance                 -0.019459       0.030683     0.007963   \n",
       "YearsAtCompany                  -0.003803       0.514285    -0.023655   \n",
       "YearsInCurrentRole              -0.002305       0.363818    -0.012815   \n",
       "YearsSinceLastPromotion         -0.018214       0.344978     0.001567   \n",
       "\n",
       "                          NumCompaniesWorked  PercentSalaryHike  \\\n",
       "Age                                 0.299635           0.003634   \n",
       "DistanceFromHome                   -0.029251           0.040235   \n",
       "Education                           0.126317          -0.011111   \n",
       "EnvironmentSatisfaction             0.012594          -0.031701   \n",
       "JobInvolvement                      0.015012          -0.017205   \n",
       "JobLevel                            0.142501          -0.034730   \n",
       "JobSatisfaction                    -0.055699           0.020002   \n",
       "MonthlyIncome                       0.149515          -0.027269   \n",
       "MonthlyRate                         0.017521          -0.006429   \n",
       "NumCompaniesWorked                  1.000000          -0.010238   \n",
       "PercentSalaryHike                  -0.010238           1.000000   \n",
       "PerformanceRating                  -0.014095           0.773550   \n",
       "RelationshipSatisfaction            0.052733          -0.040490   \n",
       "TotalWorkingYears                   0.237639          -0.020608   \n",
       "WorkLifeBalance                    -0.008366          -0.003280   \n",
       "YearsAtCompany                     -0.118421          -0.035991   \n",
       "YearsInCurrentRole                 -0.090754          -0.001520   \n",
       "YearsSinceLastPromotion            -0.036814          -0.022154   \n",
       "\n",
       "                          PerformanceRating  RelationshipSatisfaction  \\\n",
       "Age                                0.001904                  0.053535   \n",
       "DistanceFromHome                   0.027110                  0.006557   \n",
       "Education                         -0.024539                 -0.009118   \n",
       "EnvironmentSatisfaction           -0.029548                  0.007665   \n",
       "JobInvolvement                    -0.029071                  0.034297   \n",
       "JobLevel                          -0.021222                  0.021642   \n",
       "JobSatisfaction                    0.002297                 -0.012454   \n",
       "MonthlyIncome                     -0.017120                  0.025873   \n",
       "MonthlyRate                       -0.009811                 -0.004085   \n",
       "NumCompaniesWorked                -0.014095                  0.052733   \n",
       "PercentSalaryHike                  0.773550                 -0.040490   \n",
       "PerformanceRating                  1.000000                 -0.031351   \n",
       "RelationshipSatisfaction          -0.031351                  1.000000   \n",
       "TotalWorkingYears                  0.006744                  0.024054   \n",
       "WorkLifeBalance                    0.002572                  0.019604   \n",
       "YearsAtCompany                     0.003435                  0.019367   \n",
       "YearsInCurrentRole                 0.034986                 -0.015123   \n",
       "YearsSinceLastPromotion            0.017896                  0.033493   \n",
       "\n",
       "                          TotalWorkingYears  WorkLifeBalance  YearsAtCompany  \\\n",
       "Age                                0.680381        -0.021490        0.311309   \n",
       "DistanceFromHome                   0.004628        -0.026556        0.009508   \n",
       "Education                          0.148280         0.009819        0.069114   \n",
       "EnvironmentSatisfaction           -0.002693         0.027627        0.001458   \n",
       "JobInvolvement                    -0.005533        -0.014617       -0.021355   \n",
       "JobLevel                           0.782208         0.037818        0.534739   \n",
       "JobSatisfaction                   -0.020185        -0.019459       -0.003803   \n",
       "MonthlyIncome                      0.772893         0.030683        0.514285   \n",
       "MonthlyRate                        0.026442         0.007963       -0.023655   \n",
       "NumCompaniesWorked                 0.237639        -0.008366       -0.118421   \n",
       "PercentSalaryHike                 -0.020608        -0.003280       -0.035991   \n",
       "PerformanceRating                  0.006744         0.002572        0.003435   \n",
       "RelationshipSatisfaction           0.024054         0.019604        0.019367   \n",
       "TotalWorkingYears                  1.000000         0.001008        0.628133   \n",
       "WorkLifeBalance                    0.001008         1.000000        0.012089   \n",
       "YearsAtCompany                     0.628133         0.012089        1.000000   \n",
       "YearsInCurrentRole                 0.460365         0.049856        0.758754   \n",
       "YearsSinceLastPromotion            0.404858         0.008941        0.618409   \n",
       "\n",
       "                          YearsInCurrentRole  YearsSinceLastPromotion  \n",
       "Age                                 0.212901                 0.216513  \n",
       "DistanceFromHome                    0.018845                 0.010029  \n",
       "Education                           0.060236                 0.054254  \n",
       "EnvironmentSatisfaction             0.018007                 0.016194  \n",
       "JobInvolvement                      0.008717                -0.024184  \n",
       "JobLevel                            0.389447                 0.353885  \n",
       "JobSatisfaction                    -0.002305                -0.018214  \n",
       "MonthlyIncome                       0.363818                 0.344978  \n",
       "MonthlyRate                        -0.012815                 0.001567  \n",
       "NumCompaniesWorked                 -0.090754                -0.036814  \n",
       "PercentSalaryHike                  -0.001520                -0.022154  \n",
