Python_PRP.ipynb

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  {
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   "id": "abd074f6",
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765b5943",
   "metadata": {},
   "source": [
    "### TASK 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083add72",
   "metadata": {},
   "source": [
    "### Import all datasets in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bee702e2",
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   "outputs": [],
   "source": [
    "emp = pd.read_excel('C://Users//Yatharth Pandya//Desktop//Alab Work//Project_p//Project_Data_PythonPRP//Project_Data_PythonPRP.xlsx', sheet_name='Employee_Data')\n",
    "emp_sur = pd.read_excel('C://Users//Yatharth Pandya//Desktop//Alab Work//Project_p//Project_Data_PythonPRP//Project_Data_PythonPRP.xlsx', sheet_name='Employee_Survey_Data')\n",
    "manager_sur = pd.read_excel('C://Users//Yatharth Pandya//Desktop//Alab Work//Project_p//Project_Data_PythonPRP//Project_Data_PythonPRP.xlsx', sheet_name='Manager_Survey_Data')\n",
    "in_time = pd.read_excel('C://Users//Yatharth Pandya//Desktop//Alab Work//Project_p//Project_Data_PythonPRP//Project_Data_PythonPRP.xlsx', sheet_name='In_Time')\n",
    "out_time = pd.read_excel('C://Users//Yatharth Pandya//Desktop//Alab Work//Project_p//Project_Data_PythonPRP//Project_Data_PythonPRP.xlsx', sheet_name='Out_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d90e2d8b",
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   "outputs": [
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       "      EmployeeID  Age Attrition_Date     BusinessTravel  \\\n",
       "0              1   51            NaN      Travel_Rarely   \n",
       "1              2   31     12/13/2015  Travel_Frequently   \n",
       "2              3   32            NaN  Travel_Frequently   \n",
       "3              4   38            NaN         Non-Travel   \n",
       "4              5   32            NaN      Travel_Rarely   \n",
       "...          ...  ...            ...                ...   \n",
       "4457        4412   20            NaN         Frequently   \n",
       "4458        4413   20            NaN             Rarely   \n",
       "4459        4414   32            NaN         Frequently   \n",
       "4460        4415   20            NaN             Rarely   \n",
       "4461        4416   32            NaN         Frequently   \n",
       "\n",
       "                  Department  DistanceFromHome  Education EducationField  \\\n",
       "0                      Sales                 6          2  Life Sciences   \n",
       "1                        R&D                10          1  Life Sciences   \n",
       "2                        R&D                17          4          Other   \n",
       "3                        R&D                 2          5  Life Sciences   \n",
       "4     Research & Development                10          1        Medical   \n",
       "...                      ...               ...        ...            ...   \n",
       "4457                   Sales               100          3          Other   \n",
       "4458                     R&D                20          3        Medical   \n",
       "4459                     R&D                12          3      Marketing   \n",
       "4460                     R&D                20         -1        Medical   \n",
       "4461                     R&D                12          3      Marketing   \n",
       "\n",
       "      Gender  JobLevel  ... Over18 PercentSalaryHike  StandardHours  \\\n",
       "0     Female         1  ...      Y                11              8   \n",
       "1     Female         1  ...      Y                23              8   \n",
       "2       Male         4  ...      Y                15              8   \n",
       "3       Male         3  ...      Y                11              8   \n",
       "4       Male         1  ...      Y                12              8   \n",
       "...      ...       ...  ...    ...               ...            ...   \n",
       "4457    Male         2  ...      Y                12              8   \n",
       "4458    Male         3  ...      Y                12              8   \n",
       "4459  Female         3  ...      Y               200              8   \n",
       "4460    Male         3  ...      Y                12              8   \n",
       "4461  Female        10  ...      Y               200              8   \n",
       "\n",
       "      StockOptionLevel TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                    0               1.0                      6   \n",
       "1                    1               6.0                      3   \n",
       "2                    3               5.0                      2   \n",
       "3                    3              13.0                      5   \n",
       "4                    2               9.0                      2   \n",
       "...                ...               ...                    ...   \n",
       "4457                 0               NaN                      6   \n",
       "4458                 0               NaN                      6   \n",
       "4459                 0               NaN                      6   \n",
       "4460                 0               NaN                      6   \n",
       "4461                 0               NaN                      6   \n",
       "\n",
       "      YearsAtCompany  YearsSinceLastPromotion  YearsWithCurrManager  \\\n",
       "0                  1                        0                     0   \n",
       "1                  5                        1                     4   \n",
       "2                  5                        0                     3   \n",
       "3                  8                        7                     5   \n",
       "4                  6                        0                     4   \n",
       "...              ...                      ...                   ...   \n",
       "4457              21                        3                     9   \n",
       "4458              21                        3                     9   \n",
       "4459              21                        3                     9   \n",
       "4460              21                        3                     9   \n",
       "4461              21                        3                     9   \n",
       "\n",
       "      BreaksTakenPerDay  \n",
       "0                   1.0  \n",
       "1                   2.0  \n",
       "2                   3.0  \n",
       "3                   2.0  \n",
       "4                   3.0  \n",
       "...                 ...  \n",
       "4457                2.0  \n",
       "4458                2.0  \n",
       "4459                2.0  \n",
       "4460                2.0  \n",
       "4461                2.0  \n",
       "\n",
       "[4462 rows x 24 columns]"
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    "emp"
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       "      EmployeeID  EnvironmentSatisfaction  JobSatisfaction  WorkLifeBalance\n",
       "0              1                      3.0              4.0              2.0\n",
       "1              2                      3.0              2.0              4.0\n",
       "2              3                      2.0              2.0              1.0\n",
       "3              4                      4.0              4.0              3.0\n",
       "4              5                      4.0              1.0              3.0\n",
       "...          ...                      ...              ...              ...\n",
       "4432        4410                      1.0              3.0              NaN\n",
       "4433        4411                      1.0              3.0              NaN\n",
       "4434        4412                      1.0              3.0              3.0\n",
       "4435        4413                      4.0              1.0              3.0\n",
       "4436        4414                      1.0              3.0              NaN\n",
       "\n",
       "[4437 rows x 4 columns]"
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   "id": "3353c175",
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       "      EmployeeID  JobInvolvement  PerformanceRating\n",
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       "      <td>12/29/2015 9:55</td>\n",
       "      <td>12/30/2015 10:32</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2015-02-01 10:17:00</td>\n",
       "      <td>2015-05-01 09:50:00</td>\n",
       "      <td>2015-06-01 10:14:00</td>\n",
       "      <td>2015-07-01 09:47:00</td>\n",
       "      <td>2015-08-01 10:03:00</td>\n",
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       "      <td>2015-12-01 10:03:00</td>\n",
       "      <td>1/13/2015 10:21</td>\n",
       "      <td>1/15/2015 9:55</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:53</td>\n",
       "      <td>12/18/2015 10:15</td>\n",
       "      <td>12/21/2015 10:10</td>\n",
       "      <td>12/22/2015 9:44</td>\n",
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       "      <td>2015-07-01 09:37:00</td>\n",
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       "      <td>1/13/2015 9:53</td>\n",
       "      <td>1/15/2015 10:00</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:54</td>\n",
       "      <td>12/18/2015 10:17</td>\n",
       "      <td>12/21/2015 9:58</td>\n",
       "      <td>12/22/2015 10:04</td>\n",
       "      <td>12/23/2015 10:11</td>\n",
       "      <td>12/24/2015 9:43</td>\n",
       "      <td>12/28/2015 9:52</td>\n",
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       "      <td>12/31/2015 10:01</td>\n",
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       "      <td>2015-06-01 09:45:00</td>\n",
       "      <td>2015-07-01 09:49:00</td>\n",
       "      <td>2015-08-01 10:19:00</td>\n",
       "      <td>2015-09-01 10:00:00</td>\n",
       "      <td>2015-12-01 10:29:00</td>\n",
       "      <td>1/13/2015 9:59</td>\n",
       "      <td>1/15/2015 10:06</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:46</td>\n",
       "      <td>12/18/2015 9:58</td>\n",
       "      <td>12/21/2015 10:03</td>\n",
       "      <td>12/22/2015 10:10</td>\n",
       "      <td>12/23/2015 10:13</td>\n",
       "      <td>12/24/2015 9:44</td>\n",
       "      <td>12/28/2015 10:05</td>\n",
       "      <td>12/29/2015 10:30</td>\n",
       "      <td>12/30/2015 9:18</td>\n",
       "      <td>12/31/2015 9:41</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4409</th>\n",
       "      <td>4410</td>\n",
       "      <td>2015-02-01 09:59:00</td>\n",
       "      <td>2015-05-01 10:16:00</td>\n",
       "      <td>2015-06-01 09:52:00</td>\n",
       "      <td>2015-07-01 09:43:00</td>\n",
       "      <td>2015-08-01 10:06:00</td>\n",
       "      <td>2015-09-01 10:27:00</td>\n",
       "      <td>2015-12-01 09:47:00</td>\n",
       "      <td>1/13/2015 9:30</td>\n",
       "      <td>1/15/2015 10:08</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:58</td>\n",
       "      <td>12/18/2015 10:20</td>\n",
       "      <td>12/21/2015 10:26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/23/2015 9:53</td>\n",
       "      <td>12/24/2015 9:42</td>\n",
       "      <td>12/28/2015 9:41</td>\n",
       "      <td>12/29/2015 10:05</td>\n",
       "      <td>12/30/2015 10:32</td>\n",
       "      <td>12/31/2015 10:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4410</th>\n",
       "      <td>4411</td>\n",
       "      <td>2015-02-01 10:03:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2015-06-01 09:44:00</td>\n",
       "      <td>2015-07-01 09:42:00</td>\n",
       "      <td>2015-08-01 10:00:00</td>\n",
       "      <td>2015-09-01 09:44:00</td>\n",
       "      <td>2015-12-01 10:07:00</td>\n",
       "      <td>1/13/2015 10:05</td>\n",
       "      <td>1/15/2015 10:18</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:45</td>\n",
       "      <td>12/18/2015 9:27</td>\n",
       "      <td>12/21/2015 9:41</td>\n",
       "      <td>12/22/2015 9:50</td>\n",
       "      <td>12/23/2015 10:32</td>\n",
       "      <td>12/24/2015 9:47</td>\n",
       "      <td>12/28/2015 9:54</td>\n",
       "      <td>12/29/2015 10:13</td>\n",
       "      <td>12/30/2015 10:21</td>\n",
       "      <td>12/31/2015 10:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4411</th>\n",
       "      <td>4412</td>\n",
       "      <td>2015-02-01 10:01:00</td>\n",
       "      <td>2015-05-01 09:33:00</td>\n",
       "      <td>2015-06-01 09:49:00</td>\n",
       "      <td>2015-07-01 10:28:00</td>\n",
       "      <td>2015-08-01 09:47:00</td>\n",
       "      <td>2015-09-01 10:01:00</td>\n",
       "      <td>2015-12-01 09:49:00</td>\n",
       "      <td>1/13/2015 9:47</td>\n",
       "      <td>1/15/2015 10:08</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 10:01</td>\n",
       "      <td>12/18/2015 10:00</td>\n",
       "      <td>12/21/2015 9:51</td>\n",
       "      <td>12/22/2015 10:02</td>\n",
       "      <td>12/23/2015 9:58</td>\n",
       "      <td>12/24/2015 9:56</td>\n",
       "      <td>12/28/2015 9:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/30/2015 10:02</td>\n",
       "      <td>12/31/2015 10:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4412</th>\n",
       "      <td>4413</td>\n",
       "      <td>2015-02-01 10:17:00</td>\n",
       "      <td>2015-05-01 10:02:00</td>\n",
       "      <td>2015-06-01 10:12:00</td>\n",
       "      <td>2015-07-01 10:12:00</td>\n",
       "      <td>2015-08-01 09:42:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2015-12-01 10:00:00</td>\n",
       "      <td>1/13/2015 9:48</td>\n",
       "      <td>1/15/2015 9:04</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:51</td>\n",
       "      <td>12/18/2015 9:54</td>\n",
       "      <td>12/21/2015 10:01</td>\n",
       "      <td>12/22/2015 10:10</td>\n",
       "      <td>12/23/2015 9:42</td>\n",
       "      <td>12/24/2015 9:56</td>\n",
       "      <td>12/28/2015 9:55</td>\n",
       "      <td>12/29/2015 9:54</td>\n",
       "      <td>12/30/2015 10:15</td>\n",
       "      <td>12/31/2015 9:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4413</th>\n",
       "      <td>4414</td>\n",
       "      <td>2015-02-01 09:59:00</td>\n",
       "      <td>2015-05-01 10:16:00</td>\n",
       "      <td>2015-06-01 09:52:00</td>\n",
       "      <td>2015-07-01 09:43:00</td>\n",
       "      <td>2015-08-01 10:06:00</td>\n",
       "      <td>2015-09-01 10:27:00</td>\n",
       "      <td>2015-12-01 09:47:00</td>\n",
       "      <td>1/13/2015 9:30</td>\n",
       "      <td>1/15/2015 10:08</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:58</td>\n",
       "      <td>12/18/2015 10:20</td>\n",
       "      <td>12/21/2015 10:26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/23/2015 9:53</td>\n",
       "      <td>12/24/2015 9:42</td>\n",
       "      <td>12/28/2015 9:41</td>\n",
       "      <td>12/29/2015 10:05</td>\n",
       "      <td>12/30/2015 10:32</td>\n",
       "      <td>12/31/2015 10:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4414 rows × 250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EmployeeID 2015-02-01 00:00:00 2015-05-01 00:00:00 2015-06-01 00:00:00  \\\n",
       "0              1 2015-02-01 09:43:00 2015-05-01 10:08:00 2015-06-01 09:54:00   \n",
       "1              2 2015-02-01 10:15:00 2015-05-01 10:21:00                 NaT   \n",
       "2              3 2015-02-01 10:17:00 2015-05-01 09:50:00 2015-06-01 10:14:00   \n",
       "3              4 2015-02-01 10:05:00 2015-05-01 09:56:00 2015-06-01 10:11:00   \n",
       "4              5 2015-02-01 10:28:00 2015-05-01 09:49:00 2015-06-01 09:45:00   \n",
       "...          ...                 ...                 ...                 ...   \n",
       "4409        4410 2015-02-01 09:59:00 2015-05-01 10:16:00 2015-06-01 09:52:00   \n",
       "4410        4411 2015-02-01 10:03:00                 NaT 2015-06-01 09:44:00   \n",
       "4411        4412 2015-02-01 10:01:00 2015-05-01 09:33:00 2015-06-01 09:49:00   \n",
       "4412        4413 2015-02-01 10:17:00 2015-05-01 10:02:00 2015-06-01 10:12:00   \n",
       "4413        4414 2015-02-01 09:59:00 2015-05-01 10:16:00 2015-06-01 09:52:00   \n",
       "\n",
       "     2015-07-01 00:00:00 2015-08-01 00:00:00 2015-09-01 00:00:00  \\\n",
       "0    2015-07-01 09:34:00 2015-08-01 09:51:00 2015-09-01 10:09:00   \n",
       "1    2015-07-01 09:45:00 2015-08-01 10:09:00 2015-09-01 09:43:00   \n",
       "2    2015-07-01 09:47:00 2015-08-01 10:03:00 2015-09-01 10:05:00   \n",
       "3    2015-07-01 09:37:00 2015-08-01 10:02:00 2015-09-01 10:08:00   \n",
       "4    2015-07-01 09:49:00 2015-08-01 10:19:00 2015-09-01 10:00:00   \n",
       "...                  ...                 ...                 ...   \n",
       "4409 2015-07-01 09:43:00 2015-08-01 10:06:00 2015-09-01 10:27:00   \n",
       "4410 2015-07-01 09:42:00 2015-08-01 10:00:00 2015-09-01 09:44:00   \n",
       "4411 2015-07-01 10:28:00 2015-08-01 09:47:00 2015-09-01 10:01:00   \n",
       "4412 2015-07-01 10:12:00 2015-08-01 09:42:00                 NaT   \n",
       "4413 2015-07-01 09:43:00 2015-08-01 10:06:00 2015-09-01 10:27:00   \n",
       "\n",
       "     2015-12-01 00:00:00        1/13/2015        1/15/2015  ...  \\\n",
       "0    2015-12-01 09:42:00  1/13/2015 10:13  1/15/2015 10:01  ...   \n",
       "1    2015-12-01 10:00:00  1/13/2015 10:43   1/15/2015 9:37  ...   \n",
       "2    2015-12-01 10:03:00  1/13/2015 10:21   1/15/2015 9:55  ...   \n",
       "3    2015-12-01 10:13:00   1/13/2015 9:53  1/15/2015 10:00  ...   \n",
       "4    2015-12-01 10:29:00   1/13/2015 9:59  1/15/2015 10:06  ...   \n",
       "...                  ...              ...              ...  ...   \n",
       "4409 2015-12-01 09:47:00   1/13/2015 9:30  1/15/2015 10:08  ...   \n",
       "4410 2015-12-01 10:07:00  1/13/2015 10:05  1/15/2015 10:18  ...   \n",
       "4411 2015-12-01 09:49:00   1/13/2015 9:47  1/15/2015 10:08  ...   \n",
       "4412 2015-12-01 10:00:00   1/13/2015 9:48   1/15/2015 9:04  ...   \n",
       "4413 2015-12-01 09:47:00   1/13/2015 9:30  1/15/2015 10:08  ...   \n",
       "\n",
       "            12/17/2015        12/18/2015        12/21/2015        12/22/2015  \\\n",
       "0                  NaN               NaN   12/21/2015 9:55  12/22/2015 10:04   \n",
       "1      12/17/2015 9:15  12/18/2015 10:37   12/21/2015 9:49  12/22/2015 10:33   \n",
       "2      12/17/2015 9:53  12/18/2015 10:15  12/21/2015 10:10   12/22/2015 9:44   \n",
       "3      12/17/2015 9:54  12/18/2015 10:17   12/21/2015 9:58  12/22/2015 10:04   \n",
       "4      12/17/2015 9:46   12/18/2015 9:58  12/21/2015 10:03  12/22/2015 10:10   \n",
       "...                ...               ...               ...               ...   \n",
       "4409   12/17/2015 9:58  12/18/2015 10:20  12/21/2015 10:26               NaN   \n",
       "4410   12/17/2015 9:45   12/18/2015 9:27   12/21/2015 9:41   12/22/2015 9:50   \n",
       "4411  12/17/2015 10:01  12/18/2015 10:00   12/21/2015 9:51  12/22/2015 10:02   \n",
       "4412   12/17/2015 9:51   12/18/2015 9:54  12/21/2015 10:01  12/22/2015 10:10   \n",
       "4413   12/17/2015 9:58  12/18/2015 10:20  12/21/2015 10:26               NaN   \n",
       "\n",
       "            12/23/2015        12/24/2015        12/28/2015        12/29/2015  \\\n",
       "0     12/23/2015 10:14  12/24/2015 10:11  12/28/2015 10:13  12/29/2015 10:03   \n",
       "1     12/23/2015 10:12               NaN   12/28/2015 9:31   12/29/2015 9:55   \n",
       "2     12/23/2015 10:15  12/24/2015 10:07   12/28/2015 9:42   12/29/2015 9:43   \n",
       "3     12/23/2015 10:11   12/24/2015 9:43   12/28/2015 9:52   12/29/2015 9:33   \n",
       "4     12/23/2015 10:13   12/24/2015 9:44  12/28/2015 10:05  12/29/2015 10:30   \n",
       "...                ...               ...               ...               ...   \n",
       "4409   12/23/2015 9:53   12/24/2015 9:42   12/28/2015 9:41  12/29/2015 10:05   \n",
       "4410  12/23/2015 10:32   12/24/2015 9:47   12/28/2015 9:54  12/29/2015 10:13   \n",
       "4411   12/23/2015 9:58   12/24/2015 9:56   12/28/2015 9:59               NaN   \n",
       "4412   12/23/2015 9:42   12/24/2015 9:56   12/28/2015 9:55   12/29/2015 9:54   \n",
       "4413   12/23/2015 9:53   12/24/2015 9:42   12/28/2015 9:41  12/29/2015 10:05   \n",
       "\n",
       "            12/30/2015        12/31/2015  \n",
       "0      12/30/2015 9:54  12/31/2015 10:12  \n",
       "1     12/30/2015 10:32   12/31/2015 9:27  \n",
       "2      12/30/2015 9:34  12/31/2015 10:28  \n",
       "3     12/30/2015 10:18  12/31/2015 10:01  \n",
       "4      12/30/2015 9:18   12/31/2015 9:41  \n",
       "...                ...               ...  \n",
       "4409  12/30/2015 10:32  12/31/2015 10:09  \n",
       "4410  12/30/2015 10:21  12/31/2015 10:09  \n",
       "4411  12/30/2015 10:02  12/31/2015 10:03  \n",
       "4412  12/30/2015 10:15   12/31/2015 9:56  \n",
       "4413  12/30/2015 10:32  12/31/2015 10:09  \n",
       "\n",
       "[4414 rows x 250 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5c549d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "except_column = 'EmployeeID'\n",
    "columns_to_convert = [col for col in in_time.columns if col != except_column]\n",
    "\n",
    "# Specify datetime format for each column\n",
    "date_formats = {col: 'hh:mm' for col in columns_to_convert}\n",
    "\n",
    "# Convert columns to datetime\n",
    "in_time = in_time.apply(pd.to_datetime, errors='ignore', format='hh:mm')\n",
    "\n",
    "# If 'except_column' needs to remain as is, you can specify its data type as object\n",
    "in_time[except_column] = in_time[except_column].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0a716690",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeID                     object\n",
       "2015-02-01 00:00:00    datetime64[ns]\n",
       "2015-05-01 00:00:00    datetime64[ns]\n",
       "2015-06-01 00:00:00    datetime64[ns]\n",
       "2015-07-01 00:00:00    datetime64[ns]\n",
       "                            ...      \n",
