Employee_Attrition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c1868774",
   "metadata": {},
   "source": [
    "# This Analysis is for Predicting the Emplooyee Is leave the company or Not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2005e996",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f7d1891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EmployeeID</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>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>Shift</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>1313919</td>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</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>1200302</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</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>1060315</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</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>1272912</td>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</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>1414939</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EmployeeID  Age Attrition     BusinessTravel  DailyRate  Department  \\\n",
       "0     1313919   41        No      Travel_Rarely       1102  Cardiology   \n",
       "1     1200302   49        No  Travel_Frequently        279   Maternity   \n",
       "2     1060315   37       Yes      Travel_Rarely       1373   Maternity   \n",
       "3     1272912   33        No  Travel_Frequently       1392   Maternity   \n",
       "4     1414939   27        No      Travel_Rarely        591   Maternity   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  ...  \\\n",
       "0                 1          2  Life Sciences              1  ...   \n",
       "1                 8          1  Life Sciences              1  ...   \n",
       "2                 2          2          Other              1  ...   \n",
       "3                 3          4  Life Sciences              1  ...   \n",
       "4                 2          1        Medical              1  ...   \n",
       "\n",
       "   RelationshipSatisfaction StandardHours  Shift  TotalWorkingYears  \\\n",
       "0                         1            80      0                  8   \n",
       "1                         4            80      1                 10   \n",
       "2                         2            80      0                  7   \n",
       "3                         3            80      0                  8   \n",
       "4                         4            80      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 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"files/watson_healthcare_modified.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26932f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1676, 35)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f7d4a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>Shift</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>1.676000e+03</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.0</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.0</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.456796e+06</td>\n",
       "      <td>36.866348</td>\n",
       "      <td>800.557876</td>\n",
       "      <td>9.221957</td>\n",
       "      <td>2.907518</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.714797</td>\n",
       "      <td>65.470167</td>\n",
       "      <td>2.724940</td>\n",
       "      <td>2.066826</td>\n",
       "      <td>...</td>\n",
       "      <td>2.718377</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.806086</td>\n",
       "      <td>11.338902</td>\n",
       "      <td>2.805489</td>\n",
       "      <td>2.766110</td>\n",
       "      <td>7.033413</td>\n",
       "      <td>4.264916</td>\n",
       "      <td>2.200477</td>\n",
       "      <td>4.135442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.487486e+05</td>\n",
       "      <td>9.129126</td>\n",
       "      <td>401.594438</td>\n",
       "      <td>8.158118</td>\n",
       "      <td>1.025835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.097534</td>\n",
       "      <td>20.207572</td>\n",
       "      <td>0.714121</td>\n",
       "      <td>1.113423</td>\n",
       "      <td>...</td>\n",
       "      <td>1.078162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.855527</td>\n",
       "      <td>7.834996</td>\n",
       "      <td>1.288431</td>\n",
       "      <td>0.702369</td>\n",
       "      <td>6.098991</td>\n",
       "      <td>3.627456</td>\n",
       "      <td>3.229587</td>\n",
       "      <td>3.559662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.025177e+06</td>\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>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>1.235832e+06</td>\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>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>1.464606e+06</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>796.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>65.500000</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>1.667992e+06</td>\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>4.000000</td>\n",
       "      <td>83.000000</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>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>1.886378e+06</td>\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>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": [
       "         EmployeeID          Age    DailyRate  DistanceFromHome    Education  \\\n",
       "count  1.676000e+03  1676.000000  1676.000000       1676.000000  1676.000000   \n",
       "mean   1.456796e+06    36.866348   800.557876          9.221957     2.907518   \n",