       "PerformanceRating                   0.034986                 0.017896  \n",
       "RelationshipSatisfaction           -0.015123                 0.033493  \n",
       "TotalWorkingYears                   0.460365                 0.404858  \n",
       "WorkLifeBalance                     0.049856                 0.008941  \n",
       "YearsAtCompany                      0.758754                 0.618409  \n",
       "YearsInCurrentRole                  1.000000                 0.548056  \n",
       "YearsSinceLastPromotion             0.548056                 1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations = data.corr()\n",
    "correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JobLevel           MonthlyIncome        0.950300\n",
      "MonthlyIncome      JobLevel             0.950300\n",
      "JobLevel           TotalWorkingYears    0.782208\n",
      "TotalWorkingYears  JobLevel             0.782208\n",
      "PercentSalaryHike  PerformanceRating    0.773550\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Найдем несколько наиболее коррелирующих между собой признаков. \n",
    "# Исключим единицы на главной диагонали (корреляции признаков самих с собой) и возьмем значения по модулю \n",
    "# (чтобы при сортировке учесть как положительные, так и отрицательные корреляции):\n",
    "correlations = correlations.replace(1, 0).abs()\n",
    "print(correlations.unstack().sort_values(kind=\"quicksort\", ascending=False).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VB65X1fUi0gA8KyKPq+qfMqb7g6pedAjxjTHGFFm+S2f3AveJyGb3MtoFUdXd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\nomCMMcbz/wP3StunPGGDMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd699370>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Странно, но не похоже, что число лет проработанных на одной и той же должности влияет на удовлетворённость работой...\n",
    "# Видно только, что мало кто в принципе долго работает на одной и той же должности \n",
    "plt.plot(data['YearsInCurrentRole'], data['JobSatisfaction'], 'o', alpha = 0.05)\n",
    "plt.xlabel('YearsInCurrentRole')\n",
    "plt.ylabel('JobSatisfaction')\n",
    "plt.title('Years In Current Role vs Job Satisfaction')\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0023047852298301056"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# И правда, эти признаки почти не коррелируют\n",
    "correlations[\"YearsInCurrentRole\"][\"JobSatisfaction\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    1006\n",
       "True      464\n",
       "Name: EducationField, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посчитаем, сколько сотрудников имеют и не имеют медицинское образование\n",
    "(data[\"EducationField\"] == \"Medical\").value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7vfj8XnZsTv8eGQ7btkFBgXODnult2/bf3pj9tz8QR8o6H45ldaQy1u6702qf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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb2a450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Видно, что по большей части дольше всего в компании работают мужчины\n",
    "# А степень удовлетворённости работой примерна одинаково распределена как для мужчин, так и для женщин\n",
    "plt.figure(figsize = (10, 4))\n",
    "\n",
    "np.random.seed(0)\n",
    "job_sat_rnd = data['JobSatisfaction'] + np.random.randint(data.shape[0])\n",
    "\n",
    "plt.scatter(data[data['Gender'] == \"Male\"]['YearsAtCompany'],\n",
    "            data[data['Gender'] == \"Male\"]['JobSatisfaction'],\n",
    "            alpha = 0.15,\n",
    "            label = 'Male',\n",
    "            color = 'b')\n",
    "\n",
    "plt.scatter(data[data['Gender'] == \"Female\"]['YearsAtCompany'],\n",
    "            data[data['Gender'] == \"Female\"]['JobSatisfaction'],\n",
    "            alpha = 0.07,\n",
    "            label = 'Female',\n",
    "            color = 'r')\n",
    "\n",
    "plt.xlabel('YearsAtCompany')\n",
    "plt.ylabel('JobSatisfaction')\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Attrition', 'BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole', 'MaritalStatus', 'OverTime']\n",
      "['Age', 'DistanceFromHome', 'Education', 'EnvironmentSatisfaction', 'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction', 'TotalWorkingYears', 'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion']\n"
     ]
    }
   ],
   "source": [
    "categorical_columns = [c for c in data.columns if data[c].dtype.name == 'object']\n",
    "numerical_columns   = [c for c in data.columns if data[c].dtype.name != 'object']\n",
    "print(categorical_columns)\n",
    "print(numerical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition ['Yes' 'No']\n",
      "BusinessTravel ['Travel_Rarely' 'Travel_Frequently' 'Non-Travel']\n",
      "Department ['Sales' 'Research & Development' 'Human Resources']\n",
      "EducationField ['Life Sciences' 'Other' 'Medical' 'Marketing' 'Technical Degree'\n",
      " 'Human Resources']\n",
      "Gender ['Female' 'Male']\n",
      "JobRole ['Sales Executive' 'Research Scientist' 'Laboratory Technician'\n",
      " 'Manufacturing Director' 'Healthcare Representative' 'Manager'\n",
      " 'Sales Representative' 'Research Director' 'Human Resources']\n",
      "MaritalStatus ['Single' 'Married' 'Divorced']\n",
      "OverTime ['Yes' 'No']\n"
     ]
    }
   ],
   "source": [
    "for c in categorical_columns:\n",
    "    print(c, data[c].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         0\n",
       "Attrition                   0\n",
       "BusinessTravel              0\n",
       "Department                  0\n",
       "DistanceFromHome            0\n",
       "Education                   0\n",
       "EducationField              0\n",
       "EnvironmentSatisfaction     0\n",
       "Gender                      0\n",
       "JobInvolvement              0\n",
       "JobLevel                    0\n",
       "JobRole                     0\n",
       "JobSatisfaction             0\n",
       "MaritalStatus               0\n",
       "MonthlyIncome               0\n",
       "MonthlyRate                 0\n",
       "NumCompaniesWorked          0\n",
       "OverTime                    0\n",
       "PercentSalaryHike           0\n",
       "PerformanceRating           0\n",