       "12/24/2015                     object\n",
       "12/28/2015                     object\n",
       "12/29/2015                     object\n",
       "12/30/2015                     object\n",
       "12/31/2015                     object\n",
       "Length: 250, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_time.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8756debf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EmployeeID</th>\n",
       "      <th>2015-02-01 00:00:00</th>\n",
       "      <th>2015-05-01 00:00:00</th>\n",
       "      <th>2015-06-01 00:00:00</th>\n",
       "      <th>2015-07-01 00:00:00</th>\n",
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       "      <th>2015-09-01 00:00:00</th>\n",
       "      <th>2015-12-01 00:00:00</th>\n",
       "      <th>1/13/2015</th>\n",
       "      <th>1/15/2015</th>\n",
       "      <th>...</th>\n",
       "      <th>12/17/2015</th>\n",
       "      <th>12/18/2015</th>\n",
       "      <th>12/21/2015</th>\n",
       "      <th>12/22/2015</th>\n",
       "      <th>12/23/2015</th>\n",
       "      <th>12/24/2015</th>\n",
       "      <th>12/28/2015</th>\n",
       "      <th>12/29/2015</th>\n",
       "      <th>12/30/2015</th>\n",
       "      <th>12/31/2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-02-01 09:43:00</td>\n",
       "      <td>2015-05-01 10:08:00</td>\n",
       "      <td>2015-06-01 09:54:00</td>\n",
       "      <td>2015-07-01 09:34:00</td>\n",
       "      <td>2015-08-01 09:51:00</td>\n",
       "      <td>2015-09-01 10:09:00</td>\n",
       "      <td>2015-12-01 09:42:00</td>\n",
       "      <td>1/13/2015 10:13</td>\n",
       "      <td>1/15/2015 10:01</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/21/2015 9:55</td>\n",
       "      <td>12/22/2015 10:04</td>\n",
       "      <td>12/23/2015 10:14</td>\n",
       "      <td>12/24/2015 10:11</td>\n",
       "      <td>12/28/2015 10:13</td>\n",
       "      <td>12/29/2015 10:03</td>\n",
       "      <td>12/30/2015 9:54</td>\n",
       "      <td>12/31/2015 10:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-01 10:15:00</td>\n",
       "      <td>2015-05-01 10:21:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2015-07-01 09:45:00</td>\n",
       "      <td>2015-08-01 10:09:00</td>\n",
       "      <td>2015-09-01 09:43:00</td>\n",
       "      <td>2015-12-01 10:00:00</td>\n",
       "      <td>1/13/2015 10:43</td>\n",
       "      <td>1/15/2015 9:37</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:15</td>\n",
       "      <td>12/18/2015 10:37</td>\n",
       "      <td>12/21/2015 9:49</td>\n",
       "      <td>12/22/2015 10:33</td>\n",
       "      <td>12/23/2015 10:12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/28/2015 9:31</td>\n",
       "      <td>12/29/2015 9:55</td>\n",
       "      <td>12/30/2015 10:32</td>\n",
       "      <td>12/31/2015 9:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-02-01 10:17:00</td>\n",
       "      <td>2015-05-01 09:50:00</td>\n",
       "      <td>2015-06-01 10:14:00</td>\n",
       "      <td>2015-07-01 09:47:00</td>\n",
       "      <td>2015-08-01 10:03:00</td>\n",
       "      <td>2015-09-01 10:05:00</td>\n",
       "      <td>2015-12-01 10:03:00</td>\n",
       "      <td>1/13/2015 10:21</td>\n",
       "      <td>1/15/2015 9:55</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:53</td>\n",
       "      <td>12/18/2015 10:15</td>\n",
       "      <td>12/21/2015 10:10</td>\n",
       "      <td>12/22/2015 9:44</td>\n",
       "      <td>12/23/2015 10:15</td>\n",
       "      <td>12/24/2015 10:07</td>\n",
       "      <td>12/28/2015 9:42</td>\n",
       "      <td>12/29/2015 9:43</td>\n",
       "      <td>12/30/2015 9:34</td>\n",
       "      <td>12/31/2015 10:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-02-01 10:05:00</td>\n",
       "      <td>2015-05-01 09:56:00</td>\n",
       "      <td>2015-06-01 10:11:00</td>\n",
       "      <td>2015-07-01 09:37:00</td>\n",
       "      <td>2015-08-01 10:02:00</td>\n",
       "      <td>2015-09-01 10:08:00</td>\n",
       "      <td>2015-12-01 10:13:00</td>\n",
       "      <td>1/13/2015 9:53</td>\n",
       "      <td>1/15/2015 10:00</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:54</td>\n",
       "      <td>12/18/2015 10:17</td>\n",
       "      <td>12/21/2015 9:58</td>\n",
       "      <td>12/22/2015 10:04</td>\n",
       "      <td>12/23/2015 10:11</td>\n",
       "      <td>12/24/2015 9:43</td>\n",
       "      <td>12/28/2015 9:52</td>\n",
       "      <td>12/29/2015 9:33</td>\n",
       "      <td>12/30/2015 10:18</td>\n",
       "      <td>12/31/2015 10:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2015-02-01 10:28:00</td>\n",
       "      <td>2015-05-01 09:49:00</td>\n",
       "      <td>2015-06-01 09:45:00</td>\n",
       "      <td>2015-07-01 09:49:00</td>\n",
       "      <td>2015-08-01 10:19:00</td>\n",
       "      <td>2015-09-01 10:00:00</td>\n",
       "      <td>2015-12-01 10:29:00</td>\n",
       "      <td>1/13/2015 9:59</td>\n",
       "      <td>1/15/2015 10:06</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:46</td>\n",
       "      <td>12/18/2015 9:58</td>\n",
       "      <td>12/21/2015 10:03</td>\n",
       "      <td>12/22/2015 10:10</td>\n",
       "      <td>12/23/2015 10:13</td>\n",
       "      <td>12/24/2015 9:44</td>\n",
       "      <td>12/28/2015 10:05</td>\n",
       "      <td>12/29/2015 10:30</td>\n",
       "      <td>12/30/2015 9:18</td>\n",
       "      <td>12/31/2015 9:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4409</th>\n",
       "      <td>4410</td>\n",
       "      <td>2015-02-01 09:59:00</td>\n",
       "      <td>2015-05-01 10:16:00</td>\n",
       "      <td>2015-06-01 09:52:00</td>\n",
       "      <td>2015-07-01 09:43:00</td>\n",
       "      <td>2015-08-01 10:06:00</td>\n",
       "      <td>2015-09-01 10:27:00</td>\n",
       "      <td>2015-12-01 09:47:00</td>\n",
       "      <td>1/13/2015 9:30</td>\n",
       "      <td>1/15/2015 10:08</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:58</td>\n",
       "      <td>12/18/2015 10:20</td>\n",
       "      <td>12/21/2015 10:26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/23/2015 9:53</td>\n",
       "      <td>12/24/2015 9:42</td>\n",
       "      <td>12/28/2015 9:41</td>\n",
       "      <td>12/29/2015 10:05</td>\n",
       "      <td>12/30/2015 10:32</td>\n",
       "      <td>12/31/2015 10:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4410</th>\n",
       "      <td>4411</td>\n",
       "      <td>2015-02-01 10:03:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2015-06-01 09:44:00</td>\n",
       "      <td>2015-07-01 09:42:00</td>\n",
       "      <td>2015-08-01 10:00:00</td>\n",
       "      <td>2015-09-01 09:44:00</td>\n",
       "      <td>2015-12-01 10:07:00</td>\n",
       "      <td>1/13/2015 10:05</td>\n",
       "      <td>1/15/2015 10:18</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:45</td>\n",
       "      <td>12/18/2015 9:27</td>\n",
       "      <td>12/21/2015 9:41</td>\n",
       "      <td>12/22/2015 9:50</td>\n",
       "      <td>12/23/2015 10:32</td>\n",
       "      <td>12/24/2015 9:47</td>\n",
       "      <td>12/28/2015 9:54</td>\n",
       "      <td>12/29/2015 10:13</td>\n",
       "      <td>12/30/2015 10:21</td>\n",
       "      <td>12/31/2015 10:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4411</th>\n",
       "      <td>4412</td>\n",
       "      <td>2015-02-01 10:01:00</td>\n",
       "      <td>2015-05-01 09:33:00</td>\n",
       "      <td>2015-06-01 09:49:00</td>\n",
       "      <td>2015-07-01 10:28:00</td>\n",
       "      <td>2015-08-01 09:47:00</td>\n",
       "      <td>2015-09-01 10:01:00</td>\n",
       "      <td>2015-12-01 09:49:00</td>\n",
       "      <td>1/13/2015 9:47</td>\n",
       "      <td>1/15/2015 10:08</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 10:01</td>\n",
       "      <td>12/18/2015 10:00</td>\n",
       "      <td>12/21/2015 9:51</td>\n",
       "      <td>12/22/2015 10:02</td>\n",
       "      <td>12/23/2015 9:58</td>\n",
       "      <td>12/24/2015 9:56</td>\n",
       "      <td>12/28/2015 9:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/30/2015 10:02</td>\n",
       "      <td>12/31/2015 10:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4412</th>\n",
       "      <td>4413</td>\n",
       "      <td>2015-02-01 10:17:00</td>\n",
       "      <td>2015-05-01 10:02:00</td>\n",
       "      <td>2015-06-01 10:12:00</td>\n",
       "      <td>2015-07-01 10:12:00</td>\n",
       "      <td>2015-08-01 09:42:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2015-12-01 10:00:00</td>\n",
       "      <td>1/13/2015 9:48</td>\n",
       "      <td>1/15/2015 9:04</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:51</td>\n",
       "      <td>12/18/2015 9:54</td>\n",
       "      <td>12/21/2015 10:01</td>\n",
       "      <td>12/22/2015 10:10</td>\n",
       "      <td>12/23/2015 9:42</td>\n",
       "      <td>12/24/2015 9:56</td>\n",
       "      <td>12/28/2015 9:55</td>\n",
       "      <td>12/29/2015 9:54</td>\n",
       "      <td>12/30/2015 10:15</td>\n",
       "      <td>12/31/2015 9:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4413</th>\n",
       "      <td>4414</td>\n",
       "      <td>2015-02-01 09:59:00</td>\n",
       "      <td>2015-05-01 10:16:00</td>\n",
       "      <td>2015-06-01 09:52:00</td>\n",
       "      <td>2015-07-01 09:43:00</td>\n",
       "      <td>2015-08-01 10:06:00</td>\n",
       "      <td>2015-09-01 10:27:00</td>\n",
       "      <td>2015-12-01 09:47:00</td>\n",
       "      <td>1/13/2015 9:30</td>\n",
       "      <td>1/15/2015 10:08</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 9:58</td>\n",
       "      <td>12/18/2015 10:20</td>\n",
       "      <td>12/21/2015 10:26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/23/2015 9:53</td>\n",
       "      <td>12/24/2015 9:42</td>\n",
       "      <td>12/28/2015 9:41</td>\n",
       "      <td>12/29/2015 10:05</td>\n",
       "      <td>12/30/2015 10:32</td>\n",
       "      <td>12/31/2015 10:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4414 rows × 250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     EmployeeID 2015-02-01 00:00:00 2015-05-01 00:00:00 2015-06-01 00:00:00  \\\n",
       "0             1 2015-02-01 09:43:00 2015-05-01 10:08:00 2015-06-01 09:54:00   \n",
       "1             2 2015-02-01 10:15:00 2015-05-01 10:21:00                 NaT   \n",
       "2             3 2015-02-01 10:17:00 2015-05-01 09:50:00 2015-06-01 10:14:00   \n",
       "3             4 2015-02-01 10:05:00 2015-05-01 09:56:00 2015-06-01 10:11:00   \n",
       "4             5 2015-02-01 10:28:00 2015-05-01 09:49:00 2015-06-01 09:45:00   \n",
       "...         ...                 ...                 ...                 ...   \n",
       "4409       4410 2015-02-01 09:59:00 2015-05-01 10:16:00 2015-06-01 09:52:00   \n",
       "4410       4411 2015-02-01 10:03:00                 NaT 2015-06-01 09:44:00   \n",
       "4411       4412 2015-02-01 10:01:00 2015-05-01 09:33:00 2015-06-01 09:49:00   \n",
       "4412       4413 2015-02-01 10:17:00 2015-05-01 10:02:00 2015-06-01 10:12:00   \n",
       "4413       4414 2015-02-01 09:59:00 2015-05-01 10:16:00 2015-06-01 09:52:00   \n",
       "\n",
       "     2015-07-01 00:00:00 2015-08-01 00:00:00 2015-09-01 00:00:00  \\\n",
       "0    2015-07-01 09:34:00 2015-08-01 09:51:00 2015-09-01 10:09:00   \n",
       "1    2015-07-01 09:45:00 2015-08-01 10:09:00 2015-09-01 09:43:00   \n",
       "2    2015-07-01 09:47:00 2015-08-01 10:03:00 2015-09-01 10:05:00   \n",
       "3    2015-07-01 09:37:00 2015-08-01 10:02:00 2015-09-01 10:08:00   \n",
       "4    2015-07-01 09:49:00 2015-08-01 10:19:00 2015-09-01 10:00:00   \n",
       "...                  ...                 ...                 ...   \n",
       "4409 2015-07-01 09:43:00 2015-08-01 10:06:00 2015-09-01 10:27:00   \n",
       "4410 2015-07-01 09:42:00 2015-08-01 10:00:00 2015-09-01 09:44:00   \n",
       "4411 2015-07-01 10:28:00 2015-08-01 09:47:00 2015-09-01 10:01:00   \n",
       "4412 2015-07-01 10:12:00 2015-08-01 09:42:00                 NaT   \n",
       "4413 2015-07-01 09:43:00 2015-08-01 10:06:00 2015-09-01 10:27:00   \n",
       "\n",
       "     2015-12-01 00:00:00        1/13/2015        1/15/2015  ...  \\\n",
       "0    2015-12-01 09:42:00  1/13/2015 10:13  1/15/2015 10:01  ...   \n",
       "1    2015-12-01 10:00:00  1/13/2015 10:43   1/15/2015 9:37  ...   \n",
       "2    2015-12-01 10:03:00  1/13/2015 10:21   1/15/2015 9:55  ...   \n",
       "3    2015-12-01 10:13:00   1/13/2015 9:53  1/15/2015 10:00  ...   \n",
       "4    2015-12-01 10:29:00   1/13/2015 9:59  1/15/2015 10:06  ...   \n",
       "...                  ...              ...              ...  ...   \n",
       "4409 2015-12-01 09:47:00   1/13/2015 9:30  1/15/2015 10:08  ...   \n",
       "4410 2015-12-01 10:07:00  1/13/2015 10:05  1/15/2015 10:18  ...   \n",
       "4411 2015-12-01 09:49:00   1/13/2015 9:47  1/15/2015 10:08  ...   \n",
       "4412 2015-12-01 10:00:00   1/13/2015 9:48   1/15/2015 9:04  ...   \n",
       "4413 2015-12-01 09:47:00   1/13/2015 9:30  1/15/2015 10:08  ...   \n",
       "\n",
       "            12/17/2015        12/18/2015        12/21/2015        12/22/2015  \\\n",
       "0                  NaN               NaN   12/21/2015 9:55  12/22/2015 10:04   \n",
       "1      12/17/2015 9:15  12/18/2015 10:37   12/21/2015 9:49  12/22/2015 10:33   \n",
       "2      12/17/2015 9:53  12/18/2015 10:15  12/21/2015 10:10   12/22/2015 9:44   \n",
       "3      12/17/2015 9:54  12/18/2015 10:17   12/21/2015 9:58  12/22/2015 10:04   \n",
       "4      12/17/2015 9:46   12/18/2015 9:58  12/21/2015 10:03  12/22/2015 10:10   \n",
       "...                ...               ...               ...               ...   \n",
       "4409   12/17/2015 9:58  12/18/2015 10:20  12/21/2015 10:26               NaN   \n",
       "4410   12/17/2015 9:45   12/18/2015 9:27   12/21/2015 9:41   12/22/2015 9:50   \n",
       "4411  12/17/2015 10:01  12/18/2015 10:00   12/21/2015 9:51  12/22/2015 10:02   \n",
       "4412   12/17/2015 9:51   12/18/2015 9:54  12/21/2015 10:01  12/22/2015 10:10   \n",
       "4413   12/17/2015 9:58  12/18/2015 10:20  12/21/2015 10:26               NaN   \n",
       "\n",
       "            12/23/2015        12/24/2015        12/28/2015        12/29/2015  \\\n",
       "0     12/23/2015 10:14  12/24/2015 10:11  12/28/2015 10:13  12/29/2015 10:03   \n",
       "1     12/23/2015 10:12               NaN   12/28/2015 9:31   12/29/2015 9:55   \n",
       "2     12/23/2015 10:15  12/24/2015 10:07   12/28/2015 9:42   12/29/2015 9:43   \n",
       "3     12/23/2015 10:11   12/24/2015 9:43   12/28/2015 9:52   12/29/2015 9:33   \n",
       "4     12/23/2015 10:13   12/24/2015 9:44  12/28/2015 10:05  12/29/2015 10:30   \n",
       "...                ...               ...               ...               ...   \n",
       "4409   12/23/2015 9:53   12/24/2015 9:42   12/28/2015 9:41  12/29/2015 10:05   \n",
       "4410  12/23/2015 10:32   12/24/2015 9:47   12/28/2015 9:54  12/29/2015 10:13   \n",
       "4411   12/23/2015 9:58   12/24/2015 9:56   12/28/2015 9:59               NaN   \n",
       "4412   12/23/2015 9:42   12/24/2015 9:56   12/28/2015 9:55   12/29/2015 9:54   \n",
       "4413   12/23/2015 9:53   12/24/2015 9:42   12/28/2015 9:41  12/29/2015 10:05   \n",
       "\n",
       "            12/30/2015        12/31/2015  \n",
       "0      12/30/2015 9:54  12/31/2015 10:12  \n",
       "1     12/30/2015 10:32   12/31/2015 9:27  \n",
       "2      12/30/2015 9:34  12/31/2015 10:28  \n",
       "3     12/30/2015 10:18  12/31/2015 10:01  \n",
       "4      12/30/2015 9:18   12/31/2015 9:41  \n",
       "...                ...               ...  \n",
       "4409  12/30/2015 10:32  12/31/2015 10:09  \n",
       "4410  12/30/2015 10:21  12/31/2015 10:09  \n",
       "4411  12/30/2015 10:02  12/31/2015 10:03  \n",
       "4412  12/30/2015 10:15   12/31/2015 9:56  \n",
       "4413  12/30/2015 10:32  12/31/2015 10:09  \n",
       "\n",
       "[4414 rows x 250 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e462edd2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EmployeeID</th>\n",
       "      <th>2015-02-01 00:00:00</th>\n",
       "      <th>2015-05-01 00:00:00</th>\n",
       "      <th>2015-06-01 00:00:00</th>\n",
       "      <th>2015-07-01 00:00:00</th>\n",
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       "      <th>2015-09-01 00:00:00</th>\n",
       "      <th>2015-12-01 00:00:00</th>\n",
       "      <th>1/13/2015</th>\n",
       "      <th>1/15/2015</th>\n",
       "      <th>...</th>\n",
       "      <th>12/17/2015</th>\n",
       "      <th>12/18/2015</th>\n",
       "      <th>12/21/2015</th>\n",
       "      <th>12/22/2015</th>\n",
       "      <th>12/23/2015</th>\n",
       "      <th>12/24/2015</th>\n",
       "      <th>12/28/2015</th>\n",
       "      <th>12/29/2015</th>\n",
       "      <th>12/30/2015</th>\n",
       "      <th>12/31/2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-02-01 16:56:00</td>\n",
       "      <td>2015-05-01 17:20:00</td>\n",
       "      <td>2015-06-01 17:19:00</td>\n",
       "      <td>2015-07-01 16:34:00</td>\n",
       "      <td>2015-08-01 17:08:00</td>\n",
       "      <td>2015-09-01 17:38:00</td>\n",
       "      <td>2015-12-01 16:58:00</td>\n",
       "      <td>1/13/2015 18:02</td>\n",
       "      <td>1/15/2015 17:22</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/21/2015 17:15</td>\n",
       "      <td>12/22/2015 17:27</td>\n",
       "      <td>12/23/2015 16:44</td>\n",
       "      <td>12/24/2015 17:47</td>\n",
       "      <td>12/28/2015 18:00</td>\n",
       "      <td>12/29/2015 17:22</td>\n",
       "      <td>12/30/2015 17:40</td>\n",
       "      <td>12/31/2015 17:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-01 18:22:00</td>\n",
       "      <td>2015-05-01 17:48:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2015-07-01 17:09:00</td>\n",
       "      <td>2015-08-01 17:34:00</td>\n",
       "      <td>2015-09-01 16:52:00</td>\n",
       "      <td>2015-12-01 17:36:00</td>\n",
       "      <td>1/13/2015 18:00</td>\n",
       "      <td>1/15/2015 17:14</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 17:10</td>\n",
       "      <td>12/18/2015 18:31</td>\n",
       "      <td>12/21/2015 17:34</td>\n",
       "      <td>12/22/2015 18:16</td>\n",
       "      <td>12/23/2015 17:38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/28/2015 17:08</td>\n",
       "      <td>12/29/2015 17:54</td>\n",
       "      <td>12/30/2015 18:31</td>\n",
       "      <td>12/31/2015 17:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-02-01 16:59:00</td>\n",
       "      <td>2015-05-01 17:06:00</td>\n",
       "      <td>2015-06-01 16:38:00</td>\n",
       "      <td>2015-07-01 16:33:00</td>\n",
       "      <td>2015-08-01 17:24:00</td>\n",
       "      <td>2015-09-01 16:57:00</td>\n",
       "      <td>2015-12-01 17:28:00</td>\n",
       "      <td>1/13/2015 17:21</td>\n",
       "      <td>1/15/2015 17:21</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 17:06</td>\n",
       "      <td>12/18/2015 17:02</td>\n",
       "      <td>12/21/2015 17:20</td>\n",
       "      <td>12/22/2015 16:32</td>\n",
       "      <td>12/23/2015 16:59</td>\n",
       "      <td>12/24/2015 16:58</td>\n",
       "      <td>12/28/2015 16:43</td>\n",
       "      <td>12/29/2015 17:09</td>\n",
       "      <td>12/30/2015 17:06</td>\n",
       "      <td>12/31/2015 17:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-02-01 17:25:00</td>\n",
       "      <td>2015-05-01 17:14:00</td>\n",
       "      <td>2015-06-01 17:07:00</td>\n",
       "      <td>2015-07-01 16:32:00</td>\n",
       "      <td>2015-08-01 16:53:00</td>\n",
       "      <td>2015-09-01 17:19:00</td>\n",
       "      <td>2015-12-01 17:13:00</td>\n",
       "      <td>1/13/2015 17:11</td>\n",
       "      <td>1/15/2015 16:53</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 17:21</td>\n",
       "      <td>12/18/2015 17:55</td>\n",
       "      <td>12/21/2015 16:49</td>\n",
       "      <td>12/22/2015 17:24</td>\n",
       "      <td>12/23/2015 17:36</td>\n",
       "      <td>12/24/2015 16:48</td>\n",
       "      <td>12/28/2015 17:19</td>\n",
       "      <td>12/29/2015 16:58</td>\n",
       "      <td>12/30/2015 17:40</td>\n",
       "      <td>12/31/2015 17:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2015-02-01 18:31:00</td>\n",
       "      <td>2015-05-01 17:49:00</td>\n",
       "      <td>2015-06-01 17:26:00</td>\n",
       "      <td>2015-07-01 17:37:00</td>\n",
       "      <td>2015-08-01 17:59:00</td>\n",
       "      <td>2015-09-01 17:44:00</td>\n",
       "      <td>2015-12-01 18:51:00</td>\n",
       "      <td>1/13/2015 18:14</td>\n",
       "      <td>1/15/2015 18:21</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 18:05</td>\n",
       "      <td>12/18/2015 17:52</td>\n",
       "      <td>12/21/2015 17:43</td>\n",
       "      <td>12/22/2015 18:07</td>\n",
       "      <td>12/23/2015 18:00</td>\n",
       "      <td>12/24/2015 17:59</td>\n",
       "      <td>12/28/2015 17:44</td>\n",
       "      <td>12/29/2015 18:47</td>\n",
       "      <td>12/30/2015 17:15</td>\n",
       "      <td>12/31/2015 17:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4409</th>\n",
       "      <td>4410</td>\n",
       "      <td>2015-02-01 16:49:00</td>\n",
       "      <td>2015-05-01 17:33:00</td>\n",
       "      <td>2015-06-01 16:36:00</td>\n",
       "      <td>2015-07-01 16:33:00</td>\n",
       "      <td>2015-08-01 17:32:00</td>\n",
       "      <td>2015-09-01 17:25:00</td>\n",
       "      <td>2015-12-01 16:39:00</td>\n",
       "      <td>1/13/2015 16:59</td>\n",
       "      <td>1/15/2015 17:13</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 17:05</td>\n",
       "      <td>12/18/2015 17:23</td>\n",
       "      <td>12/21/2015 17:26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/23/2015 16:36</td>\n",
       "      <td>12/24/2015 16:00</td>\n",
       "      <td>12/28/2015 17:14</td>\n",
       "      <td>12/29/2015 17:16</td>\n",
