{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da311036",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:37:31.291569Z",
     "start_time": "2022-10-16T15:37:29.442960Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eed16b7b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:37:36.535108Z",
     "start_time": "2022-10-16T15:37:34.702596Z"
    }
   },
   "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>EmployeeNumber</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>41</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>49</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>37</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>27</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EmployeeNumber Attrition  Age     BusinessTravel  DailyRate  \\\n",
       "0               1       Yes   41      Travel_Rarely       1102   \n",
       "1               2        No   49  Travel_Frequently        279   \n",
       "2               3       Yes   37      Travel_Rarely       1373   \n",
       "3               4        No   33  Travel_Frequently       1392   \n",
       "4               5        No   27      Travel_Rarely        591   \n",
       "\n",
       "               Department  DistanceFromHome  Education EducationField  \\\n",
       "0                   Sales                 1          2  Life Sciences   \n",
       "1  Research & Development                 8          1  Life Sciences   \n",
       "2  Research & Development                 2          2          Other   \n",
       "3  Research & Development                 3          4  Life Sciences   \n",
       "4  Research & Development                 2          1        Medical   \n",
       "\n",
       "   EmployeeCount  ...  RelationshipSatisfaction StandardHours  \\\n",
       "0              1  ...                         1            80   \n",
       "1              1  ...                         4            80   \n",
       "2              1  ...                         2            80   \n",
       "3              1  ...                         3            80   \n",
       "4              1  ...                         4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_excel(\"/Users/rishavdas/Downloads/Data/HR_Employee_Attrition-1.xlsx\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9fcb9b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:37:43.352842Z",
     "start_time": "2022-10-16T15:37:43.347289Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2940, 35)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47a82e3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:37:49.212673Z",
     "start_time": "2022-10-16T15:37:49.188802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2940 entries, 0 to 2939\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   EmployeeNumber            2940 non-null   int64 \n",
      " 1   Attrition                 2940 non-null   object\n",
      " 2   Age                       2940 non-null   int64 \n",
      " 3   BusinessTravel            2940 non-null   object\n",
      " 4   DailyRate                 2940 non-null   int64 \n",
      " 5   Department                2940 non-null   object\n",
      " 6   DistanceFromHome          2940 non-null   int64 \n",
      " 7   Education                 2940 non-null   int64 \n",
      " 8   EducationField            2940 non-null   object\n",
      " 9   EmployeeCount             2940 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   2940 non-null   int64 \n",
      " 11  Gender                    2940 non-null   object\n",
      " 12  HourlyRate                2940 non-null   int64 \n",
      " 13  JobInvolvement            2940 non-null   int64 \n",
      " 14  JobLevel                  2940 non-null   int64 \n",
      " 15  JobRole                   2940 non-null   object\n",
      " 16  JobSatisfaction           2940 non-null   int64 \n",
      " 17  MaritalStatus             2940 non-null   object\n",
      " 18  MonthlyIncome             2940 non-null   int64 \n",
      " 19  MonthlyRate               2940 non-null   int64 \n",
      " 20  NumCompaniesWorked        2940 non-null   int64 \n",
      " 21  Over18                    2940 non-null   object\n",
      " 22  OverTime                  2940 non-null   object\n",
      " 23  PercentSalaryHike         2940 non-null   int64 \n",
      " 24  PerformanceRating         2940 non-null   int64 \n",
      " 25  RelationshipSatisfaction  2940 non-null   int64 \n",
      " 26  StandardHours             2940 non-null   int64 \n",
      " 27  StockOptionLevel          2940 non-null   int64 \n",
      " 28  TotalWorkingYears         2940 non-null   int64 \n",
      " 29  TrainingTimesLastYear     2940 non-null   int64 \n",
      " 30  WorkLifeBalance           2940 non-null   int64 \n",
      " 31  YearsAtCompany            2940 non-null   int64 \n",
      " 32  YearsInCurrentRole        2940 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   2940 non-null   int64 \n",
      " 34  YearsWithCurrManager      2940 non-null   int64 \n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 804.0+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01e67645",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:37:58.531521Z",
     "start_time": "2022-10-16T15:37:58.510001Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in dataset.select_dtypes('object').columns:\n",
    "    dataset[i] = dataset[i].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70b2933b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:38:10.451781Z",
