Human Resources Case - Retention Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "employee_df = pd.read_csv(\"Human_Resources.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
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       "      <th>EmployeeNumber</th>\n",
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       "      <th>RelationshipSatisfaction</th>\n",
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       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
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       "      <td>No</td>\n",
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       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
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       "      <td>8</td>\n",
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       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      Age Attrition     BusinessTravel  DailyRate              Department  \\\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",
       "1465                23          2        Medical              1   \n",
       "1466                 6          1        Medical              1   \n",
       "1467                 4          3  Life Sciences              1   \n",
       "1468                 2          3        Medical              1   \n",
       "1469                 8          3        Medical              1   \n",
       "\n",
       "      EmployeeNumber  ...  RelationshipSatisfaction StandardHours  \\\n",
       "1465            2061  ...                         3            80   \n",
       "1466            2062  ...                         1            80   \n",
       "1467            2064  ...                         2            80   \n",
       "1468            2065  ...                         4            80   \n",
       "1469            2068  ...                         1            80   \n",
       "\n",
       "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "1465                 1                 17                      3   \n",
       "1466                 1                  9                      5   \n",
       "1467                 1                  6                      0   \n",
       "1468                 0                 17                      3   \n",
       "1469                 0                  6                      3   \n",
       "\n",
       "     WorkLifeBalance  YearsAtCompany YearsInCurrentRole  \\\n",
       "1465               3               5                  2   \n",
       "1466               3               7                  7   \n",
       "1467               3               6                  2   \n",
       "1468               2               9                  6   \n",
       "1469               4               4                  3   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "1465                        0                     3  \n",
       "1466                        1                     7  \n",
       "1467                        0                     3  \n",
       "1468                        0                     8  \n",
       "1469                        1                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee_df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                          int64\n",
       "Attrition                   object\n",
       "BusinessTravel              object\n",
       "DailyRate                    int64\n",
       "Department                  object\n",
       "DistanceFromHome             int64\n",
       "Education                    int64\n",
       "EducationField              object\n",
       "EmployeeCount                int64\n",
       "EmployeeNumber               int64\n",
       "EnvironmentSatisfaction      int64\n",
       "Gender                      object\n",
       "HourlyRate                   int64\n",
       "JobInvolvement               int64\n",
       "JobLevel                     int64\n",
       "JobRole                     object\n",
       "JobSatisfaction              int64\n",
       "MaritalStatus               object\n",
       "MonthlyIncome                int64\n",
       "MonthlyRate                  int64\n",
       "NumCompaniesWorked           int64\n",
       "Over18                      object\n",
       "OverTime                    object\n",
       "PercentSalaryHike            int64\n",
       "PerformanceRating            int64\n",
       "RelationshipSatisfaction     int64\n",
       "StandardHours                int64\n",
       "StockOptionLevel             int64\n",
       "TotalWorkingYears            int64\n",
       "TrainingTimesLastYear        int64\n",
       "WorkLifeBalance              int64\n",
       "YearsAtCompany               int64\n",
       "YearsInCurrentRole           int64\n",
       "YearsSinceLastPromotion      int64\n",
       "YearsWithCurrManager         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.923810</td>\n",
       "      <td>802.485714</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1024.865306</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>65.891156</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>...</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.793878</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>4.123129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.135373</td>\n",
       "      <td>403.509100</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>602.024335</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>20.329428</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>...</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.852077</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>1.289271</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "      <td>3.568136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>491.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1020.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1555.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2068.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
       "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
       "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
       "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
       "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
       "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
       "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
       "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
       "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
       "\n",
       "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
       "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
       "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
       "std        602.024335                 1.093082    20.329428        0.711561   \n",
       "min          1.000000                 1.000000    30.000000        1.000000   \n",
       "25%        491.250000                 2.000000    48.000000        2.000000   \n",
       "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
       "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
       "max       2068.000000                 4.000000   100.000000        4.000000   \n",
       "\n",
       "          JobLevel  ...  RelationshipSatisfaction  StandardHours  \\\n",
       "count  1470.000000  ...               1470.000000         1470.0   \n",
       "mean      2.063946  ...                  2.712245           80.0   \n",
       "std       1.106940  ...                  1.081209            0.0   \n",
       "min       1.000000  ...                  1.000000           80.0   \n",
       "25%       1.000000  ...                  2.000000           80.0   \n",
       "50%       2.000000  ...                  3.000000           80.0   \n",
       "75%       3.000000  ...                  4.000000           80.0   \n",
       "max       5.000000  ...                  4.000000           80.0   \n",
       "\n",
       "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "count       1470.000000        1470.000000            1470.000000   \n",
       "mean           0.793878          11.279592               2.799320   \n",
       "std            0.852077           7.780782               1.289271   \n",
       "min            0.000000           0.000000               0.000000   \n",
       "25%            0.000000           6.000000               2.000000   \n",
       "50%            1.000000          10.000000               3.000000   \n",
       "75%            1.000000          15.000000               3.000000   \n",
       "max            3.000000          40.000000               6.000000   \n",
       "\n",
       "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "count      1470.000000     1470.000000         1470.000000   \n",
       "mean          2.761224        7.008163            4.229252   \n",
       "std           0.706476        6.126525            3.623137   \n",
       "min           1.000000        0.000000            0.000000   \n",
       "25%           2.000000        3.000000            2.000000   \n",
       "50%           3.000000        5.000000            3.000000   \n",
       "75%           3.000000        9.000000            7.000000   \n",
       "max           4.000000       40.000000           18.000000   \n",
       "\n",
       "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count              1470.000000           1470.000000  \n",
       "mean                  2.187755              4.123129  \n",
       "std                   3.222430              3.568136  \n",
       "min                   0.000000              0.000000  \n",
       "25%                   0.000000              2.000000  \n",
       "50%                   1.000000              3.000000  \n",
       "75%                   3.000000              7.000000  \n",
       "max                  15.000000             17.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 35)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41          1      Travel_Rarely       1102                   Sales   \n",
       "1   49          0  Travel_Frequently        279  Research & Development   \n",
       "2   37          1      Travel_Rarely       1373  Research & Development   \n",
       "3   33          0  Travel_Frequently       1392  Research & Development   \n",
       "4   27          0      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's convert some columns and make them numerical...\n",
    "employee_df['Attrition'] = employee_df['Attrition'].replace('Yes', 1)\n",
    "employee_df['Attrition'] = employee_df['Attrition'].replace('No', 0)\n",
    "employee_df['OverTime'] = employee_df['OverTime'].replace('Yes', 1)\n",
    "employee_df['OverTime'] = employee_df['OverTime'].replace('No', 0)\n",
    "employee_df['Over18'] = employee_df['Over18'].replace('Y', 1)\n",
    "employee_df['Over18'] = employee_df['Over18'].replace('N', 0)\n",
    "employee_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attrition    int64\n",
       "OverTime     int64\n",
       "Over18       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's confirm these columns are now numerical...\n",
    "employee_df[['Attrition','OverTime','Over18']].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1470\n",
       "Name: Over18, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's explore a bit more the content of \"Over18\"...\n",
    "employee_df['Over18'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can clearly delete this column. It adds no value.\n",
    "employee_df.drop(['Over18'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 34)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The column was succesfully deleted\n",
    "employee_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         False\n",
       "Attrition                   False\n",
       "BusinessTravel              False\n",
       "DailyRate                   False\n",
       "Department                  False\n",
       "DistanceFromHome            False\n",
       "Education                   False\n",
       "EducationField              False\n",
       "EmployeeCount               False\n",
       "EmployeeNumber              False\n",
       "EnvironmentSatisfaction     False\n",
       "Gender                      False\n",
       "HourlyRate                  False\n",
       "JobInvolvement              False\n",
       "JobLevel                    False\n",
       "JobRole                     False\n",
       "JobSatisfaction             False\n",
       "MaritalStatus               False\n",
       "MonthlyIncome               False\n",
       "MonthlyRate                 False\n",
       "NumCompaniesWorked          False\n",
       "OverTime                    False\n",
       "PercentSalaryHike           False\n",
       "PerformanceRating           False\n",
       "RelationshipSatisfaction    False\n",
       "StandardHours               False\n",
       "StockOptionLevel            False\n",
       "TotalWorkingYears           False\n",