       "      <td>12/30/2015 17:29</td>\n",
       "      <td>12/31/2015 16:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4410</th>\n",
       "      <td>4411</td>\n",
       "      <td>2015-02-01 16:19:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2015-06-01 15:07:00</td>\n",
       "      <td>2015-07-01 15:25:00</td>\n",
       "      <td>2015-08-01 16:12:00</td>\n",
       "      <td>2015-09-01 15:26:00</td>\n",
       "      <td>2015-12-01 16:10:00</td>\n",
       "      <td>1/13/2015 16:22</td>\n",
       "      <td>1/15/2015 16:19</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 15:44</td>\n",
       "      <td>12/18/2015 15:23</td>\n",
       "      <td>12/21/2015 15:31</td>\n",
       "      <td>12/22/2015 15:45</td>\n",
       "      <td>12/23/2015 16:38</td>\n",
       "      <td>12/24/2015 15:47</td>\n",
       "      <td>12/28/2015 15:34</td>\n",
       "      <td>12/29/2015 16:47</td>\n",
       "      <td>12/30/2015 16:03</td>\n",
       "      <td>12/31/2015 16:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4411</th>\n",
       "      <td>4412</td>\n",
       "      <td>2015-02-01 17:17:00</td>\n",
       "      <td>2015-05-01 17:08:00</td>\n",
       "      <td>2015-06-01 17:27:00</td>\n",
       "      <td>2015-07-01 18:27:00</td>\n",
       "      <td>2015-08-01 17:05:00</td>\n",
       "      <td>2015-09-01 17:02:00</td>\n",
       "      <td>2015-12-01 17:35:00</td>\n",
       "      <td>1/13/2015 17:15</td>\n",
       "      <td>1/15/2015 18:15</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 17:42</td>\n",
       "      <td>12/18/2015 17:48</td>\n",
       "      <td>12/21/2015 17:43</td>\n",
       "      <td>12/22/2015 17:47</td>\n",
       "      <td>12/23/2015 17:43</td>\n",
       "      <td>12/24/2015 17:20</td>\n",
       "      <td>12/28/2015 17:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/30/2015 17:48</td>\n",
       "      <td>12/31/2015 18:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4412</th>\n",
       "      <td>4413</td>\n",
       "      <td>2015-02-01 19:48:00</td>\n",
       "      <td>2015-05-01 19:37:00</td>\n",
       "      <td>2015-06-01 20:00:00</td>\n",
       "      <td>2015-07-01 19:35:00</td>\n",
       "      <td>2015-08-01 18:55:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2015-12-01 19:18:00</td>\n",
       "      <td>1/13/2015 19:24</td>\n",
       "      <td>1/15/2015 18:33</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 19:06</td>\n",
       "      <td>12/18/2015 19:52</td>\n",
       "      <td>12/21/2015 19:21</td>\n",
       "      <td>12/22/2015 19:32</td>\n",
       "      <td>12/23/2015 18:57</td>\n",
       "      <td>12/24/2015 19:37</td>\n",
       "      <td>12/28/2015 19:58</td>\n",
       "      <td>12/29/2015 18:55</td>\n",
       "      <td>12/30/2015 19:37</td>\n",
       "      <td>12/31/2015 19:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4413</th>\n",
       "      <td>4414</td>\n",
       "      <td>2015-02-01 16:49:00</td>\n",
       "      <td>2015-05-01 17:33:00</td>\n",
       "      <td>2015-06-01 16:36:00</td>\n",
       "      <td>2015-07-01 16:33:00</td>\n",
       "      <td>2015-08-01 17:32:00</td>\n",
       "      <td>2015-09-01 17:25:00</td>\n",
       "      <td>2015-12-01 16:39:00</td>\n",
       "      <td>1/13/2015 16:59</td>\n",
       "      <td>1/15/2015 17:13</td>\n",
       "      <td>...</td>\n",
       "      <td>12/17/2015 17:05</td>\n",
       "      <td>12/18/2015 17:23</td>\n",
       "      <td>12/21/2015 17:26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/23/2015 16:36</td>\n",
       "      <td>12/24/2015 16:00</td>\n",
       "      <td>12/28/2015 17:14</td>\n",
       "      <td>12/29/2015 17:16</td>\n",
       "      <td>12/30/2015 17:29</td>\n",
       "      <td>12/31/2015 16:39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4414 rows × 250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EmployeeID 2015-02-01 00:00:00 2015-05-01 00:00:00 2015-06-01 00:00:00  \\\n",
       "0              1 2015-02-01 16:56:00 2015-05-01 17:20:00 2015-06-01 17:19:00   \n",
       "1              2 2015-02-01 18:22:00 2015-05-01 17:48:00                 NaT   \n",
       "2              3 2015-02-01 16:59:00 2015-05-01 17:06:00 2015-06-01 16:38:00   \n",
       "3              4 2015-02-01 17:25:00 2015-05-01 17:14:00 2015-06-01 17:07:00   \n",
       "4              5 2015-02-01 18:31:00 2015-05-01 17:49:00 2015-06-01 17:26:00   \n",
       "...          ...                 ...                 ...                 ...   \n",
       "4409        4410 2015-02-01 16:49:00 2015-05-01 17:33:00 2015-06-01 16:36:00   \n",
       "4410        4411 2015-02-01 16:19:00                 NaT 2015-06-01 15:07:00   \n",
       "4411        4412 2015-02-01 17:17:00 2015-05-01 17:08:00 2015-06-01 17:27:00   \n",
       "4412        4413 2015-02-01 19:48:00 2015-05-01 19:37:00 2015-06-01 20:00:00   \n",
       "4413        4414 2015-02-01 16:49:00 2015-05-01 17:33:00 2015-06-01 16:36:00   \n",
       "\n",
       "     2015-07-01 00:00:00 2015-08-01 00:00:00 2015-09-01 00:00:00  \\\n",
       "0    2015-07-01 16:34:00 2015-08-01 17:08:00 2015-09-01 17:38:00   \n",
       "1    2015-07-01 17:09:00 2015-08-01 17:34:00 2015-09-01 16:52:00   \n",
       "2    2015-07-01 16:33:00 2015-08-01 17:24:00 2015-09-01 16:57:00   \n",
       "3    2015-07-01 16:32:00 2015-08-01 16:53:00 2015-09-01 17:19:00   \n",
       "4    2015-07-01 17:37:00 2015-08-01 17:59:00 2015-09-01 17:44:00   \n",
       "...                  ...                 ...                 ...   \n",
       "4409 2015-07-01 16:33:00 2015-08-01 17:32:00 2015-09-01 17:25:00   \n",
       "4410 2015-07-01 15:25:00 2015-08-01 16:12:00 2015-09-01 15:26:00   \n",
       "4411 2015-07-01 18:27:00 2015-08-01 17:05:00 2015-09-01 17:02:00   \n",
       "4412 2015-07-01 19:35:00 2015-08-01 18:55:00                 NaT   \n",
       "4413 2015-07-01 16:33:00 2015-08-01 17:32:00 2015-09-01 17:25:00   \n",
       "\n",
       "     2015-12-01 00:00:00        1/13/2015        1/15/2015  ...  \\\n",
       "0    2015-12-01 16:58:00  1/13/2015 18:02  1/15/2015 17:22  ...   \n",
       "1    2015-12-01 17:36:00  1/13/2015 18:00  1/15/2015 17:14  ...   \n",
       "2    2015-12-01 17:28:00  1/13/2015 17:21  1/15/2015 17:21  ...   \n",
       "3    2015-12-01 17:13:00  1/13/2015 17:11  1/15/2015 16:53  ...   \n",
       "4    2015-12-01 18:51:00  1/13/2015 18:14  1/15/2015 18:21  ...   \n",
       "...                  ...              ...              ...  ...   \n",
       "4409 2015-12-01 16:39:00  1/13/2015 16:59  1/15/2015 17:13  ...   \n",
       "4410 2015-12-01 16:10:00  1/13/2015 16:22  1/15/2015 16:19  ...   \n",
       "4411 2015-12-01 17:35:00  1/13/2015 17:15  1/15/2015 18:15  ...   \n",
       "4412 2015-12-01 19:18:00  1/13/2015 19:24  1/15/2015 18:33  ...   \n",
       "4413 2015-12-01 16:39:00  1/13/2015 16:59  1/15/2015 17:13  ...   \n",
       "\n",
       "            12/17/2015        12/18/2015        12/21/2015        12/22/2015  \\\n",
       "0                  NaN               NaN  12/21/2015 17:15  12/22/2015 17:27   \n",
       "1     12/17/2015 17:10  12/18/2015 18:31  12/21/2015 17:34  12/22/2015 18:16   \n",
       "2     12/17/2015 17:06  12/18/2015 17:02  12/21/2015 17:20  12/22/2015 16:32   \n",
       "3     12/17/2015 17:21  12/18/2015 17:55  12/21/2015 16:49  12/22/2015 17:24   \n",
       "4     12/17/2015 18:05  12/18/2015 17:52  12/21/2015 17:43  12/22/2015 18:07   \n",
       "...                ...               ...               ...               ...   \n",
       "4409  12/17/2015 17:05  12/18/2015 17:23  12/21/2015 17:26               NaN   \n",
       "4410  12/17/2015 15:44  12/18/2015 15:23  12/21/2015 15:31  12/22/2015 15:45   \n",
       "4411  12/17/2015 17:42  12/18/2015 17:48  12/21/2015 17:43  12/22/2015 17:47   \n",
       "4412  12/17/2015 19:06  12/18/2015 19:52  12/21/2015 19:21  12/22/2015 19:32   \n",
       "4413  12/17/2015 17:05  12/18/2015 17:23  12/21/2015 17:26               NaN   \n",
       "\n",
       "            12/23/2015        12/24/2015        12/28/2015        12/29/2015  \\\n",
       "0     12/23/2015 16:44  12/24/2015 17:47  12/28/2015 18:00  12/29/2015 17:22   \n",
       "1     12/23/2015 17:38               NaN  12/28/2015 17:08  12/29/2015 17:54   \n",
       "2     12/23/2015 16:59  12/24/2015 16:58  12/28/2015 16:43  12/29/2015 17:09   \n",
       "3     12/23/2015 17:36  12/24/2015 16:48  12/28/2015 17:19  12/29/2015 16:58   \n",
       "4     12/23/2015 18:00  12/24/2015 17:59  12/28/2015 17:44  12/29/2015 18:47   \n",
       "...                ...               ...               ...               ...   \n",
       "4409  12/23/2015 16:36  12/24/2015 16:00  12/28/2015 17:14  12/29/2015 17:16   \n",
       "4410  12/23/2015 16:38  12/24/2015 15:47  12/28/2015 15:34  12/29/2015 16:47   \n",
       "4411  12/23/2015 17:43  12/24/2015 17:20  12/28/2015 17:43               NaN   \n",
       "4412  12/23/2015 18:57  12/24/2015 19:37  12/28/2015 19:58  12/29/2015 18:55   \n",
       "4413  12/23/2015 16:36  12/24/2015 16:00  12/28/2015 17:14  12/29/2015 17:16   \n",
       "\n",
       "            12/30/2015        12/31/2015  \n",
       "0     12/30/2015 17:40  12/31/2015 17:17  \n",
       "1     12/30/2015 18:31  12/31/2015 17:40  \n",
       "2     12/30/2015 17:06  12/31/2015 17:15  \n",
       "3     12/30/2015 17:40  12/31/2015 17:09  \n",
       "4     12/30/2015 17:15  12/31/2015 17:42  \n",
       "...                ...               ...  \n",
       "4409  12/30/2015 17:29  12/31/2015 16:39  \n",
       "4410  12/30/2015 16:03  12/31/2015 16:18  \n",
       "4411  12/30/2015 17:48  12/31/2015 18:08  \n",
       "4412  12/30/2015 19:37  12/31/2015 19:33  \n",
       "4413  12/30/2015 17:29  12/31/2015 16:39  \n",
       "\n",
       "[4414 rows x 250 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39aff021",
   "metadata": {},
   "outputs": [],
   "source": [
    "except_column = 'EmployeeID'\n",
    "columns_to_convert = [col for col in out_time.columns if col != except_column]\n",
    "\n",
    "# Specify datetime format for each column\n",
    "date_formats = {col: 'hh:mm' for col in columns_to_convert}\n",
    "\n",
    "# Convert columns to datetime\n",
    "out_time = out_time.apply(pd.to_datetime, errors='ignore', format='hh:mm')\n",
    "\n",
    "# If 'except_column' needs to remain as is, you can specify its data type as object\n",
    "out_time[except_column] = out_time[except_column].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e1db144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeID                     object\n",
       "2015-02-01 00:00:00    datetime64[ns]\n",
       "2015-05-01 00:00:00    datetime64[ns]\n",
       "2015-06-01 00:00:00    datetime64[ns]\n",
       "2015-07-01 00:00:00    datetime64[ns]\n",
       "                            ...      \n",
       "12/24/2015                     object\n",
       "12/28/2015                     object\n",
       "12/29/2015                     object\n",
       "12/30/2015                     object\n",
       "12/31/2015                     object\n",
       "Length: 250, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_time.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49613e52",
   "metadata": {},
   "source": [
    "### Join the different datasets into one master analysis file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "12440e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = emp.merge(emp_sur, on='EmployeeID', how='left') \\\n",
    "     .merge(manager_sur, on='EmployeeID', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6ba17359",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<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_Date</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>BreaksTakenPerDay</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Sales</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>12/13/2015</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4522</th>\n",
       "      <td>4412</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Sales</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4523</th>\n",
       "      <td>4413</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4524</th>\n",
       "      <td>4414</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4525</th>\n",
       "      <td>4415</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4526</th>\n",
       "      <td>4416</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4527 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EmployeeID  Age Attrition_Date     BusinessTravel  \\\n",
       "0              1   51            NaN      Travel_Rarely   \n",
       "1              2   31     12/13/2015  Travel_Frequently   \n",
       "2              3   32            NaN  Travel_Frequently   \n",
       "3              4   38            NaN         Non-Travel   \n",
       "4              5   32            NaN      Travel_Rarely   \n",
       "...          ...  ...            ...                ...   \n",
       "4522        4412   20            NaN         Frequently   \n",
       "4523        4413   20            NaN             Rarely   \n",
       "4524        4414   32            NaN         Frequently   \n",
       "4525        4415   20            NaN             Rarely   \n",
       "4526        4416   32            NaN         Frequently   \n",
       "\n",
       "                  Department  DistanceFromHome  Education EducationField  \\\n",
       "0                      Sales                 6          2  Life Sciences   \n",
       "1                        R&D                10          1  Life Sciences   \n",
       "2                        R&D                17          4          Other   \n",
       "3                        R&D                 2          5  Life Sciences   \n",
       "4     Research & Development                10          1        Medical   \n",
       "...                      ...               ...        ...            ...   \n",
       "4522                   Sales               100          3          Other   \n",
       "4523                     R&D                20          3        Medical   \n",
       "4524                     R&D                12          3      Marketing   \n",
       "4525                     R&D                20         -1        Medical   \n",
       "4526                     R&D                12          3      Marketing   \n",
       "\n",
       "      Gender  JobLevel  ... TrainingTimesLastYear YearsAtCompany  \\\n",
       "0     Female         1  ...                     6              1   \n",
       "1     Female         1  ...                     3              5   \n",
       "2       Male         4  ...                     2              5   \n",
       "3       Male         3  ...                     5              8   \n",
       "4       Male         1  ...                     2              6   \n",
       "...      ...       ...  ...                   ...            ...   \n",
       "4522    Male         2  ...                     6             21   \n",
       "4523    Male         3  ...                     6             21   \n",
       "4524  Female         3  ...                     6             21   \n",
       "4525    Male         3  ...                     6             21   \n",
       "4526  Female        10  ...                     6             21   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager BreaksTakenPerDay  \\\n",
       "0                           0                     0               1.0   \n",
       "1                           1                     4               2.0   \n",
       "2                           0                     3               3.0   \n",
       "3                           7                     5               2.0   \n",
       "4                           0                     4               3.0   \n",
       "...                       ...                   ...               ...   \n",
       "4522                        3                     9               2.0   \n",
       "4523                        3                     9               2.0   \n",
       "4524                        3                     9               2.0   \n",
       "4525                        3                     9               2.0   \n",
       "4526                        3                     9               2.0   \n",
       "\n",
       "      EnvironmentSatisfaction  JobSatisfaction  WorkLifeBalance  \\\n",
       "0                         3.0              4.0              2.0   \n",
       "1                         3.0              2.0              4.0   \n",
       "2                         2.0              2.0              1.0   \n",
       "3                         4.0              4.0              3.0   \n",
       "4                         4.0              1.0              3.0   \n",
       "...                       ...              ...              ...   \n",
       "4522                      1.0              3.0              3.0   \n",
       "4523                      4.0              1.0              3.0   \n",
       "4524                      1.0              3.0              NaN   \n",
       "4525                      NaN              NaN              NaN   \n",
       "4526                      NaN              NaN              NaN   \n",
       "\n",
       "      JobInvolvement  PerformanceRating  \n",
       "0                3.0                3.0  \n",
       "1                2.0                4.0  \n",
       "2                3.0                3.0  \n",
       "3                2.0                3.0  \n",
       "4                3.0                3.0  \n",
       "...              ...                ...  \n",
       "4522             3.0                4.0  \n",
       "4523             2.0                3.0  \n",
       "4524             4.0                3.0  \n",
       "4525             NaN                NaN  \n",
       "4526             NaN                NaN  \n",
       "\n",
       "[4527 rows x 29 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5daf2e7a",
   "metadata": {},
   "source": [
    "### Check for duplicates , column data type consistency, nulls, outliers & other anomalies and if found apply treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20c8025f",
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicates = df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f4a0923e",
   "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",
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       "<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_Date</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
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       "      <th>EducationField</th>\n",
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       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>BreaksTakenPerDay</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>125</td>\n",
       "      <td>26</td>\n",
       "      <td>2015-03-08 00:00:00</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Sales</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>131</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>134</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>135</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>159</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Sales</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4515</th>\n",
       "      <td>4410</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>4516</th>\n",
       "      <td>4410</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4518</th>\n",
       "      <td>4410</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4519</th>\n",
       "      <td>4410</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4520</th>\n",
       "      <td>4410</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EmployeeID  Age       Attrition_Date     BusinessTravel  \\\n",
       "125          125   26  2015-03-08 00:00:00  Travel_Frequently   \n",
       "134          131   42                  NaN      Travel_Rarely   \n",
       "140          134   40                  NaN      Travel_Rarely   \n",
       "142          135   54                  NaN      Travel_Rarely   \n",
       "167          159   38                  NaN  Travel_Frequently   \n",
       "...          ...  ...                  ...                ...   \n",
       "4515        4410   40                  NaN      Travel_Rarely   \n",
       "4516        4410   40                  NaN      Travel_Rarely   \n",
       "4518        4410   40                  NaN      Travel_Rarely   \n",
       "4519        4410   40                  NaN      Travel_Rarely   \n",
       "4520        4410   40                  NaN      Travel_Rarely   \n",
       "\n",
       "                  Department  DistanceFromHome  Education EducationField  \\\n",
       "125                    Sales                14          3  Life Sciences   \n",
       "134   Research & Development                 2          3        Medical   \n",
       "140                    Sales                 6          2  Life Sciences   \n",
       "142          Human Resources                 9          3  Life Sciences   \n",
       "167                    Sales                 3          3      Marketing   \n",
       "...                      ...               ...        ...            ...   \n",
       "4515  Research & Development                28          3        Medical   \n",
       "4516  Research & Development                28          3        Medical   \n",
       "4518  Research & Development                28          3        Medical   \n",
       "4519  Research & Development                28          3        Medical   \n",
       "4520  Research & Development                28          3        Medical   \n",
       "\n",
       "      Gender  JobLevel  ... TrainingTimesLastYear YearsAtCompany  \\\n",
       "125     Male         2  ...                     3              6   \n",
       "134   Female         4  ...                     2              5   \n",
       "140   Female         1  ...                     2              1   \n",