       "RelationshipSatisfaction    0\n",
       "TotalWorkingYears           0\n",
       "WorkLifeBalance             0\n",
       "YearsAtCompany              0\n",
       "YearsInCurrentRole          0\n",
       "YearsSinceLastPromotion     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# В таблице нет пропущенных значений\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Attrition', 'Gender', 'OverTime'] ['BusinessTravel', 'Department', 'EducationField', 'JobRole', 'MaritalStatus']\n"
     ]
    }
   ],
   "source": [
    "data_describe = data.describe(include = [object])\n",
    "binary_columns    = [c for c in categorical_columns if data_describe[c]['unique'] == 2]\n",
    "nonbinary_columns = [c for c in categorical_columns if data_describe[c]['unique'] > 2]\n",
    "print(binary_columns, nonbinary_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обработаем бинарные признаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.at[data['Gender'] == 'Male', 'Gender'] = 0\n",
    "data.at[data['Gender'] == 'Female', 'Gender'] = 1\n",
    "data[\"Gender\"] = data[\"Gender\"].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.at[data['Attrition'] == 'No', 'Attrition'] = 0\n",
    "data.at[data['Attrition'] == 'Yes', 'Attrition'] = 1\n",
    "data[\"Attrition\"] = data[\"Attrition\"].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.at[data['OverTime'] == 'No', 'OverTime'] = 0\n",
    "data.at[data['OverTime'] == 'Yes', 'OverTime'] = 1\n",
    "data[\"OverTime\"] = data[\"OverTime\"].astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обработаем категориальные признаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['BusinessTravel_Non-Travel', 'BusinessTravel_Travel_Frequently',\n",
      "       'BusinessTravel_Travel_Rarely', 'Department_Human Resources',\n",
      "       'Department_Research & Development', 'Department_Sales',\n",
      "       'EducationField_Human Resources', 'EducationField_Life Sciences',\n",
      "       'EducationField_Marketing', 'EducationField_Medical',\n",
      "       'EducationField_Other', 'EducationField_Technical Degree',\n",
      "       'JobRole_Healthcare Representative', 'JobRole_Human Resources',\n",
      "       'JobRole_Laboratory Technician', 'JobRole_Manager',\n",
      "       'JobRole_Manufacturing Director', 'JobRole_Research Director',\n",
      "       'JobRole_Research Scientist', 'JobRole_Sales Executive',\n",
      "       'JobRole_Sales Representative', 'MaritalStatus_Divorced',\n",
      "       'MaritalStatus_Married', 'MaritalStatus_Single'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "data_nonbinary = pd.get_dummies(data[nonbinary_columns])\n",
    "print(data_nonbinary.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.923810</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>2.728571</td>\n",
       "      <td>6502.931293</td>\n",
       "      <td>14313.103401</td>\n",
       "      <td>2.693197</td>\n",
       "      <td>15.209524</td>\n",
       "      <td>3.153741</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.135373</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>1.102846</td>\n",
       "      <td>4707.956783</td>\n",
       "      <td>7117.786044</td>\n",
       "      <td>2.498009</td>\n",
       "      <td>3.659938</td>\n",
       "      <td>0.360824</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2911.000000</td>\n",
       "      <td>8047.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4919.000000</td>\n",
       "      <td>14235.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8379.000000</td>\n",
       "      <td>20461.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19999.000000</td>\n",
       "      <td>26999.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  DistanceFromHome    Education  EnvironmentSatisfaction  \\\n",
       "count  1470.000000       1470.000000  1470.000000              1470.000000   \n",
       "mean     36.923810          9.192517     2.912925                 2.721769   \n",
       "std       9.135373          8.106864     1.024165                 1.093082   \n",
       "min      18.000000          1.000000     1.000000                 1.000000   \n",
       "25%      30.000000          2.000000     2.000000                 2.000000   \n",
       "50%      36.000000          7.000000     3.000000                 3.000000   \n",
       "75%      43.000000         14.000000     4.000000                 4.000000   \n",
       "max      60.000000         29.000000     5.000000                 4.000000   \n",
       "\n",
       "       JobInvolvement     JobLevel  JobSatisfaction  MonthlyIncome  \\\n",
       "count     1470.000000  1470.000000      1470.000000    1470.000000   \n",
       "mean         2.729932     2.063946         2.728571    6502.931293   \n",
       "std          0.711561     1.106940         1.102846    4707.956783   \n",
       "min          1.000000     1.000000         1.000000    1009.000000   \n",
       "25%          2.000000     1.000000         2.000000    2911.000000   \n",
       "50%          3.000000     2.000000         3.000000    4919.000000   \n",
       "75%          3.000000     3.000000         4.000000    8379.000000   \n",
       "max          4.000000     5.000000         4.000000   19999.000000   \n",
       "\n",
       "        MonthlyRate  NumCompaniesWorked  PercentSalaryHike  PerformanceRating  \\\n",
       "count   1470.000000         1470.000000        1470.000000        1470.000000   \n",
       "mean   14313.103401            2.693197          15.209524           3.153741   \n",
       "std     7117.786044            2.498009           3.659938           0.360824   \n",
       "min     2094.000000            0.000000          11.000000           3.000000   \n",
       "25%     8047.000000            1.000000          12.000000           3.000000   \n",
       "50%    14235.500000            2.000000          14.000000           3.000000   \n",
       "75%    20461.500000            4.000000          18.000000           3.000000   \n",
       "max    26999.000000            9.000000          25.000000           4.000000   \n",
       "\n",
       "       RelationshipSatisfaction  TotalWorkingYears  WorkLifeBalance  \\\n",