       "TrainingTimesLastYear       False\n",
       "WorkLifeBalance             False\n",
       "YearsAtCompany              False\n",
       "YearsInCurrentRole          False\n",
       "YearsSinceLastPromotion     False\n",
       "YearsWithCurrManager        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No column contains null values:\n",
    "employee_df.isnull().sum() > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see the distribution of data in every column:\n",
    "\n",
    "employee_df.hist(bins = 30, figsize=(20,20), color = 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# By looking at the distributions, we can see that the following columns add no value: EmployeeCount (it is always 1), \n",
    "# StandardHours (always 80.0), EmployeeNumber (this is the employee ID number).\n",
    "\n",
    "employee_df = employee_df.drop(['EmployeeCount', 'StandardHours','EmployeeNumber'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 31)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The column were succesfully deleted\n",
    "employee_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of employees that left: 237\n",
      "Percentage Number of employees that left: 16.122448979591837%\n",
      "Number of employees that stayed: 1233\n",
      "Percentage of employees that stayed: 83.87755102040816%\n"
     ]
    }
   ],
   "source": [
    "# Let's see how many employees left the company and how many stayed:\n",
    "left_df   = employee_df[employee_df['Attrition'] == 1]\n",
    "stayed_df = employee_df[employee_df['Attrition'] == 0] \n",
    "\n",
    "print(\"Number of employees that left: \" + str(len(left_df)))\n",
    "print(\"Percentage Number of employees that left: \" + str(len(left_df)/len(employee_df)*100)+ \"%\")\n",
    "print(\"Number of employees that stayed: \" + str(len(stayed_df)))\n",
    "print(\"Percentage of employees that stayed: \" + str(len(stayed_df)/len(employee_df)*100)+\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create separate datasets and continue our analysis:\n",
    "left_df = employee_df[employee_df['Attrition'] == 1]\n",
    "stayed_df = employee_df[employee_df['Attrition'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>...</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.0</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.607595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>750.362869</td>\n",
       "      <td>10.632911</td>\n",
       "      <td>2.839662</td>\n",
       "      <td>2.464135</td>\n",
       "      <td>65.573840</td>\n",
       "      <td>2.518987</td>\n",
       "      <td>1.637131</td>\n",
       "      <td>2.468354</td>\n",
       "      <td>...</td>\n",
       "      <td>3.156118</td>\n",
       "      <td>2.599156</td>\n",
       "      <td>0.527426</td>\n",
       "      <td>8.244726</td>\n",
       "      <td>2.624473</td>\n",
       "      <td>2.658228</td>\n",
       "      <td>5.130802</td>\n",
       "      <td>2.902954</td>\n",
       "      <td>1.945148</td>\n",
       "      <td>2.852321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.689350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>401.899519</td>\n",
       "      <td>8.452525</td>\n",
       "      <td>1.008244</td>\n",
       "      <td>1.169791</td>\n",
       "      <td>20.099958</td>\n",
       "      <td>0.773405</td>\n",
       "      <td>0.940594</td>\n",
       "      <td>1.118058</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363735</td>\n",
       "      <td>1.125437</td>\n",
       "      <td>0.856361</td>\n",
       "      <td>7.169204</td>\n",
       "      <td>1.254784</td>\n",
       "      <td>0.816453</td>\n",
       "      <td>5.949984</td>\n",
       "      <td>3.174827</td>\n",
       "      <td>3.153077</td>\n",
       "      <td>3.143349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</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>50%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1092.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1496.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Attrition    DailyRate  DistanceFromHome   Education  \\\n",
       "count  237.000000      237.0   237.000000        237.000000  237.000000   \n",
       "mean    33.607595        1.0   750.362869         10.632911    2.839662   \n",
       "std      9.689350        0.0   401.899519          8.452525    1.008244   \n",
       "min     18.000000        1.0   103.000000          1.000000    1.000000   \n",
       "25%     28.000000        1.0   408.000000          3.000000    2.000000   \n",
       "50%     32.000000        1.0   699.000000          9.000000    3.000000   \n",
       "75%     39.000000        1.0  1092.000000         17.000000    4.000000   \n",
       "max     58.000000        1.0  1496.000000         29.000000    5.000000   \n",
       "\n",
       "       EnvironmentSatisfaction  HourlyRate  JobInvolvement    JobLevel  \\\n",
       "count               237.000000  237.000000      237.000000  237.000000   \n",
       "mean                  2.464135   65.573840        2.518987    1.637131   \n",
       "std                   1.169791   20.099958        0.773405    0.940594   \n",
       "min                   1.000000   31.000000        1.000000    1.000000   \n",
       "25%                   1.000000   50.000000        2.000000    1.000000   \n",
       "50%                   3.000000   66.000000        3.000000    1.000000   \n",
       "75%                   4.000000   84.000000        3.000000    2.000000   \n",
       "max                   4.000000  100.000000        4.000000    5.000000   \n",
       "\n",
       "       JobSatisfaction  ...  PerformanceRating  RelationshipSatisfaction  \\\n",
       "count       237.000000  ...         237.000000                237.000000   \n",
       "mean          2.468354  ...           3.156118                  2.599156   \n",
       "std           1.118058  ...           0.363735                  1.125437   \n",
       "min           1.000000  ...           3.000000                  1.000000   \n",
       "25%           1.000000  ...           3.000000                  2.000000   \n",
       "50%           3.000000  ...           3.000000                  3.000000   \n",
       "75%           3.000000  ...           3.000000                  4.000000   \n",
       "max           4.000000  ...           4.000000                  4.000000   \n",
       "\n",
       "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "count        237.000000         237.000000             237.000000   \n",
       "mean           0.527426           8.244726               2.624473   \n",
       "std            0.856361           7.169204               1.254784   \n",
       "min            0.000000           0.000000               0.000000   \n",
       "25%            0.000000           3.000000               2.000000   \n",
       "50%            0.000000           7.000000               2.000000   \n",
       "75%            1.000000          10.000000               3.000000   \n",
       "max            3.000000          40.000000               6.000000   \n",
       "\n",
       "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "count       237.000000      237.000000          237.000000   \n",
       "mean          2.658228        5.130802            2.902954   \n",
       "std           0.816453        5.949984            3.174827   \n",
       "min           1.000000        0.000000            0.000000   \n",
       "25%           2.000000        1.000000            0.000000   \n",
       "50%           3.000000        3.000000            2.000000   \n",
       "75%           3.000000        7.000000            4.000000   \n",
       "max           4.000000       40.000000           15.000000   \n",
       "\n",
       "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count               237.000000            237.000000  \n",
       "mean                  1.945148              2.852321  \n",
       "std                   3.153077              3.143349  \n",
       "min                   0.000000              0.000000  \n",
       "25%                   0.000000              0.000000  \n",
       "50%                   1.000000              2.000000  \n",
       "75%                   2.000000              5.000000  \n",
       "max                  15.000000             14.000000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's compare both datasets\n",
    "left_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>...</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "      <td>1233.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.561233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>812.504461</td>\n",
       "      <td>8.915653</td>\n",
       "      <td>2.927007</td>\n",
       "      <td>2.771290</td>\n",
       "      <td>65.952149</td>\n",
       "      <td>2.770479</td>\n",
       "      <td>2.145985</td>\n",
       "      <td>2.778589</td>\n",
       "      <td>...</td>\n",
       "      <td>3.153285</td>\n",
       "      <td>2.733982</td>\n",
       "      <td>0.845093</td>\n",
       "      <td>11.862936</td>\n",
       "      <td>2.832928</td>\n",
       "      <td>2.781022</td>\n",
       "      <td>7.369019</td>\n",
       "      <td>4.484185</td>\n",
       "      <td>2.234388</td>\n",
       "      <td>4.367397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.888360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>403.208379</td>\n",
       "      <td>8.012633</td>\n",
       "      <td>1.027002</td>\n",
       "      <td>1.071132</td>\n",
       "      <td>20.380754</td>\n",
       "      <td>0.692050</td>\n",
       "      <td>1.117933</td>\n",
       "      <td>1.093277</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360408</td>\n",
       "      <td>1.071603</td>\n",
       "      <td>0.841985</td>\n",
       "      <td>7.760719</td>\n",
       "      <td>1.293585</td>\n",
       "      <td>0.681907</td>\n",
       "      <td>6.096298</td>\n",
       "      <td>3.649402</td>\n",
       "      <td>3.234762</td>\n",
       "      <td>3.594116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>817.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1176.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  Attrition    DailyRate  DistanceFromHome    Education  \\\n",
       "count  1233.000000     1233.0  1233.000000       1233.000000  1233.000000   \n",
       "mean     37.561233        0.0   812.504461          8.915653     2.927007   \n",
       "std       8.888360        0.0   403.208379          8.012633     1.027002   \n",
       "min      18.000000        0.0   102.000000          1.000000     1.000000   \n",
       "25%      31.000000        0.0   477.000000          2.000000     2.000000   \n",
       "50%      36.000000        0.0   817.000000          7.000000     3.000000   \n",
       "75%      43.000000        0.0  1176.000000         13.000000     4.000000   \n",
       "max      60.000000        0.0  1499.000000         29.000000     5.000000   \n",
       "\n",
       "       EnvironmentSatisfaction   HourlyRate  JobInvolvement     JobLevel  \\\n",
       "count              1233.000000  1233.000000     1233.000000  1233.000000   \n",
       "mean                  2.771290    65.952149        2.770479     2.145985   \n",
       "std                   1.071132    20.380754        0.692050     1.117933   \n",
       "min                   1.000000    30.000000        1.000000     1.000000   \n",
       "25%                   2.000000    48.000000        2.000000     1.000000   \n",
       "50%                   3.000000    66.000000        3.000000     2.000000   \n",
       "75%                   4.000000    83.000000        3.000000     3.000000   \n",
       "max                   4.000000   100.000000        4.000000     5.000000   \n",
       "\n",
       "       JobSatisfaction  ...  PerformanceRating  RelationshipSatisfaction  \\\n",
       "count      1233.000000  ...        1233.000000               1233.000000   \n",
       "mean          2.778589  ...           3.153285                  2.733982   \n",
       "std           1.093277  ...           0.360408                  1.071603   \n",
       "min           1.000000  ...           3.000000                  1.000000   \n",
       "25%           2.000000  ...           3.000000                  2.000000   \n",
       "50%           3.000000  ...           3.000000                  3.000000   \n",
       "75%           4.000000  ...           3.000000                  4.000000   \n",
       "max           4.000000  ...           4.000000                  4.000000   \n",
       "\n",
       "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "count       1233.000000        1233.000000            1233.000000   \n",
       "mean           0.845093          11.862936               2.832928   \n",
       "std            0.841985           7.760719               1.293585   \n",
       "min            0.000000           0.000000               0.000000   \n",