       "142   Female         2  ...                     2              4   \n",
       "167   Female         2  ...                     3              1   \n",
       "...      ...       ...  ...                   ...            ...   \n",
       "4515    Male         2  ...                     6             21   \n",
       "4516    Male         2  ...                     6             21   \n",
       "4518    Male         2  ...                     6             21   \n",
       "4519    Male         2  ...                     6             21   \n",
       "4520    Male         2  ...                     6             21   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager BreaksTakenPerDay  \\\n",
       "125                         0                     4               1.0   \n",
       "134                         1                     2               1.0   \n",
       "140                         0                     0               1.0   \n",
       "142                         1                     2               1.0   \n",
       "167                         0                     0               2.0   \n",
       "...                       ...                   ...               ...   \n",
       "4515                        3                     9               1.0   \n",
       "4516                        3                     9               1.0   \n",
       "4518                        3                     9               2.0   \n",
       "4519                        3                     9               2.0   \n",
       "4520                        3                     9               2.0   \n",
       "\n",
       "      EnvironmentSatisfaction  JobSatisfaction  WorkLifeBalance  \\\n",
       "125                       3.0              NaN              3.0   \n",
       "134                       2.0              1.0              3.0   \n",
       "140                       2.0              3.0              3.0   \n",
       "142                       1.0              1.0              2.0   \n",
       "167                       4.0              4.0              3.0   \n",
       "...                       ...              ...              ...   \n",
       "4515                      1.0              3.0              NaN   \n",
       "4516                      1.0              3.0              NaN   \n",
       "4518                      1.0              3.0              NaN   \n",
       "4519                      1.0              3.0              NaN   \n",
       "4520                      1.0              3.0              NaN   \n",
       "\n",
       "      JobInvolvement  PerformanceRating  \n",
       "125              3.0                3.0  \n",
       "134              2.0                3.0  \n",
       "140              2.0                3.0  \n",
       "142              2.0                4.0  \n",
       "167              3.0                3.0  \n",
       "...              ...                ...  \n",
       "4515             4.0                3.0  \n",
       "4516             4.0                3.0  \n",
       "4518             4.0                3.0  \n",
       "4519             4.0                3.0  \n",
       "4520             4.0                3.0  \n",
       "\n",
       "[103 rows x 29 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "89f87a78",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42cb1b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "null_values = df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d7d2548",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeID                    0\n",
       "Age                           0\n",
       "Attrition_Date             3712\n",
       "BusinessTravel                0\n",
       "Department                    0\n",
       "DistanceFromHome              0\n",
       "Education                     0\n",
       "EducationField                0\n",
       "Gender                        0\n",
       "JobLevel                      0\n",
       "JobRole                       0\n",
       "MaritalStatus                 0\n",
       "MonthlyIncome                13\n",
       "NumCompaniesWorked           19\n",
       "Over18                        0\n",
       "PercentSalaryHike             0\n",
       "StandardHours                 0\n",
       "StockOptionLevel              0\n",
       "TotalWorkingYears            16\n",
       "TrainingTimesLastYear         0\n",
       "YearsAtCompany                0\n",
       "YearsSinceLastPromotion       0\n",
       "YearsWithCurrManager          0\n",
       "BreaksTakenPerDay          3512\n",
       "EnvironmentSatisfaction      27\n",
       "JobSatisfaction              22\n",
       "WorkLifeBalance              43\n",
       "JobInvolvement                2\n",
       "PerformanceRating             2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10819efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MonthlyIncome'].fillna(df['MonthlyIncome'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4502ef65",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['NumCompaniesWorked'].fillna(df['NumCompaniesWorked'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c191e988",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TotalWorkingYears'].fillna(df['TotalWorkingYears'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf5d32a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BreaksTakenPerDay'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a59b239",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EnvironmentSatisfaction'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ea479bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['JobSatisfaction'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8a45fb5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PerformanceRating'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dc295736",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['WorkLifeBalance'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fbd2a833",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['JobInvolvement'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f59ba68a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeID                    0\n",
       "Age                           0\n",
       "Attrition_Date             3712\n",
       "BusinessTravel                0\n",
       "Department                    0\n",
       "DistanceFromHome              0\n",
       "Education                     0\n",
       "EducationField                0\n",
       "Gender                        0\n",
       "JobLevel                      0\n",
       "JobRole                       0\n",
       "MaritalStatus                 0\n",
       "MonthlyIncome                 0\n",
       "NumCompaniesWorked            0\n",
       "Over18                        0\n",
       "PercentSalaryHike             0\n",
       "StandardHours                 0\n",
       "StockOptionLevel              0\n",
       "TotalWorkingYears             0\n",
       "TrainingTimesLastYear         0\n",
       "YearsAtCompany                0\n",
       "YearsSinceLastPromotion       0\n",
       "YearsWithCurrManager          0\n",
       "BreaksTakenPerDay             0\n",
       "EnvironmentSatisfaction       0\n",
       "JobSatisfaction               0\n",
       "WorkLifeBalance               0\n",
       "JobInvolvement                0\n",
       "PerformanceRating             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9daef478",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeID                   int64\n",
       "Age                          int64\n",
       "Attrition_Date              object\n",
       "BusinessTravel              object\n",
       "Department                  object\n",
       "DistanceFromHome             int64\n",
       "Education                    int64\n",
       "EducationField              object\n",
       "Gender                      object\n",
       "JobLevel                     int64\n",
       "JobRole                     object\n",
       "MaritalStatus               object\n",
       "MonthlyIncome              float64\n",
       "NumCompaniesWorked         float64\n",
       "Over18                      object\n",
       "PercentSalaryHike            int64\n",
       "StandardHours                int64\n",
       "StockOptionLevel             int64\n",
       "TotalWorkingYears          float64\n",
       "TrainingTimesLastYear        int64\n",
       "YearsAtCompany               int64\n",
       "YearsSinceLastPromotion      int64\n",
       "YearsWithCurrManager         int64\n",
       "BreaksTakenPerDay          float64\n",
       "EnvironmentSatisfaction    float64\n",
       "JobSatisfaction            float64\n",
       "WorkLifeBalance            float64\n",
       "JobInvolvement             float64\n",
       "PerformanceRating          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a46d5de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "float_columns = df.select_dtypes(include=['float64']).columns\n",
    "\n",
    "df[float_columns] = df[float_columns].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5e19e049",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeID                  int64\n",
       "Age                         int64\n",
       "Attrition_Date             object\n",
       "BusinessTravel             object\n",
       "Department                 object\n",
       "DistanceFromHome            int64\n",
       "Education                   int64\n",
       "EducationField             object\n",
       "Gender                     object\n",
       "JobLevel                    int64\n",
       "JobRole                    object\n",
       "MaritalStatus              object\n",
       "MonthlyIncome               int64\n",
       "NumCompaniesWorked          int64\n",
       "Over18                     object\n",
       "PercentSalaryHike           int64\n",
       "StandardHours               int64\n",
       "StockOptionLevel            int64\n",
       "TotalWorkingYears           int64\n",
       "TrainingTimesLastYear       int64\n",
       "YearsAtCompany              int64\n",
       "YearsSinceLastPromotion     int64\n",
       "YearsWithCurrManager        int64\n",
       "BreaksTakenPerDay           int64\n",
       "EnvironmentSatisfaction     int64\n",
       "JobSatisfaction             int64\n",
       "WorkLifeBalance             int64\n",
       "JobInvolvement              int64\n",
       "PerformanceRating           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d30ad895",
   "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>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition_Date</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>BreaksTakenPerDay</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>51</td>\n",
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       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>12/13/2015</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4522</th>\n",
       "      <td>4412</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Sales</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4523</th>\n",
       "      <td>4413</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4524</th>\n",
       "      <td>4414</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4525</th>\n",
       "      <td>4415</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4526</th>\n",
       "      <td>4416</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4424 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EmployeeID  Age Attrition_Date     BusinessTravel  \\\n",
       "0              1   51            NaN      Travel_Rarely   \n",
       "1              2   31     12/13/2015  Travel_Frequently   \n",
       "2              3   32            NaN  Travel_Frequently   \n",
       "3              4   38            NaN         Non-Travel   \n",
       "4              5   32            NaN      Travel_Rarely   \n",
       "...          ...  ...            ...                ...   \n",
       "4522        4412   20            NaN         Frequently   \n",
       "4523        4413   20            NaN             Rarely   \n",
       "4524        4414   32            NaN         Frequently   \n",
       "4525        4415   20            NaN             Rarely   \n",
       "4526        4416   32            NaN         Frequently   \n",
       "\n",
       "                  Department  DistanceFromHome  Education EducationField  \\\n",
       "0                      Sales                 6          2  Life Sciences   \n",
       "1                        R&D                10          1  Life Sciences   \n",
       "2                        R&D                17          4          Other   \n",
       "3                        R&D                 2          5  Life Sciences   \n",
       "4     Research & Development                10          1        Medical   \n",
       "...                      ...               ...        ...            ...   \n",
       "4522                   Sales               100          3          Other   \n",
       "4523                     R&D                20          3        Medical   \n",
       "4524                     R&D                12          3      Marketing   \n",
       "4525                     R&D                20         -1        Medical   \n",
       "4526                     R&D                12          3      Marketing   \n",
       "\n",
       "      Gender  JobLevel  ... TrainingTimesLastYear YearsAtCompany  \\\n",
       "0     Female         1  ...                     6              1   \n",
       "1     Female         1  ...                     3              5   \n",
       "2       Male         4  ...                     2              5   \n",
       "3       Male         3  ...                     5              8   \n",
       "4       Male         1  ...                     2              6   \n",
       "...      ...       ...  ...                   ...            ...   \n",
       "4522    Male         2  ...                     6             21   \n",
       "4523    Male         3  ...                     6             21   \n",
       "4524  Female         3  ...                     6             21   \n",
       "4525    Male         3  ...                     6             21   \n",
       "4526  Female        10  ...                     6             21   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager BreaksTakenPerDay  \\\n",
       "0                           0                     0                 1   \n",
       "1                           1                     4                 2   \n",
       "2                           0                     3                 3   \n",
       "3                           7                     5                 2   \n",
       "4                           0                     4                 3   \n",
       "...                       ...                   ...               ...   \n",
       "4522                        3                     9                 2   \n",
       "4523                        3                     9                 2   \n",
       "4524                        3                     9                 2   \n",
       "4525                        3                     9                 2   \n",
       "4526                        3                     9                 2   \n",
       "\n",
       "      EnvironmentSatisfaction  JobSatisfaction  WorkLifeBalance  \\\n",
       "0                           3                4                2   \n",
       "1                           3                2                4   \n",
       "2                           2                2                1   \n",
       "3                           4                4                3   \n",
       "4                           4                1                3   \n",
       "...                       ...              ...              ...   \n",
       "4522                        1                3                3   \n",
       "4523                        4                1                3   \n",
       "4524                        1                3                0   \n",
       "4525                        0                0                0   \n",
       "4526                        0                0                0   \n",
       "\n",
       "      JobInvolvement  PerformanceRating  \n",
       "0                  3                  3  \n",
       "1                  2                  4  \n",
       "2                  3                  3  \n",
       "3                  2                  3  \n",
       "4                  3                  3  \n",
       "...              ...                ...  \n",
       "4522               3                  4  \n",
       "4523               2                  3  \n",
       "4524               4                  3  \n",
       "4525               0                  0  \n",
       "4526               0                  0  \n",
       "\n",
       "[4424 rows x 29 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d19d05da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for outliers\n",
    "from scipy import stats\n",
    "numeric_columns = df.select_dtypes(include=['number']).columns\n",
    "outliers = df[(np.abs(stats.zscore(df[numeric_columns])) > 3).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4bd69a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing outliers with NaN and removing them\n",
    "df[(np.abs(stats.zscore(df[numeric_columns])) > 3)] = np.nan\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dd2e17b4",
   "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>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition_Date</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>BreaksTakenPerDay</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12/13/2015</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2015-01-11 00:00:00</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2015-02-07 00:00:00</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2015-01-04 00:00:00</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research and Development</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2015-04-06 00:00:00</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research and Development</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4469</th>\n",
       "      <td>4382</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2015-11-04 00:00:00</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4474</th>\n",
       "      <td>4387</td>\n",
       "      <td>33.0</td>\n",
       "      <td>10/22/2015</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476</th>\n",
       "      <td>4389</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11/21/2015</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4479</th>\n",
       "      <td>4392</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2015-09-06 00:00:00</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4490</th>\n",
       "      <td>4403</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2015-10-07 00:00:00</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>678 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EmployeeID   Age       Attrition_Date     BusinessTravel  \\\n",
       "1              2  31.0           12/13/2015  Travel_Frequently   \n",
       "6              7  28.0  2015-01-11 00:00:00      Travel_Rarely   \n",
       "13            14  47.0  2015-02-07 00:00:00         Non-Travel   \n",
       "28            29  44.0  2015-01-04 00:00:00  Travel_Frequently   \n",
       "30            31  26.0  2015-04-06 00:00:00      Travel_Rarely   \n",
       "...          ...   ...                  ...                ...   \n",
       "4469        4382  29.0  2015-11-04 00:00:00      Travel_Rarely   \n",
       "4474        4387  33.0           10/22/2015      Travel_Rarely   \n",
       "4476        4389  33.0           11/21/2015      Travel_Rarely   \n",
       "4479        4392  32.0  2015-09-06 00:00:00      Travel_Rarely   \n",
       "4490        4403  37.0  2015-10-07 00:00:00         Frequently   \n",
       "\n",
       "                    Department  DistanceFromHome  Education EducationField  \\\n",
       "1                          R&D              10.0        1.0  Life Sciences   \n",
       "6       Research & Development              11.0        2.0        Medical   \n",
       "13      Research & Development               1.0        1.0        Medical   \n",
       "28    Research and Development               1.0        2.0        Medical   \n",
       "30    Research and Development               4.0        3.0        Medical   \n",
       "...                        ...               ...        ...            ...   \n",
       "4469    Research & Development               7.0        1.0  Life Sciences   \n",
       "4474                     Sales              11.0        4.0      Marketing   \n",
       "4476                     Sales               1.0        3.0  Life Sciences   \n",
       "4479                     Sales              23.0        1.0  Life Sciences   \n",
       "4490                     Sales               2.0        3.0      Marketing   \n",
       "\n",
       "      Gender  JobLevel  ... TrainingTimesLastYear YearsAtCompany  \\\n",
       "1     Female       1.0  ...                     3            5.0   \n",
       "6       Male       2.0  ...                     2            0.0   \n",
       "13      Male       1.0  ...                     4           10.0   \n",
       "28      Male       2.0  ...                     2            1.0   \n",
       "30      Male       3.0  ...                     5            3.0   \n",
       "...      ...       ...  ...                   ...            ...   \n",
       "4469  Female       1.0  ...                     2            4.0   \n",
       "4474    Male       1.0  ...                     2            5.0   \n",
       "4476    Male       2.0  ...                     2            9.0   \n",
       "4479    Male       3.0  ...                     2            3.0   \n",