       "count               1470.000000        1470.000000      1470.000000   \n",
       "mean                   2.712245          11.279592         2.761224   \n",
       "std                    1.081209           7.780782         0.706476   \n",
       "min                    1.000000           0.000000         1.000000   \n",
       "25%                    2.000000           6.000000         2.000000   \n",
       "50%                    3.000000          10.000000         3.000000   \n",
       "75%                    4.000000          15.000000         3.000000   \n",
       "max                    4.000000          40.000000         4.000000   \n",
       "\n",
       "       YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \n",
       "count     1470.000000         1470.000000              1470.000000  \n",
       "mean         7.008163            4.229252                 2.187755  \n",
       "std          6.126525            3.623137                 3.222430  \n",
       "min          0.000000            0.000000                 0.000000  \n",
       "25%          3.000000            2.000000                 0.000000  \n",
       "50%          5.000000            3.000000                 1.000000  \n",
       "75%          9.000000            7.000000                 3.000000  \n",
       "max         40.000000           18.000000                15.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numerical = data[numerical_columns]\n",
    "data_numerical.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "      <td>1.470000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.389667e-17</td>\n",
       "      <td>6.525392e-17</td>\n",
       "      <td>4.833624e-18</td>\n",
       "      <td>1.873029e-16</td>\n",
       "      <td>5.316986e-17</td>\n",
       "      <td>-1.812609e-17</td>\n",
       "      <td>-1.063397e-16</td>\n",
       "      <td>-3.866899e-17</td>\n",
       "      <td>2.779334e-17</td>\n",
       "      <td>8.458842e-18</td>\n",
       "      <td>1.885113e-16</td>\n",
       "      <td>-6.767074e-16</td>\n",
       "      <td>8.217161e-17</td>\n",
       "      <td>-3.141856e-17</td>\n",
       "      <td>-6.283711e-17</td>\n",
       "      <td>-9.667248e-18</td>\n",
       "      <td>1.075481e-16</td>\n",
       "      <td>-1.329247e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.071487e+00</td>\n",
       "      <td>-1.010565e+00</td>\n",
       "      <td>-1.867790e+00</td>\n",
       "      <td>-1.575150e+00</td>\n",
       "      <td>-2.431178e+00</td>\n",
       "      <td>-9.611593e-01</td>\n",
       "      <td>-1.567373e+00</td>\n",
       "      <td>-1.166946e+00</td>\n",
       "      <td>-1.716700e+00</td>\n",
       "      <td>-1.078138e+00</td>\n",
       "      <td>-1.150163e+00</td>\n",
       "      <td>-4.260850e-01</td>\n",
       "      <td>-1.583639e+00</td>\n",
       "      <td>-1.449673e+00</td>\n",
       "      <td>-2.492972e+00</td>\n",
       "      <td>-1.143905e+00</td>\n",
       "      <td>-1.167290e+00</td>\n",
       "      <td>-6.789146e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-7.579120e-01</td>\n",
       "      <td>-8.872132e-01</td>\n",
       "      <td>-8.913849e-01</td>\n",
       "      <td>-6.603060e-01</td>\n",
       "      <td>-1.025818e+00</td>\n",
       "      <td>-9.611593e-01</td>\n",
       "      <td>-6.606284e-01</td>\n",
       "      <td>-7.629491e-01</td>\n",
       "      <td>-8.803444e-01</td>\n",
       "      <td>-6.778187e-01</td>\n",
       "      <td>-8.769340e-01</td>\n",
       "      <td>-4.260850e-01</td>\n",
       "      <td>-6.587487e-01</td>\n",
       "      <td>-6.785426e-01</td>\n",
       "      <td>-1.077495e+00</td>\n",
       "      <td>-6.542311e-01</td>\n",
       "      <td>-6.152822e-01</td>\n",
       "      <td>-6.789146e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.011244e-01</td>\n",
       "      <td>-2.704519e-01</td>\n",
       "      <td>8.502032e-02</td>\n",
       "      <td>2.545383e-01</td>\n",
       "      <td>3.795430e-01</td>\n",
       "      <td>-5.776789e-02</td>\n",
       "      <td>2.461164e-01</td>\n",
       "      <td>-3.364371e-01</td>\n",
       "      <td>-1.090274e-02</td>\n",
       "      <td>-2.774999e-01</td>\n",
       "      <td>-3.304766e-01</td>\n",
       "      <td>-4.260850e-01</td>\n",
       "      <td>2.661420e-01</td>\n",
       "      <td>-1.644554e-01</td>\n",
       "      <td>3.379811e-01</td>\n",
       "      <td>-3.277818e-01</td>\n",
       "      <td>-3.392783e-01</td>\n",
       "      <td>-3.685899e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.651278e-01</td>\n",
       "      <td>5.930139e-01</td>\n",
       "      <td>1.061426e+00</td>\n",
       "      <td>1.169383e+00</td>\n",
       "      <td>3.795430e-01</td>\n",
       "      <td>8.456235e-01</td>\n",
       "      <td>1.152861e+00</td>\n",
       "      <td>3.984889e-01</td>\n",
       "      <td>8.638074e-01</td>\n",
       "      <td>5.231377e-01</td>\n",
       "      <td>7.624382e-01</td>\n",
       "      <td>-4.260850e-01</td>\n",
       "      <td>1.191033e+00</td>\n",
       "      <td>4.781535e-01</td>\n",
       "      <td>3.379811e-01</td>\n",
       "      <td>3.251169e-01</td>\n",
       "      <td>7.647374e-01</td>\n",
       "      <td>2.520597e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.526026e+00</td>\n",
       "      <td>2.443298e+00</td>\n",
       "      <td>2.037831e+00</td>\n",
       "      <td>1.169383e+00</td>\n",
       "      <td>1.784904e+00</td>\n",
       "      <td>2.652406e+00</td>\n",
       "      <td>1.152861e+00</td>\n",
       "      <td>2.866651e+00</td>\n",
       "      <td>1.782281e+00</td>\n",
       "      <td>2.524732e+00</td>\n",
       "      <td>2.675039e+00</td>\n",
       "      <td>2.345353e+00</td>\n",
       "      <td>1.191033e+00</td>\n",
       "      <td>3.691198e+00</td>\n",
       "      <td>1.753458e+00</td>\n",
       "      <td>5.385081e+00</td>\n",
       "      <td>3.800780e+00</td>\n",
       "      <td>3.975957e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age  DistanceFromHome     Education  EnvironmentSatisfaction  \\\n",
       "count  1.470000e+03      1.470000e+03  1.470000e+03             1.470000e+03   \n",