     "start_time": "2022-10-16T15:38:10.421396Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2940 entries, 0 to 2939\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype   \n",
      "---  ------                    --------------  -----   \n",
      " 0   EmployeeNumber            2940 non-null   int64   \n",
      " 1   Attrition                 2940 non-null   category\n",
      " 2   Age                       2940 non-null   int64   \n",
      " 3   BusinessTravel            2940 non-null   category\n",
      " 4   DailyRate                 2940 non-null   int64   \n",
      " 5   Department                2940 non-null   category\n",
      " 6   DistanceFromHome          2940 non-null   int64   \n",
      " 7   Education                 2940 non-null   int64   \n",
      " 8   EducationField            2940 non-null   category\n",
      " 9   EmployeeCount             2940 non-null   int64   \n",
      " 10  EnvironmentSatisfaction   2940 non-null   int64   \n",
      " 11  Gender                    2940 non-null   category\n",
      " 12  HourlyRate                2940 non-null   int64   \n",
      " 13  JobInvolvement            2940 non-null   int64   \n",
      " 14  JobLevel                  2940 non-null   int64   \n",
      " 15  JobRole                   2940 non-null   category\n",
      " 16  JobSatisfaction           2940 non-null   int64   \n",
      " 17  MaritalStatus             2940 non-null   category\n",
      " 18  MonthlyIncome             2940 non-null   int64   \n",
      " 19  MonthlyRate               2940 non-null   int64   \n",
      " 20  NumCompaniesWorked        2940 non-null   int64   \n",
      " 21  Over18                    2940 non-null   category\n",
      " 22  OverTime                  2940 non-null   category\n",
      " 23  PercentSalaryHike         2940 non-null   int64   \n",
      " 24  PerformanceRating         2940 non-null   int64   \n",
      " 25  RelationshipSatisfaction  2940 non-null   int64   \n",
      " 26  StandardHours             2940 non-null   int64   \n",
      " 27  StockOptionLevel          2940 non-null   int64   \n",
      " 28  TotalWorkingYears         2940 non-null   int64   \n",
      " 29  TrainingTimesLastYear     2940 non-null   int64   \n",
      " 30  WorkLifeBalance           2940 non-null   int64   \n",
      " 31  YearsAtCompany            2940 non-null   int64   \n",
      " 32  YearsInCurrentRole        2940 non-null   int64   \n",
      " 33  YearsSinceLastPromotion   2940 non-null   int64   \n",
      " 34  YearsWithCurrManager      2940 non-null   int64   \n",
      "dtypes: category(9), int64(26)\n",
      "memory usage: 624.6 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be361929",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:44:31.727546Z",
     "start_time": "2022-10-16T15:44:31.715896Z"
    }
   },
   "outputs": [],
   "source": [
    "def continuos_univariate_analysis(data,\n",
    "                                  feature,\n",
    "                                  figsize=(12, 8),\n",
    "                                  kde=False,\n",
    "                                  bins=None):\n",
    "    f1, (ax_box,\n",
    "         ax_hist) = plt.subplots(nrows=2,\n",
    "                                 sharex=True,\n",
    "                                 gridspec_kw={'height_ratios': (0.25, 0.75)},\n",
    "                                 figsize=figsize)\n",
    "    sns.color_palette(\"viridis\", as_cmap=True)\n",
    "    sns.boxplot(data=data,\n",
    "                x=feature,\n",
    "                ax=ax_box,\n",
    "                showmeans=True,\n",
    "                color='yellow')\n",
    "    sns.histplot(data=data,\n",
    "                 x=feature,\n",
    "                 ax=ax_hist,\n",
    "                 showmeans=True,\n",
    "                 color='crest',\n",
    "                 bins=bins,\n",
    "                 kde=kde) if bins else sns.histplot(\n",
    "                     data=data, x=feature, ax=ax_hist, kde=kde, color='blue')\n",
    "    ax_hist.axvline(data[feature].mean(), color='cyan', linestyle='--')\n",
    "    ax_hist.axvline(data[feature].median(), color='orange', linestyle=\"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "215693e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:45:31.778448Z",
     "start_time": "2022-10-16T15:45:30.944036Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "continuos_univariate_analysis(dataset, 'PercentSalaryHike', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ffd4e503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "continuos_univariate_analysis(dataset, 'Age', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4bdc643a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "continuos_univariate_analysis(dataset, 'DailyRate', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f38bfc6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "continuos_univariate_analysis(dataset, 'MonthlyIncome', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c33bb70a",
   "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>MonthlyIncome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobRole</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Healthcare Representative</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laboratory Technician</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manager</th>\n",
       "      <td>18155.567901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing Director</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Director</th>\n",