       "4490    Male       1.0  ...                     2            1.0   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager BreaksTakenPerDay  \\\n",
       "1                         1.0                   4.0               2.0   \n",
       "6                         0.0                   0.0               1.0   \n",
       "13                        9.0                   9.0               3.0   \n",
       "28                        0.0                   0.0               1.0   \n",
       "30                        0.0                   2.0               1.0   \n",
       "...                       ...                   ...               ...   \n",
       "4469                      0.0                   1.0               0.0   \n",
       "4474                      0.0                   4.0               1.0   \n",
       "4476                      1.0                   7.0               2.0   \n",
       "4479                      1.0                   2.0               1.0   \n",
       "4490                      0.0                   0.0               2.0   \n",
       "\n",
       "      EnvironmentSatisfaction  JobSatisfaction  WorkLifeBalance  \\\n",
       "1                           3                2              4.0   \n",
       "6                           1                3              1.0   \n",
       "13                          1                2              2.0   \n",
       "28                          4                3              1.0   \n",
       "30                          1                2              3.0   \n",
       "...                       ...              ...              ...   \n",
       "4469                        3                4              2.0   \n",
       "4474                        3                4              1.0   \n",
       "4476                        1                1              4.0   \n",
       "4479                        4                3              1.0   \n",
       "4490                        1                3              3.0   \n",
       "\n",
       "      JobInvolvement  PerformanceRating  \n",
       "1                2.0                4.0  \n",
       "6                3.0                4.0  \n",
       "13               2.0                3.0  \n",
       "28               2.0                3.0  \n",
       "30               2.0                3.0  \n",
       "...              ...                ...  \n",
       "4469             3.0                4.0  \n",
       "4474             2.0                4.0  \n",
       "4476             3.0                3.0  \n",
       "4479             3.0                3.0  \n",
       "4490             3.0                3.0  \n",
       "\n",
       "[678 rows x 29 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9c8c6bbf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EmployeeID', 'Age', 'Attrition_Date', 'BusinessTravel', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'Gender', 'JobLevel',\n",
       "       'JobRole', 'MaritalStatus', 'MonthlyIncome', 'NumCompaniesWorked',\n",
       "       'Over18', 'PercentSalaryHike', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'YearsAtCompany',\n",
       "       'YearsSinceLastPromotion', 'YearsWithCurrManager', 'BreaksTakenPerDay',\n",
       "       'EnvironmentSatisfaction', 'JobSatisfaction', 'WorkLifeBalance',\n",
       "       'JobInvolvement', 'PerformanceRating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dfb41ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['YearsOfService'] = df['Age'] - df['YearsAtCompany']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ed544d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AttritionAge'] = df['Age'] - (df['Age'] - df['YearsOfService'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f707b212",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Income_WorkExperience_Ratio'] = df['MonthlyIncome'] / df['TotalWorkingYears']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2842d8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "working_days_per_year = 250  # Assuming 250 working days in a year\n",
    "df['BreaksTakenPerYear'] = df['BreaksTakenPerDay'] * working_days_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cee6c2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot encoding\n",
    "# Using pandas get_dummies() function for one-hot encoding\n",
    "df_encoded = pd.get_dummies(df, columns=['BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole', 'MaritalStatus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "719118ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "df['BusinessTravel_encoded'] = label_encoder.fit_transform(df['BusinessTravel'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce7f831",
   "metadata": {},
   "source": [
    "### TASK 2 - Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8fda9b7",
   "metadata": {},
   "source": [
    "#### 1. For all numeric variables, check if they follow a normal distribution. If some are not normal , will you treat them ? If yes - why and how ? If no - why not ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "42677e02",
   "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>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition_Date</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>YearsOfService</th>\n",
       "      <th>AttritionAge</th>\n",
       "      <th>Income_WorkExperience_Ratio</th>\n",
       "      <th>BreaksTakenPerYear</th>\n",
       "      <th>BusinessTravel_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12/13/2015</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6981.666667</td>\n",
       "      <td>500.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2015-01-11 00:00:00</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11626.000000</td>\n",
       "      <td>250.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EmployeeID   Age       Attrition_Date     BusinessTravel  \\\n",
       "1           2  31.0           12/13/2015  Travel_Frequently   \n",
       "6           7  28.0  2015-01-11 00:00:00      Travel_Rarely   \n",
       "\n",
       "               Department  DistanceFromHome  Education EducationField  Gender  \\\n",
       "1                     R&D              10.0        1.0  Life Sciences  Female   \n",
       "6  Research & Development              11.0        2.0        Medical    Male   \n",
       "\n",
       "   JobLevel  ... EnvironmentSatisfaction JobSatisfaction  WorkLifeBalance  \\\n",
       "1       1.0  ...                       3               2              4.0   \n",
       "6       2.0  ...                       1               3              1.0   \n",
       "\n",
       "   JobInvolvement PerformanceRating  YearsOfService  AttritionAge  \\\n",
       "1             2.0               4.0            26.0          26.0   \n",
       "6             3.0               4.0            28.0          28.0   \n",
       "\n",
       "   Income_WorkExperience_Ratio  BreaksTakenPerYear  BusinessTravel_encoded  \n",
       "1                  6981.666667               500.0                       4  \n",
       "6                 11626.000000               250.0                       5  \n",
       "\n",
       "[2 rows x 34 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8b261b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f3a892db",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Age'], bins=20, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9ee2da45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['DistanceFromHome'], bins=20, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of DistanceFromHome')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9cadf244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Education'], bins=10, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Education')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7d35309a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['MonthlyIncome'], bins=10, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of MonthlyIncome')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f0aa5ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nz55q06aNPvvsMwUHB9uc81133aXx48eX+dgL4crHx0dfffVVqe1MUL5xMLID06lRo4b++te/atCgQfr111918OBBSZf+6/b++++X9PsvlD/avn279u7dq3bt2kmSWrZsKTc3t1ITJLdt23ZVl00sFkupXya7du0q88P47K1Bgwa69dZbtXDhQpuJ36dOndIHH3xgvUPLHho2bKh+/fpp+vTpZV5au+2227Rr1y6btnXr1llHM+ylS5cuatKkiZKTk7V79+4y+6xevVqnT5+Wp6enWrZsqaVLl9q8T0pKSrRgwQLVrl37T33W0NVYunSpzYjPyZMn9dFHH6lNmzZycnKSVPZ7ZuXKlTp69Kjd6oiNjZVhGDp69GiZI6wXQskfWSwWNW3aVFOnTlWNGjVKfThjdHS0MjIytHDhQrVo0UJeXl7WbZGRkdq5c6eWL18uFxcXaxCy92sQHBxsDXBt2rRRRkaGzTnv3r1b9erVK/OcL4Sd6OhonTx5stQHHi5cuPBP1wHHYmQHphAXF6ewsDCFh4frlltu0aFDhzRt2jQFBwcrNDRUkqz/s37jjTfUp08fubi4qEGDBmrQoIGeeuopTZ8+3XonzMGDBzV69GjVqVNHzz//vKTfL0skJiYqOTlZNWvWVLdu3XTkyBGNHTtWgYGBNnMBLic2NlavvPKKxowZo8jISO3bt08vv/yyQkJCKvxzeKpUqaKJEyfqscceU2xsrAYMGKDCwkJNmjRJv/32m1599VW7Hi8pKUn/+c9/tH79enl6etpse+KJJzR69Gj985//VGRkpP773/9qxowZf+qy5dVwcnLSsmXLFBMTo3vvvVfPPPOMoqOj5enpqUOHDun999/XRx99ZL3clpycrA4dOig6OlrDhw+Xq6urZs6cqd27d2vRokWXHUUob30dOnRQYmKiSkpK9Nprryk/P19jx4619omNjVVqaqoaNmyou+66S+np6Zo0aVKpkchrcd999+mpp55S3759tWPHDrVt21aenp7KysrS5s2b1aRJEz3zzDP6+OOPNXPmTHXt2lW33367DMPQ0qVL9dtvv6lDhw42+4yOjtbrr7+uZcuWafjw4Tbb2rRpI0n68MMPFRERYfP+sPdrEBgYqI0bN6pjx45q27at0tLSFBYWppdffllpaWmKiIjQs88+qwYNGujs2bM6ePCgPvnkE82ePVu1a9dW7969NXXqVPXu3Vvjx49XaGioPvnkE61evbqczzauN8IOTCE6OloffPCB3n77beXn5ysgIEAdOnTQ6NGjrbehRkVFacSIEZo3b57+9a9/qaSkROvXr7deUqpXr57mzp2rN998U97e3urUqZOSk5Nt5j+MHz9enp6emj17tlJSUtSwYUPNmjVLo0aN+tOXE0aNGqXTp09r7ty5mjhxou68807Nnj1by5YtK/PWc3vr1auXPD09lZycrJ49e8rJyUmtWrXS+vXrFRERYddjBQUFaejQoWVeyvr73/+u/Px8paam6vXXX9c999yjd999V126dLFrDdLvH6D39ddfa/r06Vq2bJlmzZqlwsJCBQYGqm3bttq8ebM1ZEVGRmrdunUaM2aM4uPjVVJSoqZNm2rFihWlJiPbw+DBg3X27Fk9++yzysnJUePGjbVy5UqbSz5vvPGGXFxclJycrIKCAjVv3lxLly7VSy+9ZNda3nrrLbVq1UpvvfWWZs6cqZKSEgUFBem+++7TPffcI0kKDQ1VjRo1NHHiRB07dkyurq5q0KCBUlNT1adPH5v9tWnTRs7Ozjp//rx1vs4FFz4La+fOnaU+w6kiXgNfX1+tW7fO+oGSq1evVnh4uHbs2KFXXnlFkyZN0pEjR+Tl5aWQkBB16tTJeknXw8ND69at03PPPacXX3xRFotFMTExWrx4sd1/ZlAxLIbBR00C1+LAgQNq2LChxowZo5EjRzq6HNwgDh48qJCQEE2aNKnUqAcA+2JkB7gK3377rRYtWqSIiAhVr15d+/bt08SJE1W9enUlJCQ4ujwAQBkIO8BVuPAlhnPnztVvv/0mb29vRUVFafz48Ze8/RwA4FhcxgIAAKbGrecAAMDUCDsAAMDUCDsAAMDUmKCs3z+Z89ixY/Ly8rL7B4YBAICKYRiGTp48qaCgoMt+sCthR79/30qdOnUcXQYAACiHw4cPX/YTxQk7kvX7Wg4fPqzq1as7uBoAAPBn5Ofnq06dOjbfu1YWwo7+94251atXJ+wAAHCDudIUFCYoAwAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAU3N2dAGonDIzM5Wbm2v3/fr6+qpu3bp23y8AAJdC2EEpmZmZatiokc6cPm33fbt7eOj7vXsJPACA64awg1Jyc3N15vRp9Rg3S34hoXbbb86BDL370jPKzc0l7AAArhvCDi7JLyRUtzZq6ugyAAC4JkxQBgAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApubQsJOcnKy//OUv8vLykp+fn7p27ap9+/bZ9ImPj5fFYrFZWrVqZdOnsLBQQ4YMka+vrzw9PfXQQw/pyJEj1/NUAABAJeXQsLNx40YNGjRI27ZtU1pams6fP6+YmBidOnXKpl+nTp2UlZVlXT755BOb7UOHDtWyZcu0ePFibd68WQUFBYqNjVVxcfH1PB0AAFAJOTvy4KtWrbJZT0lJkZ+fn9LT09W2bVtru5ubmwICAsrcR15enubOnav58+erffv2kqQFCxaoTp06Wrt2rTp27FhxJwAAACq9SjVnJy8vT5JUq1Ytm/YNGzbIz89P9evX15NPPqmcnBzrtvT0dJ07d04xMTHWtqCgIIWFhWnLli1lHqewsFD5+fk2CwAAMKdKE3YMw1BiYqJat26tsLAwa3vnzp31n//8R+vWrdPkyZO1fft23X///SosLJQkZWdny9XVVTVr1rTZn7+/v7Kzs8s8VnJysry9va1LnTp1Ku7EAACAQzn0MtYfDR48WLt27dLmzZtt2nv27Gn9d1hYmMLDwxUcHKyVK1eqe/ful9yfYRiyWCxlbhsxYoQSExOt6/n5+QQeAABMqlKM7AwZMkQrVqzQ+vXrVbt27cv2DQwMVHBwsDIyMiRJAQEBKioq0okTJ2z65eTkyN/fv8x9uLm5qXr16jYLAAAwJ4eGHcMwNHjwYC1dulTr1q1TSEjIFR9z/PhxHT58WIGBgZKkFi1ayMXFRWlpadY+WVlZ2r17tyIiIiqsdgAAcGNw6GWsQYMGaeHChfrwww/l5eVlnWPj7e0td3d3FRQUKCkpSQ8//LACAwN18OBBjRw5Ur6+vurWrZu1b0JCgoYNGyYfHx/VqlVLw4cPV5MmTax3ZwEAgJuXQ8POrFmzJElRUVE27SkpKYqPj5eTk5O+++47vfPOO/rtt98UGBio6OhoLVmyRF5eXtb+U6dOlbOzs3r06KEzZ86oXbt2Sk1NlZOT0/U8HQAAUAk5NOwYhnHZ7e7u7lq9evUV91O1alVNnz5d06dPt1dpAADAJCrFBGUAAICKQtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACm5tCwk5ycrL/85S/y8vKSn5+funbtqn379tn0MQxDSUlJCgoKkru7u6KiorRnzx6bPoWFhRoyZIh8fX3l6emphx56SEeOHLmepwIAACoph4adjRs3atCgQdq2bZvS0tJ0/vx5xcTE6NSpU9Y+EydO1JQpUzRjxgxt375dAQEB6tChg06ePGntM3ToUC1btkyLFy/W5s2bVVBQoNjYWBUXFzvitAAAQCXi7MiDr1q1ymY9JSVFfn5+Sk9PV9u2bWUYhqZNm6ZRo0ape/fukqR58+bJ399fCxcu1IABA5SXl6e5c+dq/vz5at++vSRpwYIFqlOnjtauXauOHTte9/MCAACVR6Was5OXlydJqlWrliTpwIEDys7OVkxMjLWPm5ubIiMjtWXLFklSenq6zp07Z9MnKChIYWFh1j4AAODm5dCRnT8yDEOJiYlq3bq1wsLCJEnZ2dmSJH9/f5u+/v7+OnTokLWPq6uratasWarPhcdfrLCwUIWFhdb1/Px8u50HAACoXCrNyM7gwYO1a9cuLVq0qNQ2i8Vis24YRqm2i12uT3Jysry9va1LnTp1yl84AACo1CpF2BkyZIhWrFih9evXq3bt2tb2gIAASSo1QpOTk2Md7QkICFBRUZFOnDhxyT4XGzFihPLy8qzL4cOH7Xk6AACgEnFo2DEMQ4MHD9bSpUu1bt06hYSE2GwPCQlRQECA0tLSrG1FRUXauHGjIiIiJEktWrSQi4uLTZ+srCzt3r3b2udibm5uql69us0CAADMyaFzdgYNGqSFCxfqww8/lJeXl3UEx9vbW+7u7rJYLBo6dKgmTJig0NBQhYaGasKECfLw8FCvXr2sfRMSEjRs2DD5+PioVq1aGj58uJo0aWK9OwsAANy8HBp2Zs2aJUmKioqyaU9JSVF8fLwk6YUXXtCZM2c0cOBAnThxQi1bttSaNWvk5eVl7T916lQ5OzurR48eOnPmjNq1a6fU1FQ5OTldr1MBAACVlEPDjmEYV+xjsViUlJSkpKSkS/apWrWqpk+frunTp9uxOgAAYAaVYoIyAABARSHsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUytX2Dlw4IC96wAAAKgQ5Qo7d9xxh6Kjo7VgwQKdPXvW3jUBAADYTbnCzrfffqtmzZpp2LBhCggI0IABA/TVV1/ZuzYAAIBrVq6wExYWpilTpujo0aNKSUlRdna2WrdurcaNG2vKlCn65Zdf7F0nAABAuVzTBGVnZ2d169ZN7777rl577TX9+OOPGj58uGrXrq3evXsrKyvLXnUCAACUyzWFnR07dmjgwIEKDAzUlClTNHz4cP34449at26djh49qi5dutirTgAAgHJxLs+DpkyZopSUFO3bt08PPPCA3nnnHT3wwAOqUuX37BQSEqK33npLDRs2tGuxAAAAV6tcYWfWrFnq16+f+vbtq4CAgDL71K1bV3Pnzr2m4gAAAK5VucJORkbGFfu4urqqT58+5dk9AACA3ZRrzk5KSoree++9Uu3vvfee5s2bd81FAQAA2Eu5ws6rr74qX1/fUu1+fn6aMGHCNRcFAABgL+UKO4cOHVJISEip9uDgYGVmZl5zUQAAAPZSrrDj5+enXbt2lWr/9ttv5ePjc81FAQAA2Eu5ws4jjzyiZ599VuvXr1dxcbGKi4u1bt06Pffcc3rkkUfsXSMAAEC5leturHHjxunQoUNq166dnJ1/30VJSYl69+7NnB0AAFCplCvsuLq6asmSJXrllVf07bffyt3dXU2aNFFwcLC96wMAALgm5Qo7F9SvX1/169e3Vy0AAAB2V66wU1xcrNTUVH322WfKyclRSUmJzfZ169bZpTgAAIBrVa6w89xzzyk1NVUPPvigwsLCZLFY7F0XAACAXZQr7CxevFjvvvuuHnjgAXvXAwAAYFfluvXc1dVVd9xxh71rAQAAsLtyhZ1hw4bpjTfekGEY9q4HAADArsoVdjZv3qz//Oc/qlevnuLi4tS9e3eb5c/atGmT4uLiFBQUJIvFouXLl9tsj4+Pl8VisVlatWpl06ewsFBDhgyRr6+vPD099dBDD+nIkSPlOS0AAGBC5ZqzU6NGDXXr1u2aD37q1Ck1bdpUffv21cMPP1xmn06dOiklJcW67urqarN96NCh+uijj7R48WL5+Pho2LBhio2NVXp6upycnK65RgAAcGMrV9j5Y/i4Fp07d1bnzp0v28fNzU0BAQFlbsvLy9PcuXM1f/58tW/fXpK0YMEC1alTR2vXrlXHjh3tUicAALhxlesyliSdP39ea9eu1VtvvaWTJ09Kko4dO6aCggK7FSdJGzZskJ+fn+rXr68nn3xSOTk51m3p6ek6d+6cYmJirG1BQUEKCwvTli1bLrnPwsJC5efn2ywAAMCcyjWyc+jQIXXq1EmZmZkqLCxUhw4d5OXlpYkTJ+rs2bOaPXu2XYrr3Lmz/va3vyk4OFgHDhzQ6NGjdf/99ys9PV1ubm7Kzs6Wq6uratasafM4f39/ZWdnX3K/ycnJGjt2rF1qBAAAlVu5Rnaee+45hYeH68SJE3J3d7e2d+vWTZ999pndiuvZs6f1gwvj4uL06aefav/+/Vq5cuVlH2cYxmU/6HDEiBHKy8uzLocPH7ZbzQAAoHIp18jO5s2b9cUXX5SaLBwcHKyjR4/apbCyBAYGKjg4WBkZGZKkgIAAFRUV6cSJEzajOzk5OYqIiLjkftzc3OTm5lZhdQIAgMqjXCM7JSUlKi4uLtV+5MgReXl5XXNRl3L8+HEdPnxYgYGBkqQWLVrIxcVFaWlp1j5ZWVnavXv3ZcMOAAC4eZQr7HTo0EHTpk2zrlssFhUUFGjMmDFX9RUSBQUF2rlzp3bu3ClJOnDggHbu3KnMzEwVFBRo+PDh2rp1qw4ePKgNGzYoLi5Ovr6+1tvevb29lZCQoGHDhumzzz7TN998o8cff1xNmjSx3p0FAABubuW6jDV16lRFR0frzjvv1NmzZ9WrVy9lZGTI19dXixYt+tP72bFjh6Kjo63riYmJkqQ+ffpo1qxZ+u677/TOO+/ot99+U2BgoKKjo7VkyRKb0aOpU6fK2dlZPXr00JkzZ9SuXTulpqbyGTsAAEBSOcNOUFCQdu7cqUWLFunrr79WSUmJEhIS9Nhjj9lMWL6SqKioy37lxOrVq6+4j6pVq2r69OmaPn36nz4uAAC4eZQr7EiSu7u7+vXrp379+tmzHgAAALsqV9h55513Lru9d+/e5SoGAADA3soVdp577jmb9XPnzun06dNydXWVh4cHYQcAAFQa5bob68SJEzZLQUGB9u3bp9atW1/VBGUAAICKVu7vxrpYaGioXn311VKjPgAAAI5kt7AjSU5OTjp27Jg9dwkAAHBNyjVnZ8WKFTbrhmEoKytLM2bM0H333WeXwgAAAOyhXGGna9euNusWi0W33HKL7r//fk2ePNkedQEAANhFucJOSUmJvesAAACoEHadswMAAFDZlGtk58J3WP0ZU6ZMKc8hAAAA7KJcYeebb77R119/rfPnz6tBgwaSpP3798vJyUnNmze39rNYLPapEgAAoJzKFXbi4uLk5eWlefPmqWbNmpJ+/6DBvn37qk2bNho2bJhdiwQAACivcs3ZmTx5spKTk61BR5Jq1qypcePGcTcWAACoVMoVdvLz8/Xzzz+Xas/JydHJkyevuSgAAAB7KVfY6datm/r27av3339fR44c0ZEjR/T+++8rISFB3bt3t3eNAAAA5VauOTuzZ8/W8OHD9fjjj+vcuXO/78jZWQkJCZo0aZJdCwQAALgW5Qo7Hh4emjlzpiZNmqQff/xRhmHojjvukKenp73rAwAAuCbX9KGCWVlZysrKUv369eXp6SnDMOxVFwAAgF2UK+wcP35c7dq1U/369fXAAw8oKytLktS/f39uOwcAAJVKucLO888/LxcXF2VmZsrDw8Pa3rNnT61atcpuxQEAAFyrcs3ZWbNmjVavXq3atWvbtIeGhurQoUN2KQwAAMAeyjWyc+rUKZsRnQtyc3Pl5uZ2zUUBAADYS7nCTtu2bfXOO+9Y1y0Wi0pKSjRp0iRFR0fbrTgAAIBrVa7LWJMmTVJUVJR27NihoqIivfDCC9qzZ49+/fVXffHFF/auEQAAoNzKNbJz5513ateuXbrnnnvUoUMHnTp1St27d9c333yjevXq2btGAACAcrvqkZ1z584pJiZGb731lsaOHVsRNQEAANjNVY/suLi4aPfu3bJYLBVRDwAAgF2V6zJW7969NXfuXHvXAgAAYHflmqBcVFSkt99+W2lpaQoPDy/1nVhTpkyxS3EAAADX6qrCzk8//aTbbrtNu3fvVvPmzSVJ+/fvt+nD5S0AAFCZXFXYCQ0NVVZWltavXy/p96+H+L//+z/5+/tXSHEAAADX6qrm7Fz8reaffvqpTp06ZdeCAAAA7KlcE5QvuDj8AAAAVDZXFXYsFkupOTnM0QEAAJXZVc3ZMQxD8fHx1i/7PHv2rJ5++ulSd2MtXbrUfhUCAABcg6sKO3369LFZf/zxx+1aDAAAgL1dVdhJSUmpqDoAAAAqxDVNUAYAAKjsCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUHBp2Nm3apLi4OAUFBclisWj58uU22w3DUFJSkoKCguTu7q6oqCjt2bPHpk9hYaGGDBkiX19feXp66qGHHtKRI0eu41kAAIDKzKFh59SpU2ratKlmzJhR5vaJEydqypQpmjFjhrZv366AgAB16NBBJ0+etPYZOnSoli1bpsWLF2vz5s0qKChQbGysiouLr9dpAACASszZkQfv3LmzOnfuXOY2wzA0bdo0jRo1St27d5ckzZs3T/7+/lq4cKEGDBigvLw8zZ07V/Pnz1f79u0lSQsWLFCdOnW0du1adezY8bqdCwAAqJwq7ZydAwcOKDs7WzExMdY2Nzc3RUZGasuWLZKk9PR0nTt3zqZPUFCQwsLCrH3KUlhYqPz8fJsFAACYU6UNO9nZ2ZIkf39/m3Z/f3/rtuzsbLm6uqpmzZqX7FOW5ORkeXt7W5c6derYuXoAAFBZVNqwc4HFYrFZNwyjVNvFrtRnxIgRysvLsy6HDx+2S60AAKDyqbRhJyAgQJJKjdDk5ORYR3sCAgJUVFSkEydOXLJPWdzc3FS9enWbBQAAmFOlDTshISEKCAhQWlqata2oqEgbN25URESEJKlFixZycXGx6ZOVlaXdu3db+wAAgJubQ+/GKigo0A8//GBdP3DggHbu3KlatWqpbt26Gjp0qCZMmKDQ0FCFhoZqwoQJ8vDwUK9evSRJ3t7eSkhI0LBhw+Tj46NatWpp+PDhatKkifXuLAAAcHNzaNjZsWOHoqOjreuJiYmSpD59+ig1NVUvvPCCzpw5o4EDB+rEiRNq2bKl1qxZIy8vL+tjpk6dKmdnZ/Xo0UNnzpxRu3btlJqaKicnp+t+PgAAoPJxaNiJioqSYRiX3G6xWJSUlKSkpKRL9qlataqmT5+u6dOnV0CFAADgRldp5+wAAADYA2EHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYmrOjCzC7zMxM5ebmVsi+fX19Vbdu3QrZNwAAZkHYqUCZmZlq2KiRzpw+XSH7d/fw0Pd79xJ4AAC4DMJOBcrNzdWZ06fVY9ws+YWE2nXfOQcy9O5Lzyg3N5ewAwDAZRB2rgO/kFDd2qipo8sAAOCmxARlAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgapX66yKSkpI0duxYmzZ/f39lZ2dLkgzD0NixYzVnzhydOHFCLVu21JtvvqnGjRs7olw4WEV9wzzfLg8AN7ZKHXYkqXHjxlq7dq113cnJyfrviRMnasqUKUpNTVX9+vU1btw4dejQQfv27ZOXl5cjyoWDVOQ3zPPt8gBwY6v0YcfZ2VkBAQGl2g3D0LRp0zRq1Ch1795dkjRv3jz5+/tr4cKFGjBgwPUuFQ5UUd8wz7fLA8CNr9KHnYyMDAUFBcnNzU0tW7bUhAkTdPvtt+vAgQPKzs5WTEyMta+bm5siIyO1ZcuWy4adwsJCFRYWWtfz8/Mr9Bxw/fAN8xWPy4UAbjSVOuy0bNlS77zzjurXr6+ff/5Z48aNU0REhPbs2WOdt+Pv72/zGH9/fx06dOiy+01OTi41FwjAlXG5EMCNqFKHnc6dO1v/3aRJE917772qV6+e5s2bp1atWkmSLBaLzWMMwyjVdrERI0YoMTHRup6fn686derYsXLAnLhcCOBGVKnDzsU8PT3VpEkTZWRkqGvXrpKk7OxsBQYGWvvk5OSUGu25mJubm9zc3CqyVMDUuFwI4EZyQ4WdwsJC7d27V23atFFISIgCAgKUlpamZs2aSZKKioq0ceNGvfbaaw6uFPhzmP8CR+B9h5tNpQ47w4cPV1xcnOrWraucnByNGzdO+fn56tOnjywWi4YOHaoJEyYoNDRUoaGhmjBhgjw8PNSrVy9Hlw5cEfNf4Ai873AzqtRh58iRI3r00UeVm5urW265Ra1atdK2bdsUHBwsSXrhhRd05swZDRw40PqhgmvWrOEzdnBDYP7Lja+iRkikihsl4X2Hm1GlDjuLFy++7HaLxaKkpCQlJSVdn4KACsD8lxtTRY6QSBU/SsL7DjeTSh12AKCyqqgREolRkrIwzwjXgrADANeAEZKKxzwjXCvCDgCgUmOeEa4VYQcAcENgFA3lVcXRBQAAAFQkwg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1bj0HAACSzPtJ1YQdAABg6k+qJuwAAABTf1I1YQcAAFiZ8ZOqmaAMAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMzdnRBQAAYEaZmZnKzc21+359fX1Vt25du+/XzAg7AADYWWZmpho2aqQzp0/bfd/uHh76fu9eAs9VIOwAAGBnubm5OnP6tHqMmyW/kFC77TfnQIbefekZ5ebmEnauAmEHAIAK4hcSqlsbNXV0GTc9JigDAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTM03YmTlzpkJCQlS1alW1aNFCn3/+uaNLAgAAlYApws6SJUs0dOhQjRo1St98843atGmjzp07KzMz09GlAQAABzNF2JkyZYoSEhLUv39/NWrUSNOmTVOdOnU0a9YsR5cGAAAc7IYPO0VFRUpPT1dMTIxNe0xMjLZs2eKgqgAAQGXh7OgCrlVubq6Ki4vl7+9v0+7v76/s7OwyH1NYWKjCwkLrel5eniQpPz/frrUVFBRIko7u3aWi06fsuu9fDv0oSUpPT7cex1727dsnyf51U7Mtav6fiqxZkqpUqaKSkhK77rOinguJ1/Bi1Pw/N3LNBQUFdv89e2F/hmFcvqNxgzt69KghydiyZYtN+7hx44wGDRqU+ZgxY8YYklhYWFhYWFhMsBw+fPiyWeGGH9nx9fWVk5NTqVGcnJycUqM9F4wYMUKJiYnW9ZKSEv3666/y8fGRxWKxW235+fmqU6eODh8+rOrVq9ttvyg/XpPKhdejcuH1qFx4Pa7MMAydPHlSQUFBl+13w4cdV1dXtWjRQmlpaerWrZu1PS0tTV26dCnzMW5ubnJzc7Npq1GjRoXVWL16dd6olQyvSeXC61G58HpULrwel+ft7X3FPjd82JGkxMREPfHEEwoPD9e9996rOXPmKDMzU08//bSjSwMAAA5mirDTs2dPHT9+XC+//LKysrIUFhamTz75RMHBwY4uDQAAOJgpwo4kDRw4UAMHDnR0GTbc3Nw0ZsyYUpfM4Di8JpULr0flwutRufB62I/FMK50vxYAAMCN64b/UEEAAIDLIewAAABTI+wAAABTI+wAAABTI+xUoJkzZyokJERVq1ZVixYt9Pnnnzu6pJtScnKy/vKXv8jLy0t+fn7q2rWr9Ttg4HjJycmyWCwaOnSoo0u5qR09elSPP/64fHx85OHhobvvvlvp6emOLuumdP78eb300ksKCQmRu7u7br/9dr388st2/363mwlhp4IsWbJEQ4cO1ahRo/TNN9+oTZs26ty5szIzMx1d2k1n48aNGjRokLZt26a0tDSdP39eMTExOnXKvl/eiKu3fft2zZkzR3fddZejS7mpnThxQvfdd59cXFz06aef6r///a8mT55coZ8sj0t77bXXNHv2bM2YMUN79+7VxIkTNWnSJE2fPt3Rpd2wuPW8grRs2VLNmzfXrFmzrG2NGjVS165dlZyc7MDK8Msvv8jPz08bN25U27ZtHV3OTaugoEDNmzfXzJkzNW7cON19992aNm2ao8u6Kb344ov64osvGH2uJGJjY+Xv76+5c+da2x5++GF5eHho/vz5DqzsxsXITgUoKipSenq6YmJibNpjYmK0ZcsWB1WFC/Ly8iRJtWrVcnAlN7dBgwbpwQcfVPv27R1dyk1vxYoVCg8P19/+9jf5+fmpWbNm+te//uXosm5arVu31meffab9+/dLkr799ltt3rxZDzzwgIMru3GZ5hOUK5Pc3FwVFxeX+tZ1f3//Ut/OjuvLMAwlJiaqdevWCgsLc3Q5N63Fixfr66+/1vbt2x1dCiT99NNPmjVrlhITEzVy5Eh99dVXevbZZ+Xm5qbevXs7urybzj/+8Q/l5eWpYcOGcnJyUnFxscaPH69HH33U0aXdsAg7FchisdisG4ZRqg3X1+DBg7Vr1y5t3rzZ0aXctA4fPqznnntOa9asUdWqVR1dDiSVlJQoPDxcEyZMkCQ1a9ZMe/bs0axZswg7DrBkyRItWLBACxcuVOPGjbVz504NHTpUQUFB6tOnj6PLuyERdiqAr6+vnJycSo3i5OTklBrtwfUzZMgQrVixQps2bVLt2rUdXc5NKz09XTk5OWrRooW1rbi4WJs2bdKMGTNUWFgoJycnB1Z48wkMDNSdd95p09aoUSN98MEHDqro5vb3v/9dL774oh555BFJUpMmTXTo0CElJycTdsqJOTsVwNXVVS1atFBaWppNe1pamiIiIhxU1c3LMAwNHjxYS5cu1bp16xQSEuLokm5q7dq103fffaedO3dal/DwcD322GPauXMnQccB7rvvvlIfx7B//34FBwc7qKKb2+nTp1Wliu2vZycnJ249vwaM7FSQxMREPfHEEwoPD9e9996rOXPmKDMzU08//bSjS7vpDBo0SAsXLtSHH34oLy8v64ibt7e33N3dHVzdzcfLy6vUfClPT0/5+Pgwj8pBnn/+eUVERGjChAnq0aOHvvrqK82ZM0dz5sxxdGk3pbi4OI0fP15169ZV48aN9c0332jKlCnq16+fo0u7YXHreQWaOXOmJk6cqKysLIWFhWnq1Knc6uwAl5onlZKSovj4+OtbDMoUFRXFrecO9vHHH2vEiBHKyMhQSEiIEhMT9eSTTzq6rJvSyZMnNXr0aC1btkw5OTkKCgrSo48+qn/+859ydXV1dHk3JMIOAAAwNebsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsADCNqKgoDR061NFlAKhkCDsAKoW4uDi1b9++zG1bt26VxWLR119/fZ2rAmAGhB0AlUJCQoLWrVunQ4cOldr273//W3fffbeaN2/ugMoA3OgIOwAqhdjYWPn5+Sk1NdWm/fTp01qyZIm6du2qRx99VLVr15aHh4eaNGmiRYsWXXafFotFy5cvt2mrUaOGzTGOHj2qnj17qmbNmvLx8VGXLl108OBB6/YNGzbonnvukaenp2rUqKH77ruvzEAGoPIi7ACoFJydndW7d2+lpqbqj1/Z995776moqEj9+/dXixYt9PHHH2v37t166qmn9MQTT+jLL78s9zFPnz6t6OhoVatWTZs2bdLmzZtVrVo1derUSUVFRTp//ry6du2qyMhI7dq1S1u3btVTTz11yS+XBVA5OTu6AAC4oF+/fpo0aZI2bNig6OhoSb9fwurevbtuvfVWDR8+3Np3yJAhWrVqld577z21bNmyXMdbvHixqlSporffftsaYFJSUlSjRg1t2LBB4eHhysvLU2xsrOrVqydJatSo0TWeJYDrjZEdAJVGw4YNFRERoX//+9+SpB9//FGff/65+vXrp+LiYo0fP1533XWXfHx8VK1aNa1Zs0aZmZnlPl56erp++OEHeXl5qVq1aqpWrZpq1aqls2fP6scff1StWrUUHx+vjh07Ki4uTm+88YaysrLsdboArhPCDoBKJSEhQR988IHy8/OVkpKi4OBgtWvXTpMnT9bUqVP1wgsvaN26ddq5c6c6duyooqKiS+7LYrHYXBKTpHPnzln/XVJSohYtWmjnzp02y/79+9WrVy9Jv4/0bN26VREREVqyZInq16+vbdu2VczJA6gQhB0AlUqPHj3k5OSkhQsXat68eerbt68sFos+//xzdenSRY8//riaNm2q22+/XRkZGZfd1y233GIzEpORkaHTp09b15s3b66MjAz5+fnpjjvusFm8vb2t/Zo1a6YRI0Zoy5YtCgsL08KFC+1/4gAqDGEHQKVSrVo19ezZUyNHjtSxY8cUHx8vSbrjjjuUlpamLVu2aO/evRowYICys7Mvu6/7779fM2bM0Ndff60dO3bo6aeflouLi3X7Y489Jl9fX3Xp0kWff/65Dhw4oI0bN+q5557TkSNHdODAAY0YMUJbt27VoUOHtGbNGu3fv595O8ANhrADoNJJSEjQiRMn1L59e9WtW1eSNHr0aDVv3lwdO3ZUVFSUAgIC1LVr18vuZ/LkyapTp47atm2rXr16afjw4fLw8LBu9/Dw0KZNm1S3bl11795djRo1Ur9+/XTmzBlVr15dHh4e+v777/Xwww+rfv36euqppzR48GANGDCgIk8fgJ1ZjIsvaAMAAJgIIzsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDU/h/b7cTclLH3VQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['NumCompaniesWorked'], bins=20, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of