       "25%            0.000000           6.000000               2.000000   \n",
       "50%            1.000000          10.000000               3.000000   \n",
       "75%            1.000000          16.000000               3.000000   \n",
       "max            3.000000          38.000000               6.000000   \n",
       "\n",
       "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "count      1233.000000     1233.000000         1233.000000   \n",
       "mean          2.781022        7.369019            4.484185   \n",
       "std           0.681907        6.096298            3.649402   \n",
       "min           1.000000        0.000000            0.000000   \n",
       "25%           2.000000        3.000000            2.000000   \n",
       "50%           3.000000        6.000000            3.000000   \n",
       "75%           3.000000       10.000000            7.000000   \n",
       "max           4.000000       37.000000           18.000000   \n",
       "\n",
       "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count              1233.000000           1233.000000  \n",
       "mean                  2.234388              4.367397  \n",
       "std                   3.234762              3.594116  \n",
       "min                   0.000000              0.000000  \n",
       "25%                   0.000000              2.000000  \n",
       "50%                   1.000000              3.000000  \n",
       "75%                   3.000000              7.000000  \n",
       "max                  15.000000             17.000000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stayed_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see (among others) that.\n",
    "- Age: The older the employees are, the more chances they stay.\n",
    "- Distance: Employees that live further away have more chances of leaving.\n",
    "- DailyRate: Employees that make more many, have more chances of staying.\n",
    "- StockOptionLevel: Employes with stock options have more chances of staying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's have a look at the different correlations:\n",
    "\n",
    "correlations = employee_df.corr()\n",
    "f, ax = plt.subplots(figsize = (20,20))\n",
    "sns.heatmap(correlations, annot=True)\n",
    "plt.show\n",
    "# Joblevel and MonthlyIncome are highly correlated.\n",
    "# Joblevel and TotalWorkingYears are highly correlated. \n",
    "# Age and MonthlyIncome are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's visualize the relations between Age and our target variable:\n",
    "plt.figure(figsize=(25,12))\n",
    "sns.countplot(x = 'Age', hue ='Attrition', data = employee_df )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRJkiRJ0oai0SD1RhExuutF6UndaC9sSZIkSZIkSZLqajTQ/C/A7RFxBdWY1J8Cvta0UkmSJEmSJEmSNggNBakzc0ZEzAPeDwTwscxc1NSSSZIkSZIkSZLWew0P2VGC0gamJUmSJEmSJEkDptExqSVJkiRJkiRJGnBNC1JHxEUR8WxELKxJGxMRN0TEI+X/6JIeEXFORDwaEQsi4h3NKpckSZIkSZIkaehoZk/qi4GDe6SdBNyYmbsBN5bXAB8Bdit/xwDfaWK5JEmSJEmSJElDRNOC1Jl5M/DrHsmHAJeU6UuAQ2vSZ2TlTmCbiNihWWWTJEmSJEmSJA0Ngz0m9esz82mA8n/7kr4T8ERNvs6StpqIOCYi5kXEvKVLlza1sJIkSZIkSZKk5hoqD06MOmlZL2NmXpCZEzNz4nbbbdfkYkmSJEmSJEmSmmnkIG/vmYjYITOfLsN5PFvSO4Gda/K1AU8NctkkSZIkbaAmnDij1UVoiflnTmt1ESRJkga9J/XVwBFl+gjgqpr0aVF5J/BC17AgkiRJkiRJkqT1V9N6UkfETOBAYGxEdAL/CEwHLo+Io4HHgU+W7NcCk4BHgZeBzzSrXJIkSZIkSZKkoaNpQerMPLyXWR+okzeB45pVFkmSJEmSJEnS0DRUHpwoSZIkSZIkSdoAGaSWJEmSJEmSJLWMQWpJkiRJkiRJUssYpJYkSZIkSZIktYxBakmSJEmSJElSy4xsdQEkSZIkSZI0sCacOKPVRWiJ+WdOa3URJK0Fe1JLkiRJkiRJklrGILUkSZIkSZIkqWUMUkuSJEmSJEmSWsYgtSRJkiRJkiSpZQxSS5IkSZIkSZJaxiC1JEmSJEmSJKllDFJLkiRJkiRJklpmZKsLIEnSunr89D1bXYSWeMMpD7S6CJIkSZIkrTN7UkuSJEmSJEmSWsYgtSRJkiRJkiSpZVoy3EdELAFeBFYAyzNzYkSMAS4D2oElwKcy8zetKN/6ztviJUmSJEmSJA0VrexJfVBmjs/MieX1ScCNmbkbcGN5LUmSJEmSJElajw2l4T4OAS4p05cAh7awLJIkSZIkSZKkQdCS4T6ABK6PiAT+LTMvAF6fmU8DZObTEbF9vQUj4hjgGIA3vOENg1VeSZIkSZLW2YQTZ7S6CC0x/8xprS6CJGkIa1WQ+j2Z+VQJRN8QEQ81umAJaF8AMHHixGxWASVJkiRJkiRJzdeSIHVmPlX+PxsRs4F9gWciYofSi3oH4NlWlE3SwPEhnZIkSZIkSVqTQR+TOiI2j4gtu6aBDwELgauBI0q2I4CrBrtskiRJkiRJkqTB1Yqe1K8HZkdE1/YvzczrIuIe4PKIOBp4HPhkC8omSZIkSZIkSRpEgx6kzsxfAnvXSV8GfGCwyyNJkiRJkiRp7fhAWA2EQR/uQ5IkSZIkSZKkLgapJUmSJEmSJEktY5BakiRJkiRJktQyrXhwoiRJkiRJ2oA8fvqerS5CS7zhlAdaXQRJGhbsSS1JkiRJkiRJahmD1JIkSZIkSZKkljFILUmSJEmSJElqGYPUkiRJkiRJkqSWMUgtSZIkSZIkSWoZg9SSJEmSJEmSpJYxSC1JkiRJkiRJahmD1JIkSZIkSZKkljFILUmSJEmSJElqmZGtLoAkSZIkSZIkDSePn75nq4vQEm845YGmrNcgtSRJUoM8EZUkSZKkgWeQWpIkSRpi/EFEkiRJG5IhNyZ1RBwcEQ9HxKMRcVKryyNJkiRJkiRJap4h1ZM6IkYA3wb+GOgE7omIqzNzUWtLJkmSJEnrH3vtS5KkoWBIBamBfYFHM/OXABExCzgEMEgtSZIkSZKkPvnjmzQ8RWa2ugwrRcQngIMz87Pl9V8A+2Xm52ryHAMcU16+BXh40As6/I0Fnmt1IbRBsK5psFjXNFisaxos1jUNFuuaBot1TYPFuqbBYl3rvzdm5nb1Zgy1ntRRJ22VKHpmXgBcMDjFWT9FxLzMnNjqcmj9Z13TYLGuabBY1zRYrGsaLNY1DRbrmgaLdU2Dxbo2sIbagxM7gZ1rXrcBT7WoLJIkSZIkSZKkJhtqQep7gN0iYlxEbAJMBa5ucZkkSZIkSZIkSU0ypIb7yMzlEfE54OfACOCizHywxcVaHzlcigaLdU2DxbqmwWJd02CxrmmwWNc0WKxrGizWNQ0W69oAGlIPTpQkSZIkSZIkbViG2nAfkiRJkiRJkqQNiEFqSZIkSZIkSVLLGKRukoj4+4h4MCIWRERHROy3hvwXR8Qn1nGbB0bEC2V7XX8fXJd1Nrjd9oj4s5rXEyPinGZvd0MSESvK/lwYET+NiG1aXaZaEfFSA3leHxHXRMT9EbEoIq5dQ/7T17b+RsT4iJhU83pyRJzUaP71VSP7qSbvqRFxQpPK8XdNWu9d5XvyeEQsrWkH2/u5ns9GxDf7ucxxEfHpPubvHBGX9Wedw1FEZER8v+b1yLIvrmnCtrYr+/y+iNi/n8uu9Xc+Iq4diDa4HDtfKeVfHBF3R8QRNfP7bLf6ua0vRsTrBmJdg6FnWxURR0bEeWu5rgO76l+ZfnfNvHU+91pbNfu/oxwTZ0TExq0oy9potB3vmS8ibm9OiTYcg9nOqvVafE15X0Q8FBFnrcv6BlNEbBMR/7u/+SJix4i4ormlG97Wk+vRI0t7eV9EPBIRP+9xXrDW1589ttNQPdTqBvIccKBFxJKIeKC0x7+IiDe2ukzrI4PUTRAR7wL+FHhHZu4FfBB4YpA2f0tmjq/5+89B2GY7sDJInZnzMvP4QdjuhuSVsj/fBvwaOG6wCxAR6/qg1dOBGzJz78zcHegz+JKZp6xD/R0PrAxAZebVmTm90fxaNxExYg1Z+hWkjsoaj1eZuV9mjgdOAS6raQeX9Gd7ayMzv52ZP+xj/hOZOaXZ5RgCfge8LSI2K6//GHiySdv6APBQZr49M2/p57L9/s531cPMnJSZz/dze73571L+twJTgS9FxGeg93ZrLdviLwL9ClI38D0ejg4E3r2mTOuin/vnv0ubtSfQBnxqgMowGPuu0XZ8lXyZ2dTPfwMxmO3sWhuA88YN3hC4pnw78HbgTyPiPeu6wkbP59bRNkAjwcFV8mXmU5nZkh8th5H14XoUqmuEt2fmbsB04CcR8Vbo/fpzLY6rjdbDddmGWuOg0h7PBf5hsDe+IRxbDVI3xw7Ac5n5e4DMfC4znwKIiFMi4p7yC+QFERE9F46ICeWXmfnl170dSvrxpbfNgoiY1WhhImKfssyoiNi8/Br/tjLvxFKeBRFxWs0y00ra/V29NXr+Ml/zK9d0YP/yy+qXyq/v10TERuXXpm1qlnk0qh6120XEj8u27xmIE58NyB3ATl0v6u3Dsp//o+y/hRExpaT3Vrf+sqzj/rJfXlfSL46Ib0TEHOCMiNgiIr5X8wvix2vK8bWy/J0R8fo65d4B6Ox6kZkLapb9clnn/RExvWbbn1hDuedGxBlR9UD8r4jYPyI2oQqITyl1ckrU/AIbEZ8sn8n9EXFzvfzrvouGj4j4aHT3Rv3PHvtu74i4KaqeBn9Z8kdEnFk+wwdq6taBETEnIi4FHihpV5Z99mBEHFPSpgOblc/6hyXtb8r6FkbEF0tae1S9S/8VuBf4PxFxdk25/zIivtGP9/mRiLgjIu6NiMsiYvOSvl9Jv798Dl2BvLZS1x6JiK+XvCMj4vmImF7y3xER25d5X60p+5vL53Z/2V57ROwaER1l/i4RcUv5zOdH6RUVER+MiBsj4icR8XBEzOjPvhxCfgb8SZk+HJjZNSMi9o2I28t7vz0i3lLSjyzv+7rymf9zzTIv1Ux/orQN44F/BiaVurRZRHwnIuaV+lZ7PNunbOv+0lZszeptxCp3DpS62F6nHu4c1XFtbM28fy/bvD5K0Ci6j7t3dH1f1vShZeYvgb8Bjq/5TLrarZ5t8eYRcVFU7fZ9EXFIyTciIs6K7jb68xFxPLAjMKcsT0QcXvIsjIgzaj/rqHoR3QW8a417ugWil/OH3upWzXLtwLFUPwR0RHfv+wNK/l/Gquc49Y5LjR4r6+6f3mTmCuBuyrG97Mczo/vY/lcl/cCojluzozofPD9KwKfnvot+nEv2UZ/qfi+jfjveaHv/Uvl/Wax6x9PFEfHx3t67VjPQ7Wxv7eekqHrS3hoR50T3XQl91ZkfRcRPgeub/zGs91p+TZmZrwAddLdPfe37q0r9ejgi/rGk1zuOfii6z8l+FBFblLzTa8p1Vknrrc0/tZRjblTtd1cHqenALqXdOTOqa5cby7YeiO72uGe+9ijH6qjOB/eo+Rznls+yX237em64Xo+uIjPnABcAXcet2uvPJeV7divwyajO368r5b4lIv6o5Ht9VMfl+8vfu1m9fkU0eP2k3kUv8ajyOf4iIi6PKiYwPSI+HdV5/wMRsUvJV/e6t4/2pC89vwN/XrbXERH/FtX5zIhS5q79/qWSd3ypowtK3Rld0udGxMQyPTYilpTp1Y6tUf88tbc6ukr8Y932wiDITP8G+A/Ygupg/l/AvwLvq5k3pmb6+8BHy/TFwCeAjYHbge1K+hTgojL9FLBpmd6mznYPBF4o2+7626XM+ypwFvBt4OSS9iGqRjmofrC4BjgA2AN4GBhbW+auMtZs76Wa7V7ToxzXlOlvAZ8p0/sB/1mmLwXeW6bfACxu9X4byn81n/UI4EfAwWvYhx8H/r1m+a3XULe2rcn7VeDzNfv8GmBEeX0G8M2avKPL/6ypy/8M/EOd9/Bh4HlgDvD3wI4l/SOlXK+rV9/WUO65wL+U6Uk19etI4Lyaba98TXUCsFPt96hn/vX1r6se9UgbDUSZ/mzN53kqcD+wGTCWqufOjqVu3VDq4uuBx6kuog6k6t01rmbdXftyM2BhVz2rLQcwoeyTzanazgepeu20A/8DvLPk2xz4b2Dj8vp2YM9e3mfP/b898IuaOvb3VL37RgGPUfVQgup7MqJ8Do8AW5ayd733kaWuf6Tk/wZwUs335otlej7d34dRVD1YdwU6StrrgFFl+o+Au8r0B4HflM9zBHBP1/sfLn/AS8BewBXlvXew6jFhK2Bkzfv9cc0++2XZB6OAXwE716kvnwAu7mU/d9W3EVRtw17AJmW9+9Ruv86ypwIn1LxeWOrgKvWwzFtC9Z1oB5YD40v65cCf1yz/7jI9HVhY57Nq75lO1fPmlZ7vj9Xb4v9bs61tqM43Ngf+GvhxzWc8prbMZXpHqu/tduWzuAk4tMxL4FNDoB6tYNVzmcdrPou65w991K0D6a5/PffzxVTH1I2A3YFHS3pvx6VGj5V1909v+5+qzs8B9iqvj6EcR4FNgXnAuPJeXgXeRFXPb6Ccl9XuO/p5LtlHfTqSBr6XPT6jXtv72tfAYcAlZXoTqnZ2s97ee6vr5FD6ozntbL32c1TZL+PKvJk12+irznRSc73j3zrt61ZeU3bt69FU5zX/q4F9/zSwbU07MJHVz+fGAjdT2kTgK1R3wI2huv6M2nLRe5t/anl/m5Z1LivvuZ2aYyvVcW6rmm0/SnXd1DPfytfAl4DTyvQOwH/19d5bXU8GsT6uD9ejR9Ljmg84FPhZ7fenTC8BvlyT70ZgtzK9H3BTmb6M7muAEeV99qxfDV8/beh/9H0OuHL/9KiTB1LFGXagahOerPkOf6GrvtD3de9q7Umdsi2h+3z6m8AxZfqtwE/pvk79V2Aa1XXuDTXLd7VrCyjtOVXHma7yzQUmlumxwJKaervy2Erv56m91dHV4h9D+W+97yreCpn5UkRMAPYHDgIui4iTMvNi4KCI+DJVkGIMVUDmpzWLvwV4G3BDVD+Ij6A64ENVmX8YEVcCV/ay+Vsy80/rpJ9OFfB4ldJLi+qA8iHgvvJ6C2A3YG/gisx8rryfXzf+7ldzGdWJx/eobmXuGpP1g8Du0f2j/1YRsWVmvrgO21qfbRZVL8x2qhPFG0p6b/vwFuCsqHrIXZOZt0TVe763uvW2iPgq1QnXFsDPa7b9o6x6eUG136Z2zcjM35TJ16hOHijl++OebyAzfx4RbwIOpmpY7ytl+iDwvcx8ueTrWd/6+k4A/KRmu+09t1vHbcDFEXF5zbIbsjaqNmoHqkDBYzXzrpADzQgAACAASURBVMqqB80rpffCvsB7gZmlTjwTEb8A9gF+C9ydmbXLHx8Rh5Xpnanq5rIe238vMDszfwcQET+hajuvBn6VmXcCZObvIuImqltOF1OdBDTa4+DdVAGo20sd2gS4leqE4vHMvLds44VSBqh+8HixvH6I6sLoWaoA4s/KeueXsq5Ufgkfm5k/Let8tWadXTYFzouIvamCnLvUzLszM58uy3R95+9s8H0OCZm5IKpeq4cDPcee3xq4JCJ2o7qYqB2D98aafbAIeCP9u635U1H14BxJdZK6e9nG05l5Tynbb8v6+/OWVtbDOh7LzI4yPR9oj+ruoS0zs2vs3UupbtduRF8Fq22LPwRMju7e36Oo6ugHgfMzczn0evzeB5ibmUsBourhegDVecUKqiB3q72S1TAYQNWDhCrYAb2cP9B33erLlZn5P8Ci6O511dtxqdFjZW/7Z3GPbe9Svue7UZ13LahZfq+a3kJblzyvUbWzvyyfy0yqNvQKVt13/T2X7K280Pj3spH2vtbPgHMiYlOq84KbM/OViOjtvT/Wy3o2SE1oZ+u1nxsBv6w5rs+k9Dak7zpzwzpeO6ho8TXl/hGxoKxnemb+v5K+pn2/DFaez723rL/2OPpOqvp1W8052R1U55GvAt+NiP+g+7qitzYf4D+y6mX++4h4lir411MA/zciDqAKlu/US75al1Nda/0j1TBMP1rDe+/Ztq+vhv31aC/6Ove6DCCq3v7vBn5UUxc3Lf/fTxWQpJTxha6esTX6c/20oevrHLAv99RcQ/033XfzPEDVfkLf17312pNOVjennC8+S/dwHx+gCkjfU+rHZmX+T4E3RcS5wH8A10d1R+c2mfmLsuwldLcxfak9tq52nrqGOjqs4h8GqZukNEBzgbkR8QBwRFS3U/0r1a8jT0TEqVQHt1oBPJiZ9W6z/ROqC8nJVLe+79F1IdqAMVSN/cZlm78r2/p6Zv7bKgWobm/IOutYThkiJqqav0kD270D2DUitqP6lfKrJX0j4F0lAKY1eyUzx5dG7RqqMcDOoZd9CNWtVFS9i78eEdcDs+m9bl1M1ZPu/nIgOLBm3u9qV0v9uvGHLD/NUV0o121bSsN6KXBpVLeMHtDHOmu32Vu5AX6/pu32KMOxUQ2v8CdAR1TDBmzIzgW+kZlXR8SBVL8kd+m5X5K+T+RW1pWyrg9Sfc9fjoi5rN7e0ej6iu9S9YB+iOqHr0YFcF1m/sUqiRHvoPe69/ua6dq69Vov6bX6qs8Af0sVFPhzqja59gEhvW13uLma6u6dA6l6VXX5J2BOZh5WAixza+b19t5rP896dYiIGAecQNVj+jcRcXHJu6b2pcvK41ud7fSsh7V6lnkz+q7Ta/J2er/Y7dkWfzwzH67NUI7Na3q/fZXv1ZqLwKGq7vlDuQDorW71pXYfRs3/ep/jxTR+rFxt/9Tx3+XYvgPV+eLkzLy6LP/5zKy9QO9qV+u1y7DqvuvXuWRv5S3HyjW2Sf1o77sLnflqyfdhqp50XcNV1H3vqmtA2tk1tJ+96avO9NVmqp9aeE15S2b+aUS8Gbg1ImaXH2X72ve9tU8928cbMvPwnoWKiH2pgj1Tgc9RBf96a/OhsXOmT1PdOTQhM/8Q1e3za2qfnoyIZRGxF1X71DXsUKNt+/pqvbgeraORc6+NgOdrg6f91J/rHfWur3hUbXvwPzWv/4fuutDXdW+j12AHUe2zi6k6gv4N1f69JDNP7pk5qk5JH6b6vnyK6k6NNb4/Vm+n1vQd6LWO1ot/dP2gOBQ5JnUTRMRbSu+FLuOpbqnrqmjPlV866j2c4WFgu6gelEFEbBwRe0Q15uDOWY2b9GW6f2Fs1AXA/wF+SHWLDFS/Th4V3eOA7RTV+Ko3UvWo2Lakjyn5l1D9QgRwCN09M16kui1+NeVAMZvqtvjFNV+G66lOPijb2NADhQ0pvV+OB06IiI3pZR9GxI7Ay5n5A6oLmHfQS90qq94SeLqs89N9FKHnfuv5K3GvIuL90T222JZUvUcfL+s8qmbemB6L9lXu3vRaJyNil8y8KzNPAZ6j6vHVa/4NwNZ0P3DpiB7zDolqLPttqU4U76G6RXNKVGNsbUd1kXN3L+v9TQlY/BFVz5kufyh1jbK+QyPidVGNE30YVc+L1WTmXVT768+oGX+zAbcD74uqJ3/XGHm7UfU6emMJVhMRW8U6PrSk9OZ4LiI+WtY5KrrHue6yNVXv3qT6zNclqDlUXQScnqv3dq+tb0c2uK5nIuKt5Th4WC95tqI6eXuh9G74SEl/CNgxIvaBqu2J6oEjPb/zS6jaya4fL8Y1WLbVlDrwYkR01fmpfeXvUoJJZ1GdQK/Jz4HPlxN0IuLtJf164NjyHmvb09r3exfV92Fsqe+HUw2HM1z0dv7QSN1qtK3v7bjU6LGyt/1TV+n5cxLQdXHzc+Cvu9rJqMa537zM2zcixpXvwxSqu0J66u+5ZL/KW9S244229z3NAj5D1Uu0Kyjd13vXqgaqne2r/XxTaZugqm9d1qbOqJ+GwjVlZv4X8HWqYTmg733/xxExJqrnMxxK1XuvpzuB90TErmX515Xv+RbA1pl5LdXDfrva9v5eM/Zs57cGni0B6oOo7h6ol6+nWVSfz9Y13zHrPcP7erSniHgf1R0i/95XvqzuxHssIj5Zlouogo9QxU7+uqSPiIitWL1+NXr9pL4toX48qlF9Xfc2rPxo9kVgWjlHvBH4RHQ/q2hMRLwxIsYCG2Xmj6lice8o35/fRPezUf6C7vPwJXS/v74e5LraeWpfdbSX+MeQZZC6Obagus1uUVS3Se0OnJqZz1M1gA9Q3fp0T88FM/M1qgp5RkTcTzUOz7upboX5QVS/oN8HnF3W11PXAwy7/j4REdOA5Zl5KdXYmPtExPsz83qqXq13lPVeQXWL8oPA14BflDJ0PZzs36kubO+mGuOm69ecBcDyqAZir/fL0GVUPQYvq0k7HpgY1WDxi6geZqQGZOZ9VGMFT+1tHwJ7AndHdUvW3wNf7aNuQdVo3kV129ZDfWz+q8DoKAPv033rTCMmAPPKd+IO4LuZeU9mXkfVG2heKe8JtQutody9mUN1a2C9ByGeGeWBYVQnDPevIf/65HUR0Vnz9zdUvyD/KCJuoTpo1bqb6takO4F/yuphPbOpvvP3U41l++XsvgW01nVUPbQWUPXqqh0u4QJgQUT8MKuhNi4u27qLql7c13NlNS4Hbqu5tW+NMvMZ4Giq27vupwpav7nc0nU48J2Sfj3dt0Wti08Df1ve+61UPXhqnQd8NiLupLpY+j3rmczszMxv1Zn1z1S9aW6jOq414iSqHjs3sepQP7Xbu5/q2PggVeDmtpL+GlVg5dyyj2+ge/zf2u/8j4ExpQ36a6pxJtfF0cAFEXEH1Y8QL/SSb5eoHt6ymKpun5uZjdwl8E9UJ+YLSlv2TyX9u1Q//i0o7/fPSvoFwM8iYk4JiJ5M9RncD9ybmVf1/y22TG/nD43UrZ8Ch8WqD05cTR/HpUaPlb3tn75cSdVG70+1HxcB95bl/43uHj13UMY5p7pNdXad8vf3XHJtyruyHafB9r7OOq6nulD/z1Jm1vDeVWOg2tk+2s9XgP8NXBfVg8OeobstW5s6o/5r5TVlrfOpHjI7jr73/a1U42N3UI2FPq9OuZZS/Xgys7ynO6mez7ElcE1J+wXdvQ37dc1YOkTdVq5VzqTqoDUxIuZRnZ891Eu+nq6g+pH58po0630xjK9HofvB2f9FdYfmxzOzkSFbPg0cXbb5IFWQFKoxjw8q730+sEed+tXo9ZP61ls8qlGn0vt1b7+U8+mZwHGZuYhq6I/rSxt2A9XQWTtR3QXTQXW929UZ4QiqmMQCqh/kTi/pZ1H9UH871ZjUvW27t/PU3upovfjHkNU1aLgkSUNeVMPEnJ2ZN7a6LFJvImKLzOx64vhJwA6Z+YUWF0vDXFS3pp6Q9Z89Ig24rrYsIoLq4euPZObZrS6Xhp4o48Zm5ufWlFeSpN7Yk1qSNORFxDalx8MrBqg1DPxJ6aWzkGoog6+uaQFJGoL+svTSepDqNunVxpyVJEkaKPakliRJkiRJkiS1jD2pJUmSJEmSJEktY5BakiRJkiRJktQyBqklSZIkSZIkSS1jkFqSJEkaQBHxUh/zDoyIa3qZtyQiHoiIBRHxi4h447psS5IkSRouDFJLkiRJQ8dBmbkXMBf4hxaXRZIkSRoUBqklSZKkARaVMyNiYekdPaVm9lYRMTsiFkXE+RFR75z8DmCnmvX9TVnXwoj4Yi/bPDEi7ik9sU8b4LckSZIkNc3IVhdAkiRJWg99DBgP7A2MBe6JiJvLvH2B3YFfAdeVvFf0WP5g4EqAiJgAfAbYDwjgroj4RWbe15U5Ij4E7FbWHcDVEXFAZt6MJEmSNMTZk1qSJEkaeO8FZmbmisx8BvgFsE+Zd3dm/jIzVwAzS94ucyLiWeCDwKU165qdmb/LzJeAnwD799jeh8rffcC9wB9RBa0lSZKkIc8gtSRJkjTwoo952cfrg4A3Ag8CpzewrtrtfT0zx5e/XTPzwoZLK0mSJLWQQWpJkiRp4N0MTImIERGxHXAAcHeZt29EjCtjUU8Bbq1dMDNfAb4ITIuIMWVdh0bE6yJic+Aw4JYe2/s5cFREbAEQETtFxPbNenOSJEnSQIrMnh05ho+xY8dme3t7q4shSZIkSZIkSerD/Pnzn8vM7erNa+qDEyNiG+C7wNuobmM8CngYuAxoB5YAn8rM30REAN8CJgEvA0dm5r19rb+9vZ158+Y1rfySJEmSJEmSpHUXEb/qbV6zh/v4FnBdZv4R1ZPNFwMnATdm5m7AjeU1wEeoHu6yG3AM8J0ml02SJEmSJEmS1GJNC1JHxFZUY+9dCJCZr2Xm88AhwCUl2yXAoWX6EGBGVu4EtomIHZpVPkmSJEmSJElS6zWzJ/WbgKXA9yLivoj4bnnQy+sz82mA8r/rgS47AU/ULN9Z0lYREcdExLyImLd06dImFl+SJEmSJEmS1GzNHJN6JPAO4POZeVdEfIvuoT3qiTppqz3VMTMvAC4AmDhx4vB96qMkSZIkSZIk9fCHP/yBzs5OXn311VYXZa2MGjWKtrY2Nt5444aXaWaQuhPozMy7yusrqILUz0TEDpn5dBnO49ma/DvXLN8GPNXE8kmSpGFowokzWl0Eqenmnzmt1UWQJElSi3R2drLlllvS3t5ORL1+vUNXZrJs2TI6OzsZN25cw8s1bbiPzPx/wBMR8ZaS9AFgEXA1cERJOwK4qkxfDUyLyjuBF7qGBZEkSZIkSZKkDcGrr77KtttuO+wC1AARwbbbbtvvXuDN7EkN8HnghxGxCfBL4DNUgfHLI+Jo4HHgkyXvtcAk4FHg5ZJXkiRJkiRJkjYowzFA3WVtyt7MByeSmR2ZOTEz98rMQzPzN5m5LDM/kJm7lf+/LnkzM4/LzF0yc8/MnNfMskmSJEmSJEnScDJ79mwigoceegiAJUuWcOmll66c39HRwbXXXtvr8vPmzeP4448HYO7cudx+++0r551//vnMmNGa4RWbGqSWJEmSJEmSJA2MmTNn8t73vpdZs2YB/QtSL1++nIkTJ3LOOecAqwepjz32WKZNa82zUZo93IckSZIkSZIkaR299NJL3HbbbcyZM4fJkydz6qmnctJJJ7F48WLGjx/P4Ycfzre//W1eeeUVbr31Vk4++WQWL17MU089xZIlSxg7dizHHHMMZ511Fueddx7nn38+I0aM4Ac/+AHnnnsuN954I1tssQUnnHACHR0dHHvssbz88svssssuXHTRRYwePZoDDzyQ/fbbjzlz5vD8889z4YUXsv/++6/ze7MntSRJkiRJkiQNcVdeeSUHH3wwb37zmxkzZgz33nsv06dPZ//996ejo4OvfOUrnH766UyZMoWOjg6mTJkCwPz587nqqqtW6XHd3t7Osccey5e+9CU6OjpWCzRPmzaNM844gwULFrDnnnty2mmnrZy3fPly7r77br75zW+ukr4uDFJLkiRJkiRJ0hA3c+ZMpk6dCsDUqVOZOXNmQ8tNnjyZzTbbrOHtvPDCCzz//PO8733vA+CII47g5ptvXjn/Yx/7GAATJkxgyZIlDa+3Lw73IUmSJEmSJElD2LJly7jppptYuHAhEcGKFSuICCZNmrTGZTfffPMBLcumm24KwIgRI1i+fPmArNOe1JIkSZIkSZI0hF1xxRVMmzaNX/3qVyxZsoQnnniCcePGsdFGG/Hiiy+uzLfllluu8rovveXdeuutGT16NLfccgsA3//+91f2qm4Wg9SSJEmSJEmSNITNnDmTww47bJW0j3/848yaNYuRI0ey9957c/bZZ3PQQQexaNEixo8fz2WXXdbnOj/60Y8ye/Zsxo8fvzIg3eWSSy7hxBNPZK+99qKjo4NTTjllwN9TrcjMpm6gmSZOnJjz5s1rdTEkSdIgmnDijFYXQWq6+WdOa3URJEmS1CKLFy/mrW99a6uLsU7qvYeImJ+ZE+vltye1JEmSJEmSJKllDFJLkiRJkiRJklrGILUkSZIkSZIkqWUMUkuSJEmSJEmSWsYgtSRJkiRJkiSpZQxSS5IkSZIkSZJapqlB6ohYEhEPRERHRMwraWMi4oaIeKT8H13SIyLOiYhHI2JBRLyjmWWTJEmSJEmSJK3Zddddx1ve8hZ23XVXpk+fPuDrHznga1zdQZn5XM3rk4AbM3N6RJxUXn8F+AiwW/nbD/hO+S9JkiRJkiRJAiacOGNA1zf/zGl9zl+xYgXHHXccN9xwA21tbeyzzz5MnjyZ3XfffcDK0IrhPg4BLinTlwCH1qTPyMqdwDYRsUMLyidJkiRJkiRJAu6++2523XVX3vSmN7HJJpswdepUrrrqqgHdRrOD1AlcHxHzI+KYkvb6zHwaoPzfvqTvBDxRs2xnSVtFRBwTEfMiYt7SpUubWHRJkiRJkiRJ2rA9+eST7Lzzzitft7W18eSTTw7oNpo93Md7MvOpiNgeuCEiHuojb9RJy9USMi8ALgCYOHHiavMlSZIkSZIkSQMjc/UQbES9UO7aa2pP6sx8qvx/FpgN7As80zWMR/n/bMneCexcs3gb8FQzyydJkiRJkiRJ6l1bWxtPPNE9AEZnZyc77rjjgG6jaUHqiNg8IrbsmgY+BCwErgaOKNmOALoGMLkamBaVdwIvdA0LIkmSJEmSJEkafPvssw+PPPIIjz32GK+99hqzZs1i8uTJA7qNZg738Xpgdun6PRK4NDOvi4h7gMsj4mjgceCTJf+1wCTgUeBl4DNNLJskSZIkSZIkaQ1GjhzJeeedx4c//GFWrFjBUUcdxR577DGw2xjQtdXIzF8Ce9dJXwZ8oE56Asc1qzySJEmSJEmSNNzNP3PaoG9z0qRJTJo0qWnrb+qY1JIkSZIkSZIk9cUgtSRJkiRJkiSpZQxSS5IkSZIkSZJaxiC1JEmSJEmSJKllDFJLkiRJkiRJklrGILUkSZIkSZIkqWUMUkuSJEmSJEmSenXUUUex/fbb87a3va0p6x/ZlLVKkiRJkiRJkgbc46fvOaDre8MpD6wxz5FHHsnnPvc5pk2bNqDb7mJPakmSJEmSJElSrw444ADGjBnTtPUbpJYkSZIkSZIktYxBakmSJEmSJElSyxikliRJkiRJkiS1jEFqSZIkSZIkSVLLND1IHREjIuK+iLimvB4XEXdFxCMRcVlEbFLSNy2vHy3z25tdNkmSJEmSJElS3w4//HDe9a538fDDD9PW1saFF144oOsfOaBrq+8LwGJgq/L6DODszJwVEecDRwPfKf9/k5m7RsTUkm/KIJRPkiRJkiRJkoaFN5zywKBvc+bMmU1df1N7UkdEG/AnwHfL6wDeD1xRslwCHFqmDymvKfM/UPJLkiRJkiRJktZTzR7u45vAl4H/Ka+3BZ7PzOXldSewU5neCXgCoMx/oeSXJEmSJEmSJK2nmhakjog/BZ7NzPm1yXWyZgPzatd7TETMi4h5S5cuHYCSSpIkSZIkSZJapZk9qd8DTI6IJcAsqmE+vglsExFdY2G3AU+V6U5gZ4Ayf2vg1z1XmpkXZObEzJy43XbbNbH4kiRJkiRJkjT4MlfruztsrE3ZmxakzsyTM7MtM9uBqcBNmflpYA7wiZLtCOCqMn11eU2Zf1MO570hSZIkSZIkSf00atQoli1bNiwD1ZnJsmXLGDVqVL+WG7nmLAPuK8CsiPgqcB9wYUm/EPh+RDxK1YN6agvKJkmSJEmSJEkt09bWRmdnJ8N1qONRo0bR1tbWr2UGJUidmXOBuWX6l8C+dfK8CnxyMMojSZIkSZIkSUPRxhtvzLhx41pdjEHVzDGpJUmSJEmSJEnqk0FqSZIkSZIkSVLLGKSWJEmSJEmSJLWMQWpJkiRJkiRJUss0FKSOiBsbSZMkSZIkSZIkqT9G9jUzIkYBrwPGRsRoIMqsrYAdm1w2SZIkSZIkSdJ6rs8gNfBXwBepAtLz6Q5S/xb4dhPLtd6bcOKMVhdBarr5Z05rdREkSZIkSZI0xPUZpM7MbwHfiojPZ+a5g1QmSZIkSZIkSdIGYk09qQHIzHMj4t1Ae+0ymWl3YEmSJEmSJEnSWmsoSB0R3wd2ATqAFSU5AYPUkqT/z97dh+tV1XfC//5IQKgi8hIdJdCgoqMWiRLRjqKobUVawdcSnmpQcRh6YUVbGXHmGUZpO6ODU9/wKWUKCloTFIswDr4Vob5VMcEUkNCR0SgBRxAFoUo18ff8cXbwEE/CSTh37pPk87mu+zp7r7X22r87f5w753vWWRsAAABgi00rpE6yKMnju7tHWQwAAAAAADuWnaY57tok/2qUhQAAAAAAsOOZ7krqfZJcV1VXJvmX9Y3dfdRIqgIAAAAAYIcw3ZD6LaMsAgAAAACAHdO0Quru/vvNnbiqdk3y+SQPGO5zYXf/56o6IMmyJHsluSrJK7r7Z1X1gEw8iPGQJLclOaa7V2/ufQEAAAAA2HZMa0/qqrqzqn48vO6uqnVV9eP7uOxfkjynuw9OsjDJEVX1tCRvT/LO7j4wyY+SHD+MPz7Jj7r70UneOYwDAAAAAGA7Nq2Qurt37+4HD69dk7wkyZn3cU13913D6c7Dq5M8J8mFQ/t5SV44HB89nGfof25V1bTfCQAAAAAA25xphdQb6u6PZyJs3qSqmlNVK5PckuSzSf5Pktu7e+0wZE2SfYfjfZPcOMy/NskdSfbekvoAAAAAANg2TGtP6qp68aTTnZIsysSq6E3q7nVJFlbVQ5JclORxUw1bf5tN9E2u5YQkJyTJ/vvvf18lAAAAAAAwi00rpE7ygknHa5OszsT2HNPS3bdX1RVJnpbkIVU1d1gtPT/JzcOwNUn2S7KmquYm2SPJD6eY6+wkZyfJokWL7jMoBwAAAJJDTjl/3CXAyK04Y8m4SwC2wLRC6u5+1eZOXFXzkvx8CKh3S/JbmXgY4uVJXppkWZLjklw8XHLJcP4PQ//nulsIDQAAAACwHZvWntRVNb+qLqqqW6rq+1X1saqafx+XPTzJ5VV1dZKvJflsd38iyZuS/HFV3ZCJPafPGcafk2Tvof2Pk5y6JW8IAAAAAIBtx3S3+3h/kg8nedlw/vKh7bc3dkF3X53kSVO0fyvJoVO03z1pfgAAAAAAdgDTWkmdZF53v7+71w6vDySZN8K6AAAAAADYAUw3pP5BVb28quYMr5cnuW2UhQEAAAAAsP2bbkj96iS/n+T/JvleJh5suNkPUwQAAAAAgMmmuyf1nyY5rrt/lCRVtVeSd2QivAYAAAAAgC0y3ZXUT1wfUCdJd/8wUzwUEQAAAAAANsd0Q+qdqmrP9SfDSurprsIGAAAAAIApTTdo/u9JvlxVFybpTOxP/ecjqwoAAAAAgB3CtELq7j6/qpYneU6SSvLi7r5upJUBAAAAALDdm/aWHUMoLZgGAAAAAGDGTHdPagAAAAAAmHFCagAAAAAAxkZIDQAAAADA2AipAQAAAAAYGyE1AAAAAABjM7KQuqr2q6rLq2pVVX2jqk4e2veqqs9W1TeHr3sO7VVV76mqG6rq6qp68qhqAwAAAABgdhjlSuq1Sf6kux+X5GlJTqqqxyc5Ncll3X1gksuG8yR5fpIDh9cJSf5yhLUBAAAAADALjCyk7u7vdfdVw/GdSVYl2TfJ0UnOG4adl+SFw/HRSc7vCV9J8pCqevio6gMAAAAAYPy2yp7UVbUgyZOSfDXJw7r7e8lEkJ3kocOwfZPcOOmyNUMbAAAAAADbqZGH1FX1oCQfS/L67v7xpoZO0dZTzHdCVS2vquW33nrrTJUJAAAAAMAYjDSkrqqdMxFQ/013/+3Q/P3123gMX28Z2tck2W/S5fOT3LzhnN19dncv6u5F8+bNG13xAAAAAACM3MhC6qqqJOckWdXdfzGp65Ikxw3HxyW5eFL7kprwtCR3rN8WBAAAAACA7dPcEc799CSvSHJNVa0c2v5Dkrcl+UhVHZ/ku0leNvRdmuTIJDck+UmSV42wNgAAAAAAZoGRhdTd/cVMvc90kjx3ivGd5KRR1QMAAAAAwOwz8gcnAgAAAADAxgipAQAAAAAYGyE1AAAAAABjI6QGAAAAAGBshNQAAAAAAIyNkBoAAAAAgLERUgMAAAAAMDZCagAAAAAAxkZIDQAAAADA2AipAQAAAAAYGyE1AAAAAABjI6QGAAAAAGBshNQAAAAAAIzN3HEXAGy/vnv6QeMuAUZu/9OuGXcJAAAAsE0b2Urqqjq3qm6pqmsnte1VVZ+tqm8OX/cc2quq3lNVN1TV1VX15FHVBQAAAADA7DHK7T4+kOSIDdpOTXJZdx+Y5LLhPEmen+TA4XVCkr8cYV0AAAAAAMwSIwupu/vzSX64QfPRSc4bjs9L8sJJ7ef3hK8keUhVfC5QOgAAIABJREFUPXxUtQEAAAAAMDts7QcnPqy7v5ckw9eHDu37Jrlx0rg1QxsAAAAAANuxrR1Sb0xN0dZTDqw6oaqWV9XyW2+9dcRlAQAAAAAwSls7pP7++m08hq+3DO1rkuw3adz8JDdPNUF3n93di7p70bx580ZaLAAAAAAAo7W1Q+pLkhw3HB+X5OJJ7UtqwtOS3LF+WxAAAAAAALZfc0c1cVUtTXJ4kn2qak2S/5zkbUk+UlXHJ/lukpcNwy9NcmSSG5L8JMmrRlUXAADMdt89/aBxlwAjt/9p14y7BABglhhZSN3dx26k67lTjO0kJ42qFgAAAAAAZqfZ8uBEAAAAAAB2QEJqAAAAAADGRkgNAAAAAMDYCKkBAAAAABgbITUAAAAAAGMjpAYAAAAAYGyE1AAAAAAAjI2QGgAAAACAsRFSAwAAAAAwNkJqAAAAAADGZu64CwAAAACAmfDd0w8adwkwcvufds24S5hxVlIDAAAAADA2QmoAAAAAAMZGSA0AAAAAwNjMupC6qo6oqn+qqhuq6tRx1wMAAAAAwOjMqpC6quYkeV+S5yd5fJJjq+rx460KAAAAAIBRmVUhdZJDk9zQ3d/q7p8lWZbk6DHXBAAAAADAiFR3j7uGe1TVS5Mc0d2vGc5fkeSp3f3aSWNOSHLCcPrYJP+01QsFpmufJD8YdxEAsA3yGQoAW8ZnKMxev97d86bqmLu1K7kPNUXbvVL07j47ydlbpxzg/qiq5d29aNx1AMC2xmcoAGwZn6GwbZpt232sSbLfpPP5SW4eUy0AAAAAAIzYbAupv5bkwKo6oKp2SbI4ySVjrgkAAAAAgBGZVdt9dPfaqnptkk8nmZPk3O7+xpjLAracrXkAYMv4DAWALeMzFLZBs+rBiQAAAAAA7Fhm23YfAAAAAADsQITUAAAAAACMjZAauE9V9R+r6htVdXVVrayqp1bVX1fV47dwvgVVde1M1wkA41ZVXVUfnHQ+t6purapPzMDcX97M8W+pqjfe3/sCwChU1brh58tvVNU/VtUfV9VOQ9+iqnrPLKjx8Jn4DAfu26x6cCIw+1TVbyb5vSRP7u5/qap9kuzS3a8Zc2kAMBv9c5LfqKrduvunSX47yU2bM0FVze3utZPO53T3uu7+NzNcKwCM00+7e2GSVNVDk3w4yR5J/nN3L0+y/P7eYMPPVGD2spIauC8PT/KD7v6XJOnuH3T3zVV1RVUtSpKququq/nz47fdXquphQ/ujhvOvVdXpVXXXhpNX1ZyqOmMYc3VV/but+u4AYOZ9MsnvDsfHJlm6vqOqDq2qL1fV14evjx3aX1lVH62q/5nkM8PKrcur6sNJrhnG3DVpnlMmfXa+dVL7f6yqf6qqv0vy2NG/VQC4/7r7liQnJHltTTi8qj5RVTtV1eqqesj6sVV1Q1U9rKp+vaouGz4LL6uq/Yf+D1TVX1TV5UneXlUPqqr3V9U1w9iXDON+p6r+oaquGj6DHzS0H1FV11fVF5O8eOv/a8COSUgN3JfPJNmvqv53Vf1/VfWsKcY8MMlXuvvgJJ9P8m+H9ncneXd3PyXJzRuZ//gkdwxjnpLk31bVATP7FgBgq1qWZHFV7ZrkiUm+Oqnv+iTP7O4nJTktyX+Z1PebSY7r7ucM54cm+Y/dfa/ttarqd5IcOPQvTHJIVT2zqg5JsjjJkzLxQ/VTZvydAcCIdPe3MpFTPXRS2y+SXJzkRUlSVU9Nsrq7v5/kzCTnd/cTk/xNksnbgzwmyW91958k+U+Z+JnzoGHs54a/EP5/hzFPzsSq7T8ePrv/R5IXJDksyb8a5XsGfklIDWxSd9+V5JBM/Fb71iQXVNUrNxj2syTr9+lakWTBcPybST46HH94I7f4nSRLqmplJn6I3zsTP3gDwDapu6/OxGfhsUku3aB7jyQfHZ7N8M4kT5jU99nu/uGk8yu7+9tT3OJ3htfXk1yV5F9n4rPzsCQXdfdPuvvHSS6ZgbcDAFtTTdF2QZJjhuPFw3ky8fPm+p8zP5jkGZOu+Wh3rxuOfyvJ+9Z3dPePkjwtyeOTfGn4WfS4JL+eic/Ub3f3N7u7k3zofr8jYFrsSQ3cp+HD/YokV1TVNZn4AJ/s58MHeJKsy+Z9b6kkf9Tdn77fhQLA7HFJknckOTwTv4Bd70+TXN7dL6qqBZn4fF3vnzeYY8Pz9SrJf+3uv7pXY9Xrk/TUlwDA7FZVj8zEz5O3JHncpK5/SPLoqpqX5IVJ/mwjU0z+DJz8GVr51c/HysQvh4/doIaFU4wFtgIrqYFNqqrHVtXklc0Lk3xnmpd/JclLhuPFGxnz6SR/WFU7D/d7TFU9cIuKBYDZ49wkp3f3NRu075FfPkjxlVs496eTvHrS3pn7Dg+c+nySF1XVblW1eyb+VBkAZr0hgD4ryZmTFkAlSYbzi5L8RZJV3X3b0PXl/PLnzD9I8sWNTP+ZJK+ddK89M/Gz6tOr6tFD269V1WMysS3XAVX1qGH4sRtOBoyGkBq4Lw9Kcl5VXVdVV2fiT6LeMs1rX5+Jfb2uzMQDGO+YYsxfJ7kuyVXDnz7/VfyVBwDbuO5e093vnqLrvyX5r1X1pSRztnDuz2Tiz5v/YfgLpwuT7N7dV2XiT6BXJvlYki9sUfEAsHXsVlUrq+obSf4uE2HyWzcy9oIkL88vt/pIktcledXwc+orkpy8kWv/LMmeVXVtVf1jkmd3962Z+GXx0uH6ryT51919dya2uvxfw4MTp7tAC7ifaoNfUAHMmKr6tSQ/7e6uqsVJju3uo8ddFwAAAACzh9WKwCgdkuTMqqoktyd59ZjrAQAAAGCWsZIaAAAAAICxsSc1AAAAAABjI6QGAAAAAGBshNQAAAAAAIyNkBoAADZQVV1VH5x0Preqbq2qT2zmPI+oqguH44VVdeQ0rjl8/X2q6mFV9Ymq+sequq6qLh3aF1TV/zONuaY1DgAAxklIDQAAv+qfk/xGVe02nP92kps2Z4KqmtvdN3f3S4emhUnuM6TewOlJPtvdB3f345OcOrQvSDKd8Hm64wAAYGyE1AAAMLVPJvnd4fjYJEvXd1TVoVX15ar6+vD1sUP7K6vqo1X1P5N8ZljJfG1V7ZKJwPmYqlpZVcdsbI4NPDzJmvUn3X31cPi2JIcNc71huM8Xquqq4fVvNjLulVV15qT38Ylh5facqvrAUOs1VfWGmfknBACA+zZ33AUAAMAstSzJacPWG09Mcm6Sw4a+65M8s7vXVtVvJfkvSV4y9P1mkid29w+rakGSdPfPquq0JIu6+7VJUlUP3sQc670vyQVV9dokf5fk/d19cyZWVL+xu39vmOvXkvx2d99dVQdmIlBfNMW4V27kvS5Msm93/8Yw7iGb/88FAABbRkgNAABT6O6rh5D52CSXbtC9R5LzhkC4k+w8qe+z3f3DadxiU3Osr+HTVfXIJEckeX6Sr1fVb0wx185JzqyqhUnWJXnMNO4/2beSPLKq3pvkfyX5zGZeDwAAW8x2HwAAsHGXJHlHJm31MfjTJJcPK49fkGTXSX3/PM25NzXHPbr7h9394e5+RZKvJXnmFMPekOT7SQ7OxArqXTZyz7W5988Auw73+NFw7RVJTkry19N8DwAAcL8JqQEAYOPOTXJ6d1+zQfse+eWDFF85zbnuTLL75sxRVc8ZtvJIVe2e5FFJvruRub7X3b9I8ookczZyz9VJFlbVTlW1X5JDh7n3SbJTd38syX9K8uRpvicAALjfqrvHXcMW22effXrBggXjLgMAAAAAgE1YsWLFD7p73lR92/Se1AsWLMjy5cvHXQYAAAAAAJtQVd/ZWJ/tPgAAAAAAGBshNQAAAAAAYyOkBgAAAABgbLbpPakBAAAAALYnP//5z7NmzZrcfffd4y5li+y6666ZP39+dt5552lfI6QGAAAAAJgl1qxZk9133z0LFixIVY27nM3S3bntttuyZs2aHHDAAdO+TkgNAABs1CGnnD/uEthGrDhjybhLAIDtwt13371NBtRJUlXZe++9c+utt27WdfakBgAAAACYRbbFgHq9LaldSA0AAAAAwNgIqQEAAAAAtgEXXXRRqirXX399kmT16tX58Ic/fE//ypUrc+mll270+uXLl+d1r3tdkuSKK67Il7/85Xv6zjrrrJx//ni2ehNSAwAAAABsA5YuXZpnPOMZWbZsWZLNC6nXrl2bRYsW5T3veU+SXw2pTzzxxCxZMp5nTHhwIgAAAADALHfXXXflS1/6Ui6//PIcddRRectb3pJTTz01q1atysKFC3Psscfmfe97X37605/mi1/8Yt785jdn1apVufnmm7N69erss88+OeGEE/KOd7wjZ555Zs4666zMmTMnH/rQh/Le9743l112WR70oAfljW98Y1auXJkTTzwxP/nJT/KoRz0q5557bvbcc88cfvjheepTn5rLL788t99+e84555wcdthh9/u9WUkNAAAAADDLffzjH88RRxyRxzzmMdlrr71y1VVX5W1ve1sOO+ywrFy5Mm9605ty+umn55hjjsnKlStzzDHHJElWrFiRiy+++F4rrhcsWJATTzwxb3jDG7Jy5cpfCZqXLFmSt7/97bn66qtz0EEH5a1vfes9fWvXrs2VV16Zd73rXfdqvz+E1AAAAAAAs9zSpUuzePHiJMnixYuzdOnSaV131FFHZbfddpv2fe64447cfvvtedaznpUkOe644/L5z3/+nv4Xv/jFSZJDDjkkq1evnva8m2K7DwAAAACAWey2227L5z73uVx77bWpqqxbty5VlSOPPPI+r33gAx84o7U84AEPSJLMmTMna9eunZE5raQGAAAAAJjFLrzwwixZsiTf+c53snr16tx444054IADstNOO+XOO++8Z9zuu+9+r/NN2djYPfbYI3vuuWe+8IUvJEk++MEP3rOqelSE1AAAAAAAs9jSpUvzohe96F5tL3nJS7Js2bLMnTs3Bx98cN75znfm2c9+dq677rosXLgwF1xwwSbnfMELXpCLLrooCxcuvCeQXu+8887LKaeckic+8YlZuXJlTjvttBl/T5NVd4/0BqO0aNGiXr58+bjLAACA7dYhp5w/7hLYRqw4Y8m4SwCA7cKqVavyuMc9btxl3C9TvYeqWtHdi6YabyU1AAAAAABjI6QGAAAAAGBshNQAAAAAAIyNkBoAAAAAgLERUgMAAAAAMDZCagAAAAAAxmbuuAsAAAAAAGB6Djnl/Bmdb8UZS+5zzKc+9amcfPLJWbduXV7zmtfk1FNPndEarKQGAAAAAGBK69aty0knnZRPfvKTue6667J06dJcd911M3oPITUAAAAAAFO68sor8+hHPzqPfOQjs8suu2Tx4sW5+OKLZ/QeQmoAAAAAAKZ00003Zb/99rvnfP78+bnppptm9B4jDamr6iFVdWFVXV9Vq6rqN6tqr6r6bFV9c/i65zC2quo9VXVDVV1dVU8eZW0AAAAAAGxad/9KW1XN6D1GvZL63Uk+1d3/OsnBSVYlOTXJZd19YJLLhvMkeX6SA4fXCUn+csS1AQAAAACwCfPnz8+NN954z/maNWvyiEc8YkbvMbKQuqoenOSZSc5Jku7+WXffnuToJOcNw85L8sLh+Ogk5/eEryR5SFU9fFT1AQAAAACwaU95ylPyzW9+M9/+9rfzs5/9LMuWLctRRx01o/eYO6Oz3dsjk9ya5P1VdXCSFUlOTvKw7v5eknT396rqocP4fZPcOOn6NUPb90ZYIwAAAADANmPFGUu26v3mzp2bM888M8973vOybt26vPrVr84TnvCEmb3HjM72q3M/OckfdfdXq+rd+eXWHlOZaiOTX9nwpKpOyMR2INl///1nok4AAAAAADbiyCOPzJFHHjmy+Ue5J/WaJGu6+6vD+YWZCK2/v34bj+HrLZPG7zfp+vlJbt5w0u4+u7sXdfeiefPmjax4AAAAAABGb2QhdXf/3yQ3VtVjh6bnJrkuySVJjhvajkty8XB8SZIlNeFpSe5Yvy0IAAAAAADbp1Fu95Ekf5Tkb6pqlyTfSvKqTATjH6mq45N8N8nLhrGXJjkyyQ1JfjKMBQAAAABgOzbSkLq7VyZZNEXXc6cY20lOGmU9AAAAAADMLqPckxoAAAAAADZJSA0AAAAAwNiMek9qAAAAAABmyHdPP2hG59v/tGvuc8yrX/3qfOITn8hDH/rQXHvttTN6/8RKagAAAAAANuGVr3xlPvWpT41sfiE1AAAAAAAb9cxnPjN77bXXyOYXUgMAAAAAMDZCagAAAAAAxkZIDQAAAADA2AipAQAAAAAYm7njLgAAAAAAgOnZ/7Rrtvo9jz322FxxxRX5wQ9+kPnz5+etb31rjj/++BmbX0gNAAAAAMBGLV26dKTz2+4DAAAAAICxEVIDAAAAADA2QmoAAAAAgFmku8ddwhbbktqF1AAAAAAAs8Suu+6a2267bZsMqrs7t912W3bdddfNus6DEwEAAAAAZon58+dnzZo1ufXWW8ddyhbZddddM3/+/M26RkgNAAAAADBL7LzzzjnggAPGXcZWZbsPAAAAAADGRkgNAAAAAMDYCKkBAAAAABgbITUAAAAAAGMjpAYAAAAAYGymFVJX1WXTaQMAAAAAgM0xd1OdVbVrkl9Lsk9V7Zmkhq4HJ3nEiGsDAAAAAGA7t8mQOsm/S/L6TATSK/LLkPrHSd43wroAAAAAANgBbDKk7u53J3l3Vf1Rd793K9UEAAAAAMAO4r5WUidJuvu9VfVvkiyYfE13nz+iugAAAAAA2AFMK6Suqg8meVSSlUnWDc2dREgNAAAAAMAWm1ZInWRRksd3d2/uDapqTpLlSW7q7t+rqgOSLEuyV5Krkryiu39WVQ/IROh9SJLbkhzT3as3934AAAAAAGw7dprmuGuT/KstvMfJSVZNOn97knd294FJfpTk+KH9+CQ/6u5HJ3nnMA4AAAAAgO3YdEPqfZJcV1WfrqpL1r/u66Kqmp/kd5P89XBeSZ6T5MJhyHlJXjgcHz2cZ+h/7jAeAAAAAIDt1HS3+3jLFs7/riT/Psnuw/neSW7v7rXD+Zok+w7H+ya5MUm6e21V3TGM/8HkCavqhCQnJMn++++/hWUBAAAAADAbTCuk7u6/39yJq+r3ktzS3Suq6vD1zVNNP42+ybWcneTsJFm0aNFm75ENAAAAAMDsMa2QuqruzC8D412S7Jzkn7v7wZu47OlJjqqqI5PsmuTBmVhZ/ZCqmjuspp6f5OZh/Jok+yVZU1Vzk+yR5Ieb+X4AAAAAANiGTGtP6u7evbsfPLx2TfKSJGfexzVv7u753b0gyeIkn+vuP0hyeZKXDsOOS3LxcHzJcJ6h/3PdbaU0AAAAAMB2bLoPTryX7v54Jh6AuCXelOSPq+qGTOw5fc7Qfk6SvYf2P05y6hbODwAAAADANmK62328eNLpTkkWZYr9ojemu69IcsVw/K0kh04x5u4kL5vunAAAAAAAbPumFVInecGk47VJVic5esarAQAAAABghzKtkLq7XzXqQgAAAAAA2PFMa0/qqppfVRdV1S1V9f2q+lhVzR91cQAAAAAAbN+m++DE9ye5JMkjkuyb5H8ObQAAAAAAsMWmG1LP6+73d/fa4fWBJPNGWBcAAAAAADuA6YbUP6iql1fVnOH18iS3jbIwAAAAAAC2f9MNqV+d5PeT/N8k30vy0iQepggAAAAAwP0yd5rj/jTJcd39oySpqr2SvCMT4TUAAAAAAGyR6a6kfuL6gDpJuvuHSZ40mpIAAAAAANhRTDek3qmq9lx/Mqyknu4qbAAAAAAAmNJ0g+b/nuTLVXVhks7E/tR/PrKqAAAAAADYIUwrpO7u86tqeZLnJKkkL+7u60ZaGQBsxCGnnD/uEthGrDhjybhLAAAA4D5Me8uOIZQWTAMAAAAAMGOmuyc1AAAAAADMOCE1AAAAAABjI6QGAAAAAGBshNQAAAAAAIyNkBoAAAAAgLERUgMAAAAAMDZCagAAAAAAxkZIDQAAAADA2AipAQAAAAAYGyE1AAAAAABjI6QGAAAAAGBshNQAAAAAAIyNkBoAAAAAgLERUgMAAAAAMDYjC6mrar+quryqVlXVN6rq5KF9r6r6bFV9c/i659BeVfWeqrqhqq6uqiePqjYAAAAAAGaHUa6kXpvkT7r7cUmeluSkqnp8klOTXNbdBya5bDhPkucnOXB4nZDkL0dYGwAAAAAAs8DIQuru/l53XzUc35lkVZJ9kxyd5Lxh2HlJXjgcH53k/J7wlSQPqaqHj6o+AAAAAADGb6vsSV1VC5I8KclXkzysu7+XTATZSR46DNs3yY2TLlsztG041wlVtbyqlt96662jLBsAAAAAgBEbeUhdVQ9K8rEkr+/uH29q6BRt/SsN3Wd396LuXjRv3ryZKhMAAAAAgDEYaUhdVTtnIqD+m+7+26H5++u38Ri+3jK0r0my36TL5ye5eZT1AQAAAAAwXiMLqauqkpyTZFV3/8WkrkuSHDccH5fk4kntS2rC05LcsX5bEAAAAAAAtk9zRzj305O8Isk1VbVyaPsPSd6W5CNVdXyS7yZ52dB3aZIjk9yQ5CdJXjXC2gAAAAAAmAVGFlJ39xcz9T7TSfLcKcZ3kpNGVQ8AAAAAALPPyB+cCAAAAAAAGyOkBgAAAABgbITUAAAAAACMjZAaAAAAAICxEVIDAAAAADA2QmoAAAAAAMZGSA0AAAAAwNjMHXcBAAAAADu6Q045f9wlsA1ZccaScZcAM0pIDQAAwP323dMPGncJbEP2P+2acZcAwCxiuw8AAAAAAMZGSA0AAAAAwNgIqQEAAAAAGBshNQAAAAAAYyOkBgAAAABgbITUAAAAAACMjZAaAAAAAICxEVIDAAAAADA2QmoAAAAAAMZm7rgLAAAYle+eftC4S2Absv9p14y7BAAA2CFZSQ0AAAAAwNgIqQEAAAAAGBvbfcyQQ045f9wlsA1ZccaScZcAAAAAALOCldQAAAAAAIyNkBoAAAAAgLERUgMAAAAAMDZCagAAAAAAxsaDE2EMvnv6QeMugW3E/qddM+4SAAAAAEZq1q2krqojquqfquqGqjp13PUAAAAAADA6s2oldVXNSfK+JL+dZE2Sr1XVJd193XgrAwAAAIDZwV9oM13byl9oz7aV1IcmuaG7v9XdP0uyLMnRY64JAAAAAIARmW0h9b5Jbpx0vmZoAwAAAABgO1TdPe4a7lFVL0vyvO5+zXD+iiSHdvcfTRpzQpIThtPHJvmnrV4o3H/7JPnBuIsA2AH4fguw9fieC7D1+J7LtujXu3veVB2zak/qTKyc3m/S+fwkN08e0N1nJzl7axYFM62qlnf3onHXAbC98/0WYOvxPRdg6/E9l+3NbNvu42tJDqyqA6pqlySLk1wy5poAAAAAABiRWbWSurvXVtVrk3w6yZwk53b3N8ZcFgAAAAAAIzKrQuok6e5Lk1w67jpgxGxZA7B1+H4LsPX4nguw9fiey3ZlVj04EQAAAACAHcts25MaAAAAAIAdiJAatqKqOreqbqmqa8ddC8D2rKr2q6rLq2pVVX2jqk4ed00A26uq2rWqrqyqfxy+57513DUBbM+qak5Vfb2qPjHuWmCmCKlh6/pAkiPGXQTADmBtkj/p7scleVqSk6rq8WOuCWB79S9JntPdBydZmOSIqnramGsC2J6dnGTVuIuAmSSkhq2ouz+f5IfjrgNge9fd3+vuq4bjOzPxn/h9x1sVwPapJ9w1nO48vDz8CGAEqmp+kt9N8tfjrgVmkpAaANiuVdWCJE9K8tXxVgKw/Rr+9HxlkluSfLa7fc8FGI13Jfn3SX4x7kJgJgmpAYDtVlU9KMnHkry+u3887noAtlfdva67FyaZn+TQqvqNcdcEsL2pqt9Lckt3rxh3LTDThNQAwHapqnbORED9N939t+OuB2BH0N23J7kinsMCMApPT3JUVa1OsizJc6rqQ+MtCWaGkBoA2O5UVSU5J8mq7v6LcdcDsD2rqnlV9ZDheLckv5Xk+vFWBbD96e43d/f87l6QZHGSz3X3y8dcFswIITVsRVW1NMk/JHlsVa2pquPHXRPAdurpSV6RidUlK4fXkeMuCmA79fAkl1fV1Um+lok9qT8x5poAgG1IdXvoMgAAAAAA42ElNQAAAAAAYyOkBgAAAABgbITUAAAAAACMjZAaAAAAAICxEVIDAAAAADA2QmoAAHZoVXXXJvoOr6pPbKRvdVXtM8O1XFFVi2Zyzvurql5fVb827joAANh+CakBAIBNeX0SITUAACMjpAYAYIdXE86oqmur6pqqOmZS94Or6qKquq6qzqqqnTa4dkFVraqq/1FV36iqz1TVblX1uKq6coNxVw/Hz62qrw/3OreqHrDBnH9YVf9t0vkrq+q9w/HLq+rKqlpZVX9VVXOG9ruq6u1VtaKq/q6qDh1WZn+rqo4axswZ3ufXqurqqvp3Q/vhw9gLq+r6qvqb4d/kdUkekeTyqrp8Rv/RAQBgIKQGAIDkxUkWJjk4yW8lOaOqHj70HZrkT5IclORRw9gNHZjkfd39hCS3J3lJd69KsktVPXIYc0ySj1TVrkk+kOSY7j4oydwkf7jBfBducJ9jklxQVY8bjp/e3QuTrEvyB8OYBya5orsPSXJnkj9L8ttJXpTk9GHM8Unu6O6nJHlKkn9bVQcMfU/KxKrpxyd55HCP9yS5Ocmzu/vZm/j3AwCALSakBgCA5BlJlnb3uu7+fpK/z0SImyRXdve3untdkqXD2A19u7tXDscrkiwYjj+S5PeH42OSXJDkscP4/z20n5fkmZMn6+5bk3yrqp5WVXsP13wpyXOTHJLka1W1cjhfH4L/LMmnhuNrkvx9d/98OF5fz+8kWTJc+9Uke2ciYF//Ptd09y+SrJx0DQAAjNTcUU5eVaszsYpjXZK13b2oqvbKxH/OFyRCU/+4AAAY60lEQVRZneT3u/tHVVVJ3p3kyCQ/SfLK7r5qlPUBAMCgNtHX93GeJP8y6Xhdkt2G4wuSfLSq/jZJd/c3q2rhNGu6IBMB9/VJLuruHv7PfF53v3mK8T/v7vW1/WJ9Td39i6pa///+SvJH3f3pyRdW1eFTvIeR/qwAAADrbY2V1M/u7oXdvf4p5acmuay7D0xy2XCeJM/PxCqOA5OckOQvt0JtAACQJJ9PcsywZ/O8TKxsXr+f9KFVdcCwF/UxSb443Um7+/9kIvD9T5kInZOJ0HlBVT16OH9FJlZub+hvk7wwybGTrr0syUur6qFJUlV7VdWvT7eeJJ9O8odVtfNw/WOq6oH3cc2dSXbfjHsAAMBmqV8uthjB5BMrqRd19w8mtf1TksO7+3vDPn9XdPdjq+qvhuOlG47b2Pz77LNPL1iwYGT1AwAAAABw/61YseIH3T1vqr5R/wlfJ/lMVXWSv+rus5M8bH3wPATVDx3G7pvkxknXrhnaNhpSL1iwIMuXLx9N5QAAAAAAzIiq+s7G+kYdUj+9u28egujPVtX1mxg71T6Av7LMu6pOyMR2INl///1npkoAAAAAAMZipHtSd/fNw9dbklyU5NAk3x+2+cjw9ZZh+Jok+026fH6Sm6eY8+zuXtTdi+bNm3J1OAAAAAAA24iRhdRV9cCq2n39cZLfSXJtkkuSHDcMOy7JxcPxJUmW1ISnJbljU/tRAwAAAACw7Rvldh8PS3JRVa2/z4e7+1NV9bUkH6mq45N8N8nLhvGXJjkyyQ1JfpLkVSOsDQAAAABg1vn5z3+eNWvW5O677x53KVtk1113zfz587PzzjtP+5qRhdTd/a0kB0/RfluS507R3klOGlU9AAAAAACz3Zo1a7L77rtnwYIFGRYAbzO6O7fddlvWrFmTAw44YNrXjfrBiQDch0NOOX/cJXA/rDhjybhLAAAAYDty9913b5MBdZJUVfbee+/ceuutm3XdSB+cCAAAAADA5tkWA+r1tqR2ITUAAAAAAGMjpAYAAAAA2AZcdNFFqapcf/31SZLVq1fnwx/+8D39K1euzKWXXrrR65cvX57Xve51SZIrrrgiX/7yl+/pO+uss3L++ePZklRIDQAAAACwDVi6dGme8YxnZNmyZUk2L6Reu3ZtFi1alPe85z1JfjWkPvHEE7NkyXieu+TBiQAAAAAAs9xdd92VL33pS7n88stz1FFH5S1veUtOPfXUrFq1KgsXLsyxxx6b973vffnpT3+aL37xi3nzm9+cVatW5eabb87q1auzzz775IQTTsg73vGOnHnmmTnrrLMyZ86cfOhDH8p73/veXHbZZXnQgx6UN77xjVm5cmVOPPHE/OQnP8mjHvWonHvuudlzzz1z+OGH56lPfWouv/zy3H777TnnnHNy2GGH3e/3ZiU1AAAAAMAs9/GPfzxHHHFEHvOYx2SvvfbKVVddlbe97W057LDDsnLlyrzpTW/K6aefnmOOOSYrV67MMccckyRZsWJFLr744nutuF6wYEFOPPHEvOENb8jKlSt/JWhesmRJ3v72t+fqq6/OQQcdlLe+9a339K1duzZXXnll3vWud92r/f4QUgMAAAAAzHJLly7N4sWLkySLFy/O0qVLp3XdUUcdld12223a97njjjty++2351nPelaS5LjjjsvnP//5e/pf/OIXJ0kOOeSQrF69etrzbortPgAAAAAAZrHbbrstn/vc53LttdemqrJu3bpUVY488sj7vPaBD3zgjNbygAc8IEkyZ86crF27dkbmtJIaAAAAAGAWu/DCC7NkyZJ85zvfyerVq3PjjTfmgAMOyE477ZQ777zznnG77777vc43ZWNj99hjj+y55575whe+kCT54Ac/eM+q6lERUgMAAAAAzGJLly7Ni170onu1veQlL8myZcsyd+7cHHzwwXnnO9+ZZz/72bnuuuuycOHCXHDBBZuc8wUveEEuuuiiLFy48J5Aer3zzjsvp5xySp74xCdm5cqVOe2002b8PU1W3T3SG4zSokWLevny5eMuA+B+OeSU88ddAvfDijOWjLsEAAAAtiOrVq3K4x73uHGXcb9M9R6qakV3L5pqvJXUAAAAAACMjZAaAAAAAICxEVIDAAAAADA2QmoAAAAAAMZGSA0AAAAAwNgIqQEAAAAAGJu54y4AAAAAAIDpOeSU82d0vhVnLLnPMZ/61Kdy8sknZ926dXnNa16TU089dUZrGPlK6qqaU1Vfr6pPDOcHVNVXq+qbVXVBVe0ytD9gOL9h6F8w6toAAAAAANi4devW5aSTTsonP/nJXHfddVm6dGmuu+66Gb3H1tju4+Qkqyadvz3JO7v7wCQ/SnL80H58kh9196OTvHMYBwAAAADAmFx55ZV59KMfnUc+8pHZZZddsnjx4lx88cUzeo+RhtRVNT/J7yb56+G8kjwnyYXDkPOSvHA4Pno4z9D/3GE8AAAAAABjcNNNN2W//fa753z+/Pm56aabZvQeo15J/a4k/z7JL4bzvZPc3t1rh/M1SfYdjvdNcmOSDP13DOMBAAAAABiD7v6VtpleWzyyBydW1e8luaW7V1TV4eubpxja0+ibPO8JSU5Ikv33338GKp2+md6UnK1rOpvAAwAAAAC/NH/+/Nx44433nK9ZsyaPeMQjZvQeo1xJ/fQkR1XV6iTLMrHNx7uSPKSq1ofj85PcPByvSbJfkgz9eyT54YaTdvfZ3b2ouxfNmzdvhOUDAAAAAOzYnvKUp+Sb3/xmvv3tb+dnP/tZli1blqOOOmpG7zGyldTd/eYkb06SYSX1G7v7D6rqo0lemong+rgk63fZvmQ4/4eh/3M91VpyAAAAAIAd1NbeLWDu3Lk588wz87znPS/r1q3Lq1/96jzhCU+Y2XvM6GzT86Yky6rqz5J8Pck5Q/s5ST5YVTdkYgX14jHUBgAA/397dx+rZ1nfAfz7k1br8AUZxVUOHcQRoxuKUjsyF+LQGeycGCeiiYDKgkvQaGZ07iWouCUuZDpdjAsbKnUG4nyZjBmdQasxU3lRpBVmZI5gA1qor8yogf32x7k7j3B6+nDap/d5Tj+f5Mm57+u+nvt8zz93mm+uXjcAALDAli1bsmXLlqnd/6CU1N29Lcm24fibSTYvMucnSc48GHkAAAAAAFgZprknNQAAAAAALElJDQAAAADAaJTUAAAAAACMRkkNAAAAAMBolNQAAAAAAIxmzdgBAAAAAACYzG0XnXhA77fxwu37nPPyl788V111VY4++ujs2LHjgP7+xEpqAAAAAACW8NKXvjSf+MQnpnZ/JTUAAAAAAHt16qmn5sgjj5za/ZXUAAAAAACMRkkNAAAAAMBolNQAAAAAAIxGSQ0AAAAAwGjWjB0AAAAAAIDJbLxw+0H/nS9+8Yuzbdu23HXXXZmbm8ub3/zmnHfeeQfs/kpqAAAAAAD26vLLL5/q/W33AQAAAADAaJTUAAAAAACMRkkNAAAAALCCdPfYEZZtOdmV1AAAAAAAK8S6deuye/fumSyquzu7d+/OunXrHtD3vDgRAAAAAGCFmJuby86dO3PnnXeOHWVZ1q1bl7m5uQf0namV1FW1Lsnnkjxk+D0f6u43VtXxSa5IcmSSLyc5u7t/VlUPSbI1yclJdic5q7tvnVY+AAAAAICVZu3atTn++OPHjnFQTXO7j58mOa27n5TkpCSnV9UpSf46ydu7+4Qk30ty3jD/vCTf6+5fS/L2YR4AAAAAAKvY1Erqnnf3cLp2+HSS05J8aBi/LMnzhuMzhvMM159RVTWtfAAAAAAAjG+qL06sqsOq6oYku5J8Ksl/Jfl+d98zTNmZ5Jjh+Jgk30qS4foPkvzyNPMBAAAAADCuqZbU3X1vd5+UZC7J5iSPX2za8HOxVdP3e4VlVZ1fVddV1XWzunk4AAAAAADzplpS79Hd30+yLckpSY6oqj0vbJxLcvtwvDPJsUkyXH9kku8ucq9LuntTd29av379tKMDAAAAADBFE5XUVXX1JGP3ub6+qo4Yjh+a5JlJbk7ymSQvGKadm+Rjw/GVw3mG65/u7vutpAYAAAAAYPVYs9TFqlqX5JeSHFVVj8rPt+R4RJLH7OPeG5JcVlWHZb4M/2B3X1VVNyW5oqr+MslXklw6zL80yfur6pbMr6B+0XL+IAAAAAAAZseSJXWSVyR5TeYL6evz85L6h0netdQXu/vGJE9eZPybmd+f+r7jP0ly5r4jAwAAAACwWixZUnf3O5K8o6pe1d1/d5AyAQAAAABwiNjXSuokSXf/XVX9VpLjFn6nu7dOKRcAAAAAAIeAiUrqqnp/kscmuSHJvcNwJ1FSAwAAAACwbBOV1Ek2JXlCd/c0wwAAAAAAcGh50ITzdiT5lWkGAQAAAADg0DPpSuqjktxUVdck+emewe5+7lRSAQAAAABwSJi0pH7TNEMAAAAAAHBomqik7u7PTjsIAAAAAACHnolK6qr6UZI9L018cJK1Sf6nux8xrWAAAAAAAKx+k66kfvjC86p6XpLNU0kEAAAAAMAh40HL+VJ3/0uS0w5wFgAAAAAADjGTbvfx/AWnD0qyKT/f/gMAAAAAAJZlopI6ye8vOL4nya1JzjjgaQAAAAAAOKRMuif1y6YdBAAAAACAQ89Ee1JX1VxVfbSqdlXVd6rqw1U1N+1wAAAAAACsbpO+OPG9Sa5M8pgkxyT512EMAAAAAACWbdKSen13v7e77xk+70uyfoq5AAAAAAA4BExaUt9VVS+pqsOGz0uS7J5mMAAAAAAAVr9JS+qXJ3lhkm8nuSPJC5J4mSIAAAAAAPtl0pL6LUnO7e713X105kvrNy31hao6tqo+U1U3V9XXqurVw/iRVfWpqvrG8PNRw3hV1Tur6paqurGqnrIffxcAAAAAADNg0pL6id39vT0n3f3dJE/ex3fuSfLa7n58klOSXFBVT0jyhiRXd/cJSa4ezpPk2UlOGD7nJ3n3xH8FAAAAAAAzadKS+kF7Vjwn86uhk6xZ6gvdfUd3f3k4/lGSm5Mck+SMJJcN0y5L8rzh+IwkW3veF5McUVUbJv5LAAAAAACYOUsWzQv8TZL/qKoPJenM70/9V5P+kqo6LvMrr7+U5NHdfUcyX2RX1dHDtGOSfGvB13YOY3dM+nsAAAAAAJgtE5XU3b21qq5LclqSSvL87r5pku9W1cOSfDjJa7r7h1W116mL/epF7nd+5rcDycaNGyeJAAAAAADACjXpSuoMpfRExfQeVbU28wX1B7r7I8Pwd6pqw7CKekOSXcP4ziTHLvj6XJLbF8lxSZJLkmTTpk33K7EBAAAAAJgdk+5J/YDV/JLpS5Pc3N1vW3DpyiTnDsfnJvnYgvFzat4pSX6wZ1sQAAAAAABWp4lXUi/D05KcnWR7Vd0wjP1Zkrcm+WBVnZfktiRnDtc+nmRLkluS/DjJy6aYDQAAAACAFWBqJXV3fz6L7zOdJM9YZH4nuWBaeQAADiUnv27r2BHYD9dffM7YEQAA4KCZ2nYfAAAAAACwL0pqAAAAAABGo6QGAAAAAGA0SmoAAAAAAEajpAYAAAAAYDRKagAAAAAARqOkBgAAAABgNEpqAAAAAABGo6QGAAAAAGA0SmoAAAAAAEajpAYAAAAAYDRrxg4AB8ttF504dgSWaeOF28eOAAAAAMCUWEkNAAAAAMBolNQAAAAAAIxGSQ0AAAAAwGiU1AAAAAAAjEZJDQAAAADAaJTUAAAAAACMRkkNAAAAAMBolNQAAAAAAIxmaiV1Vb2nqnZV1Y4FY0dW1aeq6hvDz0cN41VV76yqW6rqxqp6yrRyAQAAAACwckxzJfX7kpx+n7E3JLm6u09IcvVwniTPTnLC8Dk/ybunmAsAAAAAgBVizbRu3N2fq6rj7jN8RpKnD8eXJdmW5E+G8a3d3Um+WFVHVNWG7r5jWvkAAAB4YE5+3daxI7Afrr/4nLEjAMCiDvae1I/eUzwPP48exo9J8q0F83YOY/dTVedX1XVVdd2dd9451bAAAAAAAEzXSnlxYi0y1otN7O5LuntTd29av379lGMBAAAAADBNB7uk/k5VbUiS4eeuYXxnkmMXzJtLcvtBzgYAAAAAwEF2sEvqK5OcOxyfm+RjC8bPqXmnJPmB/agBAAAAAFa/qb04saouz/xLEo+qqp1J3pjkrUk+WFXnJbktyZnD9I8n2ZLkliQ/TvKyaeUCAAAAAGDlmFpJ3d0v3sulZywyt5NcMK0sAAAAAACsTCvlxYkAAAAAAByClNQAAAAAAIxGSQ0AAAAAwGiU1AAAAAAAjGZqL04EAAAAgH05+XVbx47Afrj+4nPGjsAqYCU1AAAAAACjUVIDAAAAADAa230AwH647aITx47AMm28cPvYEQAAAIiV1AAAAAAAjEhJDQAAAADAaJTUAAAAAACMRkkNAAAAAMBolNQAAAAAAIxmzdgBAACAX3TbRSeOHYFl2njh9rEjAADMHCupAQAAAAAYjZIaAAAAAIDRKKkBAAAAABiNkhoAAAAAgNEoqQEAAAAAGM2KK6mr6vSq+npV3VJVbxg7DwAAAAAA07Nm7AALVdVhSd6V5HeT7ExybVVd2d03jZsMAAAAZtttF504dgSWaeOF28eOAHvl2TK7VtKzZaWtpN6c5Jbu/mZ3/yzJFUnOGDkTAAAAAABTUt09dob/V1UvSHJ6d//hcH52kt/s7lcumHN+kvOH08cl+fpBD8qsOirJXWOHAFYdzxZgGjxbgGnwbAGmwbOFSf1qd69f7MKK2u4jSS0y9gstendfkuSSgxOH1aSqruvuTWPnAFYXzxZgGjxbgGnwbAGmwbOFA2GlbfexM8mxC87nktw+UhYAAAAAAKZspZXU1yY5oaqOr6oHJ3lRkitHzgQAAAAAwJSsqO0+uvueqnplkk8mOSzJe7r7ayPHYvWwTQwwDZ4twDR4tgDT4NkCTINnC/ttRb04EQAAAACAQ8tK2+4DAAAAAIBDiJIaAAAAAIDRKKlZ9arqPVW1q6p2jJ0FWB2q6tiq+kxV3VxVX6uqV4+dCZh9VbWuqq6pqq8Oz5Y3j50JWD2q6rCq+kpVXTV2FmB1qKpbq2p7Vd1QVdeNnYfZZk9qVr2qOjXJ3Um2dvdvjJ0HmH1VtSHJhu7+clU9PMn1SZ7X3TeNHA2YYVVVSQ7v7ruram2Szyd5dXd/ceRowCpQVX+cZFOSR3T3c8bOA8y+qro1yabuvmvsLMw+K6lZ9br7c0m+O3YOYPXo7ju6+8vD8Y+S3JzkmHFTAbOu5909nK4dPlaUAPutquaS/F6Sfxw7CwAsRkkNAPuhqo5L8uQkXxo3CbAaDP8d/4Yku5J8qrs9W4AD4W+TvD7J/44dBFhVOsm/V9X1VXX+2GGYbUpqAFimqnpYkg8neU13/3DsPMDs6+57u/ukJHNJNleVrcqA/VJVz0myq7uvHzsLsOo8rbufkuTZSS4YtluFZVFSA8AyDPvFfjjJB7r7I2PnAVaX7v5+km1JTh85CjD7npbkucPesVckOa2q/mncSMBq0N23Dz93Jfloks3jJmKWKakB4AEaXm52aZKbu/ttY+cBVoeqWl9VRwzHD03yzCT/OW4qYNZ1959291x3H5fkRUk+3d0vGTkWMOOq6vDhJfKpqsOTPCvJjnFTMcuU1Kx6VXV5ki8keVxV7ayq88bOBMy8pyU5O/MrkW4YPlvGDgXMvA1JPlNVNya5NvN7Ul81ciYAgMU8Osnnq+qrSa5J8m/d/YmRMzHDqtsLwwEAAAAAGIeV1AAAAAAAjEZJDQAAAADAaJTUAAAAAACMRkkNAAAAAMBolNQAAAAAAIxGSQ0AAPupqu5e4trTq+qqvVy7taqOmlKmvf5eAABYSZTUAAAAAACMRkkNAAAHQM27uKp2VNX2qjprweVHVNVHq+qmqvr7qtrrv8Or6vCqek9VXVtVX6mqM4bxL1XVry+Yt62qTt7bfAAAmBVKagAAODCen+SkJE9K8swkF1fVhuHa5iSvTXJikscOc/fmz5N8urufmuR3hvscnuSKJC9MkuG+j+nu65eYDwAAM0FJDQAAB8ZvJ7m8u+/t7u8k+WySpw7Xrunub3b3vUkuH+buzbOSvKGqbkiyLcm6JBuTfDDJmcOcFyb5533MBwCAmbBm7AAAALBK1BLXeh/n973PH3T31+93oWp3VT0xyVlJXrHU/Kp69L4jAwDA+KykBgCAA+NzSc6qqsOqan2SU5NcM1zbXFXHD3tRn5Xk80vc55NJXlVVlSRV9eQF165I8vokj+zu7RPMBwCAFU9JDQAA+6Gq1iT5aZKPJrkxyVeTfDrJ67v728O0LyR5a5IdSf57mLvHjVW1c/i8LclbkqwdxncM53t8KMmLMr/1xx5LzQcAgBWvupf6n4YAAMBSqupJSf6huzePnQUAAGaRldQAALBMVfVHmX8R4l+MnQUAAGaVldQAAAAAAIzGSmoAAAAAAEajpAYAAAAAYDRKagAAAAAARqOkBgAAAABgNEpqAAAAAABG839jv8RI2+XqZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's visualize the interaction with other variables as well:\n",
    "plt.figure(figsize=(25,12))\n",
    "plt.subplot(411)\n",
    "sns.countplot(x = 'JobRole', hue ='Attrition', data = employee_df )\n",