       "mean  -1.389667e-17      6.525392e-17  4.833624e-18             1.873029e-16   \n",
       "std    1.000000e+00      1.000000e+00  1.000000e+00             1.000000e+00   \n",
       "min   -2.071487e+00     -1.010565e+00 -1.867790e+00            -1.575150e+00   \n",
       "25%   -7.579120e-01     -8.872132e-01 -8.913849e-01            -6.603060e-01   \n",
       "50%   -1.011244e-01     -2.704519e-01  8.502032e-02             2.545383e-01   \n",
       "75%    6.651278e-01      5.930139e-01  1.061426e+00             1.169383e+00   \n",
       "max    2.526026e+00      2.443298e+00  2.037831e+00             1.169383e+00   \n",
       "\n",
       "       JobInvolvement      JobLevel  JobSatisfaction  MonthlyIncome  \\\n",
       "count    1.470000e+03  1.470000e+03     1.470000e+03   1.470000e+03   \n",
       "mean     5.316986e-17 -1.812609e-17    -1.063397e-16  -3.866899e-17   \n",
       "std      1.000000e+00  1.000000e+00     1.000000e+00   1.000000e+00   \n",
       "min     -2.431178e+00 -9.611593e-01    -1.567373e+00  -1.166946e+00   \n",
       "25%     -1.025818e+00 -9.611593e-01    -6.606284e-01  -7.629491e-01   \n",
       "50%      3.795430e-01 -5.776789e-02     2.461164e-01  -3.364371e-01   \n",
       "75%      3.795430e-01  8.456235e-01     1.152861e+00   3.984889e-01   \n",
       "max      1.784904e+00  2.652406e+00     1.152861e+00   2.866651e+00   \n",
       "\n",
       "        MonthlyRate  NumCompaniesWorked  PercentSalaryHike  PerformanceRating  \\\n",
       "count  1.470000e+03        1.470000e+03       1.470000e+03       1.470000e+03   \n",
       "mean   2.779334e-17        8.458842e-18       1.885113e-16      -6.767074e-16   \n",
       "std    1.000000e+00        1.000000e+00       1.000000e+00       1.000000e+00   \n",
       "min   -1.716700e+00       -1.078138e+00      -1.150163e+00      -4.260850e-01   \n",
       "25%   -8.803444e-01       -6.778187e-01      -8.769340e-01      -4.260850e-01   \n",
       "50%   -1.090274e-02       -2.774999e-01      -3.304766e-01      -4.260850e-01   \n",
       "75%    8.638074e-01        5.231377e-01       7.624382e-01      -4.260850e-01   \n",
       "max    1.782281e+00        2.524732e+00       2.675039e+00       2.345353e+00   \n",
       "\n",
       "       RelationshipSatisfaction  TotalWorkingYears  WorkLifeBalance  \\\n",
       "count              1.470000e+03       1.470000e+03     1.470000e+03   \n",
       "mean               8.217161e-17      -3.141856e-17    -6.283711e-17   \n",
       "std                1.000000e+00       1.000000e+00     1.000000e+00   \n",
       "min               -1.583639e+00      -1.449673e+00    -2.492972e+00   \n",
       "25%               -6.587487e-01      -6.785426e-01    -1.077495e+00   \n",
       "50%                2.661420e-01      -1.644554e-01     3.379811e-01   \n",
       "75%                1.191033e+00       4.781535e-01     3.379811e-01   \n",
       "max                1.191033e+00       3.691198e+00     1.753458e+00   \n",
       "\n",
       "       YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \n",
       "count    1.470000e+03        1.470000e+03             1.470000e+03  \n",
       "mean    -9.667248e-18        1.075481e-16            -1.329247e-17  \n",
       "std      1.000000e+00        1.000000e+00             1.000000e+00  \n",
       "min     -1.143905e+00       -1.167290e+00            -6.789146e-01  \n",
       "25%     -6.542311e-01       -6.152822e-01            -6.789146e-01  \n",
       "50%     -3.277818e-01       -3.392783e-01            -3.685899e-01  \n",
       "75%      3.251169e-01        7.647374e-01             2.520597e-01  \n",
       "max      5.385081e+00        3.800780e+00             3.975957e+00  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numerical = (data_numerical - data_numerical.mean(axis = 0))/data_numerical.std(axis = 0)\n",
    "data_numerical.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1470, 45)\n"
     ]
    }
   ],
   "source": [
    "# Соединим все вместе\n",
    "data = pd.concat((data_numerical, data_nonbinary, data[binary_columns]), axis = 1)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Решим задачу предсказания \"выгорания\" сотрудника на работе по его личным данным"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data.drop(('Attrition'), axis = 1) \n",
    "y = data['Attrition']\n",
    "feature_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Разделим выборку на обучающую и тестовую (30% тестовая, 70%  обучающая)\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Метод K ближайших соседей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Для начала возьмем число соседей наугад, пусть это будет 5\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.137026239067 0.131519274376\n"
     ]
    }
   ],
   "source": [
    "y_train_predict = knn.predict(X_train)\n",
    "y_test_predict = knn.predict(X_test)\n",
    "\n",
    "err_train = np.mean(y_train != y_train_predict)\n",
    "err_test  = np.mean(y_test  != y_test_predict)\n",
    "\n",
    "print(err_train, err_test)\n",
    "\n",
    "# Сохраним значение ошибки на тестовой выборке для метода 5ти ближайших соседей\n",
    "err_test_before_parameter_selection = err_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[377   3]\n",
      " [ 55   6]]\n"
     ]
    }
   ],
   "source": [
    "# Матрица ошибок\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.162293488824 10\n"
     ]
    }
   ],
   "source": [
    "# Проведем подбор параметра K для метода kNN \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "nnb = [1, 3, 5, 10, 15, 20, 25, 35, 45, 55]\n",
    "knn = KNeighborsClassifier()\n",
    "grid = GridSearchCV(knn, param_grid = {'n_neighbors': nnb}, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "best_cv_err = 1 - grid.best_score_\n",
    "best_n_neighbors = grid.best_estimator_.n_neighbors\n",
    "print(best_cv_err, best_n_neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.157434402332 0.140589569161\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = best_n_neighbors).fit(X_train, y_train)\n",
    "\n",
    "err_train = np.mean(y_train != knn.predict(X_train))\n",
    "err_test  = np.mean(y_test  != knn.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)\n",
    "\n",
    "# Сохраним значение ошибки на тестовой выборке для метода 10ти ближайших соседей (подобранный параметр)\n",
    "err_test_after_parameter_selection = err_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0090702947845805182"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посчитаем, насколько уменьшилась ошибка после подбора наилучшего параметра\n",
    "err_diff = err_test_after_parameter_selection - err_test_before_parameter_selection\n",
    "err_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.14864865  0.16216216  0.16216216  0.15646259  0.15646259  0.15646259\n",
      "  0.15646259  0.15068493  0.15753425  0.15068493]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.15577274226980684"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Попробуем воспользоваться методом кросс-валидации (разобьем данные на 10 блоков для поочередного использования одного из них в качестве тестовой выборки)\n",
    "from sklearn import cross_validation\n",
    "knn_scores = cross_validation.cross_val_score(knn, X, y, cv = 10)\n",
    "knn_errors = 1 - knn_scores # Поскольку раньше мы оперировали ошибками, для удобства посчитаем ошибку и тут\n",
    "print(knn_errors)\n",
    "knn_errors.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кросс-валидация для метода kNN дала большую ошибку, чем была при единичном разбиении (т.е. удачность разбиения действительно влияет на результат, и в теории, не использование кросс-валидации может ввести в заблуждение относительно качества модели). Ошибка для каждого варианта разбиения в данном случае варьируется в пределах +-1% (не очень сильно, что показывает, что датасет составлен хорошо) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.115646258503 0.133786848073\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)\n",
    "\n",
    "err_train = np.mean(y_train != svc.predict(X_train))\n",
    "err_test  = np.mean(y_test  != svc.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\sklearn\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12730806608357625 100.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "Cs = 10.**np.arange(-5, 5)\n",
    "gamma_array = 10.**np.arange(-5, 5)\n",
    "svc = SVC()\n",
    "grid = GridSearchCV(svc, param_grid = {'C': Cs, 'gamma': gamma_array})\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "best_cv_err = 1 - grid.best_score_\n",
    "best_C = grid.best_estimator_.C\n",
    "print(best_cv_err, best_C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.12925170068\n"
     ]
    }
   ],
   "source": [
    "from sklearn import ensemble\n",
    "rf = ensemble.RandomForestClassifier(n_estimators = 1000)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "err_train = np.mean(y_train != rf.predict(X_train))\n",
    "err_test  = np.mean(y_test  != rf.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extremely Randomized Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.124716553288\n"
     ]
    }
   ],
   "source": [
    "ert = ensemble.ExtraTreesClassifier(n_estimators = 100).fit(X_train, y_train)\n",
    "\n",
    "err_train = np.mean(y_train != ert.predict(X_train))\n",
    "err_test  = np.mean(y_test  != ert.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0272108843537 0.167800453515\n"
     ]
    }
   ],
   "source": [
    "ada = ensemble.AdaBoostClassifier(n_estimators = 1000)\n",
    "ada.fit(X_train, y_train)\n",
    "\n",
    "err_train = np.mean(y_train != ada.predict(X_train))\n",
    "err_test = np.mean(y_test != ada.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GBT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.12925170068\n"
     ]
    }
   ],
   "source": [
    "gbt = ensemble.GradientBoostingClassifier(n_estimators = 1000)\n",
    "gbt.fit(X_train, y_train)\n",
    "\n",
    "err_train = np.mean(y_train != gbt.predict(X_train))\n",
    "err_test = np.mean(y_test != gbt.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наилучшие результаты (наим.ошибку) на тестовой выборке показали алгоритмы GBT и Extremely Randomized Trees. Возможно, результаты были бы несколько другими, если бы во всех перечисленных методах использовалась кросс-валидация."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Значимость признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      " 1. feature 'MonthlyIncome' (0.088316)\n",
      " 2. feature '  Age' (0.072554)\n",
      " 3. feature 'TotalWorkingYears' (0.059905)\n",
      " 4. feature 'OverTime' (0.059313)\n",
      " 5. feature 'MonthlyRate' (0.057617)\n",
      " 6. feature 'DistanceFromHome' (0.054028)\n",
      " 7. feature 'YearsAtCompany' (0.053748)\n",
      " 8. feature 'NumCompaniesWorked' (0.043056)\n",
      " 9. feature 'YearsInCurrentRole' (0.041495)\n",
      "10. feature 'PercentSalaryHike' (0.038880)\n",
      "11. feature 'MaritalStatus_Single' (0.030524)\n",
      "12. feature 'EnvironmentSatisfaction' (0.030462)\n",
      "13. feature 'YearsSinceLastPromotion' (0.030292)\n",
      "14. feature 'JobSatisfaction' (0.029159)\n",
      "15. feature 'RelationshipSatisfaction' (0.027691)\n",
      "16. feature 'JobInvolvement' (0.025251)\n",
      "17. feature 'WorkLifeBalance' (0.024647)\n",
      "18. feature 'Education' (0.023625)\n",
      "19. feature 'JobLevel' (0.023218)\n",
      "20. feature 'BusinessTravel_Travel_Frequently' (0.014642)\n",
      "21. feature 'Gender' (0.012517)\n",
      "22. feature 'MaritalStatus_Divorced' (0.011346)\n",
      "23. feature 'MaritalStatus_Married' (0.010912)\n",
      "24. feature 'EducationField_Life Sciences' (0.010729)\n",
      "25. feature 'JobRole_Sales Representative' (0.010612)\n",
      "26. feature 'EducationField_Medical' (0.009998)\n",
      "27. feature 'EducationField_Technical Degree' (0.009974)\n",
      "28. feature 'BusinessTravel_Travel_Rarely' (0.009905)\n",
      "29. feature 'Department_Sales' (0.009869)\n",
      "30. feature 'JobRole_Laboratory Technician' (0.009448)\n",
      "31. feature 'Department_Research & Development' (0.009395)\n",
      "32. feature 'EducationField_Marketing' (0.008890)\n",
      "33. feature 'JobRole_Sales Executive' (0.007694)\n",
      "34. feature 'JobRole_Research Scientist' (0.007467)\n",
      "35. feature 'PerformanceRating' (0.005999)\n",
      "36. feature 'EducationField_Other' (0.005033)\n",