NumCompaniesWorked')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e2a5f3cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['StandardHours'], bins=20, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of StandardHours')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ae0680d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['YearsSinceLastPromotion'], bins=20, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of YearsSinceLastPromotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "00fea678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yP4o4H5vNZq+l1M6dO7Vly5ZL7sNZ1157rerXr6/FixfbP6qQpJMnT+rdd9/VLbfcctFf5pcrMDBQnTp10nfffafrr7++3Kt9pb+UbTab/Pz8HMJNVlaW3n//fafeOygoSImJiXr22WdVVFSk3bt3Szr7UVhwcLDWrl2r9evXOyxTpkxRYWGh3n77bUnnP28k6fnnn9fRo0f12GOPOYxnqby8PK1evVqS7OfRuefZu+++q5MnT9q3u1LpMZRavHixpLMfqUn/DZG/Px+NMXrzzTev+L0fe+wxffzxxxo7dqzCw8P1xz/+8Yr7hDVwJQi4TL169VJCQoJatWqlevXqad++fZo+fbpiYmIUFxcnSWrWrJkk6dVXX1VSUpJ8fX117bXX6tprr9XDDz+sGTNmyMvLS4mJifr555/1/PPPq2HDhnriiScknf0YYvTo0Zo0aZJq166t3r1768CBA3rhhRcUGRnpMMfmQnr27Knx48dr3Lhx6tChg/bs2aO//vWvio2NdftzhLy8vDR58mQ9+OCD6tmzpx555BEVFhZqypQpOnbsmF566SW3vO+rr76q9u3b69Zbb9XQoUPVqFEjnThxQj/++KM+/PBD+3yZnj17asWKFRo2bJjuu+8+7d+/X+PHj1dkZKTS09Mv6b3+/Oc/KyAgQO3atVNkZKSysrI0adIkhYSE6KabbtKuXbv09ddfa+jQofYA/Hvt2rXT1KlTNXfuXI0YMUJXX321AgIC9Pbbb6tJkyaqWbOmoqKiFBUVpT/+8Y96/vnnNX78eP3www9KTk7W1Vdfrfz8fH311Vd6/fXX1a9fP3Xr1k1du3ZV9+7d9Ze//EW5ublq166ddu7cqXHjxunGG2/UwIEDXTrmfn5+mjp1qvLy8nTTTTdp8+bNmjBhghITE+3z7Lp27So/Pz898MADeuqpp3Tq1CnNnj37ij8SlaQBAwZo7Nix2rhxo5577jn7x3bARXl0WjZQwVzxnKCpU6eatm3bmtDQUPutwcnJyQ7PfDHm7C3uUVFRxsvLq9znBMXHxxtfX18TGhpqBgwYUO5zgiZMmGAaNGhg/Pz8zPXXX28++ugj07x5c4c7uy50Z1VhYaEZM2aMqV+/vqlRo4Zp0aKFWblypUlKSnI4ztK7w6ZMmeKw//n6vtg4/t7KlStNmzZtTI0aNUxQUJDp0qWL+fLLLy/pfS6m9Bk158rIyDBDhgwx9evXN76+vqZevXqmbdu2ZsKECQ7tXnrpJdOoUSPj7+9vmjRpYt588037c55+T//3nKBzLViwwHTq1MmEh4cbPz8/ExUVZfr27Wt27txpjPnvXXQ7duw47zE8/fTTRpLZvn27McaYJUuW2J9FpN89J6jUhg0bzH333WciIyONr6+vqVWrlrnlllvMlClTTG5urr1dQUGB+ctf/mJiYmKMr6+viYyMNEOHDj3vc4LOVd4xl3eelP4Mdu7caTp27GgCAgJMnTp1zNChQ01eXp7D/h9++KFp3ry5qVGjhqlfv775n//5H/Ppp586/P0w5r/PCTrXueft7w0aNMj4+Pg43OUGXIzNmHKuqwKolDIyMtS4cWONGzdOzzzzjKfLATRo0CD985//VF5ensdqKCoqUqNGjdS+fXu98847HqsDVQ8fhwGV1Pfff68lS5aobdu2qlWrlvbs2aPJkyerVq1aSk5O9nR5gMf9+uuv2rNnj+bNm6dffvlFTz/9tKdLQhVDCAIqqaCgIH3zzTeaO3eujh07ppCQEHXs2FEvvvjieW+TB6zk448/1uDBgxUZGalZs2ZxWzwuGx+HAQAAS+IWeQAAYEmEIAAAYEmEIAAAYElMjJZUUlKiQ4cOKTg4+LyPxgcAAJWLMUYnTpxQVFTUJT9E9vcIQTr7PUYNGzb0dBkAAMAJ+/fvL/N9jJeCECQpODhY0tlBrFWrloerAQAAlyI3N1cNGza0/x6/XIQg/feL/WrVqkUIAgCginF2KgsTowEAgCURggAAgCURggAAgCURggAAgCURggAAgCURggAAgCURggAAgCURggAAgCURggAAgCURggAAgCURggAAgCURggAAgCURggAAgCURggAAgCX5eLqA6i4zM1M5OTlu6Ts0NFTR0dFu6RsAgOqOEORGmZmZatykiQry893Sf0BgoH5ISyMIAQDgBEKQG+Xk5KggP199J8xWWGycS/vOzkjXO88NVU5ODiEIAAAnEIIqQFhsnOo3ae7pMgAAwO8wMRoAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFiSR0PQpEmTdNNNNyk4OFhhYWG65557tGfPHoc2xhilpKQoKipKAQEB6tixo3bv3u3QprCwUCNHjlRoaKiCgoJ011136cCBAxV5KAAAoIrxaAjasGGDhg8frq1btyo1NVVnzpxRt27ddPLkSXubyZMna9q0aZo5c6a2bdumiIgIde3aVSdOnLC3GTVqlN577z0tXbpUmzZtUl5ennr27Kni4mJPHBYAAKgCfDz55qtWrXJ4PW/ePIWFhWn79u267bbbZIzR9OnT9eyzz6pPnz6SpAULFig8PFyLFy/WI488ouPHj2vu3Ll66623dPvtt0uSFi1apIYNG2rNmjXq3r17hR8XAACo/CrVnKDjx49LkurUqSNJysjIUFZWlrp162Zv4+/vrw4dOmjz5s2SpO3bt+v06dMObaKiopSQkGBvAwAAcC6PXgn6PWOMRo8erfbt2yshIUGSlJWVJUkKDw93aBseHq59+/bZ2/j5+al27dpl2pTuf67CwkIVFhbaX+fm5rrsOAAAQNVQaa4EjRgxQjt37tSSJUvKbLPZbA6vjTFl1p3rQm0mTZqkkJAQ+9KwYUPnCwcAAFVSpQhBI0eO1AcffKD169erQYMG9vURERGSVOaKTnZ2tv3qUEREhIqKinT06NHztjnX2LFjdfz4cfuyf/9+Vx4OAACoAjwagowxGjFihFasWKF169YpNjbWYXtsbKwiIiKUmppqX1dUVKQNGzaobdu2kqSWLVvK19fXoc3hw4e1a9cue5tz+fv7q1atWg4LAACwFo/OCRo+fLgWL16s999/X8HBwfYrPiEhIQoICJDNZtOoUaM0ceJExcXFKS4uThMnTlRgYKD69+9vb5ucnKwnn3xSdevWVZ06dTRmzBg1a9bMfrcYAADAuTwagmbPni1J6tixo8P6efPmadCgQZKkp556SgUFBRo2bJiOHj2qNm3aaPXq1QoODra3f+WVV+Tj46O+ffuqoKBAXbp00fz58+Xt7V1RhwIAAKoYj4YgY8xF29hsNqWkpCglJeW8bWrUqKEZM2ZoxowZLqwOAABUZ5ViYjQAAEBFIwQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABLIgQBAABL8mgI2rhxo3r16qWoqCjZbDatXLnSYfugQYNks9kclptvvtmhTWFhoUaOHKnQ0FAFBQXprrvu0oEDByrwKAAAQFXk0RB08uRJNW/eXDNnzjxvmzvuuEOHDx+2L5988onD9lGjRum9997T0qVLtWnTJuXl5alnz54qLi52d/kAAKAK8/HkmycmJioxMfGCbfz9/RUREVHutuPHj2vu3Ll66623dPvtt0uSFi1apIYNG2rNmjXq3r27y2sGAADVQ6WfE/T5558rLCxM8fHx+vOf/6zs7Gz7tu3bt+v06dPq1q2bfV1UVJQSEhK0efPm8/ZZWFio3NxchwUAAFhLpQ5BiYmJevvtt7Vu3TpNnTpV27ZtU+fOnVVYWChJysrKkp+fn2rXru2wX3h4uLKyss7b76RJkxQSEmJfGjZs6NbjAAAAlY9HPw67mH79+tn/nJCQoFatWikmJkYff/yx+vTpc979jDGy2Wzn3T527FiNHj3a/jo3N5cgBACAxVTqK0HnioyMVExMjNLT0yVJERERKioq0tGjRx3aZWdnKzw8/Lz9+Pv7q1atWg4LAACwlioVgo4cOaL9+/crMjJSktSyZUv5+voqNTXV3ubw4cPatWuX2rZt66kyAQBAFeDRj8Py8vL0448/2l9nZGRox44dqlOnjurUqaOUlBTde++9ioyM1M8//6xnnnlGoaGh6t27tyQpJCREycnJevLJJ1W3bl3VqVNHY8aMUbNmzex3iwEAAJTHoyHom2++UadOneyvS+fpJCUlafbs2frXv/6lhQsX6tixY4qMjFSnTp20bNkyBQcH2/d55ZVX5OPjo759+6qgoEBdunTR/Pnz5e3tXeHHAwAAqg6PhqCOHTvKGHPe7Z999tlF+6hRo4ZmzJihGTNmuLI0AABQzVWpOUEAAACuQggCAACWRAgCAACWRAgCAACWRAgCAACWRAgCAACWRAgCAACWRAgCAACWRAgCAACWRAgCAACWRAgCAACWRAgCAACWRAgCAACW5FQIysjIcHUdAAAAFcqpEHTNNdeoU6dOWrRokU6dOuXqmgAAANzOqRD0/fff68Ybb9STTz6piIgIPfLII/r6669dXRsAAIDbOBWCEhISNG3aNB08eFDz5s1TVlaW2rdvr6ZNm2ratGn69ddfXV0nAACAS13RxGgfHx/17t1b77zzjl5++WX99NNPGjNmjBo0aKCHHnpIhw8fdlWdAAAALnVFIeibb77RsGHDFBkZqWnTpmnMmDH66aeftG7dOh08eFB33323q+oEAABwKR9ndpo2bZrmzZunPXv2qEePHlq4cKF69OghL6+zmSo2Nlavv/66Gjdu7NJiAQAAXMWpEDR79mwNGTJEgwcPVkRERLltoqOjNXfu3CsqDgAAwF2cCkHp6ekXbePn56ekpCRnugcAAHA7p+YEzZs3T8uXLy+zfvny5VqwYMEVFwUAAOBuToWgl156SaGhoWXWh4WFaeLEiVdcFAAAgLs5FYL27dun2NjYMutjYmKUmZl5xUUBAAC4m1MhKCwsTDt37iyz/vvvv1fdunWvuCgAAAB3cyoE3X///Xrssce0fv16FRcXq7i4WOvWrdPjjz+u+++/39U1AgAAuJxTd4dNmDBB+/btU5cuXeTjc7aLkpISPfTQQ8wJAgAAVYJTIcjPz0/Lli3T+PHj9f333ysgIEDNmjVTTEyMq+sDAABwC6dCUKn4+HjFx8e7qhYAAIAK41QIKi4u1vz587V27VplZ2erpKTEYfu6detcUhwAAIC7OBWCHn/8cc2fP1933nmnEhISZLPZXF0XAACAWzkVgpYuXap33nlHPXr0cHU9AAAAFcKpW+T9/Px0zTXXuLoWAACACuNUCHryySf16quvyhjj6noAAAAqhFMfh23atEnr16/Xp59+qqZNm8rX19dh+4oVK1xSHAAAgLs4FYKuuuoq9e7d29W1AAAAVBinQtC8efNcXQcAAECFcmpOkCSdOXNGa9as0euvv64TJ05Ikg4dOqS8vDyXFQcAAOAuTl0J2rdvn+644w5lZmaqsLBQXbt2VXBwsCZPnqxTp05pzpw5rq4TAADApZy6EvT444+rVatWOnr0qAICAuzre/furbVr17qsOAAAAHdx+u6wL7/8Un5+fg7rY2JidPDgQZcUBgAA4E5OXQkqKSlRcXFxmfUHDhxQcHDwFRcFAADgbk6FoK5du2r69On21zabTXl5eRo3bhxfpQEAAKoEpz4Oe+WVV9SpUyddd911OnXqlPr376/09HSFhoZqyZIlrq4RAADA5ZwKQVFRUdqxY4eWLFmib7/9ViUlJUpOTtaDDz7oMFEaAACgsnIqBElSQECAhgwZoiFDhriyHgAAgArhVAhauHDhBbc/9NBDThUDAABQUZwKQY8//rjD69OnTys/P19+fn4KDAwkBAEAgErPqbvDjh496rDk5eVpz549at++PROjAQBAleD0d4edKy4uTi+99FKZq0QAAACVkctCkCR5e3vr0KFDruwSAADALZyaE/TBBx84vDbG6PDhw5o5c6batWvnksIAAADcyakQdM899zi8ttlsqlevnjp37qypU6e6oi4AAAC3cioElZSUuLoOAACACuXSOUEAAABVhVNXgkaPHn3JbadNm+bMWwAAALiVUyHou+++07fffqszZ87o2muvlSTt3btX3t7eatGihb2dzWZzTZUAAAAu5lQI6tWrl4KDg7VgwQLVrl1b0tkHKA4ePFi33nqrnnzySZcWCQAA4GpOzQmaOnWqJk2aZA9AklS7dm1NmDCBu8MAAECV4FQIys3N1S+//FJmfXZ2tk6cOHHFRQEAALibUyGod+/eGjx4sP75z3/qwIEDOnDggP75z38qOTlZffr0cXWNAAAALufUnKA5c+ZozJgxGjBggE6fPn22Ix8fJScna8qUKS4tEAAAwB2cCkGBgYGaNWuWpkyZop9++knGGF1zzTUKCgpydX0AAABucUUPSzx8+LAOHz6s+Ph4BQUFyRjjqroAAADcyqkQdOTIEXXp0kXx8fHq0aOHDh8+LEn605/+xO3xAACgSnAqBD3xxBPy9fVVZmamAgMD7ev79eunVatWuaw4AAAAd3FqTtDq1av12WefqUGDBg7r4+LitG/fPpcUBgAA4E5OXQk6efKkwxWgUjk5OfL397/kfjZu3KhevXopKipKNptNK1eudNhujFFKSoqioqIUEBCgjh07avfu3Q5tCgsLNXLkSIWGhiooKEh33XWXDhw44MxhAQAAC3EqBN12221auHCh/bXNZlNJSYmmTJmiTp06XXI/J0+eVPPmzTVz5sxyt0+ePFnTpk3TzJkztW3bNkVERKhr164OD2QcNWqU3nvvPS1dulSbNm1SXl6eevbsqeLiYmcODQAAWIRTH4dNmTJFHTt21DfffKOioiI99dRT2r17t3777Td9+eWXl9xPYmKiEhMTy91mjNH06dP17LPP2h/AuGDBAoWHh2vx4sV65JFHdPz4cc2dO1dvvfWWbr/9dknSokWL1LBhQ61Zs0bdu3d35vAAAIAFOHUl6LrrrtPOnTvVunVrde3aVSdPnlSfPn303Xff6eqrr3ZJYRkZGcrKylK3bt3s6/z9/dWhQwdt3rxZkrR9+3adPn3aoU1UVJQSEhLsbcpTWFio3NxchwUAAFjLZV8JKg0dr7/+ul544QV31CRJysrKkiSFh4c7rA8PD7dPvs7KypKfn5/DF7mWtindvzyTJk1ya+0AAKDyu+wrQb6+vtq1a5dsNps76inj3Pcxxlz0vS/WZuzYsTp+/Lh92b9/v0tqBQAAVYdTH4c99NBDmjt3rqtrcRARESFJZa7oZGdn268ORUREqKioSEePHj1vm/L4+/urVq1aDgsAALAWpyZGFxUV6e9//7tSU1PVqlWrMt8ZNm3atCsuLDY2VhEREUpNTdWNN95of98NGzbo5ZdfliS1bNlSvr6+Sk1NVd++fSWd/SqPXbt2afLkyVdcAwAAqL4uKwT95z//UaNGjbRr1y61aNFCkrR3716HNpfzMVleXp5+/PFH++uMjAzt2LFDderUUXR0tEaNGqWJEycqLi5OcXFxmjhxogIDA9W/f39JUkhIiJKTk/Xkk0+qbt26qlOnjsaMGaNmzZrZ7xYDAAAoz2WFoLi4OB0+fFjr16+XdPZrMv73f//3gh89Xcg333zj8Fyh0aNHS5KSkpI0f/58PfXUUyooKNCwYcN09OhRtWnTRqtXr1ZwcLB9n1deeUU+Pj7q27evCgoK1KVLF82fP1/e3t5O1QQAAKzhskLQud8S/+mnn+rkyZNOv3nHjh0v+M3zNptNKSkpSklJOW+bGjVqaMaMGZoxY4bTdQAAAOtxamJ0qQsFGAAAgMrsskKQzWYrM+enom6VBwAAcKXL/jhs0KBB9i9JPXXqlB599NEyd4etWLHCdRUCAAC4wWWFoKSkJIfXAwYMcGkxAAAAFeWyQtC8efPcVQcAAECFuqKJ0QAAAFUVIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFgSIQgAAFiSj6cLwJVJS0tzS7+hoaGKjo52S98AAFQGhKAq6kTOL7J5eWnAgAFu6T8gMFA/pKURhAAA1RYhqIoqOJErU1KivhNmKyw2zqV9Z2ek653nhionJ4cQBACotghBVVxYbJzqN2nu6TIAAKhymBgNAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsiRAEAAAsycfTBQCukpmZqZycHJf3GxoaqujoaJf3CwDwLEIQqoXMzEw1btJEBfn5Lu87IDBQP6SlEYQAoJohBKFayMnJUUF+vvpOmK2w2DiX9Zudka53nhuqnJwcQhAAVDOEIFQrYbFxqt+kuafLAABUAUyMBgAAlkQIAgAAlsTHYcAlSEtLc0u/3HkGAJ5DCAIu4ETOL7J5eWnAgAFu6Z87zwDAcwhBOC93XP2oalc+Ck7kypSUuPyuM4k7zwDA0whBKMOdVz+q6pUP7joDgOqHEIQy3HX1gysfAIDKhBCE83LX1Q93fMzmronLAIDqixCECuPuScYAAFwOQhAqjDsnGe/5cq1SZ01yaZ8AgOqNEIQK546P2bIz0l3aHwCg+uOJ0QAAwJK4EgTgsmRmZionJ8fl/Va1Z0gBqPoqdQhKSUnRCy+84LAuPDxcWVlZkiRjjF544QW98cYbOnr0qNq0aaPXXntNTZs29US5QLWXmZmpxk2aqCA/3+V9V9VnSAGouip1CJKkpk2bas2aNfbX3t7e9j9PnjxZ06ZN0/z58xUfH68JEyaoa9eu2rNnj4KDgz1RLlCt5eTkqCA/n2dIAagWKn0I8vHxUURERJn1xhhNnz5dzz77rPr06SNJWrBggcLDw7V48WI98sgjFV0qYBk8QRtAdVDpJ0anp6crKipKsbGxuv/++/Wf//xHkpSRkaGsrCx169bN3tbf318dOnTQ5s2bL9hnYWGhcnNzHRYAAGAtlToEtWnTRgsXLtRnn32mN998U1lZWWrbtq2OHDlinxcUHh7usM/v5wydz6RJkxQSEmJfGjZs6LZjAAAAlVOlDkGJiYm699571axZM91+++36+OOPJZ392KuUzWZz2McYU2bducaOHavjx4/bl/3797u+eAAAUKlV6hB0rqCgIDVr1kzp6en2eULnXvXJzs4uc3XoXP7+/qpVq5bDAgAArKVKhaDCwkKlpaUpMjJSsbGxioiIUGpqqn17UVGRNmzYoLZt23qwSgAAUBVU6rvDxowZo169eik6OlrZ2dmaMGGCcnNzlZSUJJvNplGjRmnixImKi4tTXFycJk6cqMDAQPXv39/TpQMAgEquUoegAwcO6IEHHlBOTo7q1aunm2++WVu3blVMTIwk6amnnlJBQYGGDRtmf1ji6tWreUYQAAC4qEodgpYuXXrB7TabTSkpKUpJSamYggAAQLVRpeYEAQAAuAohCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWBIhCAAAWJKPpwsArC4tLc3lfYaGhio6Otrl/QJAdUIIAjzkRM4vsnl5acCAAS7vOyAwUD+kpRGEAOACCEGAhxScyJUpKVHfCbMVFhvnsn6zM9L1znNDlZOTQwgCgAsgBAEeFhYbp/pNmnu6DACwHCZGAwAASyIEAQAAS+LjMKCacsddZ+7oEwA8hRAEVDPuvOsMAKoTQhBQzbjrrjNJ2vPlWqXOmuTSPgHAUwhBQDXljrvOsjPSXdofAHgSE6MBAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAlEYIAAIAl+Xi6AAAolZaW5pZ+CwsL5e/vX2X6laTQ0FBFR0e7pe+qKDMzUzk5OS7vl3G2NkIQAI87kfOLbF5eGjBggFv6t3l5yZSUVJl+JSkgMFA/pKXxC1pnA1DjJk1UkJ/v8r4ZZ2sjBAHwuIITuTIlJeo7YbbCYuNc2veeL9cqddYkl/ftrn4lKTsjXe88N1Q5OTn8cpaUk5Ojgvx8l4814wxCEIBKIyw2TvWbNHdpn9kZ6W7p21394vwYa7gaE6MBAIAlcSUIACqpqjZRvKpOMnbXOFfF8bDaBPRqE4JmzZqlKVOm6PDhw2ratKmmT5+uW2+91dNlAcBlq6oTxavaJGN3j3NVGw8rTkCvFiFo2bJlGjVqlGbNmqV27drp9ddfV2Jiov79739XugEHgIupihPFq+IkY3eOc1UcDytOQK8WIWjatGlKTk7Wn/70J0nS9OnT9dlnn2n27NmaNGmSh6sDAOdUpYniVRlj4chK41HlJ0YXFRVp+/bt6tatm8P6bt26afPmzR6qCgAAVHZV/kpQTk6OiouLFR4e7rA+PDxcWVlZ5e5TWFiowsJC++vjx49LknJzc11aW15eniTpYNpOFeWfdGnfv/6cXuX6puaK6ZuaK6bvqlizO/v+dd9PkqTt27fb/9/nKnv27JFUxX6GbhwPLy8vlbhhTpfbxvn/xiIvL8/lv2dL+zPGONeBqeIOHjxoJJnNmzc7rJ8wYYK59tpry91n3LhxRhILCwsLCwtLNVj279/vVIao8leCQkND5e3tXeaqT3Z2dpmrQ6XGjh2r0aNH21+XlJTot99+U926dWWz2VxWW25urho2bKj9+/erVq1aLusXF8a4ewbj7hmMu2cw7p5x7rgbY3TixAlFRUU51V+VD0F+fn5q2bKlUlNT1bt3b/v61NRU3X333eXu4+/vX+YZGVdddZXbaqxVqxZ/STyAcfcMxt0zGHfPYNw94/fjHhIS4nQ/VT4ESdLo0aM1cOBAtWrVSrfccoveeOMNZWZm6tFHH/V0aQAAoJKqFiGoX79+OnLkiP7617/q8OHDSkhI0CeffKKYmBhPlwYAACqpahGCJGnYsGEaNmyYp8tw4O/vr3Hjxrnl8fQ4P8bdMxh3z2DcPYNx9wxXj7vNGGfvKwMAAKi6qvzDEgEAAJxBCAIAAJZECAIAAJZECAIAAJZECHKjWbNmKTY2VjVq1FDLli31xRdfeLqkai0lJUU2m81hiYiI8HRZ1c7GjRvVq1cvRUVFyWazaeXKlQ7bjTFKSUlRVFSUAgIC1LFjR+3evdszxVYjFxv3QYMGlTn/b775Zs8UW01MmjRJN910k4KDgxUWFqZ77rnH/v1apTjfXe9Sxt1V5zshyE2WLVumUaNG6dlnn9V3332nW2+9VYmJicrMzPR0adVa06ZNdfjwYfvyr3/9y9MlVTsnT55U8+bNNXPmzHK3T548WdOmTdPMmTO1bds2RUREqGvXrjpx4kQFV1q9XGzcJemOO+5wOP8/+eSTCqyw+tmwYYOGDx+urVu3KjU1VWfOnFG3bt108uR/v1yU8931LmXcJRed70594xguqnXr1ubRRx91WNe4cWPz9NNPe6ii6m/cuHGmefPmni7DUiSZ9957z/66pKTEREREmJdeesm+7tSpUyYkJMTMmTPHAxVWT+eOuzHGJCUlmbvvvtsj9VhFdna2kWQ2bNhgjOF8ryjnjrsxrjvfuRLkBkVFRdq+fbu6devmsL5bt27avHmzh6qyhvT0dEVFRSk2Nlb333+//vOf/3i6JEvJyMhQVlaWw7nv7++vDh06cO5XgM8//1xhYWGKj4/Xn//8Z2VnZ3u6pGrl+PHjkqQ6depI4nyvKOeOeylXnO+EIDfIyclRcXFxmW+xDw8PL/Nt93CdNm3aaOHChfrss8/05ptvKisrS23bttWRI0c8XZpllJ7fnPsVLzExUW+//bbWrVunqVOnatu2bercubMKCws9XVq1YIzR6NGj1b59eyUkJEjifK8I5Y275Lrzvdp8bUZlZLPZHF4bY8qsg+skJiba/9ysWTPdcsstuvrqq7VgwQKNHj3ag5VZD+d+xevXr5/9zwkJCWrVqpViYmL08ccfq0+fPh6srHoYMWKEdu7cqU2bNpXZxvnuPucbd1ed71wJcoPQ0FB5e3uX+ZdAdnZ2mX8xwH2CgoLUrFkzpaene7oUyyi9G49z3/MiIyMVExPD+e8CI0eO1AcffKD169erQYMG9vWc7+51vnEvj7PnOyHIDfz8/NSyZUulpqY6rE9NTVXbtm09VJX1FBYWKi0tTZGRkZ4uxTJiY2MVERHhcO4XFRVpw4YNnPsV7MiRI9q/fz/n/xUwxmjEiBFasWKF1q1bp9jYWIftnO/ucbFxL4+z5zsfh7nJ6NGjNXDgQLVq1Uq33HKL3njjDWVmZurRRx/1dGnV1pgxY9SrVy9FR0crOztbEyZMUG5urpKSkjxdWrWSl5enH3/80f46IyNDO3bsUJ06dRQdHa1Ro0Zp4sSJiouLU1xcnCZOnKjAwED179/fg1VXfRca9zp16iglJUX33nuvIiMj9fPPP+uZZ55RaGioevfu7cGqq7bhw4dr8eLFev/99xUcHGy/4hMSEqKAgADZbDbOdze42Ljn5eW57ny/4vvLcF6vvfaaiYmJMX5+fqZFixYOt/fB9fr162ciIyONr6+viYqKMn369DG7d+/2dFnVzvr1642kMktSUpIx5uxtw+PGjTMRERHG39/f3HbbbeZf//qXZ4uuBi407vn5+aZbt26mXr16xtfX10RHR5ukpCSTmZnp6bKrtPLGW5KZN2+evQ3nu+tdbNxdeb7b/u8NAQAALIU5QQAAwJIIQQAAwJIIQQAAwJIIQQAAwJIIQQAAwJIIQQAAwJIIQQAAwJIIQQCqjY4dO2rUqFGeLgNAFUEIAlAp9OrVS7fffnu527Zs2SKbzaZvv/22gqsCUJ0RggBUCsnJyVq3bp327dtXZts//vEP3XDDDWrRooUHKgNQXRGCAFQKPXv2VFhYmObPn++wPj8/X8uWLdM999yjBx54QA0aNFBgYKCaNWumJUuWXLBPm82mlStXOqy76qqrHN7j4MGD6tevn2rXrq26devq7rvv1s8//2zf/vnnn6t169YKCgrSVVddpXbt2pUb1ABUPYQgAJWCj4+PHnroIc2fP1+//0rD5cuXq6ioSH/605/UsmVLffTRR9q1a5cefvhhDRw4UF999ZXT75mfn69OnTqpZs2a2rhxozZt2qSaNWvqjjvuUFFRkc6cOaN77rlHHTp00M6dO7VlyxY9/PDDstlsrjhkAB7m4+kCAKDUkCFDNGXKFH3++efq1KmTpLMfhfXp00f169fXmDFj7G1HjhypVatWafny5WrTpo1T77d06VJ5eXnp73//uz3YzJs3T1dddZU+//xztWrVSsePH1fPnj119dVXS5KaNGlyhUcJoLLgShCASqNx48Zq27at/vGPf0iSfvrpJ33xxRcaMmSIiouL9eKLL+r6669X3bp1VbNmTa1evVqZmZlOv9/27dv1448/Kjg4WDVr1lTNmjVVp04dnTp1Sj/99JPq1KmjQYMGqXv37urVq5deffVVHT582FWHC8DDCEEAKpXk5GS9++67ys3N1bx58xQTE6MuXbpo6tSpeuWVV/TUU09p3bp12rFjh7p3766ioqLz9mWz2Rw+WpOk06dP2/9cUlKili1baseOHQ7L3r171b9/f0lnrwxt2bJFbdu21bJlyxQfH6+tW7e65+ABVChCEIBKpW/fvvL29tbixYu1YMECDR48WDabTV988YXuvvtuDRgwQM2bN9cf/vAHpaenX7CvevXqOVy5SU9PV35+vv11ixYtlJ6errCwMF1zzTUOS0hIiL3djTfeqLFjx2rz5s1KSEjQ4sWLXX/gACocIQhApVKzZk3169dPzzzzjA4dOqRBgwZJkq655hqlpqZq8+bNSktL0yOPPKKsrKwL9tW5c2fNnDlT3377rb755hs9+uij8vX1tW9/8MEHFRoaqrvvvltffPGFMjIytGHDBj3++OM6cOCAMjIyNHbsWG3ZskX79u3T6tWrtXfvXuYFAdUEIQhApZOcnKyjR4/q9ttvV3R0tCTp+eefV4sWLdS9e3d17NhRERERuueeey7Yz9SpU9WwYUPddttt6t+/v8aMGaPAwED79sDAQG3cuFHR0dHq06ePmjRpoiFDhqigoEC1atVSYGCgfvjhB917772Kj4/Xww8/rBEjRuiRRx5x5+EDqCA2c+4H5gAAABbAlSAAAGBJhCAAAGBJhCAAAGBJhCAAAGBJhCAAAGBJhCAAAGBJhCAAAGBJhCAAAGBJhCAAAGBJhCAAAGBJhCAAAGBJhCAAAGBJ/x9WDp2Rdw7skQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['YearsAtCompany'], bins=20, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of YearsAtCompany')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "82dd2bf5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['YearsOfService'], bins=20, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of YearsOfService')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6d0f100d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['BreaksTakenPerYear'], bins=10, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of BreaksTakenPerYear')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a349946b",
   "metadata": {},
   "source": [
    "#### Not all numerical columns follow normal distribution.\n",
    "#### Moreover, we will not treat them because:\n",
    "\n",
    "#### i. The analysis does not rely on the assumption of normality.\n",
    "#### ii. The dataset consists of non-normal variables (e.g., counts, percentages, years) that do not require transformation for the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79dc4d9",
   "metadata": {},
   "source": [
    "#### 2. What is the annual attrition rate of the company overall and when you slice the data on different dimensions? Does any particular segment stand out (why) ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "497bd839",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annual Attrition Rate Overall: 1.0014771048744462\n",
      "\n",
      "Annual Attrition Rate by Year:\n",
      "Attrition_Year\n",
      "2015    1.001477\n",
      "Name: EmployeeID, dtype: float64\n",
      "\n",
      "Annual Attrition Rate by Dimension:\n",
      "Attrition_Year  Department              \n",
      "2015            HR                          1.000000\n",
      "                Human Resources             1.000000\n",
      "                R&D                         1.000000\n",
      "                Research & Development      1.002331\n",
      "                Research and Development    1.000000\n",
      "                Sales                       1.000000\n",
      "Name: EmployeeID, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Convert 'Attrition_Date' to datetime if it's not already in datetime format\n",
    "df['Attrition_Date'] = pd.to_datetime(df['Attrition_Date'], errors='coerce')\n",
    "\n",
    "# Extract year from 'Attrition_Date'\n",
    "df['Attrition_Year'] = df['Attrition_Date'].dt.year\n",
    "\n",
    "# Calculate the total number of employees at the beginning of each year\n",
    "employee_count_per_year = df.groupby(df['Attrition_Year'])['EmployeeID'].nunique()\n",
    "\n",
    "# Calculate the number of employees who left the company within a year\n",
    "attrition_count_per_year = df.groupby(df['Attrition_Year'])['EmployeeID'].count()\n",
    "\n",
    "# Calculate the annual attrition rate overall\n",
    "annual_attrition_rate_overall = attrition_count_per_year.sum() / employee_count_per_year.min()\n",
    "\n",
    "# Calculate the annual attrition rate for each year\n",
    "annual_attrition_rate_per_year = attrition_count_per_year / employee_count_per_year\n",
    "\n",
    "# Slice the data on different dimensions (e.g., Department, Gender, JobRole) and calculate the annual attrition rate\n",
    "annual_attrition_rate_by_dimension = df.groupby(['Attrition_Year', 'Department'])['EmployeeID'].count() / df.groupby(['Attrition_Year', 'Department'])['EmployeeID'].nunique()\n",
    "\n",
    "# Repeat the above step for other dimensions as needed\n",
    "\n",
    "# Print results\n",
    "print(\"Annual Attrition Rate Overall:\", annual_attrition_rate_overall)\n",
    "print(\"\\nAnnual Attrition Rate by Year:\")\n",
    "print(annual_attrition_rate_per_year)\n",
    "print(\"\\nAnnual Attrition Rate by Dimension:\")\n",
    "print(annual_attrition_rate_by_dimension)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dee61d8",
   "metadata": {},
   "source": [
    "#### 3. What all factors impact attrition the most (statistical analysis expected) ? Are there any early signs which can help us be more proactive ? (E.g. Satisfaction dropping, Last rating not good)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e9c2ef23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation with Attrition (numeric representation of date):\n",
      "DistanceFromHome           0.030531\n",
      "MonthlyIncome              0.018557\n",
      "NumCompaniesWorked         0.071292\n",
      "PercentSalaryHike          0.095451\n",
      "YearsAtCompany             0.025252\n",
      "YearsSinceLastPromotion    0.002019\n",
      "YearsWithCurrManager       0.005730\n",
      "BreaksTakenPerDay         -0.002983\n",
      "EnvironmentSatisfaction    0.053667\n",
      "JobSatisfaction            0.046200\n",
      "WorkLifeBalance            0.025683\n",
      "JobInvolvement             0.071061\n",
      "PerformanceRating          0.117443\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Convert 'Attrition_Date' to numeric representation (number of days since a reference date)\n",
    "df['Attrition_Date_numeric'] = (df['Attrition_Date'] - df['Attrition_Date'].min()).dt.days\n",
    "\n",
    "# List of numerical columns\n",
    "num_cols = ['DistanceFromHome', 'MonthlyIncome', 'NumCompaniesWorked', 'PercentSalaryHike', 'YearsAtCompany', \n",
    "            'YearsSinceLastPromotion', 'YearsWithCurrManager', 'BreaksTakenPerDay', 'EnvironmentSatisfaction', \n",
    "            'JobSatisfaction', 'WorkLifeBalance', 'JobInvolvement', 'PerformanceRating']\n",
    "\n",
    "# Calculate the correlation coefficients\n",
    "correlation_results = df[num_cols].corrwith(df['Attrition_Date_numeric'])\n",
    "\n",
    "# Print correlation coefficients\n",
    "print(\"Correlation with Attrition (numeric representation of date):\")\n",
    "print(correlation_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9dc4872",
   "metadata": {},
   "source": [
    "#### Factors affecting attrition most are -\n",
    "i. Performance Rating\n",
    "ii. Percentage Salary Hike\n",
    "iii. Number of companies worked\n",
    "iv. Environment Satisfaction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54785f2c",
   "metadata": {},
   "source": [
    "#### 4. How are the employee job satisfaction scores overall and when you slice data by different dimensions ? Does any particular segment stand out (why) ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0e94386a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall Job Satisfaction Statistics:\n",
      "count    678.000000\n",
      "mean       2.458702\n",
      "std        1.116774\n",
      "min        0.000000\n",
      "25%        1.000000\n",
      "50%        3.000000\n",
      "75%        3.000000\n",
      "max        4.000000\n",
      "Name: JobSatisfaction, dtype: float64\n",
      "\n",
      "\n",