    "plt.subplot(412)\n",
    "sns.countplot(x = 'MaritalStatus', hue ='Attrition', data = employee_df )\n",
    "plt.subplot(413)\n",
    "sns.countplot(x = 'JobInvolvement', hue ='Attrition', data = employee_df )\n",
    "plt.subplot(414)\n",
    "sns.countplot(x = 'JobLevel', hue ='Attrition', data = employee_df )\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# JobRole: Employees in the Role of SalesRepresenative have a high tendency to leave their position. The opposite happens with\n",
    "# Managers and Directors.\n",
    "# MaritalStatus: Employees who are single leave the company more frequently than others.\n",
    "# JobInvolvment: The higher the involvement, the smaller the chances that an employees leaves the company.\n",
    "# JobLevel: The lower the level, the higher the chances that an employee leaves the company."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.kdeplot(left_df['DistanceFromHome'], label=\"Employees that leave\", shade = True, color = 'r')\n",
    "sns.kdeplot(stayed_df['DistanceFromHome'], label=\"Employees that stay\", shade = True, color = 'b')\n",
    "plt.xlabel('Distance From Home')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.kdeplot(left_df['YearsWithCurrManager'], label=\"Employees that leave\", shade = True, color = 'r')\n",
    "sns.kdeplot(stayed_df['YearsWithCurrManager'], label=\"Employees that stay\", shade = True, color = 'b')\n",
    "plt.xlabel('Years with current manager')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.kdeplot(left_df['TotalWorkingYears'], label=\"Employees that leave\", shade = True, color = 'r')\n",
    "sns.kdeplot(stayed_df['TotalWorkingYears'], label=\"Employees that stay\", shade = True, color = 'b')\n",
    "plt.xlabel('Total Working Years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender vs Montly Income\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.boxplot('MonthlyIncome', 'Gender', data=employee_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Monthly Income vs JobRole\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.boxplot('MonthlyIncome', 'JobRole', data=employee_df)\n",
    "plt.show()\n",
    "\n",
    "# We previously notices that Sales Representative tend to leave the company in a high proportion. We now see that their salary \n",
    "# is low, when compared to other roles.\n",
    "\n",
    "# We also notices that Directors and Managers have a high tendence to stay. We can see that their salaries are high, when \n",
    "# compared to others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cat = employee_df[['BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole', 'MaritalStatus']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>MaritalStatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      BusinessTravel              Department EducationField  Gender  \\\n",
       "0      Travel_Rarely                   Sales  Life Sciences  Female   \n",
       "1  Travel_Frequently  Research & Development  Life Sciences    Male   \n",
       "2      Travel_Rarely  Research & Development          Other    Male   \n",
       "3  Travel_Frequently  Research & Development  Life Sciences  Female   \n",
       "4      Travel_Rarely  Research & Development        Medical    Male   \n",
       "\n",
       "                 JobRole MaritalStatus  \n",
       "0        Sales Executive        Single  \n",
       "1     Research Scientist       Married  \n",
       "2  Laboratory Technician        Single  \n",
       "3     Research Scientist       Married  \n",
       "4  Laboratory Technician       Married  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "onehotencoder = OneHotEncoder()\n",
    "X_cat = onehotencoder.fit_transform(X_cat).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 26)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>1470 rows × 26 columns</p>\n",
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      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9   ...   16   17   18  \\\n",
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       "3     0.0  1.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "4     0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  ...  1.0  0.0  0.0   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "1465  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  ...  1.0  0.0  0.0   \n",
       "1466  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0   \n",
       "1467  0.0  0.0  1.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  ...  0.0  0.0  1.0   \n",
       "1468  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0   \n",
       "1469  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  ...  1.0  0.0  0.0   \n",
       "\n",
       "       19   20   21   22   23   24   25  \n",
       "0     0.0  0.0  1.0  0.0  0.0  0.0  1.0  \n",
       "1     0.0  1.0  0.0  0.0  0.0  1.0  0.0  \n",
       "2     0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "3     0.0  1.0  0.0  0.0  0.0  1.0  0.0  \n",
       "4     0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "...   ...  ...  ...  ...  ...  ...  ...  \n",
       "1465  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "1466  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "1467  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "1468  0.0  0.0  1.0  0.0  0.0  1.0  0.0  \n",
       "1469  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "\n",
       "[1470 rows x 26 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cat = pd.DataFrame(X_cat)\n",
    "X_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Age  DailyRate  DistanceFromHome  Education  EnvironmentSatisfaction  \\\n",
       "0   41       1102                 1          2                        2   \n",
       "1   49        279                 8          1                        3   \n",
       "2   37       1373                 2          2                        4   \n",
       "3   33       1392                 3          4                        4   \n",
       "4   27        591                 2          1                        1   \n",
       "\n",
       "   HourlyRate  JobInvolvement  JobLevel  JobSatisfaction  MonthlyIncome  ...  \\\n",
       "0          94               3         2                4           5993  ...   \n",
       "1          61               2         2                2           5130  ...   \n",
       "2          92               2         1                3           2090  ...   \n",
       "3          56               3         1                3           2909  ...   \n",
       "4          40               3         1                2           3468  ...   \n",
       "\n",
       "   PerformanceRating  RelationshipSatisfaction  StockOptionLevel  \\\n",
       "0                  3                         1                 0   \n",
       "1                  4                         4                 1   \n",
       "2                  3                         2                 0   \n",
       "3                  3                         3                 0   \n",
       "4                  3                         4                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0                1               6   \n",
       "1                 10                      3                3              10   \n",
       "2                  7                      3                3               0   \n",
       "3                  8                      3                3               8   \n",
       "4                  6                      3                3               2   \n",
       "\n",
       "   YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                   4                        0                     5  \n",
       "1                   7                        1                     7  \n",
       "2                   0                        0                     0  \n",
       "3                   7                        3                     0  \n",
       "4                   2                        2                     2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We put here all the numerical variables, with the exception of Attrition (that is our target)\n",
    "X_numerical = employee_df[['Age', 'DailyRate', 'DistanceFromHome',\t'Education', 'EnvironmentSatisfaction', 'HourlyRate', 'JobInvolvement',\t'JobLevel',\t'JobSatisfaction',\t'MonthlyIncome',\t'MonthlyRate',\t'NumCompaniesWorked',\t'OverTime',\t'PercentSalaryHike', 'PerformanceRating',\t'RelationshipSatisfaction',\t'StockOptionLevel',\t'TotalWorkingYears'\t,'TrainingTimesLastYear'\t, 'WorkLifeBalance',\t'YearsAtCompany'\t,'YearsInCurrentRole', 'YearsSinceLastPromotion',\t'YearsWithCurrManager']]\n",
    "X_numerical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
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       "      <th>YearsWithCurrManager</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>7</td>\n",
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       "      <td>7</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>7</td>\n",
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       "      <th>4</th>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9  ...  PerformanceRating  \\\n",
       "0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  ...                  3   \n",
       "1  0.0  1.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  ...                  4   \n",
       "2  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  ...                  3   \n",
       "3  0.0  1.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  ...                  3   \n",
       "4  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  ...                  3   \n",
       "\n",
       "   RelationshipSatisfaction  StockOptionLevel  TotalWorkingYears  \\\n",
       "0                         1                 0                  8   \n",
       "1                         4                 1                 10   \n",
       "2                         2                 0                  7   \n",
       "3                         3                 0                  8   \n",
       "4                         4                 1                  6   \n",
       "\n",
       "   TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                      0                1               6                   4   \n",
       "1                      3                3              10                   7   \n",
       "2                      3                3               0                   0   \n",
       "3                      3                3               8                   7   \n",
       "4                      3                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We create a new dataset, with our numerical variables + the categorical variables converted into numerical.\n",
    "X_all = pd.concat([X_cat, X_numerical], axis = 1)\n",
    "X_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will use MinMaxScaler, to normalize our data.\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(X_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X will contain our normalized predicting variables.\n",
    "# y will contain our target variable.\n",
    "\n",
    "y = employee_df['Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train = pd.DataFrame(X_train)\n",
    "#X_test  = pd.DataFrame(X_test)\n",
    "#y_train = pd.DataFrame(y_train)\n",
    "#y_test  = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train.to_csv('X_train.csv')\n",
    "#X_test.to_csv('X_test.csv')\n",
    "#y_train.to_csv('y_train.csv')\n",
    "#y_test.to_csv('y_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train = np.array(X_train)\n",
    "#X_test = np.array(X_train)\n",
    "#y_train = np.array(y_train)\n",
    "#y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1102, 50)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(368, 50)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models Creation\n",
    "\n",
    "We will now proceed to create three models.\n",
    "- A Logistic Regression Model\n",
    "- A Random Forest Model\n",
    "- A Deep Learning Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>303</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0        0   1\n",
       "Attrition         \n",
       "0          303   3\n",
       "1           44  18"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Logistic_Crosstab = pd.crosstab(y_test, y_pred)\n",
    "Logistic_Crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 87.22826086956522 %\n",
      "Precision 85.71428571428571 %\n",
      "Recall 29.03225806451613 %\n",
      "F1Score 43.373493975903614 %\n"
     ]
    }
   ],
   "source": [
    "Accuracy  = (Logistic_Crosstab[0][0] + Logistic_Crosstab[1][1]) / len(y_test)\n",
    "Precision = (Logistic_Crosstab[1][1]) /  (Logistic_Crosstab[1][1] + Logistic_Crosstab[1][0])\n",
    "Recall    = (Logistic_Crosstab[1][1]) /  (Logistic_Crosstab[1][1] + Logistic_Crosstab[0][1])\n",
    "F1Score   = 2*(Precision*Recall) / (Precision+Recall)\n",