       "std    2.487486e+05     9.129126   401.594438          8.158118     1.025835   \n",
       "min    1.025177e+06    18.000000   102.000000          1.000000     1.000000   \n",
       "25%    1.235832e+06    30.000000   465.000000          2.000000     2.000000   \n",
       "50%    1.464606e+06    36.000000   796.500000          7.000000     3.000000   \n",
       "75%    1.667992e+06    43.000000  1157.000000         14.000000     4.000000   \n",
       "max    1.886378e+06    60.000000  1499.000000         29.000000     5.000000   \n",
       "\n",
       "       EmployeeCount  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
       "count         1676.0              1676.000000  1676.000000     1676.000000   \n",
       "mean             1.0                 2.714797    65.470167        2.724940   \n",
       "std              0.0                 1.097534    20.207572        0.714121   \n",
       "min              1.0                 1.000000    30.000000        1.000000   \n",
       "25%              1.0                 2.000000    48.000000        2.000000   \n",
       "50%              1.0                 3.000000    65.500000        3.000000   \n",
       "75%              1.0                 4.000000    83.000000        3.000000   \n",
       "max              1.0                 4.000000   100.000000        4.000000   \n",
       "\n",
       "          JobLevel  ...  RelationshipSatisfaction  StandardHours        Shift  \\\n",
       "count  1676.000000  ...               1676.000000         1676.0  1676.000000   \n",
       "mean      2.066826  ...                  2.718377           80.0     0.806086   \n",
       "std       1.113423  ...                  1.078162            0.0     0.855527   \n",
       "min       1.000000  ...                  1.000000           80.0     0.000000   \n",
       "25%       1.000000  ...                  2.000000           80.0     0.000000   \n",
       "50%       2.000000  ...                  3.000000           80.0     1.000000   \n",
       "75%       3.000000  ...                  4.000000           80.0     1.000000   \n",
       "max       5.000000  ...                  4.000000           80.0     3.000000   \n",
       "\n",
       "       TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  \\\n",
       "count        1676.000000            1676.000000      1676.000000   \n",
       "mean           11.338902               2.805489         2.766110   \n",
       "std             7.834996               1.288431         0.702369   \n",
       "min             0.000000               0.000000         1.000000   \n",
       "25%             6.000000               2.000000         2.000000   \n",
       "50%            10.000000               3.000000         3.000000   \n",
       "75%            15.000000               3.000000         3.000000   \n",
       "max            40.000000               6.000000         4.000000   \n",
       "\n",
       "       YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "count     1676.000000         1676.000000              1676.000000   \n",
       "mean         7.033413            4.264916                 2.200477   \n",
       "std          6.098991            3.627456                 3.229587   \n",
       "min          0.000000            0.000000                 0.000000   \n",
       "25%          3.000000            2.000000                 0.000000   \n",
       "50%          5.000000            3.000000                 1.000000   \n",
       "75%         10.000000            7.000000                 3.000000   \n",
       "max         40.000000           18.000000                15.000000   \n",
       "\n",
       "       YearsWithCurrManager  \n",
       "count           1676.000000  \n",
       "mean               4.135442  \n",
       "std                3.559662  \n",
       "min                0.000000  \n",
       "25%                2.000000  \n",
       "50%                3.000000  \n",
       "75%                7.000000  \n",
       "max               17.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "354910a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeID                  0\n",
       "Age                         0\n",
       "Attrition                   0\n",
       "BusinessTravel              0\n",
       "DailyRate                   0\n",
       "Department                  0\n",
       "DistanceFromHome            0\n",
       "Education                   0\n",
       "EducationField              0\n",
       "EmployeeCount               0\n",
       "EnvironmentSatisfaction     0\n",
       "Gender                      0\n",
       "HourlyRate                  0\n",
       "JobInvolvement              0\n",
       "JobLevel                    0\n",
       "JobRole                     0\n",
       "JobSatisfaction             0\n",
       "MaritalStatus               0\n",
       "MonthlyIncome               0\n",
       "MonthlyRate                 0\n",
       "NumCompaniesWorked          0\n",
       "Over18                      0\n",
       "OverTime                    0\n",
       "PercentSalaryHike           0\n",
       "PerformanceRating           0\n",
       "RelationshipSatisfaction    0\n",
       "StandardHours               0\n",
       "Shift                       0\n",
       "TotalWorkingYears           0\n",
       "TrainingTimesLastYear       0\n",
       "WorkLifeBalance             0\n",
       "YearsAtCompany              0\n",
       "YearsInCurrentRole          0\n",
       "YearsSinceLastPromotion     0\n",
       "YearsWithCurrManager        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4149607b",
   "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>Attrition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>1477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Attrition\n",