      "Job Satisfaction Statistics by Different Dimensions:\n",
      "                                                                                                      count  \\\n",
      "Department      Gender JobRole                MaritalStatus DistanceFromHome YearsSinceLastPromotion          \n",
      "HR              Female Research Scientist     Married       25.0             5.0                        1.0   \n",
      "                Male   Manufacturing Director Married       28.0             0.0                        1.0   \n",
      "                       Sales Executive        Single        6.0              0.0                        1.0   \n",
      "Human Resources Female Laboratory Technician  Married       1.0              0.0                        3.0   \n",
      "                                              Single        6.0              1.0                        3.0   \n",
      "...                                                                                                     ...   \n",
      "Sales           Male   Sales Executive        Married       1.0              5.0                        3.0   \n",
      "                                                            10.0             9.0                        3.0   \n",
      "                                              Single        2.0              4.0                        3.0   \n",
      "                                                            8.0              0.0                        3.0   \n",
      "                       Sales Representative   Single        7.0              4.0                        3.0   \n",
      "\n",
      "                                                                                                      mean  \\\n",
      "Department      Gender JobRole                MaritalStatus DistanceFromHome YearsSinceLastPromotion         \n",
      "HR              Female Research Scientist     Married       25.0             5.0                       4.0   \n",
      "                Male   Manufacturing Director Married       28.0             0.0                       2.0   \n",
      "                       Sales Executive        Single        6.0              0.0                       3.0   \n",
      "Human Resources Female Laboratory Technician  Married       1.0              0.0                       2.0   \n",
      "                                              Single        6.0              1.0                       3.0   \n",
      "...                                                                                                    ...   \n",
      "Sales           Male   Sales Executive        Married       1.0              5.0                       1.0   \n",
      "                                                            10.0             9.0                       3.0   \n",
      "                                              Single        2.0              4.0                       2.0   \n",
      "                                                            8.0              0.0                       4.0   \n",
      "                       Sales Representative   Single        7.0              4.0                       1.0   \n",
      "\n",
      "                                                                                                      std  \\\n",
      "Department      Gender JobRole                MaritalStatus DistanceFromHome YearsSinceLastPromotion        \n",
      "HR              Female Research Scientist     Married       25.0             5.0                      NaN   \n",
      "                Male   Manufacturing Director Married       28.0             0.0                      NaN   \n",
      "                       Sales Executive        Single        6.0              0.0                      NaN   \n",
      "Human Resources Female Laboratory Technician  Married       1.0              0.0                      0.0   \n",
      "                                              Single        6.0              1.0                      0.0   \n",
      "...                                                                                                   ...   \n",
      "Sales           Male   Sales Executive        Married       1.0              5.0                      0.0   \n",
      "                                                            10.0             9.0                      0.0   \n",
      "                                              Single        2.0              4.0                      0.0   \n",
      "                                                            8.0              0.0                      0.0   \n",
      "                       Sales Representative   Single        7.0              4.0                      0.0   \n",
      "\n",
      "                                                                                                      min  \\\n",
      "Department      Gender JobRole                MaritalStatus DistanceFromHome YearsSinceLastPromotion        \n",
      "HR              Female Research Scientist     Married       25.0             5.0                      4.0   \n",
      "                Male   Manufacturing Director Married       28.0             0.0                      2.0   \n",
      "                       Sales Executive        Single        6.0              0.0                      3.0   \n",
      "Human Resources Female Laboratory Technician  Married       1.0              0.0                      2.0   \n",
      "                                              Single        6.0              1.0                      3.0   \n",
      "...                                                                                                   ...   \n",
      "Sales           Male   Sales Executive        Married       1.0              5.0                      1.0   \n",
      "                                                            10.0             9.0                      3.0   \n",
      "                                              Single        2.0              4.0                      2.0   \n",
      "                                                            8.0              0.0                      4.0   \n",
      "                       Sales Representative   Single        7.0              4.0                      1.0   \n",
      "\n",
      "                                                                                                      25%  \\\n",
      "Department      Gender JobRole                MaritalStatus DistanceFromHome YearsSinceLastPromotion        \n",
      "HR              Female Research Scientist     Married       25.0             5.0                      4.0   \n",
      "                Male   Manufacturing Director Married       28.0             0.0                      2.0   \n",
      "                       Sales Executive        Single        6.0              0.0                      3.0   \n",
      "Human Resources Female Laboratory Technician  Married       1.0              0.0                      2.0   \n",
      "                                              Single        6.0              1.0                      3.0   \n",
      "...                                                                                                   ...   \n",
      "Sales           Male   Sales Executive        Married       1.0              5.0                      1.0   \n",
      "                                                            10.0             9.0                      3.0   \n",
      "                                              Single        2.0              4.0                      2.0   \n",
      "                                                            8.0              0.0                      4.0   \n",
      "                       Sales Representative   Single        7.0              4.0                      1.0   \n",
      "\n",
      "                                                                                                      50%  \\\n",
      "Department      Gender JobRole                MaritalStatus DistanceFromHome YearsSinceLastPromotion        \n",
      "HR              Female Research Scientist     Married       25.0             5.0                      4.0   \n",
      "                Male   Manufacturing Director Married       28.0             0.0                      2.0   \n",
      "                       Sales Executive        Single        6.0              0.0                      3.0   \n",
      "Human Resources Female Laboratory Technician  Married       1.0              0.0                      2.0   \n",
      "                                              Single        6.0              1.0                      3.0   \n",
      "...                                                                                                   ...   \n",
      "Sales           Male   Sales Executive        Married       1.0              5.0                      1.0   \n",
      "                                                            10.0             9.0                      3.0   \n",
      "                                              Single        2.0              4.0                      2.0   \n",
      "                                                            8.0              0.0                      4.0   \n",
      "                       Sales Representative   Single        7.0              4.0                      1.0   \n",
      "\n",
      "                                                                                                      75%  \\\n",
      "Department      Gender JobRole                MaritalStatus DistanceFromHome YearsSinceLastPromotion        \n",
      "HR              Female Research Scientist     Married       25.0             5.0                      4.0   \n",
      "                Male   Manufacturing Director Married       28.0             0.0                      2.0   \n",
      "                       Sales Executive        Single        6.0              0.0                      3.0   \n",
      "Human Resources Female Laboratory Technician  Married       1.0              0.0                      2.0   \n",
      "                                              Single        6.0              1.0                      3.0   \n",
      "...                                                                                                   ...   \n",
      "Sales           Male   Sales Executive        Married       1.0              5.0                      1.0   \n",
      "                                                            10.0             9.0                      3.0   \n",
      "                                              Single        2.0              4.0                      2.0   \n",
      "                                                            8.0              0.0                      4.0   \n",
      "                       Sales Representative   Single        7.0              4.0                      1.0   \n",
      "\n",
      "                                                                                                      max  \n",
      "Department      Gender JobRole                MaritalStatus DistanceFromHome YearsSinceLastPromotion       \n",
      "HR              Female Research Scientist     Married       25.0             5.0                      4.0  \n",
      "                Male   Manufacturing Director Married       28.0             0.0                      2.0  \n",
      "                       Sales Executive        Single        6.0              0.0                      3.0  \n",
      "Human Resources Female Laboratory Technician  Married       1.0              0.0                      2.0  \n",
      "                                              Single        6.0              1.0                      3.0  \n",
      "...                                                                                                   ...  \n",
      "Sales           Male   Sales Executive        Married       1.0              5.0                      1.0  \n",
      "                                                            10.0             9.0                      3.0  \n",
      "                                              Single        2.0              4.0                      2.0  \n",
      "                                                            8.0              0.0                      4.0  \n",
      "                       Sales Representative   Single        7.0              4.0                      1.0  \n",
      "\n",
      "[233 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "# Overall analysis\n",
    "overall_job_satisfaction_stats = df['JobSatisfaction'].describe()\n",
    "\n",
    "# Analysis by different dimensions\n",
    "dimension_columns = ['Department', 'Gender', 'JobRole', 'MaritalStatus', 'DistanceFromHome', 'YearsSinceLastPromotion']\n",
    "segmented_job_satisfaction_stats = df.groupby(dimension_columns)['JobSatisfaction'].describe()\n",
    "\n",
    "# Print overall job satisfaction statistics\n",
    "print(\"Overall Job Satisfaction Statistics:\")\n",
    "print(overall_job_satisfaction_stats)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Print job satisfaction statistics by different dimensions\n",
    "print(\"Job Satisfaction Statistics by Different Dimensions:\")\n",
    "print(segmented_job_satisfaction_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46df8f3c",
   "metadata": {},
   "source": [
    "#### 5. What all factors impact job satisfaction the most (statistical analysis expected) ? Is there any overlap with factors impacting attrition ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1b84d7c9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation with Job Satisfaction:\n",
      "JobSatisfaction                1.000000\n",
      "PerformanceRating              0.193773\n",
      "PercentSalaryHike              0.120144\n",
      "YearsSinceLastPromotion        0.119667\n",
      "TrainingTimesLastYear          0.090250\n",
      "Income_WorkExperience_Ratio    0.054166\n",
      "BusinessTravel_encoded         0.049451\n",
      "BreaksTakenPerYear             0.046812\n",
      "BreaksTakenPerDay              0.046812\n",
      "Attrition_Date_numeric         0.046200\n",
      "EnvironmentSatisfaction        0.043606\n",
      "YearsAtCompany                 0.041697\n",
      "EmployeeID                     0.015733\n",
      "YearsWithCurrManager           0.007706\n",
      "MonthlyIncome                  0.002818\n",
      "Age                           -0.002027\n",
      "NumCompaniesWorked            -0.019937\n",
      "YearsOfService                -0.021538\n",
      "AttritionAge                  -0.021538\n",
      "JobInvolvement                -0.035335\n",
      "Education                     -0.047621\n",
      "WorkLifeBalance               -0.047751\n",
      "JobLevel                      -0.051624\n",
      "DistanceFromHome              -0.066313\n",
      "TotalWorkingYears             -0.067496\n",
      "StockOptionLevel              -0.069272\n",
      "StandardHours                       NaN\n",
      "Attrition_Year                      NaN\n",
      "Name: JobSatisfaction, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yatharth Pandya\\AppData\\Local\\Temp\\ipykernel_20816\\1794555605.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  correlation_results = df.corr()['JobSatisfaction'].sort_values(ascending=False)\n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation coefficients between job satisfaction and other numerical columns\n",
    "correlation_results = df.corr()['JobSatisfaction'].sort_values(ascending=False)\n",
    "\n",
    "# Print correlation coefficients\n",
    "print(\"Correlation with Job Satisfaction:\")\n",
    "print(correlation_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdba8f07",
   "metadata": {},
   "source": [
    "#### Factors that impact Job Satisfaction are -\n",
    "i. PerformanceRating\n",
    "ii. PercentSalaryHike\n",
    "iii. YearsSinceLastPromotion\n",
    "iv. TrainingTimesLastYear\n",
    "v. Income_WorkExperience_Ratio\n",
    "\n",
    "#### Performance Rating and Percent Salary Hike are overlapping factors that also affect attrition."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bda364",
   "metadata": {},
   "source": [
    "#### 6. When we look at factors impacting attrition , is there any inter-relationship between the top factors ? If yes how did you identify this ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "658dd081",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix:\n",
      "                         PerformanceRating  PercentSalaryHike  \\\n",
      "PerformanceRating                 1.000000           0.790853   \n",
      "PercentSalaryHike                 0.790853           1.000000   \n",
      "NumCompaniesWorked                0.025076           0.053982   \n",
      "EnvironmentSatisfaction           0.072855           0.065990   \n",
      "\n",
      "                         NumCompaniesWorked  EnvironmentSatisfaction  \n",
      "PerformanceRating                  0.025076                 0.072855  \n",
      "PercentSalaryHike                  0.053982                 0.065990  \n",
      "NumCompaniesWorked                 1.000000                -0.176576  \n",
      "EnvironmentSatisfaction           -0.176576                 1.000000  \n"
     ]
    }
   ],
   "source": [
    "attrition_factors = ['PerformanceRating','PercentSalaryHike','NumCompaniesWorked','EnvironmentSatisfaction']\n",
    "\n",
    "selected_columns = df[attrition_factors]\n",
    "\n",
    "# Perform correlation analysis\n",
    "correlation_matrix = selected_columns.corr()\n",
    "\n",
    "# Print correlation matrix\n",
    "print(\"Correlation Matrix:\")\n",
    "print(correlation_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "278f5341",
   "metadata": {},
   "source": [
    "#### PerformanceRating and PercentSalaryHike have a correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32716355",
   "metadata": {},
   "source": [
    "#### 7. The HR Head has a opinion that people who have changed a lot of jobs in past are the ones with very high attrition and should be the biggest focus of retention program. How will you test this hypothesis ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b882eeef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition Rate by Number of Companies Worked For:\n",
      "NumCompaniesWorked\n",
      "0   2015-07-17 02:17:08.571428608\n",
      "1   2015-07-11 20:10:26.086956544\n",
      "2   2015-07-12 23:32:18.461538560\n",
      "3   2015-08-01 15:49:47.234042624\n",
      "4   2015-07-26 03:50:24.000000000\n",
      "5   2015-08-04 09:36:00.000000000\n",
      "6   2015-07-02 07:08:56.170212864\n",
      "7   2015-07-12 13:38:10.909090816\n",
      "8   2015-08-20 22:40:00.000000000\n",
      "9   2015-08-17 09:20:00.000000000\n",
      "Name: Attrition_Date, dtype: datetime64[ns]\n",
      "\n",
      "Chi-square test results:\n",
      "Chi-square statistic: 2386.299399339893\n",
      "p-value: 0.8434827029783265\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Group the data by 'NumCompaniesWorked' and calculate attrition rate for each group\n",
    "attrition_by_companies = df.groupby('NumCompaniesWorked')['Attrition_Date'].mean()\n",
    "\n",
    "# Print attrition rates for each group\n",
    "print(\"Attrition Rate by Number of Companies Worked For:\")\n",
    "print(attrition_by_companies)\n",
    "\n",
    "# Hypothesis testing (chi-square test)\n",
    "observed = pd.crosstab(df['Attrition_Date'], df['NumCompaniesWorked'])\n",
    "chi2, p, dof, expected = chi2_contingency(observed)\n",
    "\n",
    "# Print chi-square test results\n",
    "print(\"\\nChi-square test results:\")\n",
    "print(\"Chi-square statistic:\", chi2)\n",
    "print(\"p-value:\", p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "433f04fd",
   "metadata": {},
   "source": [
    "#### The hypothesis is valid as it indicates that people who have changed jobs a lot are higher prone to attrition."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb186cd",
   "metadata": {},
   "source": [
    "#### 8. Create at least any two analysis with proper visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fc14a8",
   "metadata": {},
   "source": [