      "37. feature 'BusinessTravel_Non-Travel' (0.004476)\n",
      "38. feature 'JobRole_Manufacturing Director' (0.003187)\n",
      "39. feature 'EducationField_Human Resources' (0.003061)\n",
      "40. feature 'JobRole_Human Resources' (0.002919)\n",
      "41. feature 'JobRole_Healthcare Representative' (0.002688)\n",
      "42. feature 'Department_Human Resources' (0.002376)\n",
      "43. feature 'JobRole_Manager' (0.001766)\n",
      "44. feature 'JobRole_Research Director' (0.001314)\n"
     ]
    }
   ],
   "source": [
    "N, d = X.shape\n",
    "importances = rf.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(d):\n",
    "    print(\"%2d. feature '%5s' (%f)\" % (f + 1, feature_names[indices[f]], importances[indices[f]]))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1, 20)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rcYNxwPxm1vb67JbRovTvATayLjwm5NPLD9H/o3kCN/dkuSxKusHMNpR0NW7m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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xefeed70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_first = 20\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(d_first), importances[indices[:d_first]], align = 'center', color = 'r')\n",
    "plt.xticks(range(d_first), feature_names[indices[:d_first]], rotation = 90)\n",
    "plt.xlim([-1, d_first])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##  Обучение неглубокой полносвязной нейронной сети"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обучим классификатор с одним скрытым слоем и параметром альфа = 0.0001 и 14ю нейронами в каждом слое (случайный выбор параметров)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(14,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=12, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "random_alpha = 0.0001\n",
    "random_hidden_layer_size = 14\n",
    "mlp_model = MLPClassifier(hidden_layer_sizes=(random_hidden_layer_size,), alpha=random_alpha, solver='lbfgs', random_state=12)\n",
    "mlp_model.fit(X_train, y_train) # Объем обучающей выборки - 70%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Найдем ошибки на обучающей и тестовой выборках"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.181405895692\n"
     ]
    }
   ],
   "source": [
    "err_train = np.mean(y_train != mlp_model.predict(X_train))\n",
    "err_test  = np.mean(y_test  != mlp_model.predict(X_test))\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что ошибка на тестовой выборке довольно сильно превосходит ошибку на обучающей выборке (равную нулю). Следовательно, при данных значениях параметра классификатор переобучился. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Попробуем подобрать оптимальные значения параметров классификатора вручную"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "alpha_values = np.logspace(start=-5, stop=2, num=10, base=10) # alpha - 10 значений от 10^-4 до 10^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "errors_train = []\n",
    "errors_test = []\n",
    "\n",
    "# Зафиксируем hidden_layer_size на выбранном нами случайном значении. \n",
    "# Потенциально это не очень хорошо для подбора оптимального alpha (если выбранное случайно число нейронов - неудачное) \n",
    "for alpha in alpha_values:\n",
    "    mlp_model = MLPClassifier(alpha=alpha, hidden_layer_sizes=(random_hidden_layer_size,), solver='lbfgs', random_state=12)\n",
    "    mlp_model.fit(X_train, y_train)\n",
    "\n",
    "    errors_train.append(np.mean(y_train != mlp_model.predict(X_train)))\n",
    "    errors_test.append(np.mean(y_test != mlp_model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Elp56Z4giMsaYqmHJpgr98fv2TMi/mcyjjTiqQubRRtyffzN//N6KA4wx0c2q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xefee0b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(alpha_values, errors_train, label = 'train', marker='o')\n",
    "plt.semilogx(alpha_values, errors_test, label = 'test', marker='o')\n",
    "plt.xlim([np.max(alpha_values), np.min(alpha_values)])\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Alpha')\n",
    "plt.ylabel('Error')\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.113378684807\n"
     ]
    }
   ],
   "source": [
    "min_error_train = np.min(errors_train)\n",
    "min_error_test = np.min(errors_test)\n",
    "print(min_error_train, min_error_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как можно видеть, подбор оптимального параметра alpha почти вдвое снизил разницу между минимальными ошибками на обучающей и тестовой выборках. \n",
    "\n",
    "Оптимальные значения alpha (при фиксированном на случайном значении числе нейронов):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 16.68100537]\n"
     ]
    }
   ],
   "source": [
    "best_alphas = alpha_values[errors_test == min_error_test]\n",
    "print(best_alphas)\n",
    "best_alpha = best_alphas[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь попробуем подобрать оптимальное число нейронов в скрытом слое:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hidden_layer_sizes = [(i,) for i in np.arange(2,22,4)] # от 2х до 20 с шагом 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "errors_train = []\n",
    "errors_test = []\n",
    "\n",
    "for layer_size in hidden_layer_sizes:\n",
    "    mlp_model = MLPClassifier(alpha=best_alpha, hidden_layer_sizes=layer_size, solver='lbfgs', random_state=12)\n",
    "    mlp_model.fit(X_train, y_train)\n",
    "\n",
    "    errors_train.append(np.mean(y_train != mlp_model.predict(X_train)))\n",
    "    errors_test.append(np.mean(y_test != mlp_model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hidden_layer_sizes = np.array([size[0] for size in hidden_layer_sizes]) # tuple to ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkjSljPVmv9/MzNdPQnsawpv9SpKkshjrzX7Hek7c7RHxZxFxWEQcNPzaxzZK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\nKiFDnCRJUgn9f6mmf9JvLuEiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xefbebd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(hidden_layer_sizes, errors_train, label = 'train', marker='o')\n",
    "plt.plot(hidden_layer_sizes, errors_test, label = 'test', marker='o')\n",
    "\n",
    "plt.ylim([0, 0.2])\n",
    "plt.legend()\n",
    "plt.xlabel('Hidden layer size')\n",