       "No        1477\n",
       "Yes        199"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attrition_count = pd.DataFrame(df.Attrition.value_counts())\n",
    "attrition_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2160fc44",
   "metadata": {},
   "source": [
    "So this dataset represents that most of them are like to stay in the company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b849419f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1a8c8f282e0>,\n",
       "  <matplotlib.patches.Wedge at 0x1a8c8f281f0>],\n",
       " [Text(-1.2106016300689784, 0.47375488733556376, 'Yes'),\n",
       "  Text(1.0243552348260097, -0.4008694960764047, 'No')])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(attrition_count['Attrition'],labels=[\"Yes\",\"No\"],explode=(0.2,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f7e4c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Attrition', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x=\"Attrition\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "318517e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the unwanted columns\n",
    "df = df.drop(['EmployeeCount','EmployeeID'],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f0f4ff1",
   "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>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>Shift</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>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</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>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</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>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</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>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</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>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</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 × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate  Department  DistanceFromHome  \\\n",
       "0   41        No      Travel_Rarely       1102  Cardiology                 1   \n",
       "1   49        No  Travel_Frequently        279   Maternity                 8   \n",
       "2   37       Yes      Travel_Rarely       1373   Maternity                 2   \n",
       "3   33        No  Travel_Frequently       1392   Maternity                 3   \n",
       "4   27        No      Travel_Rarely        591   Maternity                 2   \n",
       "\n",
       "   Education EducationField  EnvironmentSatisfaction  Gender  ...  \\\n",
       "0          2  Life Sciences                        2  Female  ...   \n",
       "1          1  Life Sciences                        3    Male  ...   \n",
       "2          2          Other                        4    Male  ...   \n",
       "3          4  Life Sciences                        4  Female  ...   \n",
       "4          1        Medical                        1    Male  ...   \n",
       "\n",
       "   RelationshipSatisfaction  StandardHours  Shift TotalWorkingYears  \\\n",
       "0                         1             80      0                 8   \n",
       "1                         4             80      1                10   \n",
       "2                         2             80      0                 7   \n",
       "3                         3             80      0                 8   \n",
       "4                         4             80      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 33 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b0b680f",
   "metadata": {},
   "outputs": [
    {
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       "   No  Yes\n",
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     "execution_count": 16,
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   ],
   "source": [
    "attrition_dummies = pd.get_dummies(df.Attrition)\n",
    "attrition_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "54f71676",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df,attrition_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f5788e3a",
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 35 columns</p>\n",
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       "   Age Attrition     BusinessTravel  DailyRate  Department  DistanceFromHome  \\\n",
       "0   41        No      Travel_Rarely       1102  Cardiology                 1   \n",
       "1   49        No  Travel_Frequently        279   Maternity                 8   \n",
       "2   37       Yes      Travel_Rarely       1373   Maternity                 2   \n",
       "3   33        No  Travel_Frequently       1392   Maternity                 3   \n",
       "4   27        No      Travel_Rarely        591   Maternity                 2   \n",
       "\n",
       "   Education EducationField  EnvironmentSatisfaction  Gender  ...  Shift  \\\n",
       "0          2  Life Sciences                        2  Female  ...      0   \n",
       "1          1  Life Sciences                        3    Male  ...      1   \n",
       "2          2          Other                        4    Male  ...      0   \n",
       "3          4  Life Sciences                        4  Female  ...      0   \n",
       "4          1        Medical                        1    Male  ...      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  No Yes  \n",
       "0                  4                        0                     5   1   0  \n",
       "1                  7                        1                     7   1   0  \n",
       "2                  0                        0                     0   0   1  \n",