    "i. Distribution of Attrition by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2b107628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b2rVrm8cee8x8/vnnxZ40UdpnZszvTxx46aWXHMepUaOGueaaa8xDDz1k9uzZc8F6ExMTTUBAgPnmm29MQkKC8fPzMyEhIebvf/+7ycnJKfE9KSkpRpJZuHDhBY99vsLCQjNv3jzToUMHExYWZry9vU1wcLC56aabzFNPPWUOHTpU7D2zZ882LVu2NAEBAcbPz8/Ur1/fDBo0yGzZssUxpm3btua6664r8drOf8LE7t27TYcOHUz16tVNSEiIeeCBBxyPOTv/yR7r16833bp1MyEhIcbHx8fUrl3bdOvWzXz00UeOMeeelnDkyBGXPgsAl8dmzHlP3QYA4BLddddd+uqrr7R//363P9UCAMqCZQkAgMuSl5enbdu26b///a8++eQTTZ06lWALwGOYuQUAXJb9+/crLi5OQUFBGjBggKZPn37ZT8UAgEtFuAUAAIBl8CgwAAAAWAbhFgAAAJZBuAUAAIBl8LQESUVFRfrll18UGBjIzyQCAABUQsYYnTx5UtHR0apWrfT5WcKtpF9++UUxMTGeLgMAAAAXcfDgQV155ZWl9hNuJcfPcR48eFBBQUEergYAAADny87OVkxMTIk/o/5HhFvJsRQhKCiIcAsAAFCJXWwJKV8oAwAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGV4NNxOnDhRN954owIDAxUeHq4777xTP/zwg9MYY4ySk5MVHR0tPz8/JSQkaNeuXU5j8vLyNHLkSIWFhSkgIEA9e/bUoUOHKvJSAAAAUAl4NNyuX79ew4cP11dffaXU1FSdPXtWHTt2VG5urmPM5MmTNXXqVE2fPl1paWmKjIxUhw4ddPLkSceYUaNG6ZNPPtHChQu1ceNG5eTkqHv37iosLPTEZQEAAMBDbMYY4+kizjly5IjCw8O1fv16tWnTRsYYRUdHa9SoUXr88ccl/T5LGxERoUmTJumhhx5SVlaWrrjiCr377ru65557JEm//PKLYmJitGzZMnXq1Omi583OzlZwcLCysrIUFBRUrtcIAAAA15U1r1WqNbdZWVmSpJCQEElSenq6MjIy1LFjR8cYu92utm3batOmTZKkrVu3qqCgwGlMdHS04uPjHWPOl5eXp+zsbKcNAAAAVV+lCbfGGI0ePVq33nqr4uPjJUkZGRmSpIiICKexERERjr6MjAz5+vqqVq1apY4538SJExUcHOzYYmJi3H05AAAA8IBKE25HjBihb775Ru+//36xPpvN5rRvjCnWdr4LjUlKSlJWVpZjO3jw4KUXDgAAgEqjUoTbkSNH6tNPP9XatWt15ZVXOtojIyMlqdgMbGZmpmM2NzIyUvn5+Tp+/HipY85nt9sVFBTktAEAAKDq82i4NcZoxIgRWrRokdasWaO4uDin/ri4OEVGRio1NdXRlp+fr/Xr16tVq1aSpObNm8vHx8dpzOHDh7Vz507HGAAAAPw5eHvy5MOHD9eCBQv073//W4GBgY4Z2uDgYPn5+clms2nUqFGaMGGCGjRooAYNGmjChAny9/fXgAEDHGMfeOABjRkzRqGhoQoJCdHYsWPVuHFj3XHHHZ68PAAAAFQwj4bbmTNnSpISEhKc2ufMmaPBgwdLksaNG6fTp09r2LBhOn78uFq2bKmVK1cqMDDQMf7ll1+Wt7e3+vXrp9OnT6t9+/ZKSUmRl5dXRV0KAAAAKoFK9ZxbT7nc59wOeHqd+4tCpbXgnwmeLgEAgD+dsuY1j87cAnDNjy8N9nQJqEANx6Z4ugQAqHIqxdMSAAAAAHcg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDG9PFwAAqHzGrRvt6RJQgSYnTPV0CYDbMHMLAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsw6Ph9osvvlCPHj0UHR0tm82mxYsXO/XbbLYStxdffNExJiEhoVh///79K/hKAAAAUBl4NNzm5uaqSZMmmj59eon9hw8fdtpmz54tm82mu+66y2nc0KFDnca9+eabFVE+AAAAKhlvT568S5cu6tKlS6n9kZGRTvv//ve/1a5dO9WrV8+p3d/fv9hYAAAA/PlUmTW3v/76q5YuXaoHHnigWN/8+fMVFham6667TmPHjtXJkycveKy8vDxlZ2c7bQAAAKj6PDpz64q5c+cqMDBQffr0cWofOHCg4uLiFBkZqZ07dyopKUk7duxQampqqceaOHGixo8fX94lAwAAoIJVmXA7e/ZsDRw4UNWrV3dqHzp0qON1fHy8GjRooBYtWmjbtm1q1qxZicdKSkrS6NGjHfvZ2dmKiYkpn8IBAABQYapEuN2wYYN++OEHffDBBxcd26xZM/n4+GjPnj2lhlu73S673e7uMgEAAOBhVWLN7axZs9S8eXM1adLkomN37dqlgoICRUVFVUBlAAAAqEw8OnObk5OjvXv3OvbT09O1fft2hYSEqE6dOpJ+XzLw0UcfacqUKcXev2/fPs2fP19du3ZVWFiYdu/erTFjxqhp06Zq3bp1hV0HAAAAKgePhtstW7aoXbt2jv1z62ATExOVkpIiSVq4cKGMMbr33nuLvd/X11erV6/Wq6++qpycHMXExKhbt2565pln5OXlVSHXAAAAgMrDo+E2ISFBxpgLjnnwwQf14IMPltgXExOj9evXl0dpAAAAqIKqxJpbAAAAoCwItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAy/D2dAEAAODP66tHHvF0CahAN7/6armfg5lbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGR4Nt1988YV69Oih6Oho2Ww2LV682Kl/8ODBstlsTtvNN9/sNCYvL08jR45UWFiYAgIC1LNnTx06dKgCrwIAAACVhUfDbW5urpo0aaLp06eXOqZz5846fPiwY1u2bJlT/6hRo/TJJ59o4cKF2rhxo3JyctS9e3cVFhaWd/kAAACoZLw9efIuXbqoS5cuFxxjt9sVGRlZYl9WVpZmzZqld999V3fccYck6b333lNMTIxWrVqlTp06lfi+vLw85eXlOfazs7Mv8QoAAABQmVT6Nbfr1q1TeHi4GjZsqKFDhyozM9PRt3XrVhUUFKhjx46OtujoaMXHx2vTpk2lHnPixIkKDg52bDExMeV6DQAAAKgYlTrcdunSRfPnz9eaNWs0ZcoUpaWl6fbbb3fMumZkZMjX11e1atVyel9ERIQyMjJKPW5SUpKysrIc28GDB8v1OgAAAFAxPLos4WLuuecex+v4+Hi1aNFCsbGxWrp0qfr06VPq+4wxstlspfbb7XbZ7Xa31goAAADPq9Qzt+eLiopSbGys9uzZI0mKjIxUfn6+jh8/7jQuMzNTERERnigRAAAAHlSlwu3Ro0d18OBBRUVFSZKaN28uHx8fpaamOsYcPnxYO3fuVKtWrTxVJgAAADzEo8sScnJytHfvXsd+enq6tm/frpCQEIWEhCg5OVl33XWXoqKitH//fv2///f/FBYWpt69e0uSgoOD9cADD2jMmDEKDQ1VSEiIxo4dq8aNGzuengAAAIA/D4+G2y1btqhdu3aO/dGjR0uSEhMTNXPmTH377beaN2+eTpw4oaioKLVr104ffPCBAgMDHe95+eWX5e3trX79+un06dNq3769UlJS5OXlVeHXAwAAAM/yaLhNSEiQMabU/hUrVlz0GNWrV9e0adM0bdo0d5YGAACAKqhKrbkFAAAALoRwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDI+G2y+++EI9evRQdHS0bDabFi9e7OgrKCjQ448/rsaNGysgIEDR0dEaNGiQfvnlF6djJCQkyGazOW39+/ev4CsBAABAZeDRcJubm6smTZpo+vTpxfpOnTqlbdu26amnntK2bdu0aNEi/fjjj+rZs2exsUOHDtXhw4cd25tvvlkR5QMAAKCS8fbkybt06aIuXbqU2BccHKzU1FSntmnTpummm27SgQMHVKdOHUe7v7+/IiMjy7VWAAAAVH5Vas1tVlaWbDabatas6dQ+f/58hYWF6brrrtPYsWN18uTJCx4nLy9P2dnZThsAAACqPo/O3LrizJkz+sc//qEBAwYoKCjI0T5w4EDFxcUpMjJSO3fuVFJSknbs2FFs1vePJk6cqPHjx1dE2QAAAKhAVSLcFhQUqH///ioqKtKMGTOc+oYOHep4HR8frwYNGqhFixbatm2bmjVrVuLxkpKSNHr0aMd+dna2YmJiyqd4AAAAVJhKH24LCgrUr18/paena82aNU6ztiVp1qyZfHx8tGfPnlLDrd1ul91uL49yAQAA4EGXvOZ27969WrFihU6fPi1JMsa4rahzzgXbPXv2aNWqVQoNDb3oe3bt2qWCggJFRUW5vR4AAABUbi7P3B49elT33HOP1qxZI5vNpj179qhevXr661//qpo1a2rKlCllPlZOTo727t3r2E9PT9f27dsVEhKi6Oho9e3bV9u2bdOSJUtUWFiojIwMSVJISIh8fX21b98+zZ8/X127dlVYWJh2796tMWPGqGnTpmrdurWrlwYAAIAqzuWZ20cffVTe3t46cOCA/P39He333HOPli9f7tKxtmzZoqZNm6pp06aSpNGjR6tp06Z6+umndejQIX366ac6dOiQbrjhBkVFRTm2TZs2SZJ8fX21evVqderUSVdffbUefvhhdezYUatWrZKXl5erlwYAAIAqzuWZ25UrV2rFihW68sorndobNGign3/+2aVjJSQkXHA5w8WWOsTExGj9+vUunRMAAADW5fLMbW5urtOM7Tm//fYbX9ICAACAR7kcbtu0aaN58+Y59m02m4qKivTiiy+qXbt2bi0OAAAAcIXLyxJefPFFJSQkaMuWLcrPz9e4ceO0a9cuHTt2TF9++WV51AgAAACUicszt40aNdI333yjm266SR06dFBubq769Omjr7/+WvXr1y+PGgEAAIAycXnm9sCBA4qJiSnx52sPHDigOnXquKUwAAAAwFUuz9zGxcXpyJEjxdqPHj2quLg4txQFAAAAXAqXw60xRjabrVh7Tk6Oqlev7paiAAAAgEtR5mUJo0ePlvT70xGeeuopp8eBFRYW6j//+Y9uuOEGtxcIAAAAlFWZw+3XX38t6feZ22+//Va+vr6OPl9fXzVp0kRjx451f4UAAABAGZU53K5du1aSdP/99+vVV19VUFBQuRUFAAAAXAqXn5YwZ86c8qgDAAAAuGwuh1tJSktL00cffaQDBw4oPz/fqW/RokVuKQwAAABwlctPS1i4cKFat26t3bt365NPPlFBQYF2796tNWvWKDg4uDxqBAAAAMrE5XA7YcIEvfzyy1qyZIl8fX316quv6rvvvlO/fv34AQcAAAB4lMvhdt++ferWrZskyW63Kzc3VzabTY8++qjeeusttxcIAAAAlJXL4TYkJEQnT56UJNWuXVs7d+6UJJ04cUKnTp1yb3UAAACAC1z+Qtltt92m1NRUNW7cWP369dMjjzyiNWvWKDU1Ve3bty+PGgEAAIAycTncTp8+XWfOnJEkJSUlycfHRxs3blSfPn301FNPub1AAAAAoKxcDrchISGO19WqVdO4ceM0btw4SWJZAgAAADzK5TW3JTlz5oymTp2qevXqueNwAAAAwCUpc7jNz8/XE088oRtvvFGtWrXS4sWLJf3+i2X16tXTlClT9Mgjj5RXnQAAAMBFlXlZQnJysl5//XV16NBBX375pe6++24NGTJE69at08SJEzVgwAD5+PiUZ60AAADABZU53H744YdKSUlR7969tWPHDjVt2lTZ2dnatWuXvL0v6Vd8AQAAALcq87KEgwcP6sYbb5QkNWnSRL6+vnr88ccJtgAAAKg0yhxuCwoK5Ovr69j38fFRcHBwuRQFAAAAXAqXpl2ffvpp+fv7S/r9C2bPPfdcsYA7depU91UHAAAAuKDM4bZNmzb64YcfHPutWrXSTz/95DTGZrO5rzIAAADARWUOt+vWrSvHMgAAAIDL55YfcQAAAAAqA8ItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwjEv6ebETJ07ov//9rzIzM1VUVOTUN2jQILcUBgAAALjK5XD72WefaeDAgcrNzVVgYKDTs21tNhvhFgAAAB7j8rKEMWPGaMiQITp58qROnDih48ePO7Zjx46VR40AAABAmbgcbv/3v//p4YcfdvwMLwAAAFBZuBxuO3XqpC1btpRHLQAAAMBlcXnNbbdu3fTYY49p9+7daty4sXx8fJz6e/bs6bbiAAAAAFe4HG6HDh0qSfrnP/9ZrM9ms6mwsPDyqwIAAAAugcvh9vxHfwEAAACVBT/iAAAAAMu4pHC7fv169ejRQ1dddZUaNGignj17asOGDe6uDQAAAHCJy+H2vffe0x133CF/f389/PDDGjFihPz8/NS+fXstWLCgPGoEAAAAysTlNbfPP/+8Jk+erEcffdTR9sgjj2jq1Kl69tlnNWDAALcWCAAAAJSVyzO3P/30k3r06FGsvWfPnkpPT3fpWF988YV69Oih6Oho2Ww2LV682KnfGKPk5GRFR0fLz89PCQkJ2rVrl9OYvLw8jRw5UmFhYQoICFDPnj116NAhVy8LAAAAFuByuI2JidHq1auLta9evVoxMTEuHSs3N1dNmjTR9OnTS+yfPHmypk6dqunTpystLU2RkZHq0KGDTp486RgzatQoffLJJ1q4cKE2btyonJwcde/enUeSAQAA/Am5vCxhzJgxevjhh7V9+3a1atVKNptNGzduVEpKil599VWXjtWlSxd16dKlxD5jjF555RU98cQT6tOnjyRp7ty5ioiI0IIFC/TQQw8pKytLs2bN0rvvvqs77rhD0u9rgmNiYrRq1Sp16tTJ1csDAABAFeZyuP373/+uyMhITZkyRR9++KEk6dprr9UHH3ygXr16ua2w9PR0ZWRkqGPHjo42u92utm3batOmTXrooYe0detWFRQUOI2Jjo5WfHy8Nm3aVGq4zcvLU15enmM/OzvbbXUDAADAc1wOt5LUu3dv9e7d2921OMnIyJAkRUREOLVHRETo559/dozx9fVVrVq1io059/6STJw4UePHj3dzxQAAAPC0Sv8jDjabzWnfGFOs7XwXG5OUlKSsrCzHdvDgQbfUCgAAAM8q08xtSEiIfvzxR4WFhalWrVoXDI7Hjh1zS2GRkZGSfp+djYqKcrRnZmY6ZnMjIyOVn5+v48ePO83eZmZmqlWrVqUe2263y263u6VOAAAAVB5lCrcvv/yyAgMDHa8vNnPqDnFxcYqMjFRqaqqaNm0qScrPz9f69es1adIkSVLz5s3l4+Oj1NRU9evXT5J0+PBh7dy5U5MnTy73GgEAAFC5lCncJiYmOl4PHjzYbSfPycnR3r17Hfvp6enavn27QkJCVKdOHY0aNUoTJkxQgwYN1KBBA02YMEH+/v6OH4oIDg7WAw88oDFjxig0NFQhISEaO3asGjdu7Hh6AgAAAP48XP5CmZeXlw4fPqzw8HCn9qNHjyo8PNyl58tu2bJF7dq1c+yPHj1a0u9hOiUlRePGjdPp06c1bNgwHT9+XC1bttTKlSsds8jS7zPJ3t7e6tevn06fPq327dsrJSVFXl5erl4aAAAAqjiXw60xpsT2vLw8+fr6unSshISEUo8n/f5lsuTkZCUnJ5c6pnr16po2bZqmTZvm0rkBAABgPWUOt6+99pqk3wPnO++8oxo1ajj6CgsL9cUXX+iaa65xf4UAAABAGZU53L788suSfp+5feONN5z+2d/X11d169bVG2+84f4KAQAAgDIqc7hNT0+XJLVr106LFi0q9sMJAAAAgKe5vOZ27dq15VEHAAAAcNnKFG5Hjx6tZ599VgEBAY4nGpRm6tSpbikMAAAAcFWZwu3XX3+tgoICSdK2bdtK/RGHivhxBwAAAKA0ZQq3f1yKsG7duvKqBQAAALgs1VwZfPbsWXl7e2vnzp3lVQ8AAABwyVwKt97e3oqNjXXpV8gAAACAiuJSuJWkJ598UklJSTp27Fh51AMAAABcMpcfBfbaa69p7969io6OVmxsrAICApz6t23b5rbiAAAAAFe4HG579erFUxEAAABQKbkcbpOTk8uhDAAAAODyubzmtl69ejp69Gix9hMnTqhevXpuKQoAAAC4FC6H2/3795f4tIS8vDwdOnTILUUBAAAAl6LMyxI+/fRTx+sVK1YoODjYsV9YWKjVq1crLi7OvdUBAAAALihzuL3zzjsdrxMTE536fHx8VLduXU2ZMsVthQEAAACuKnO4LSoqkiTFxcUpLS1NYWFh5VYUAAAAcClcXnM7fvx4BQYGFmvPz8/XvHnz3FIUAAAAcClcDrf333+/srKyirWfPHlS999/v1uKAgAAAC6Fy+HWGFPijzgcOnTI6UtmAAAAQEUr85rbpk2bymazyWazqX379vL2/r+3FhYWKj09XZ07dy6XIgEAAICycPlpCdu3b1enTp1Uo0YNR5+vr6/q1q2ru+66y+0FAgAAAGVV5nD7zDPPSJLq1q2re+65R9WrVy82Zvv27brhhhvcVhwAAADgCpfX3CYmJjoF26ysLM2YMUPNmjVT8+bN3VocAAAA4AqXw+05a9as0X333aeoqChNmzZNXbt21ZYtW9xZGwAAAOCSMi9LkH5/IkJKSopmz56t3Nxc9evXTwUFBfrXv/6lRo0alVeNAAAAQJmUeea2a9euatSokXbv3q1p06bpl19+0bRp08qzNgAAAMAlZZ65XblypR5++GH9/e9/V4MGDcqzJgAAAOCSlHnmdsOGDTp58qRatGihli1bavr06Tpy5Eh51gYAAAC4pMzh9pZbbtHbb7+tw4cP66GHHtLChQtVu3ZtFRUVKTU1VSdPnizPOgEAAICLcvlpCf7+/hoyZIg2btyob7/9VmPGjNELL7yg8PBw9ezZszxqBAAAAMrkkh8FJklXX321Jk+erEOHDun99993V00AAADAJbmscHuOl5eX7rzzTn366afuOBwAAABwSdwSbgEAAIDKgHALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIqfbitW7eubDZbsW348OGSpMGDBxfru/nmmz1cNQAAADzB29MFXExaWpoKCwsd+zt37lSHDh109913O9o6d+6sOXPmOPZ9fX0rtEYAAABUDpU+3F5xxRVO+y+88ILq16+vtm3bOtrsdrsiIyMrujQAAABUMpV+WcIf5efn67333tOQIUNks9kc7evWrVN4eLgaNmyooUOHKjMz84LHycvLU3Z2ttMGAACAqq9KhdvFixfrxIkTGjx4sKOtS5cumj9/vtasWaMpU6YoLS1Nt99+u/Ly8ko9zsSJExUcHOzYYmJiKqB6AAAAlLdKvyzhj2bNmqUuXbooOjra0XbPPfc4XsfHx6tFixaKjY3V0qVL1adPnxKPk5SUpNGjRzv2s7OzCbgAAAAWUGXC7c8//6xVq1Zp0aJFFxwXFRWl2NhY7dmzp9Qxdrtddrvd3SUCAADAw6rMsoQ5c+YoPDxc3bp1u+C4o0eP6uDBg4qKiqqgygAAAFBZVIlwW1RUpDlz5igxMVHe3v832ZyTk6OxY8dq8+bN2r9/v9atW6cePXooLCxMvXv39mDFAAAA8IQqsSxh1apVOnDggIYMGeLU7uXlpW+//Vbz5s3TiRMnFBUVpXbt2umDDz5QYGCgh6oFAACAp1SJcNuxY0cZY4q1+/n5acWKFR6oCAAAAJVRlViWAAAAAJQF4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmVOtwmJyfLZrM5bZGRkY5+Y4ySk5MVHR0tPz8/JSQkaNeuXR6sGAAAAJ5UqcOtJF133XU6fPiwY/v2228dfZMnT9bUqVM1ffp0paWlKTIyUh06dNDJkyc9WDEAAAA8pdKHW29vb0VGRjq2K664QtLvs7avvPKKnnjiCfXp00fx8fGaO3euTp06pQULFni4agAAAHhCpQ+3e/bsUXR0tOLi4tS/f3/99NNPkqT09HRlZGSoY8eOjrF2u11t27bVpk2bLnjMvLw8ZWdnO20AAACo+ip1uG3ZsqXmzZunFStW6O2331ZGRoZatWqlo0ePKiMjQ5IUERHh9J6IiAhHX2kmTpyo4OBgxxYTE1Nu1wAAAICKU6nDbZcuXXTXXXepcePGuuOOO7R06VJJ0ty5cx1jbDab03uMMcXazpeUlKSsrCzHdvDgQfcXDwAAgApXqcPt+QICAtS4cWPt2bPH8dSE82dpMzMzi83mns9utysoKMhpAwAAQNVXpcJtXl6evvvuO0VFRSkuLk6RkZFKTU119Ofn52v9+vVq1aqVB6sEAACAp3h7uoALGTt2rHr06KE6deooMzNTzz33nLKzs5WYmCibzaZRo0ZpwoQJatCggRo0aKAJEybI399fAwYM8HTpAAAA8IBKHW4PHTqke++9V7/99puuuOIK3Xzzzfrqq68UGxsrSRo3bpxOnz6tYcOG6fjx42rZsqVWrlypwMBAD1cOAAAAT6jU4XbhwoUX7LfZbEpOTlZycnLFFAQAAIBKrUqtuQUAAAAuhHALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAso1KH24kTJ+rGG29UYGCgwsPDdeedd+qHH35wGjN48GDZbDan7eabb/ZQxQAAAPCkSh1u169fr+HDh+urr75Samqqzp49q44dOyo3N9dpXOfOnXX48GHHtmzZMg9VDAAAAE/y9nQBF7J8+XKn/Tlz5ig8PFxbt25VmzZtHO12u12RkZEVXR4AAAAqmUo9c3u+rKwsSVJISIhT+7p16xQeHq6GDRtq6NChyszMvOBx8vLylJ2d7bQBAACg6qsy4dYYo9GjR+vWW29VfHy8o71Lly6aP3++1qxZoylTpigtLU2333678vLySj3WxIkTFRwc7NhiYmIq4hIAAABQzir1soQ/GjFihL755htt3LjRqf2ee+5xvI6Pj1eLFi0UGxurpUuXqk+fPiUeKykpSaNHj3bsZ2dnE3ABAAAsoEqE25EjR+rTTz/VF198oSuvvPKCY6OiohQbG6s9e/aUOsZut8tut7u7TAAAAHhYpQ63xhiNHDlSn3zyidatW6e4uLiLvufo0aM6ePCgoqKiKqBCAAAAVCaVes3t8OHD9d5772nBggUKDAxURkaGMjIydPr0aUlSTk6Oxo4dq82bN2v//v1at26devToobCwMPXu3dvD1QMAAKCiVeqZ25kzZ0qSEhISnNrnzJmjwYMHy8vLS99++63mzZunEydOKCoqSu3atdMHH3ygwMBAD1QMAAAAT6rU4dYYc8F+Pz8/rVixooKqAQAAQGVXqZclAAAAAK4g3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMuwTLidMWOG4uLiVL16dTVv3lwbNmzwdEkAAACoYJYItx988IFGjRqlJ554Ql9//bVuu+02denSRQcOHPB0aQAAAKhAlgi3U6dO1QMPPKC//vWvuvbaa/XKK68oJiZGM2fO9HRpAAAAqEDeni7gcuXn52vr1q36xz/+4dTesWNHbdq0qcT35OXlKS8vz7GflZUlScrOzr6kGgryci/pfaiaLvU+cYecM/keOzcqnifvtbzcvIsPgmV48l7LzeNe+zO5nHvt3HuNMRccV+XD7W+//abCwkJFREQ4tUdERCgjI6PE90ycOFHjx48v1h4TE1MuNcJaPp7s6Qrwp/HU+56uAH8Sr2mGp0vAn8Wbb172IU6ePKng4OBS+6t8uD3HZrM57RtjirWdk5SUpNGjRzv2i4qKdOzYMYWGhpb6HjjLzs5WTEyMDh48qKCgIE+XAwvjXkNF4V5DReFeuzTGGJ08eVLR0dEXHFflw21YWJi8vLyKzdJmZmYWm809x263y263O7XVrFmzvEq0tKCgIP6HiQrBvYaKwr2GisK95roLzdieU+W/UObr66vmzZsrNTXVqT01NVWtWrXyUFUAAADwhCo/cytJo0eP1l/+8he1aNFCt9xyi9566y0dOHBAf/vb3zxdGgAAACqQJcLtPffco6NHj+qf//ynDh8+rPj4eC1btkyxsbGeLs2y7Ha7nnnmmWLLOwB3415DReFeQ0XhXitfNnOx5ykAAAAAVUSVX3MLAAAAnEO4BQAAgGUQbgEAAGAZhFtUuLp16+qVV17xdBmowvbv3y+bzabt27d7uhQAQCVDuLW4wYMHy2azFdv27t3r6dLwJ3PuXizpEX3Dhg2TzWbT4MGDK74w/KlxX6Ki8fdy+SPc/gl07txZhw8fdtri4uI8XRb+hGJiYrRw4UKdPn3a0XbmzBm9//77qlOnjgcrw58Z9yUqGn8vly/C7Z+A3W5XZGSk0+bl5aXPPvtMzZs3V/Xq1VWvXj2NHz9eZ8+edbzPZrPpzTffVPfu3eXv769rr71Wmzdv1t69e5WQkKCAgADdcsst2rdvn+M9+/btU69evRQREaEaNWroxhtv1KpVqy5YX1ZWlh588EGFh4crKChIt99+u3bs2FFunwc8p1mzZqpTp44WLVrkaFu0aJFiYmLUtGlTR9vy5ct16623qmbNmgoNDVX37t2d7rOS7N69W127dlWNGjUUERGhv/zlL/rtt9/K7VpgHWW9LwF3Ke3vZbgH4fZPasWKFbrvvvv08MMPa/fu3XrzzTeVkpKi559/3mncs88+q0GDBmn79u265pprNGDAAD300ENKSkrSli1bJEkjRoxwjM/JyVHXrl21atUqff311+rUqZN69OihAwcOlFiHMUbdunVTRkaGli1bpq1bt6pZs2Zq3769jh07Vn4fADzm/vvv15w5cxz7s2fP1pAhQ5zG5ObmavTo0UpLS9Pq1atVrVo19e7dW0VFRSUe8/Dhw2rbtq1uuOEGbdmyRcuXL9evv/6qfv36leu1wDrKcl8CqCIMLC0xMdF4eXmZgIAAx9a3b19z2223mQkTJjiNfffdd01UVJRjX5J58sknHfubN282ksysWbMcbe+//76pXr36BWto1KiRmTZtmmM/NjbWvPzyy8YYY1avXm2CgoLMmTNnnN5Tv3598+abb7p8vai8EhMTTa9evcyRI0eM3W436enpZv/+/aZ69ermyJEjplevXiYxMbHE92ZmZhpJ5ttvvzXGGJOenm4kma+//toYY8xTTz1lOnbs6PSegwcPGknmhx9+KM/LQhV3OfclcClK+3sZ7mOJn9/FhbVr104zZ8507AcEBOiqq65SWlqa00xtYWGhzpw5o1OnTsnf31+SdP311zv6IyIiJEmNGzd2ajtz5oyys7MVFBSk3NxcjR8/XkuWLNEvv/yis2fP6vTp06XO3G7dulU5OTkKDQ11aj99+vRF/xkaVVNYWJi6deumuXPnOmbuw8LCnMbs27dPTz31lL766iv99ttvjhnbAwcOKD4+vtgxt27dqrVr16pGjRrF+vbt26eGDRuWz8XAMspyXwLuUtLfy3Afwu2fwLkw+0dFRUUaP368+vTpU2x89erVHa99fHwcr202W6lt58LHY489phUrVuill17SVVddJT8/P/Xt21f5+fkl1lZUVKSoqCitW7euWF/NmjXLdoGocoYMGeJYzvL6668X6+/Ro4diYmL09ttvKzo6WkVFRYqPj7/gfdSjRw9NmjSpWF9UVJR7i4dlXey+BNylpL+X4T6E2z+pZs2a6YcffnD7/7g2bNigwYMHq3fv3pJ+X4O7f//+C9aRkZEhb29v1a1b1621oPLq3LmzI6h26tTJqe/o0aP67rvv9Oabb+q2226TJG3cuPGCx2vWrJn+9a9/qW7duvL25o81XJoL3ZcAqg6+UPYn9fTTT2vevHlKTk7Wrl279N133+mDDz7Qk08+eVnHveqqq7Ro0SJt375dO3bs0IABA0r9EpAk3XHHHbrlllt05513asWKFdq/f782bdqkJ5980vGFNViPl5eXvvvuO3333XfFviFcq1YthYaG6q233tLevXu1Zs0ajR49+oLHGz58uI4dO6Z7771X//3vf/XTTz9p5cqVGjJkiAoLC8vzUmAhF7ovAVQdhNs/qU6dOmnJkiVKTU3VjTfeqJtvvllTp05VbGzsZR335ZdfVq1atdSqVSv16NFDnTp1UrNmzUodb7PZtGzZMrVp00ZDhgxRw4YN1b9/f+3fv9+xxhfWFBQUpKCgoGLt1apV08KFC7V161bFx8fr0Ucf1YsvvnjBY0VHR+vLL79UYWGhOnXqpPj4eD3yyCMKDg5WtWr8MYeyK+2+BFB12IwxxtNFAAAAAO7AlAYAAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0A/EklJCRo1KhRni4DANyKcAsAHpSRkaFHHnlEV111lapXr66IiAjdeuuteuONN3Tq1ClPlwcAVY63pwsAgD+rn376Sa1bt1bNmjU1YcIENW7cWGfPntWPP/6o2bNnKzo6Wj179vR0maUqLCyUzWZTtWrMkwCoPPgTCQA8ZNiwYfL29taWLVvUr18/XXvttWrcuLHuuusuLV26VD169JAkZWVl6cEHH1R4eLiCgoJ0++23a8eOHY7jJCcn64YbbtC7776runXrKjg4WP3799fJkycdY3JzczVo0CDVqFFDUVFRmjJlSrF68vPzNW7cONWuXVsBAQFq2bKl1q1b5+hPSUlRzZo1tWTJEjVq1Eh2u10///xz+X1AAHAJCLcA4AFHjx7VypUrNXz4cAUEBJQ4xmazyRijbt26KSMjQ8uWLdPWrVvVrFkztW/fXseOHXOM3bdvnxYvXqwlS5ZoyZIlWr9+vV544QVH/2OPPaa1a9fqk08+0cqVK7Vu3Tpt3brV6Xz333+/vvzySy1cuFDffPON7r77bnXu3Fl79uxxjDl16pQmTpyod955R7t27VJ4eLibPxkAuDwsSwAAD9i7d6+MMbr66qud2sPCwnTmzBlJ0vDhw9WpUyd9++23yszMlN1ulyS99NJLWrx4sT7++GM9+OCDkqSioiKlpKQoMDBQkvSXv/xFq1ev1vPPP6+cnBzNmjVL8+bNU4cOHSRJc+fO1ZVXXuk47759+/T+++/r0KFDio6OliSNHTtWy5cv15w5czRhwgRJUkFBgWbMmKEmTZqU46cDAJeOcAsAHmSz2Zz2//vf/6qoqEgDBw5UXl6etm7dqpycHIWGhjqNO336tPbt2+fYr1u3riPYSlJUVJQyMzMl/R5c8/Pzdcsttzj6Q0JCnIL1tm3bZIxRw4YNnc6Tl5fndG5fX19df/31l3HFAFC+CLcA4AFXXXWVbDabvv/+e6f2evXqSZL8/Pwk/T4jGxUV5bT29ZyaNWs6Xvv4+Dj12Ww2FRUVSZKMMRetp6ioSF5eXtq6dau8vLyc+mrUqOF47efnVyyQA0BlQrgFAA8IDQ1Vhw4dNH36dI0cObLUdbfNmjVTRkaGvL29Vbdu3Us611VXXSUfHx999dVXqlOnjiTp+PHj+vHHH9W2bVtJUtOmTVVYWKjMzEzddtttl3QeAKgM+EIZAHjIjBkzdPbsWbVo0UIffPCBvvvuO/3www9677339P3338vLy0t33HGHbrnlFt15551asWKF9u/fr02bNunJJ5/Uli1bynSeGjVq6IEHHtBjjz2m1atXa+fOnRo8eLDTI7waNmyogQMHatCgQVq0aJHS09OVlpamSZMmadmyZeX1EQCA2zFzCwAeUr9+fX399deaMGGCkpKSdOjQIdntdjVq1Ehjx47VsGHDZLPZtGzZMj3xxBMaMmSIjhw5osjISLVp00YRERFlPteLL76onJwc9ezZU4GBgRozZoyysrKcxsyZM0fPPfecxowZo//9738KDQ3VLbfcoq5du7r70gGg3NhMWRZjAQAAAFUAyxIAAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJbx/wEYLoVjTld54wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Group data by gender and calculate attrition rate\n",
    "attrition_by_gender = df.groupby('Gender')['Attrition_Date_numeric'].mean().sort_values(ascending=False)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=attrition_by_gender.index, y=attrition_by_gender.values, palette='muted')\n",
    "plt.title('Attrition Rate by Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Attrition Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9d8177",
   "metadata": {},
   "source": [
    "ii. Relationship between Age and Job Satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4b455db5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of age vs. job satisfaction\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(data=df, x='Age', y='JobSatisfaction', alpha=0.5)\n",
    "plt.title('Relationship between Age and Job Satisfaction')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Job Satisfaction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d083ade6",
   "metadata": {},
   "source": [
    "### TASK 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3fa900",
   "metadata": {},
   "source": [
    "#### 1. Calculate the average time duration spent by each employee using In Time & Out Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "792b87d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     EmployeeID  AverageTime(hrs)\n",
      "0             1          7.374138\n",
      "1             2          7.717726\n",
      "2             3          7.013499\n",
      "3             4          7.193475\n",
      "4             5          8.006259\n",
      "...         ...               ...\n",
      "4409       4410          6.987787\n",
      "4410       4411          6.093084\n",
      "4411       4412          7.706926\n",
      "4412       4413          9.492324\n",
      "4413       4414          6.987787\n",
      "\n",
      "[4414 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "merged_time = pd.merge(in_time, out_time, on='EmployeeID')\n",
    "\n",
    "# Calculate the time duration spent by each employee for each day\n",
    "for date in in_time.columns[1:]:  # Exclude the first column (EmployeeID)\n",
    "    in_time_col = str(date) + '_x'  # Convert date to string\n",
    "    out_time_col = str(date) + '_y'  # Convert date to string\n",
    "    merged_time[date] = pd.to_datetime(merged_time[out_time_col]) - pd.to_datetime(merged_time[in_time_col])\n",
    "    merged_time[date] = merged_time[date].dt.total_seconds() / 3600  # Convert to hours\n",
    "\n",
    "# Calculate the average time duration spent by each employee across all days\n",
    "merged_time['AverageTime(hrs)'] = merged_time.iloc[:, 1:].mean(axis=1)\n",
    "\n",
    "# Display the resulting dataframe with average time duration\n",
    "print(merged_time[['EmployeeID', 'AverageTime(hrs)']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f4bbe84",
   "metadata": {},
   "source": [
    "#### 2. Create Bins based on the average time duration into below bins\n",
    "#### a. High: If employee spent more than 8 Hours\n",
    "#### b. Medium: if employee spent more than 6 Hours\n",
    "#### c. Low: If employee spent less than 6 Hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4f9894cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     EmployeeID  AverageTime(hrs) TimeCategory\n",
      "0             1          7.374138       Medium\n",
      "1             2          7.717726       Medium\n",
      "2             3          7.013499       Medium\n",
      "3             4          7.193475       Medium\n",
      "4             5          8.006259         High\n",
      "...         ...               ...          ...\n",
      "4409       4410          6.987787       Medium\n",
      "4410       4411          6.093084       Medium\n",
      "4411       4412          7.706926       Medium\n",
      "4412       4413          9.492324         High\n",
      "4413       4414          6.987787       Medium\n",
      "\n",
      "[4414 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Define the bins and labels\n",
    "bins = [0, 6, 8, 20]  # Adjusted bin edges\n",
    "labels = ['Low', 'Medium', 'High']\n",
    "\n",
    "# Create a new column 'TimeCategory' based on the average time duration bins\n",
    "merged_time['TimeCategory'] = pd.cut(merged_time['AverageTime(hrs)'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Display the resulting dataframe with time category\n",
    "print(merged_time[['EmployeeID', 'AverageTime(hrs)', 'TimeCategory']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c7b84f3",
   "metadata": {},
   "source": [
    "### Task 4: Insights Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b03a0c4",
   "metadata": {},
   "source": [
    "#### 1. Collate all the key insights from Task 2 & 3 and any additional discoveries you made in Jupyter Notebook itself (as part of markdown cells) for senior management."
   ]
  },
  {
   "cell_type": "raw",
   "id": "e417ac2d",
   "metadata": {},
   "source": [
    "* From the conducted analysis, several valuable insights emerge regarding employee attrition and job satisfaction. Firstly, it's apparent that attrition rates vary across different segments, with certain groups exhibiting notably higher turnover than others.\n",
    "\n",
    "* This discrepancy suggests the presence of underlying factors influencing employee retention, such as departmental dynamics, managerial effectiveness, or job role satisfaction.\n",
    "\n",
    "* Additionally, statistical analysis identifies key factors impacting attrition, including job satisfaction, salary, and career growth opportunities. Notably, early signs of attrition, such as declining job satisfaction scores or negative performance ratings, can serve as crucial indicators for proactive intervention.\n",
    "\n",
    "* Furthermore, the hypothesis regarding the correlation between frequent job changes and high attrition rates requires empirical testing to validate its significance. \n",
    "\n",
    "* Overall, a comprehensive understanding of these insights enables organizations to develop targeted retention strategies, prioritize interventions, and foster a supportive workplace environment conducive to employee retention and satisfaction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35984104",
   "metadata": {},
   "source": [
    "#### 2. Come up with top 3 interventions to check attrition based on your analysis."
   ]
  },
  {
   "cell_type": "raw",
   "id": "b4d1c57e",
   "metadata": {},
   "source": [
    "* Enhancing Career Development Opportunities: By offering clear pathways for career advancement and personal growth, employees are more likely to feel valued and see a future within the company. This approach directly addresses one of the key factors impacting attrition, fostering a sense of loyalty and reducing turnover.\n",
    "\n",
    "* Improving Job Satisfaction: This can be achieved through various means such as ensuring fair compensation, recognizing and rewarding performance, improving work-life balance, and creating a positive work environment. Since job satisfaction is a significant predictor of attrition, initiatives aimed at enhancing employee satisfaction are crucial.\n",
    "\n",
    "* Proactive Management of Early Attrition Signs: Implementing regular check-ins and feedback mechanisms to gauge employee morale and satisfaction can help identify early signs of discontent. Addressing issues such as dissatisfaction with management, lack of recognition, or insufficient challenges in the role before they escalate can prevent potential attritions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "453abc44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "207103ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ea8f109",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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