    "\n",
    "print(\"Accuracy {} %\".format(100*Accuracy))\n",
    "print(\"Precision {} %\".format(100*Precision))\n",
    "print(\"Recall {} %\".format(100*Recall))\n",
    "print(\"F1Score {} %\".format(100*F1Score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Model Finetuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will try to improve our model, by using a threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test2 = pd.DataFrame(y_test)\n",
    "y_test2 = pd.DataFrame.reset_index(y_test2) \n",
    "probs = model.predict_proba(X_test)\n",
    "prob = probs[:,1]\n",
    "prob_df = pd.DataFrame(prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When Threshold equals 0.1, F1Score equals 0.4317180616740088\n",
      "When Threshold equals 0.2, F1Score equals 0.5276073619631902\n",
      "When Threshold equals 0.3, F1Score equals 0.592\n",
      "When Threshold equals 0.33, F1Score equals 0.5811965811965812\n",
      "When Threshold equals 0.35, F1Score equals 0.5840707964601771\n",
      "When Threshold equals 0.38, F1Score equals 0.5636363636363636\n",
      "When Threshold equals 0.4, F1Score equals 0.5688073394495413\n",
      "When Threshold equals 0.5, F1Score equals 0.43373493975903615\n",
      "When Threshold equals 0.6, F1Score equals 0.2972972972972973\n",
      "When Threshold equals 0.7, F1Score equals 0.22535211267605634\n"
     ]
    }
   ],
   "source": [
    "threshold = [0.1, 0.2, 0.3, 0.33, 0.35, 0.38, 0.4, 0.5, 0.6, 0.7]\n",
    "Recall = [1]\n",
    "Precision = [1]\n",
    "F1Score = [1]\n",
    "\n",
    "for t in threshold:\n",
    "    prob_df[\"prediction\"] = np.where(prob_df[0]>= t, 1, 0)\n",
    "    prob_df[\"actual\"] = y_test2['Attrition']\n",
    "    confusion_matrix = pd.crosstab(prob_df.prediction, prob_df.actual)\n",
    "    TN = confusion_matrix[0][0]\n",
    "    TP = confusion_matrix[1][1]\n",
    "    FP = confusion_matrix[0][1]\n",
    "    FN = confusion_matrix[1][0]\n",
    "    rec = TP / (TP+FN)\n",
    "    pre_1= 1-(TP/(TP+FP))\n",
    "    pre = TP/(TP+FP)\n",
    "    f1 = 2*(pre*rec) / (pre+rec)\n",
    "    Recall.append(rec)\n",
    "    Precision.append(pre_1)\n",
    "    F1Score.append(f1)\n",
    "    print(\"When Threshold equals \" + str(t) + \", F1Score equals \" + str(f1))\n",
    "\n",
    "Recall.append(0)\n",
    "Precision.append(0)\n",
    "F1Score.append(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 86.1413043478261 %\n",
      "Precision 58.730158730158735 %\n",
      "Recall 59.67741935483871 %\n",
      "F1Score 59.199999999999996 %\n"
     ]
    }
   ],
   "source": [
    "prob_df = pd.DataFrame(prob)\n",
    "threshold = 0.30\n",
    "prob_df[\"prediction\"] = np.where(prob_df[0]>= threshold, 1, 0)\n",
    "prob_df['actual'] = y_test2['Attrition']\n",
    "Logistic_Crosstab = pd.crosstab(prob_df['actual'], prob_df[\"prediction\"])\n",
    "Accuracy  = (Logistic_Crosstab[0][0] + Logistic_Crosstab[1][1]) / len(prob_df)\n",
    "Precision = (Logistic_Crosstab[1][1]) /  (Logistic_Crosstab[1][1] + Logistic_Crosstab[1][0])\n",
    "Recall    = (Logistic_Crosstab[1][1]) /  (Logistic_Crosstab[1][1] + Logistic_Crosstab[0][1])\n",
    "F1Score   = 2*(Precision*Recall) / (Precision+Recall)\n",
    "print(\"Accuracy {} %\".format(100*Accuracy))\n",
    "print(\"Precision {} %\".format(100*Precision))\n",
    "print(\"Recall {} %\".format(100*Recall))\n",
    "print(\"F1Score {} %\".format(100*F1Score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 84.51086956521739 %\n",
      "Precision 77.77777777777779 %\n",
      "Recall 11.29032258064516 %\n",
      "F1Score 19.718309859154928 %\n"
     ]
    }
   ],
   "source": [
    "Forest_Crosstab = pd.crosstab(y_test, y_pred)\n",
    "Accuracy  = (Forest_Crosstab[0][0] + Forest_Crosstab[1][1]) / len(X_test)\n",
    "Precision = (Forest_Crosstab[1][1]) /  (Forest_Crosstab[1][1] + Forest_Crosstab[1][0])\n",
    "Recall    = (Forest_Crosstab[1][1]) /  (Forest_Crosstab[1][1] + Forest_Crosstab[0][1])\n",
    "F1Score   = 2*(Precision*Recall) / (Precision+Recall)\n",
    "print(\"Accuracy {} %\".format(100*Accuracy))\n",
    "print(\"Precision {} %\".format(100*Precision))\n",
    "print(\"Recall {} %\".format(100*Recall))\n",
    "print(\"F1Score {} %\".format(100*F1Score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>304</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0        0  1\n",
       "Attrition        \n",
       "0          304  2\n",
       "1           55  7"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Forest_Crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see, for all the KPI's our results are worse than when using a Logistic Regression.\n",
    "# Let's check what happens if we increase the number of trees used (n_estimators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Model Finetuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of estimators: 500. F1Score: 19.444444444444446 %\n",
      "Number of estimators: 1000. F1Score: 22.22222222222222 %\n",
      "Number of estimators: 2500. F1Score: 24.657534246575345 %\n",
      "Number of estimators: 5000. F1Score: 19.718309859154928 %\n",
      "Number of estimators: 10000. F1Score: 19.718309859154928 %\n",
      "Number of estimators: 15000. F1Score: 19.718309859154928 %\n",
      "Number of estimators: 20000. F1Score: 19.718309859154928 %\n"
     ]
    }
   ],
   "source": [
    "estimators = [500, 1000, 2500, 5000, 10000, 15000, 20000]\n",
    "for e in estimators:\n",
    "    model = RandomForestClassifier(n_estimators = e)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    Forest_Crosstab = pd.crosstab(y_test, y_pred)\n",
    "    Accuracy  = (Forest_Crosstab[0][0] + Forest_Crosstab[1][1]) / len(X_test)\n",
    "    Precision = (Forest_Crosstab[1][1]) /  (Forest_Crosstab[1][1] + Forest_Crosstab[1][0])\n",
    "    Recall    = (Forest_Crosstab[1][1]) /  (Forest_Crosstab[1][1] + Forest_Crosstab[0][1])\n",
    "    F1Score   = 2*(Precision*Recall) / (Precision+Recall)\n",
    "    print(\"Number of estimators: \" + str(e) + \". F1Score: {} %\".format(100*F1Score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see above, our model is stabilized after a certain amount of trees is used. \n",
    "# However, using that quantity does not affect the predicting capabilities of our model.\n",
    "# Let's finally use a Random Tree Cross Validation Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Tree Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = KFold(n_splits=10, shuffle=True, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.79591837, 0.85714286, 0.86394558, 0.82993197, 0.86394558,\n",
       "       0.85034014, 0.8707483 , 0.85034014, 0.82993197, 0.81632653])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(tree, X, y, scoring=\"accuracy\", cv = cv, n_jobs=1)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8428571428571427"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = np.mean(scores)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score para i =  1  es de  0.8387755102040815\n",
      "Score para i =  2  es de  0.8414965986394559\n",
      "Score para i =  3  es de  0.8428571428571427\n",
      "Score para i =  4  es de  0.8401360544217686\n",
      "Score para i =  5  es de  0.8421768707482993\n",
      "Score para i =  6  es de  0.8238095238095239\n",
      "Score para i =  7  es de  0.8163265306122449\n",
      "Score para i =  8  es de  0.8156462585034013\n",
      "Score para i =  9  es de  0.8108843537414966\n",
      "Score para i =  10  es de  0.8013605442176871\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,11):\n",
    "    tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=i, min_samples_split=5, random_state=99)\n",
    "    tree.fit(X,y)\n",
    "    cv = KFold(n_splits=10, shuffle=True, random_state=1)\n",
    "    scores = cross_val_score(tree, X, y, scoring=\"accuracy\", cv = cv, n_jobs=1)\n",
    "    score = np.mean(scores)\n",
    "    print(\"Score para i = \",i,\" es de \", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
       "                       max_depth=3, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=5,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=99, splitter='best')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3, min_samples_split=5, random_state=99)\n",
    "tree.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = tree.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "CV_crosstab = pd.crosstab(y,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 85.85034013605443 %\n",
      "Precision 67.05882352941175 %\n",
      "Recall 24.050632911392405 %\n",
      "F1Score 35.40372670807453 %\n"
     ]
    }
   ],
   "source": [
    "Accuracy  = (CV_crosstab[0][0] + CV_crosstab[1][1]) / len(y_preds)\n",
    "Precision = (CV_crosstab[1][1]) /  (CV_crosstab[1][1] + CV_crosstab[1][0])\n",
    "Recall    = (CV_crosstab[1][1]) /  (CV_crosstab[1][1] + CV_crosstab[0][1])\n",
    "F1Score   = 2*(Precision*Recall) / (Precision+Recall)\n",
    "print(\"Accuracy {} %\".format(100*Accuracy))\n",
    "print(\"Precision {} %\".format(100*Precision))\n",
    "print(\"Recall {} %\".format(100*Recall))\n",
    "print(\"F1Score {} %\".format(100*F1Score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1205</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>180</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0         0   1\n",
       "Attrition          \n",
       "0          1205  28\n",
       "1           180  57"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CV_crosstab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Dense(units = 500, activation='relu', input_shape=(50, )))\n",
    "model.add(tf.keras.layers.Dense(units = 500, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(units = 500, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(units = 1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 500)               25500     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 500)               250500    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 500)               250500    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 501       \n",
      "=================================================================\n",
      "Total params: 527,001\n",
      "Trainable params: 527,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='Adam', loss = 'binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1102 samples\n",
      "Epoch 1/100\n",
      "1102/1102 [==============================] - 0s 262us/sample - loss: 0.4237 - acc: 0.8094\n",
      "Epoch 2/100\n",
      "1102/1102 [==============================] - 0s 189us/sample - loss: 0.3388 - acc: 0.8666\n",
      "Epoch 3/100\n",
      "1102/1102 [==============================] - 0s 180us/sample - loss: 0.3148 - acc: 0.8766\n",
      "Epoch 4/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 0.2813 - acc: 0.8956\n",
      "Epoch 5/100\n",
      "1102/1102 [==============================] - 0s 169us/sample - loss: 0.2475 - acc: 0.9083\n",
      "Epoch 6/100\n",
      "1102/1102 [==============================] - 0s 158us/sample - loss: 0.2333 - acc: 0.9111\n",
      "Epoch 7/100\n",
      "1102/1102 [==============================] - 0s 169us/sample - loss: 0.1905 - acc: 0.9292\n",
      "Epoch 8/100\n",
      "1102/1102 [==============================] - 0s 169us/sample - loss: 0.1661 - acc: 0.9392\n",
      "Epoch 9/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 0.1475 - acc: 0.9456\n",
      "Epoch 10/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 0.1165 - acc: 0.9537\n",
      "Epoch 11/100\n",
      "1102/1102 [==============================] - 0s 178us/sample - loss: 0.0966 - acc: 0.9628\n",
      "Epoch 12/100\n",
      "1102/1102 [==============================] - 0s 161us/sample - loss: 0.0863 - acc: 0.9719\n",
      "Epoch 13/100\n",
      "1102/1102 [==============================] - 0s 176us/sample - loss: 0.0860 - acc: 0.9673\n",
      "Epoch 14/100\n",
      "1102/1102 [==============================] - 0s 169us/sample - loss: 0.1473 - acc: 0.9456\n",
      "Epoch 15/100\n",
      "1102/1102 [==============================] - 0s 165us/sample - loss: 0.0819 - acc: 0.9728\n",
      "Epoch 16/100\n",
      "1102/1102 [==============================] - 0s 161us/sample - loss: 0.0390 - acc: 0.9855\n",
      "Epoch 17/100\n",
      "1102/1102 [==============================] - 0s 169us/sample - loss: 0.0153 - acc: 0.9982\n",
      "Epoch 18/100\n",
      "1102/1102 [==============================] - 0s 165us/sample - loss: 0.0086 - acc: 0.9991\n",
      "Epoch 19/100\n",
      "1102/1102 [==============================] - 0s 174us/sample - loss: 0.0083 - acc: 0.9982\n",
      "Epoch 20/100\n",
      "1102/1102 [==============================] - 0s 168us/sample - loss: 0.0118 - acc: 0.9973\n",
      "Epoch 21/100\n",
      "1102/1102 [==============================] - 0s 162us/sample - loss: 0.0129 - acc: 0.9955\n",
      "Epoch 22/100\n",
      "1102/1102 [==============================] - 0s 157us/sample - loss: 0.0057 - acc: 1.0000\n",