       "3                  7                        3                     0   1   0  \n",
       "4                  2                        2                     2   1   0  \n",
       "\n",
       "[5 rows x 35 columns]"
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     },
     "execution_count": 18,
     "metadata": {},
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   ]
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "26ed7d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Attrition','No'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ad83481",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     BusinessTravel  DailyRate  Department  DistanceFromHome  Education  \\\n",
       "0   41      Travel_Rarely       1102  Cardiology                 1          2   \n",
       "1   49  Travel_Frequently        279   Maternity                 8          1   \n",
       "2   37      Travel_Rarely       1373   Maternity                 2          2   \n",
       "3   33  Travel_Frequently       1392   Maternity                 3          4   \n",
       "4   27      Travel_Rarely        591   Maternity                 2          1   \n",
       "\n",
       "  EducationField  EnvironmentSatisfaction  Gender  HourlyRate  ...  \\\n",
       "0  Life Sciences                        2  Female          94  ...   \n",
       "1  Life Sciences                        3    Male          61  ...   \n",
       "2          Other                        4    Male          92  ...   \n",
       "3  Life Sciences                        4  Female          56  ...   \n",
       "4        Medical                        1    Male          40  ...   \n",
       "\n",
       "   StandardHours  Shift TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0             80      0                 8                      0   \n",
       "1             80      1                10                      3   \n",
       "2             80      0                 7                      3   \n",
       "3             80      0                 8                      3   \n",
       "4             80      1                 6                      3   \n",
       "\n",
       "  WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0               1               6                   4   \n",
       "1               3              10                   7   \n",
       "2               3               0                   0   \n",
       "3               3               8                   7   \n",
       "4               3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion YearsWithCurrManager Yes  \n",
       "0                        0                    5   0  \n",
       "1                        1                    7   0  \n",
       "2                        0                    0   1  \n",
       "3                        3                    0   0  \n",
       "4                        2                    2   0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e006801",
   "metadata": {},
   "source": [
    "Now i just removed the NO and Attrition features , since the Yes feature says even the employee leave the company or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e33ad7ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Yes'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df,x='Gender',y='Yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e291fc4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Department', ylabel='Yes'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2hu5z6tSpSklJsT+vqqoiBAEXoGm9Drh7CACaCLcFIH9/f3l6ejrNzOzdu9dpBudctG/f/pz7tFqtslqt571NAADQvLjtFJiPj48iIiKUl5fn0J6Xl6e+ffued7+RkZFOfa5Zs+Z39QkAAC4sbj0FlpKSotjYWPXu3VuRkZFauHChysrKFB8fL+nEqak9e/Zo+fLl9nWKi4slSYcPH1ZFRYWKi4vl4+Oj7t27S5ImTZqk/v3767nnntPw4cP1zjvv6MMPP9THH3/c6PsHAACaJrcGoFGjRmnfvn1KS0uTzWZTeHi4cnNzFRwcLOnEBx/+9jOBevXqZf+5sLBQK1euVHBwsEpLSyVJffv21Wuvvabp06friSeeUJcuXZSTk6Prr7++0fYLAAA0bW6/CDohIUEJCQkuly1dutSpzTCMM/Y5cuRIjRw58vcODQAAXKDc/lUYAAAAjY0ABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATOecA9DPP/+so0eP2p/v2rVLGRkZWrNmTb0ODAAAoKGccwAaPny4/ctJDxw4oOuvv16zZ8/W8OHDlZWVVe8DBAAAqG/nHIC2bdumqKgoSdIbb7yhgIAA7dq1S8uXL9e8efPqfYAAAAD17ZwD0NGjR9WqVStJ0po1a3TnnXfKw8NDN9xwg3bt2lXvAwQAAKhv5xyArrjiCr399tvavXu3PvjgA0VHR0uS9u7dq9atW9f7AAEAAOrbOQegJ598Uo8++qhCQkJ03XXXKTIyUtKJ2aBevXrV+wABAADqm9e5rjBy5EjdeOONstls6tmzp739lltu0R133FGvgwMAAGgI5/U5QO3bt1erVq2Ul5enn3/+WZLUp08fdevWrV4HBwAA0BDOOQDt27dPt9xyi6666ioNHTpUNptNkhQXF6e//vWv9T5AAACA+nbOASg5OVne3t4qKyvTRRddZG8fNWqUVq9eXa+DAwAAaAjnfA3QmjVr9MEHH+jyyy93aL/yyiu5DR4AADQL5zwDdOTIEYeZn5MqKytltVrrZVAAAAAN6awD0H//+19JUlRUlP2rMCTJYrGorq5OL7zwggYOHFj/IwQAAKhnZ30KLDw8XC+++KJmz56tAQMGaOvWraqpqdHjjz+uL7/8Uvv379emTZsacqwAAAD14qwD0MyZM5WYmKjBgwersLBQL7/8sjw9PXXkyBHdeeedSkxMVGBgYEOOFQAAoF6cdQBKSEjQkCFDNH78ePXp00f//Oc/9cwzzzTk2AAAABrEOd0FFhoaqnXr1umll17SyJEjFRYWJi8vxy62bdtWrwMEAACob+d8G/yuXbu0atUqXXrppRo+fLhTAAIAAGjqzim9ZGdn669//asGDRqkL774Qu3atWuocQEAADSYsw5At956q7Zs2aKXXnpJY8aMacgxAQAANKizDkC1tbX6/PPPnT4BGgAAoLk56wCUl5fXkOMAAABoNOf8VRj1LTMzU6GhofL19VVERIQ2btx42vqPPvpIERER8vX1VefOnbVgwQKnmoyMDHXt2lUtWrRQUFCQkpOT9csvvzTULgAAgGbGrQEoJydHSUlJSk1NVVFRkaKiojRkyBCVlZW5rN+5c6eGDh2qqKgoFRUVadq0aZo4caJWrVplr1mxYoWmTJmip556SiUlJVq0aJFycnI0derUxtotAADQxLn1HvY5c+Zo/PjxiouLk3Ri5uaDDz5QVlaW0tPTneoXLFigTp06KSMjQ5IUFhamrVu3atasWbrrrrskSQUFBerXr59Gjx4tSQoJCdG9996rLVu2NM5OAQCAJs9tM0A1NTUqLCxUdHS0Q3t0dLTy8/NdrlNQUOBUHxMTo61bt+rYsWOSpBtvvFGFhYX2wPP9998rNzdXt9122ynHUl1draqqKocHAAC4cLltBqiyslK1tbUKCAhwaA8ICFB5ebnLdcrLy13WHz9+XJWVlQoMDNQ999yjiooK3XjjjTIMQ8ePH9cjjzyiKVOmnHIs6enpfK0HAAAm4vaLoC0Wi8NzwzCc2s5U/+v2DRs2aMaMGcrMzNS2bdv05ptv6l//+peeffbZU/Y5depUHTx40P7YvXv3+e4OAABoBtw2A+Tv7y9PT0+n2Z69e/c6zfKc1L59e5f1Xl5eatu2rSTpiSeeUGxsrP26oquvvlpHjhzRQw89pNTUVHl4OGc+q9Uqq9VaH7sFAACaAbfNAPn4+CgiIsLp84Xy8vLUt29fl+tERkY61a9Zs0a9e/eWt7e3JOno0aNOIcfT01OGYdhniwAAgLm59RRYSkqKXn75ZS1evFglJSVKTk5WWVmZ4uPjJZ04NfXrr92Ij4/Xrl27lJKSopKSEi1evFiLFi3So48+aq8ZNmyYsrKy9Nprr2nnzp3Ky8vTE088odtvv12enp6Nvo8AAKDpcett8KNGjdK+ffuUlpYmm82m8PBw5ebmKjg4WJJks9kcPhMoNDRUubm5Sk5O1vz589WhQwfNmzfPfgu8JE2fPl0Wi0XTp0/Xnj171K5dOw0bNkwzZsxo9P0DAJxevxf7uXsIbmetssqiE9exlleVm/412TRhU6Nsx2JwXshJVVWV/Pz8dPDgQbVu3fq8+4l4bHk9jgrNXeEL7v8S4bK0q909BDQhnZ7c7u4hmP6XvSRZP7DK8vOJAGS0MFQdU+3mEbnX7wlA5/L72+13gQEAADQ2AhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdtwegzMxMhYaGytfXVxEREdq4ceNp6z/66CNFRETI19dXnTt31oIFC5xqDhw4oMTERAUGBsrX11dhYWHKzc1tqF0AAADNjFsDUE5OjpKSkpSamqqioiJFRUVpyJAhKisrc1m/c+dODR06VFFRUSoqKtK0adM0ceJErVq1yl5TU1OjwYMHq7S0VG+88YZ27Nih7OxsdezYsbF2CwAANHFe7tz4nDlzNH78eMXFxUmSMjIy9MEHHygrK0vp6elO9QsWLFCnTp2UkZEhSQoLC9PWrVs1a9Ys3XXXXZKkxYsXa//+/crPz5e3t7ckKTg4uHF2CAAANAtumwGqqalRYWGhoqOjHdqjo6OVn5/vcp2CggKn+piYGG3dulXHjh2TJL377ruKjIxUYmKiAgICFB4erpkzZ6q2tvaUY6murlZVVZXDAwAAXLjcFoAqKytVW1urgIAAh/aAgACVl5e7XKe8vNxl/fHjx1VZWSlJ+v777/XGG2+otrZWubm5mj59umbPnq0ZM2accizp6eny8/OzP4KCgn7n3gEAgKbM7RdBWywWh+eGYTi1nan+1+11dXW67LLLtHDhQkVEROiee+5RamqqsrKyTtnn1KlTdfDgQftj9+7d57s7AACgGXDbNUD+/v7y9PR0mu3Zu3ev0yzPSe3bt3dZ7+XlpbZt20qSAgMD5e3tLU9PT3tNWFiYysvLVVNTIx8fH6d+rVarrFbr790lAADQTLhtBsjHx0cRERHKy8tzaM/Ly1Pfvn1drhMZGelUv2bNGvXu3dt+wXO/fv307bffqq6uzl7z9ddfKzAw0GX4AQAA5uPWU2ApKSl6+eWXtXjxYpWUlCg5OVllZWWKj4+XdOLU1JgxY+z18fHx2rVrl1JSUlRSUqLFixdr0aJFevTRR+01jzzyiPbt26dJkybp66+/1r///W/NnDlTiYmJjb5/AACgaXLrbfCjRo3Svn37lJaWJpvNpvDwcOXm5tpvW7fZbA6fCRQaGqrc3FwlJydr/vz56tChg+bNm2e/BV6SgoKCtGbNGiUnJ6tHjx7q2LGjJk2apMmTJzf6/gEAgKbJrQFIkhISEpSQkOBy2dKlS53aBgwYoG3btp22z8jISH3yySf1MTwAAHABcvtdYAAAAI2NAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEzHy90DAADAzIwWhsuf0bAIQAAAuFFN/xp3D8GUOAUGAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMx+0BKDMzU6GhofL19VVERIQ2btx42vqPPvpIERER8vX1VefOnbVgwYJT1r722muyWCwaMWJEPY8aAAA0Z24NQDk5OUpKSlJqaqqKiooUFRWlIUOGqKyszGX9zp07NXToUEVFRamoqEjTpk3TxIkTtWrVKqfaXbt26dFHH1VUVFRD7wYAAGhm3BqA5syZo/HjxysuLk5hYWHKyMhQUFCQsrKyXNYvWLBAnTp1UkZGhsLCwhQXF6dx48Zp1qxZDnW1tbW677779Mwzz6hz586NsSsAAKAZcVsAqqmpUWFhoaKjox3ao6OjlZ+f73KdgoICp/qYmBht3bpVx44ds7elpaWpXbt2Gj9+/FmNpbq6WlVVVQ4PAABw4XJbAKqsrFRtba0CAgIc2gMCAlReXu5ynfLycpf1x48fV2VlpSRp06ZNWrRokbKzs896LOnp6fLz87M/goKCznFvAABAc+L2i6AtFovDc8MwnNrOVH+y/dChQ/rzn/+s7Oxs+fv7n/UYpk6dqoMHD9ofu3fvPoc9AAAAzY2Xuzbs7+8vT09Pp9mevXv3Os3ynNS+fXuX9V5eXmrbtq2+/PJLlZaWatiwYfbldXV1kiQvLy/t2LFDXbp0cerXarXKarX+3l0CAADNhNtmgHx8fBQREaG8vDyH9ry8PPXt29flOpGRkU71a9asUe/eveXt7a1u3bpp+/btKi4utj9uv/12DRw4UMXFxZzaAgAAktw4AyRJKSkpio2NVe/evRUZGamFCxeqrKxM8fHxkk6cmtqzZ4+WL18uSYqPj9dLL72klJQUPfjggyooKNCiRYv06quvSpJ8fX0VHh7usI1LLrlEkpzaAQCAebk1AI0aNUr79u1TWlqabDabwsPDlZubq+DgYEmSzWZz+Eyg0NBQ5ebmKjk5WfPnz1eHDh00b9483XXXXe7aBQAA0Ay5NQBJUkJCghISElwuW7p0qVPbgAEDtG3btrPu31UfAADA3Nx+FxgAAEBjIwABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTcXsAyszMVGhoqHx9fRUREaGNGzeetv6jjz5SRESEfH191blzZy1YsMBheXZ2tqKiotSmTRu1adNGgwYN0pYtWxpyFwAAQDPj1gCUk5OjpKQkpaamqqioSFFRURoyZIjKyspc1u/cuVNDhw5VVFSUioqKNG3aNE2cOFGrVq2y12zYsEH33nuv1q9fr4KCAnXq1EnR0dHas2dPY+0WAABo4twagObMmaPx48crLi5OYWFhysjIUFBQkLKyslzWL1iwQJ06dVJGRobCwsIUFxencePGadasWfaaFStWKCEhQddcc426deum7Oxs1dXVae3atY21WwAAoIlzWwCqqalRYWGhoqOjHdqjo6OVn5/vcp2CggKn+piYGG3dulXHjh1zuc7Ro0d17NgxXXrppaccS3V1taqqqhweAADgwuW2AFRZWana2loFBAQ4tAcEBKi8vNzlOuXl5S7rjx8/rsrKSpfrTJkyRR07dtSgQYNOOZb09HT5+fnZH0FBQee4NwAAoDlx+0XQFovF4blhGE5tZ6p31S5Jzz//vF599VW9+eab8vX1PWWfU6dO1cGDB+2P3bt3n8suAACAZsbLXRv29/eXp6en02zP3r17nWZ5Tmrfvr3Lei8vL7Vt29ahfdasWZo5c6Y+/PBD9ejR47RjsVqtslqt57EXAACgOXLbDJCPj48iIiKUl5fn0J6Xl6e+ffu6XCcyMtKpfs2aNerdu7e8vb3tbS+88IKeffZZrV69Wr17967/wQMAgGbNrafAUlJS9PLLL2vx4sUqKSlRcnKyysrKFB8fL+nEqakxY8bY6+Pj47Vr1y6lpKSopKREixcv1qJFi/Too4/aa55//nlNnz5dixcvVkhIiMrLy1VeXq7Dhw83+v4BAICmyW2nwCRp1KhR2rdvn9LS0mSz2RQeHq7c3FwFBwdLkmw2m8NnAoWGhio3N1fJycmaP3++OnTooHnz5umuu+6y12RmZqqmpkYjR4502NZTTz2lp59+ulH2CwAANG1uDUCSlJCQoISEBJfLli5d6tQ2YMAAbdu27ZT9lZaW1tPIAADAhcrtd4EBAAA0NgIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHbcHoMzMTIWGhsrX11cRERHauHHjaes/+ugjRUREyNfXV507d9aCBQucalatWqXu3bvLarWqe/fueuuttxpq+AAAoBlyawDKyclRUlKSUlNTVVRUpKioKA0ZMkRlZWUu63fu3KmhQ4cqKipKRUVFmjZtmiZOnKhVq1bZawoKCjRq1CjFxsbqs88+U2xsrO6++25t3ry5sXYLAAA0cRbDMAx3bfz666/Xtddeq6ysLHtbWFiYRowYofT0dKf6yZMn691331VJSYm9LT4+Xp999pkKCgokSaNGjVJVVZXef/99e82tt96qNm3a6NVXXz2rcVVVVcnPz08HDx5U69atz3f3FPHY8vNeFxeewhfGuHsIKku72t1DQBPS6cnt7h6C+r3Yz91DQBOzacKm8173XH5/e533Vn6nmpoaFRYWasqUKQ7t0dHRys/Pd7lOQUGBoqOjHdpiYmK0aNEiHTt2TN7e3iooKFBycrJTTUZGxinHUl1drerqavvzgwcPSjrxQv4etdU//671cWH5vcdTfTj0S627h4AmpCkck8d/Pu7uIaCJ+T3H5cl1z2Zux20BqLKyUrW1tQoICHBoDwgIUHl5uct1ysvLXdYfP35clZWVCgwMPGXNqfqUpPT0dD3zzDNO7UFBQWe7O8AZ+b0Y7+4hAI7S/dw9AsCJ3+Tff1weOnRIfn6n78dtAegki8Xi8NwwDKe2M9X/tv1c+5w6dapSUlLsz+vq6rR//361bdv2tOvhzKqqqhQUFKTdu3f/rtOJQH3hmERTxHFZPwzD0KFDh9ShQ4cz1rotAPn7+8vT09NpZmbv3r1OMzgntW/f3mW9l5eX2rZte9qaU/UpSVarVVar1aHtkksuOdtdwVlo3bo1/6jRpHBMoiniuPz9zjTzc5Lb7gLz8fFRRESE8vLyHNrz8vLUt29fl+tERkY61a9Zs0a9e/eWt7f3aWtO1ScAADAft54CS0lJUWxsrHr37q3IyEgtXLhQZWVlio8/ca3E1KlTtWfPHi1ffuJuqvj4eL300ktKSUnRgw8+qIKCAi1atMjh7q5Jkyapf//+eu655zR8+HC98847+vDDD/Xxxx+7ZR8BAEDT49YANGrUKO3bt09paWmy2WwKDw9Xbm6ugoODJUk2m83hM4FCQ0OVm5ur5ORkzZ8/Xx06dNC8efN011132Wv69u2r1157TdOnT9cTTzyhLl26KCcnR9dff32j7x9OnF586qmnnE4xAu7CMYmmiOOy8bn1c4AAAADcwe1fhQEAANDYCEAAAMB0CEAAAMB0CED43UJCQhy+asRisejtt98+6/Xvv/9+jRgxot7HBTSU3x7zQEMpLS2VxWJRcXGxu4dywSEAXaDKy8s1YcIEde7cWVarVUFBQRo2bJjWrl3b4Nu22WwaMmRIg28Hzdf9998vi8Vi/8iLX0tISJDFYtH9999/Vn254xfEp59+qoceesj+/FxDP5qOk8fi3//+d4f2t99+m28CuMARgC5ApaWlioiI0Lp16/T8889r+/btWr16tQYOHKjExMTz6rO2tlZ1dXVnVdu+fXtu5cQZBQUF6bXXXtPPP///Lw3+5Zdf9Oqrr6pTp05uGdOxY8fOqq5du3a66KKLGng0aCy+vr567rnn9NNPPzXqds/2eEPDIABdgE7+D3rLli0aOXKkrrrqKv3hD39QSkqKPvnkE0nSnDlzdPXVV6tly5YKCgpSQkKCDh8+bO9j6dKluuSSS/Svf/1L3bt3l9Vq1a5du7R3714NGzZMLVq0UGhoqFasWOG0/d/+b3j79u26+eab1aJFC7Vt21YPPfSQw7Z+q7q6WhMnTtRll10mX19f3Xjjjfr0008dat59911deeWVatGihQYOHKhly5bJYrHowIEDOnLkiFq3bq033njDYZ333ntPLVu21KFDh87nZUU9u/baa9WpUye9+eab9rY333xTQUFB6tWrl71t9erVuvHGG3XJJZeobdu2+uMf/6jvvvvOvjw0NFSS1KtXL1ksFt100032ZUuWLFFYWJh8fX3VrVs3ZWZm2pednDl6/fXXddNNN8nX11f/8z//Yz8lO2vWLAUGBqpt27ZKTEx0+GX161NgISEhkqQ77rhDFotFISEhKi0tlYeHh7Zu3eqwzy+++KKCg4PP6puq0XgGDRqk9u3bKz09/ZQ1+fn56t+/v1q0aKGgoCBNnDhRR44csS93NQt4ySWXaOnSpZJOfbzV1dUpLS1Nl19+uaxWq6655hqtXr36tOP96KOPdN1118lqtSowMFBTpkzR8ePH7csPHTqk++67Ty1btlRgYKD+8Y9/6KabblJSUpIkKS0tTVdffbVTvxEREXryySfP8GpdOAhAF5j9+/dr9erVSkxMVMuWLZ2Wn/yOMw8PD82bN09ffPGFli1bpnXr1unxxx93qD169KjS09P18ssv68svv9Rll12m+++/X6WlpVq3bp3eeOMNZWZmau/evaccz9GjR3XrrbeqTZs2+vTTT/W///u/+vDDD/WXv/zllOs8/vjjWrVqlZYtW6Zt27bpiiuuUExMjPbv3y/pxBvJyJEjNWLECBUXF+vhhx9Wamqqff2WLVvqnnvu0ZIlSxz6XbJkiUaOHKlWrVqd8XVE43jggQcc/p4WL16scePGOdQcOXJEKSkp+vTTT7V27Vp5eHjojjvusM9IbtmyRZL04Ycfymaz2QNVdna2UlNTNWPGDJWUlGjmzJl64okntGzZMof+J0+erIkTJ6qkpEQxMTGSpPXr1+u7777T+vXrtWzZMi1dutT+i+y3TobzJUuWyGaz6dNPP1VISIgGDRrk8hg8ecoFTYenp6dmzpypF198Uf/973+dlm/fvl0xMTG688479fnnnysnJ0cff/zxad/HTuW3x9vcuXM1e/ZszZo1S59//rliYmJ0++2365tvvnG5/p49ezR06FD16dNHn332mbKysrRo0SL97W9/s9ekpKRo06ZNevfdd5WXl6eNGzdq27Zt9uXjxo3TV1995fAfy88//1xFRUVnfer5gmDggrJ582ZDkvHmm2+e03qvv/660bZtW/vzJUuWGJKM4uJie9uOHTsMScYnn3xibyspKTEkGf/4xz/sbZKMt956yzAMw1i4cKHRpk0b4/Dhw/bl//73vw0PDw+jvLzcMAzDGDt2rDF8+HDDMAzj8OHDhre3t7FixQp7fU1NjdGhQwfj+eefNwzDMCZPnmyEh4c7jD81NdWQZPz000/218HT09PYs2ePYRiGUVFRYXh7exsbNmw4p9cFDePk33lFRYVhtVqNnTt3GqWlpYavr69RUVFhDB8+3Bg7dqzLdffu3WtIMrZv324YhmHs3LnTkGQUFRU51AUFBRkrV650aHv22WeNyMhIh/UyMjKcxhYcHGwcP37c3vanP/3JGDVqlP15cHDwKY/5k3Jycow2bdoYv/zyi2EYhlFcXGxYLBZj586dZ3p50Ih+/f5zww03GOPGjTMMwzDeeust4+SvyNjYWOOhhx5yWG/jxo2Gh4eH8fPPPxuG4foY8PPzM5YsWWIYxqmPtw4dOhgzZsxwaOvTp4+RkJDgsN7J43vatGlG165djbq6Onv9/PnzjYsvvtiora01qqqqDG9vb+N///d/7csPHDhgXHTRRcakSZPsbUOGDDEeeeQR+/OkpCTjpptuOtPLdUFhBugCY/zf1PqZ/oe5fv16DR48WB07dlSrVq00ZswY7du3z2FK18fHRz169LA/LykpkZeXl3r37m1v69atm31WyZWSkhL17NnTYTaqX79+qqur044dO5zqv/vuOx07dkz9+vWzt3l7e+u6665TSUmJJGnHjh3q06ePw3rXXXed0/M//OEP9u+Re+WVV9SpUyf179//dC8LGpm/v79uu+02LVu2TEuWLNFtt90mf39/h5rvvvtOo0ePVufOndW6dWv7Ka9ff03Ob1VUVGj37t0aP368Lr74Yvvjb3/7m8PpM0kOx/NJf/jDH+Tp6Wl/HhgYeNqZTldGjBghLy8vvfXWW5JOzG4NHDjQfsoMTc9zzz2nZcuW6auvvnJoLyws1NKlSx2OpZiYGNXV1Wnnzp3ntI1fH29VVVX64YcfHN7vpBPvkSff736rpKREkZGRDu/x/fr10+HDh/Xf//5X33//vY4dO+bwnujn56euXbs69PPggw/q1Vdf1S+//KJjx45pxYoVTrOvFzoC0AXmyiuvlMViOeU/HknatWuXhg4dqvDwcK1atUqFhYWaP3++JMeL8lq0aOHwj+xsw9WvGYZxynpX7afaxq/7cdWn4eKairi4OPspiCVLluiBBx7g1EMTNG7cOC1dulTLli1z+QY8bNgw7du3T9nZ2dq8ebM2b94sSaqpqTllnydPj2VnZ6u4uNj++OKLL+zXwZ3k6lSxt7e3w3OLxXLWNwGc5OPjo9jYWC1ZskQ1NTVauXKl6X7BNDf9+/dXTEyMpk2b5tBeV1enhx9+2OFY+uyzz/TNN9+oS5cukk4cI799H3J1kbOr4+1073e/dbr3v1+P4UzvkcOGDZPVatVbb72l9957T9XV1Q7fq2kGBKALzKWXXqqYmBjNnz/fYTbnpAMHDmjr1q06fvy4Zs+erRtuuEFXXXWVfvjhhzP2HRYWpuPHjztc2Lljxw4dOHDglOt0795dxcXFDmPZtGmTPDw8dNVVVznVX3HFFfLx8dHHH39sbzt27Ji2bt2qsLAwSSdmnX57UfRvLzaVpD//+c8qKyvTvHnz9OWXX2rs2LFn3Ec0vltvvVU1NTWqqamxX4Nz0r59+1RSUqLp06frlltuUVhYmNOdOj4+PpJO3Kl4UkBAgDp27Kjvv/9eV1xxhcPj5AxSffL29nbY/klxcXH68MMPlZmZqWPHjunOO++s922jfv3973/Xe++9p/z8fHvbtddeqy+//NLpWDr5fiWduDPQZrPZ1/nmm2909OjR026rdevW6tChg8P7nXTiguuT73e/1b17d+Xn5zsEmvz8fLVq1UodO3ZUly5d5O3tbb82Tjox0/Tba4q8vLw0duxYLVmyREuWLNE999xjujsbCUAXoMzMTNXW1uq6667TqlWr9M0336ikpETz5s1TZGSkunTpouPHj+vFF1/U999/r1deeUULFiw4Y79du3bVrbfeqgcffFCbN29WYWGh4uLi1KJFi1Ouc99998nX11djx47VF198ofXr12vChAmKjY1VQECAU33Lli31yCOP6LHHHtPq1av11Vdf6cEHH9TRo0c1fvx4SdLDDz+s//znP5o8ebK+/vprvf766/YLVH/9v542bdrozjvv1GOPPabo6Ghdfvnl5/hKojF4enqqpKREJSUlDqedpBN/h23bttXChQv17bffat26dUpJSXGoueyyy9SiRQutXr1aP/74ow4ePChJevrpp5Wenq65c+fq66+/1vbt27VkyRLNmTOn3vchJCREa9euVXl5uUNACwsL0w033KDJkyfr3nvvPe2/FTQNV199te677z69+OKL9rbJkyeroKBAiYmJKi4u1jfffKN3331XEyZMsNfcfPPNeumll7Rt2zZt3bpV8fHxTjOJrjz22GN67rnnlJOTox07dmjKlCkqLi7WpEmTXNYnJCRo9+7dmjBhgv7zn//onXfe0VNPPaWUlBR5eHioVatWGjt2rB577DGtX79eX375pcaNGycPDw+nWaG4uDitW7dO77//vjlnJ91x4REa3g8//GAkJiYawcHBho+Pj9GxY0fj9ttvN9avX28YhmHMmTPHCAwMNFq0aGHExMQYy5cvd7iIeMmSJYafn59TvzabzbjtttsMq9VqdOrUyVi+fPkZLwj9/PPPjYEDBxq+vr7GpZdeajz44IPGoUOH7Mt/fRGiYRjGzz//bEyYMMHw9/c3rFar0a9fP2PLli0O43jnnXeMK664wrBarcZNN91kZGVlGZLsFySetHbtWkOS8frrr5/X64iG8du/89/69UXQeXl5RlhYmGG1Wo0ePXoYGzZscDrGsrOzjaCgIMPDw8MYMGCAvX3FihXGNddcY/j4+Bht2rQx+vfvb79B4FQXT7sa26RJkxz6/e0x/+677xpXXHGF4eXlZQQHBzusu2jRIkOS0zGMpsHV33dpaalhtVqNX/+K3LJlizF48GDj4osvNlq2bGn06NHD4eLlPXv2GNHR0UbLli2NK6+80sjNzXV5EfRvj7fa2lrjmWeeMTp27Gh4e3sbPXv2NN5//337clfrbdiwwejTp4/h4+NjtG/f3pg8ebJx7Ngx+/Kqqipj9OjRxkUXXWS0b9/emDNnjnHdddcZU6ZMcdr/qKgoo3v37ufxyjV/FsPgAynQ/M2YMUMLFizQ7t27HdpXrFihSZMm6YcffrBPVQONacaMGXrttde0fft2dw8FJnXkyBF17NhRs2fPts+kSyeuC+rWrZsefvhhp5lVM/By9wCA85GZmak+ffqobdu22rRpk1544QWHz+Q4evSodu7cqfT0dD388MOEHzS6w4cPq6SkRC+++KKeffZZdw8HJlJUVKT//Oc/uu6663Tw4EGlpaVJkoYPH26v2bt3r1555RXt2bNHDzzwgLuG6lYEIDRL33zzjf72t79p//796tSpk/76179q6tSp9uXPP/+8ZsyYof79+zu0A43lL3/5i1599VWNGDHCnNdXwK1mzZqlHTt2yMfHRxEREdq4caPDR0wEBATI399fCxcuVJs2bdw4UvfhFBgAADAd7gIDAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACUO/uv/9+WSwWWSwWeXt7KyAgQIMHD9bixYvP+VvVG8qGDRtksVhO+2W+jc1isejtt9929zAAUyAAAWgQt956q2w2m0pLS/X+++9r4MCBmjRpkv74xz/q+PHjbh3bsWPH3Lp9AO5HAALQIKxWq9q3b6+OHTvq2muv1bRp0/TOO+/o/fff19KlSyVJBw8e1EMPPaTLLrtMrVu31s0336zPPvvM3sfTTz+ta665Rv/85z8VFBSkiy66SH/6058cZm0+/fRTDR48WP7+/vLz89OAAQO0bds2h7FYLBYtWLBAw4cPV8uWLRUXF6eBAwdKOvGN8xaLRffff78k6aabbtKECROUlJSkNm3aKCAgQAsXLtSRI0f0wAMPqFWrVurSpYvef/99h2189dVXGjp0qC6++GIFBAQoNjZWlZWV9uU33XSTJk6cqMcff1yXXnqp2rdvr6efftq+PCQkRJJ0xx13yGKx2J8DaBgEIACN5uabb1bPnj315ptvyjAM3XbbbSovL1dubq4KCwt17bXX6pZbbtH+/fvt63z77bd6/fXX9d5772n16tUqLi5WYmKiffmhQ4c0duxYbdy4UZ988omuvPJKDR06VIcOHXLY9lNPPaXhw4dr+/btSktL06pVqyRJO3bskM1m09y5c+21y5Ytk7+/v7Zs2aIJEybokUce0Z/+9Cf17dtX27ZtU0xMjGJjY3X06FFJks