    "plt.ylabel('Error')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Минимальные ошибки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.103012633625 0.111111111111\n"
     ]
    }
   ],
   "source": [
    "min_error_train = np.min(errors_train)\n",
    "min_error_test = np.min(errors_test)\n",
    "\n",
    "print(min_error_train, min_error_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оптимальное число нейронов в скрытом слое:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2 10]\n"
     ]
    }
   ],
   "source": [
    "best_hidden_layer_sizes = hidden_layer_sizes[errors_test == min_error_test]\n",
    "print(best_hidden_layer_sizes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найденные таким образом оптимальные значения параметров могут на самом деле быть не самыми оптимальными, т.к. мы подбирали их по очереди, фиксируя при подборе первого параметра второй параметр на случайном (необязательно оптимальном значении). Чтобы найти действительно лучшие параметры, надо перебирать все их комбинации. Кроме того, для повышения надёжности результатов, стоит воспользоваться кросс-валидацией."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Попробуем найти комбинацию оптимальных параметров перебором и с использованием кросс-валидации:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = GridSearchCV(MLPClassifier(), param_grid={'solver':['lbfgs'], 'hidden_layer_sizes': hidden_layer_sizes, 'alpha': alpha_values}, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'solver': ['lbfgs'], 'hidden_layer_sizes': array([ 2,  6, 10, 14, 18]), 'alpha': array([  1.00000e-05,   5.99484e-05,   3.59381e-04,   2.15443e-03,\n",
       "         1.29155e-02,   7.74264e-02,   4.64159e-01,   2.78256e+00,\n",
       "         1.66810e+01,   1.00000e+02])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11632653061224485 0.111111111111\n"
     ]
    }
   ],
   "source": [
    "min_error = 1 - clf.best_score_\n",
    "print(min_error, min_error_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найденное нами ранее значение min_error_test получилось все-таки немного меньше, чем то же значение ошибки, полученное после полного перебора. Но поскольку в случае полного перебора мы использовали кросс-валидацию, значение ошибки min_error надёжнее. \n",
    "\n",
    "Оптимальные параметры:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 16.681005372000591, 'hidden_layer_sizes': 2, 'solver': 'lbfgs'}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Попробуем разное число скрытых слоёв при оптимальных значениях параметров:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Два скрытых слоя:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.103012633625 0.113378684807\n"
     ]
    }
   ],
   "source": [
    "best_hidden_layer_size = clf.best_params_['hidden_layer_sizes']\n",
    "best_alpha = clf.best_params_['alpha']\n",
    "\n",
    "mlp_model = MLPClassifier(alpha = best_alpha, hidden_layer_sizes = (best_hidden_layer_size, best_hidden_layer_size,),\n",
    "                          solver = 'lbfgs', random_state = 12)\n",
    "mlp_model.fit(X_train, y_train)\n",
    "\n",
    "err_train = np.mean(y_train != mlp_model.predict(X_train))\n",
    "err_test = np.mean(y_test != mlp_model.predict(X_test))\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Один скрытый слой:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.115646258503 0.111111111111\n"
     ]
    }
   ],
   "source": [
    "mlp_model = MLPClassifier(alpha = best_alpha, hidden_layer_sizes = (best_hidden_layer_size,),\n",
    "                          solver = 'lbfgs', random_state = 12)\n",
    "mlp_model.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = mlp_model.predict(X_train)\n",
    "y_test_pred = mlp_model.predict(X_test)\n",
    "\n",
    "err_train = np.mean(y_train != y_train_pred)\n",
    "err_test = np.mean(y_test != y_test_pred)\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Итак, наименьшую ошибку показала нейронная сеть с одним скрытым слоем."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Метрики качества"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.99      0.94       380\n",
      "          1       0.77      0.28      0.41        61\n",
      "\n",
      "avg / total       0.88      0.89      0.87       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 375, FP: 44, FN: 5, TP: 17\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>375</td>\n",
       "      <td>5</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>419</td>\n",
       "      <td>22</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "Predicted    0   1  All\n",
       "True                   \n",
       "0          375   5  380\n",
       "1           44  17   61\n",
       "All        419  22  441"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "conf_matrix = confusion_matrix(y_test_pred, y_test)\n",
    "tn, fp, fn, tp = conf_matrix.ravel()\n",
    "print(\"TN: {}, FP: {}, FN: {}, TP: {}\".format(tn, fp, fn, tp))\n",
    "\n",
    "# Та же матрица, но в более читабельном виде:\n",
    "pd.crosstab(y_test, y_test_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Получили сравнительно высокую ошибку 1го рода, т.е. много ложных срабатываний. Кроме того, можно видеть, что для 1го класса значения метрик recall & f1-score очень низкие. Это можно объяснить в том числе и несбалансированностью выборки (объектов выборки, принадлежащих в действительности классу 0 намного больше, чем объектов класса 1). В дальнейшем можно попробовать с этим бороться (например, методом Undersampling - выкидыванием части объектов превалирующего класса; или oversampling - дублированием объектов меньшего класса). Плюс при кросс-валидации надо разбивать выборку так, чтобы в каждом блоке сохранялось исходное соотношение классов. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Итак, обучение неглубокой полносвязной нейронной сети позволило слегка снизить ошибку с 12,5% (минимальная ошибка, полученная на рассмотренных ранее методах (GBT & Extremely Randomized Trees)) до 11%. Вероятно, данное значение можно улучшить, например, путем поиска еще более лучших параметров (перебором большего их числа); или повышением сбалансированности выборки."
   ]
  }
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