      "Epoch 23/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 0.0102 - acc: 0.9973\n",
      "Epoch 24/100\n",
      "1102/1102 [==============================] - 0s 186us/sample - loss: 0.1579 - acc: 0.9437\n",
      "Epoch 25/100\n",
      "1102/1102 [==============================] - 0s 176us/sample - loss: 0.2104 - acc: 0.9220\n",
      "Epoch 26/100\n",
      "1102/1102 [==============================] - 0s 165us/sample - loss: 0.1032 - acc: 0.9664\n",
      "Epoch 27/100\n",
      "1102/1102 [==============================] - 0s 166us/sample - loss: 0.0343 - acc: 0.9900\n",
      "Epoch 28/100\n",
      "1102/1102 [==============================] - 0s 191us/sample - loss: 0.0262 - acc: 0.9891\n",
      "Epoch 29/100\n",
      "1102/1102 [==============================] - 0s 196us/sample - loss: 0.0070 - acc: 1.0000\n",
      "Epoch 30/100\n",
      "1102/1102 [==============================] - 0s 181us/sample - loss: 0.0033 - acc: 1.0000\n",
      "Epoch 31/100\n",
      "1102/1102 [==============================] - 0s 172us/sample - loss: 0.0014 - acc: 1.0000\n",
      "Epoch 32/100\n",
      "1102/1102 [==============================] - 0s 165us/sample - loss: 0.0012 - acc: 1.0000\n",
      "Epoch 33/100\n",
      "1102/1102 [==============================] - 0s 165us/sample - loss: 7.6565e-04 - acc: 1.0000\n",
      "Epoch 34/100\n",
      "1102/1102 [==============================] - 0s 166us/sample - loss: 5.6660e-04 - acc: 1.0000\n",
      "Epoch 35/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 4.4673e-04 - acc: 1.0000\n",
      "Epoch 36/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 3.7120e-04 - acc: 1.0000\n",
      "Epoch 37/100\n",
      "1102/1102 [==============================] - 0s 178us/sample - loss: 3.2102e-04 - acc: 1.0000\n",
      "Epoch 38/100\n",
      "1102/1102 [==============================] - 0s 165us/sample - loss: 2.7166e-04 - acc: 1.0000\n",
      "Epoch 39/100\n",
      "1102/1102 [==============================] - 0s 170us/sample - loss: 2.3325e-04 - acc: 1.0000\n",
      "Epoch 40/100\n",
      "1102/1102 [==============================] - 0s 168us/sample - loss: 2.0423e-04 - acc: 1.0000\n",
      "Epoch 41/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 1.8022e-04 - acc: 1.0000\n",
      "Epoch 42/100\n",
      "1102/1102 [==============================] - 0s 176us/sample - loss: 1.6041e-04 - acc: 1.0000\n",
      "Epoch 43/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 1.4396e-04 - acc: 1.0000\n",
      "Epoch 44/100\n",
      "1102/1102 [==============================] - 0s 163us/sample - loss: 1.2875e-04 - acc: 1.0000\n",
      "Epoch 45/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 1.1668e-04 - acc: 1.0000\n",
      "Epoch 46/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 1.1506e-04 - acc: 1.0000\n",
      "Epoch 47/100\n",
      "1102/1102 [==============================] - 0s 166us/sample - loss: 9.5374e-05 - acc: 1.0000\n",
      "Epoch 48/100\n",
      "1102/1102 [==============================] - 0s 160us/sample - loss: 8.4435e-05 - acc: 1.0000\n",
      "Epoch 49/100\n",
      "1102/1102 [==============================] - 0s 191us/sample - loss: 7.7760e-05 - acc: 1.0000\n",
      "Epoch 50/100\n",
      "1102/1102 [==============================] - 0s 235us/sample - loss: 7.0782e-05 - acc: 1.0000\n",
      "Epoch 51/100\n",
      "1102/1102 [==============================] - 0s 172us/sample - loss: 6.7928e-05 - acc: 1.0000\n",
      "Epoch 52/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 6.1176e-05 - acc: 1.0000\n",
      "Epoch 53/100\n",
      "1102/1102 [==============================] - 0s 160us/sample - loss: 7.2174e-05 - acc: 1.0000\n",
      "Epoch 54/100\n",
      "1102/1102 [==============================] - 0s 160us/sample - loss: 8.7645e-05 - acc: 1.0000\n",
      "Epoch 55/100\n",
      "1102/1102 [==============================] - 0s 162us/sample - loss: 4.9496e-05 - acc: 1.0000\n",
      "Epoch 56/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 4.1214e-05 - acc: 1.0000\n",
      "Epoch 57/100\n",
      "1102/1102 [==============================] - 0s 179us/sample - loss: 3.8062e-05 - acc: 1.0000\n",
      "Epoch 58/100\n",
      "1102/1102 [==============================] - 0s 162us/sample - loss: 3.6033e-05 - acc: 1.0000\n",
      "Epoch 59/100\n",
      "1102/1102 [==============================] - 0s 161us/sample - loss: 3.3110e-05 - acc: 1.0000\n",
      "Epoch 60/100\n",
      "1102/1102 [==============================] - 0s 192us/sample - loss: 3.1205e-05 - acc: 1.0000\n",
      "Epoch 61/100\n",
      "1102/1102 [==============================] - 0s 161us/sample - loss: 2.9378e-05 - acc: 1.0000\n",
      "Epoch 62/100\n",
      "1102/1102 [==============================] - 0s 165us/sample - loss: 2.7633e-05 - acc: 1.0000\n",
      "Epoch 63/100\n",
      "1102/1102 [==============================] - 0s 181us/sample - loss: 2.6073e-05 - acc: 1.0000\n",
      "Epoch 64/100\n",
      "1102/1102 [==============================] - 0s 162us/sample - loss: 2.4636e-05 - acc: 1.0000\n",
      "Epoch 65/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 2.3199e-05 - acc: 1.0000\n",
      "Epoch 66/100\n",
      "1102/1102 [==============================] - 0s 172us/sample - loss: 2.1941e-05 - acc: 1.0000\n",
      "Epoch 67/100\n",
      "1102/1102 [==============================] - 0s 169us/sample - loss: 2.0857e-05 - acc: 1.0000\n",
      "Epoch 68/100\n",
      "1102/1102 [==============================] - 0s 157us/sample - loss: 1.9798e-05 - acc: 1.0000\n",
      "Epoch 69/100\n",
      "1102/1102 [==============================] - 0s 164us/sample - loss: 1.9072e-05 - acc: 1.0000\n",
      "Epoch 70/100\n",
      "1102/1102 [==============================] - 0s 164us/sample - loss: 1.7935e-05 - acc: 1.0000\n",
      "Epoch 71/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 1.7117e-05 - acc: 1.0000\n",
      "Epoch 72/100\n",
      "1102/1102 [==============================] - 0s 178us/sample - loss: 1.6307e-05 - acc: 1.0000\n",
      "Epoch 73/100\n",
      "1102/1102 [==============================] - 0s 163us/sample - loss: 1.5583e-05 - acc: 1.0000\n",
      "Epoch 74/100\n",
      "1102/1102 [==============================] - 0s 164us/sample - loss: 1.4908e-05 - acc: 1.0000\n",
      "Epoch 75/100\n",
      "1102/1102 [==============================] - 0s 160us/sample - loss: 1.4232e-05 - acc: 1.0000\n",
      "Epoch 76/100\n",
      "1102/1102 [==============================] - 0s 176us/sample - loss: 1.3651e-05 - acc: 1.0000\n",
      "Epoch 77/100\n",
      "1102/1102 [==============================] - 0s 166us/sample - loss: 1.3015e-05 - acc: 1.0000\n",
      "Epoch 78/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1102/1102 [==============================] - 0s 167us/sample - loss: 1.2702e-05 - acc: 1.0000\n",
      "Epoch 79/100\n",
      "1102/1102 [==============================] - 0s 159us/sample - loss: 1.2089e-05 - acc: 1.0000\n",
      "Epoch 80/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 1.1436e-05 - acc: 1.0000\n",
      "Epoch 81/100\n",
      "1102/1102 [==============================] - 0s 151us/sample - loss: 1.1202e-05 - acc: 1.0000\n",
      "Epoch 82/100\n",
      "1102/1102 [==============================] - 0s 164us/sample - loss: 1.0454e-05 - acc: 1.0000\n",
      "Epoch 83/100\n",
      "1102/1102 [==============================] - 0s 161us/sample - loss: 1.0109e-05 - acc: 1.0000\n",
      "Epoch 84/100\n",
      "1102/1102 [==============================] - 0s 157us/sample - loss: 9.5568e-06 - acc: 1.0000\n",
      "Epoch 85/100\n",
      "1102/1102 [==============================] - 0s 171us/sample - loss: 9.1753e-06 - acc: 1.0000\n",
      "Epoch 86/100\n",
      "1102/1102 [==============================] - 0s 157us/sample - loss: 8.9437e-06 - acc: 1.0000\n",
      "Epoch 87/100\n",
      "1102/1102 [==============================] - 0s 162us/sample - loss: 8.2657e-06 - acc: 1.0000\n",
      "Epoch 88/100\n",
      "1102/1102 [==============================] - 0s 166us/sample - loss: 8.2666e-06 - acc: 1.0000\n",
      "Epoch 89/100\n",
      "1102/1102 [==============================] - 0s 163us/sample - loss: 7.8250e-06 - acc: 1.0000\n",
      "Epoch 90/100\n",
      "1102/1102 [==============================] - 0s 175us/sample - loss: 7.4980e-06 - acc: 1.0000\n",
      "Epoch 91/100\n",
      "1102/1102 [==============================] - 0s 157us/sample - loss: 7.2911e-06 - acc: 1.0000\n",
      "Epoch 92/100\n",
      "1102/1102 [==============================] - 0s 155us/sample - loss: 6.9831e-06 - acc: 1.0000\n",
      "Epoch 93/100\n",
      "1102/1102 [==============================] - 0s 169us/sample - loss: 6.6929e-06 - acc: 1.0000\n",
      "Epoch 94/100\n",
      "1102/1102 [==============================] - 0s 161us/sample - loss: 6.4635e-06 - acc: 1.0000\n",
      "Epoch 95/100\n",
      "1102/1102 [==============================] - 0s 158us/sample - loss: 6.2715e-06 - acc: 1.0000\n",
      "Epoch 96/100\n",
      "1102/1102 [==============================] - 0s 162us/sample - loss: 6.0505e-06 - acc: 1.0000\n",
      "Epoch 97/100\n",
      "1102/1102 [==============================] - 0s 159us/sample - loss: 5.8226e-06 - acc: 1.0000\n",
      "Epoch 98/100\n",
      "1102/1102 [==============================] - 0s 160us/sample - loss: 5.6451e-06 - acc: 1.0000\n",
      "Epoch 99/100\n",
      "1102/1102 [==============================] - 0s 167us/sample - loss: 5.4667e-06 - acc: 1.0000\n",
      "Epoch 100/100\n",
      "1102/1102 [==============================] - 0s 165us/sample - loss: 5.2855e-06 - acc: 1.0000\n"
     ]
    }
   ],
   "source": [
    "# oversampler = SMOTE(random_state=0)\n",
    "# smote_train, smote_target = oversampler.fit_sample(X_train, y_train)\n",
    "# epochs_hist = model.fit(smote_train, smote_target, epochs = 100, batch_size = 50)\n",
    "epochs_hist = model.fit(X_train, y_train, epochs = 100, batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 72,
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    "y_pred = (y_pred > 0.5)\n",
    "y_pred"
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  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "dict_keys(['loss', 'acc'])"
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     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
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     "execution_count": 74,
     "metadata": {},
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs_hist.history['loss'])\n",
    "plt.title(\"Función de Pérdidas del Modelo durante el Entrenamiento\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Error de Entrenamiento\")\n",
    "plt.legend([\"Error de Entrenamiento\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b22f4dca08>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs_hist.history['acc'])\n",
    "plt.title(\"Tasa de Acierto del Modelo durante el Entrenamiento\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy de Entrenamiento\")\n",
    "plt.legend([\"Accuracy de Entrenamiento\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y_pred</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_test</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>288</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "y_pred  0.0  1.0\n",
       "y_test          \n",
       "0       288   18\n",
       "1        42   20"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Deep = y_test.reset_index()\n",
    "Deep['y_test'] = Deep['Attrition']\n",
    "Deep = Deep.drop(['index', 'Attrition'], axis=1)\n",
    "Deep['y_pred'] = y_pred\n",
    "Deep['y_pred'] = Deep['y_pred'].replace(False, 0)\n",
    "Deep['y_pred'] = Deep['y_pred'].replace(True, 1)\n",
    "Deep_Crosstab = pd.crosstab(Deep['y_test'], Deep['y_pred'])\n",
    "Deep_Crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 83.69565217391305 %\n",
      "Precision 52.63157894736842 %\n",
      "Recall 32.25806451612903 %\n",
      "F1Score 40.0 %\n"
     ]
    }
   ],
   "source": [
    "Accuracy  = (Deep_Crosstab[0][0] + Deep_Crosstab[1][1]) / len(y_test)\n",
    "Precision = (Deep_Crosstab[1][1]) /  (Deep_Crosstab[1][1] + Deep_Crosstab[1][0])\n",
    "Recall    = (Deep_Crosstab[1][1]) /  (Deep_Crosstab[1][1] + Deep_Crosstab[0][1])\n",
    "F1Score   = 2*(Precision*Recall) / (Precision+Recall)\n",
    "print(\"Accuracy {} %\".format(100*Accuracy))\n",
    "print(\"Precision {} %\".format(100*Precision))\n",
    "print(\"Recall {} %\".format(100*Recall))\n",
    "print(\"F1Score {} %\".format(100*F1Score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y_pred</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_test</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>288</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "y_pred  0.0  1.0\n",
       "y_test          \n",
       "0       288   18\n",
       "1        42   20"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Deep_Crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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