1m04ABA3TNNddo69atWr16tX788UfdfffdDmNYtmyZWrZsqc2bN+v5559XWlqa8vLyJJ0IcpK0ZMkS2Ww2+3MADcSdX0UP4MI0duxYY/jw4S6XjRo1yggLCzPWrl1rtG7d2vjll18clnfp0sX45z//aRiGYTz11FOGp6ensXv3bvvy999/3/Dw8DBsNpvL/o8fP260atXKeO+99+xtkoykpCSHuvXr1xuSjJ9++smhfcCAAcaNN97o0F/Lli2N2NhYe5vNZjMkGQUFBYZhGMYTTzxhREdHO/Sze/duQ5KxY8cOl/0ahmH06dPHmDx5ssM433rrLZf7BaB+8WWoABqVYRiyWCwqLCzU4cOH1bZtW4flP//8s7777jv7806dOunyyy+3P4+MjFRdXZ127Nih9u3ba+/evXryySe1bt06/fjjj6qtrdXRo0dVVlbm0G/v3r3Peow9evSw/+zp6am2bdvq6quvtrcFBARIOvGN2pJUWFio9evX6+KLL3bq67vvvtNVV13l1K8kBQYG2vsA0LgIQAAaVUlJiUJDQ1VXV6fAwEBt2LDBqeaSSy455foWi8Xhz/vvv18VFRXKyMhQcHCwrFarIiMjVVNT47Bey5Ytz3qM3t7eTtv8ddvJbZ+8o62urk7Dhg3Tc88959RXYGDgafttKnfFAWZDAALQaNatW6ft27crOTlZl19+ucrLy+Xl5XXaC37Lysr0ww8/qEOHDpKkgoICeXh42GdVNm7cqMzMTA0dOlSStHv3boeLj0/Fx8dHklRbW/s790q69tprtWrVKoWEhMjL6/zfVr29vetlPADOjIugATSI6upqlZeXa8+ePdq2bZtmzpyp4cOH649//KPGjBmjQYMGKTIyUiNGjNAHH3yg0tJS5efna/r06dq6dau9H19fX40dO1afffaZNm7cqIkTJ+ruu+9W+/btJUlXXHGFXnnlFZWUlGjz5s2677771KJFizOOLzg4WBaLRf/6179UUVGhw4cPn/e+JiYmav/+/br33nu1ZcsWff/991qzZo3GjRt3ToEmJCREa9euVXl5uX766afzHg+AMyMAAWgQq1evVmBgoEJCQnTrrbdq/fr1mjdvnt555x15enrKYrEoNzdX/fv317hx43TVVVfpnnvuUWlpqf0aG+lEwLnzzjs1dOhQRUdHKzw8XJmZmfblixcv1k8//aRevXopNjZWEydO1GWXXXbG8XXs2FHPPPOMpkyZooCAAP3lL385733t0KGDNm3apNraWsXExCg8PFyTJk2Sn5+fPDzO/m129uzZysvLU1BQkHr16nXe4wFwZhbDMAx3DwIAXHn66af19ttvq7i42N1DAXCBYQYIAACYDgEIAACYDqfAAACA6TADBAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATOf/AeWmW+7Ra7A8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df,x='Department',y='Yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8bd07890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BusinessTravel', ylabel='Yes'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='BusinessTravel', y='Yes', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ae64da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop (['Age', 'JobLevel'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e89b7649",
   "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>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>...</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>Shift</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      BusinessTravel  DailyRate  Department  DistanceFromHome  Education  \\\n",
       "0      Travel_Rarely       1102  Cardiology                 1          2   \n",
       "1  Travel_Frequently        279   Maternity                 8          1   \n",
       "2      Travel_Rarely       1373   Maternity                 2          2   \n",
       "3  Travel_Frequently       1392   Maternity                 3          4   \n",
       "4      Travel_Rarely        591   Maternity                 2          1   \n",
       "\n",
       "  EducationField  EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  \\\n",
       "0  Life Sciences                        2  Female          94               3   \n",
       "1  Life Sciences                        3    Male          61               2   \n",
       "2          Other                        4    Male          92               2   \n",
       "3  Life Sciences                        4  Female          56               3   \n",
       "4        Medical                        1    Male          40               3   \n",
       "\n",
       "   ... StandardHours  Shift TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0  ...            80      0                 8                      0   \n",
       "1  ...            80      1                10                      3   \n",
       "2  ...            80      0                 7                      3   \n",
       "3  ...            80      0                 8                      3   \n",
       "4  ...            80      1                 6                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion  \\\n",
       "0                1               6                  4                       0   \n",
       "1                3              10                  7                       1   \n",
       "2                3               0                  0                       0   \n",
       "3                3               8                  7                       3   \n",
       "4                3               2                  2                       2   \n",
       "\n",
       "   YearsWithCurrManager  Yes  \n",
       "0                     5    0  \n",
       "1                     7    0  \n",
       "2                     0    1  \n",
       "3                     0    0  \n",
       "4                     2    0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "811f2301",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30380\\4259945406.py:3: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if df[column].dtype==np.number:\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "for column in df.columns:\n",
    "    if df[column].dtype==np.number:\n",
    "        continue\n",
    "    else:\n",
    "        df[column]=LabelEncoder().fit_transform(df[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9960cd40",
   "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>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>...</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>Shift</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>805</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>820</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   BusinessTravel  DailyRate  Department  DistanceFromHome  Education  \\\n",
       "0               2        624           0                 0          1   \n",
       "1               1        113           1                 7          0   \n",
       "2               2        805           1                 1          1   \n",
       "3               1        820           1                 2          3   \n",
       "4               2        312           1                 1          0   \n",
       "\n",
       "   EducationField  EnvironmentSatisfaction  Gender  HourlyRate  \\\n",
       "0               1                        1       0          64   \n",
       "1               1                        2       1          31   \n",
       "2               4                        3       1          62   \n",
       "3               1                        3       0          26   \n",
       "4               3                        0       1          10   \n",
       "\n",
       "   JobInvolvement  ...  StandardHours  Shift  TotalWorkingYears  \\\n",
       "0               2  ...              0      0                  8   \n",
       "1               1  ...              0      1                 10   \n",
       "2               1  ...              0      0                  7   \n",
       "3               2  ...              0      0                  8   \n",
       "4               2  ...              0      1                  6   \n",
       "\n",
       "   TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                      0                0               6                   4   \n",
       "1                      3                2              10                   7   \n",
       "2                      3                2               0                   0   \n",
       "3                      3                2               8                   7   \n",
       "4                      3                2               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  Yes  \n",
       "0                        0                     5    0  \n",
       "1                        1                     7    0  \n",
       "2                        0                     0    1  \n",
       "3                        3                     0    0  \n",
       "4                        2                     2    0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f412b946",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1676 entries, 0 to 1675\n",
      "Data columns (total 31 columns):\n",
      " #   Column                    Non-Null Count  Dtype\n",
      "---  ------                    --------------  -----\n",
      " 0   BusinessTravel            1676 non-null   int32\n",
      " 1   DailyRate                 1676 non-null   int64\n",
      " 2   Department                1676 non-null   int32\n",
      " 3   DistanceFromHome          1676 non-null   int64\n",
      " 4   Education                 1676 non-null   int64\n",
      " 5   EducationField            1676 non-null   int32\n",
      " 6   EnvironmentSatisfaction   1676 non-null   int64\n",
      " 7   Gender                    1676 non-null   int32\n",
      " 8   HourlyRate                1676 non-null   int64\n",
      " 9   JobInvolvement            1676 non-null   int64\n",
      " 10  JobRole                   1676 non-null   int32\n",
      " 11  JobSatisfaction           1676 non-null   int64\n",
      " 12  MaritalStatus             1676 non-null   int32\n",
      " 13  MonthlyIncome             1676 non-null   int64\n",
      " 14  MonthlyRate               1676 non-null   int64\n",
      " 15  NumCompaniesWorked        1676 non-null   int64\n",
      " 16  Over18                    1676 non-null   int32\n",
      " 17  OverTime                  1676 non-null   int32\n",
      " 18  PercentSalaryHike         1676 non-null   int64\n",
      " 19  PerformanceRating         1676 non-null   int64\n",
      " 20  RelationshipSatisfaction  1676 non-null   int64\n",
      " 21  StandardHours             1676 non-null   int64\n",
      " 22  Shift                     1676 non-null   int64\n",
      " 23  TotalWorkingYears         1676 non-null   int64\n",
      " 24  TrainingTimesLastYear     1676 non-null   int64\n",
      " 25  WorkLifeBalance           1676 non-null   int64\n",
      " 26  YearsAtCompany            1676 non-null   int64\n",
      " 27  YearsInCurrentRole        1676 non-null   int64\n",
      " 28  YearsSinceLastPromotion   1676 non-null   int64\n",
      " 29  YearsWithCurrManager      1676 non-null   int64\n",
      " 30  Yes                       1676 non-null   int64\n",
      "dtypes: int32(8), int64(23)\n",
      "memory usage: 353.7 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e383a5c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3f53cb7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('Yes',axis=1)\n",
    "Y = df.Yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "127f197f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc3f9d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6fc6f739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "055ed61a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9304174950298211"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e1901277",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model2 = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3cf6d612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bc0c6014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8727634194831014"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5d2d5ac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model3 = SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b0163fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4857031",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "42e593e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9145129224652088"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.score(X_test,Y_test)"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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