       "      <td>18269.622222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Scientist</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Executive</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Representative</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           MonthlyIncome\n",
       "JobRole                                 \n",
       "Healthcare Representative            NaN\n",
       "Human Resources                      NaN\n",
       "Laboratory Technician                NaN\n",
       "Manager                     18155.567901\n",
       "Manufacturing Director               NaN\n",
       "Research Director           18269.622222\n",
       "Research Scientist                   NaN\n",
       "Sales Executive                      NaN\n",
       "Sales Representative                 NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['MonthlyIncome'] > 16000].groupby(['JobRole']).agg({'MonthlyIncome':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "304eb673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "continuos_univariate_analysis(dataset, 'DistanceFromHome', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f0a020e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:46:35.434024Z",
     "start_time": "2022-10-16T15:46:35.425479Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attrition\n",
       "No     15.231144\n",
       "Yes    15.097046\n",
       "Name: PercentSalaryHike, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(['Attrition'])['PercentSalaryHike'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "db0d6441",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:51:00.041943Z",
     "start_time": "2022-10-16T15:51:00.034773Z"
    }
   },
   "outputs": [],
   "source": [
    "bins = [10, 15, 20, 25]\n",
    "hike_cat = ['AVGHIKE', 'MEDHIKE', 'HIGHHIKE']\n",
    "dataset['hike_cat'] = pd.cut(dataset['PercentSalaryHike'], bins=bins, labels=hike_cat, include_lowest = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6fc3d2e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T15:52:21.260507Z",
     "start_time": "2022-10-16T15:52:21.234928Z"
    }
   },
   "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>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hike_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AVGHIKE</th>\n",
       "      <td>1538</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDHIKE</th>\n",
       "      <td>646</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIGHHIKE</th>\n",
       "      <td>282</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attrition    No  Yes\n",
       "hike_cat            \n",
       "AVGHIKE    1538  300\n",
       "MEDHIKE     646  114\n",
       "HIGHHIKE    282   60"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(['Attrition', 'hike_cat']).agg({'EmployeeNumber':'count', 'MonthlyIncome':'mean'}).reset_index().pivot_table(index='hike_cat', columns='Attrition', values='EmployeeNumber')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "846ee0c8",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "1. Higher Hike will reduce the Attrition rate given  Monthly income is lesser than Avg \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3798f01f",
   "metadata": {},
   "source": [
    "## Univariate Discrete Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74a734bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T16:22:55.725358Z",
     "start_time": "2022-10-16T16:22:55.717332Z"
    }
   },
   "outputs": [],
   "source": [
    "def discrete_univariate_analysis(data, feature, perc=False, n=None):\n",
    "    total = len(data[feature])\n",
    "    count = data[feature].nunique()\n",
    "    if n is None:\n",
    "        plt.figure(figsize=(count + 1, 5))\n",
    "    else:\n",
    "        plt.figure(figsize=(n + 1, 5))\n",
    "    plt.xticks(rotation=90, fontsize=15)\n",
    "    ax = sns.countplot(\n",
    "        data=data,\n",
    "        x=feature,\n",
    "        palette=\"flare\",\n",
    "        order=data[feature].value_counts().index[:n].sort_values(\n",
    "            ascending=False))\n",
    "    for p in ax.patches:\n",
    "        if perc == True:\n",
    "            label = \"{:.1f}%\".format(100 * p.get_height() / total)\n",
    "        else:\n",
    "            label = p.get_height()\n",
    "        x = p.get_x() + p.get_width() / 2\n",
    "        y = p.get_height()\n",
    "        ax.annotate(label, (x, y),\n",
    "                    ha=\"center\",\n",
    "                    va=\"center\",\n",
    "                    size=12,\n",
    "                    xytext=(0, 5),\n",
    "                    textcoords=\"offset points\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "904ac87e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T16:23:39.249873Z",
     "start_time": "2022-10-16T16:23:39.092914Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(dataset, 'WorkLifeBalance', perc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc1901eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T16:49:43.237806Z",
     "start_time": "2022-10-16T16:49:43.226365Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attrition  WorkLifeBalance\n",
       "No         1                   110\n",
       "           2                   572\n",
       "           3                  1532\n",
       "           4                   252\n",
       "Yes        1                    50\n",
       "           2                   116\n",
       "           3                   254\n",
       "           4                    54\n",
       "Name: EmployeeNumber, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(['Attrition', 'WorkLifeBalance'])['EmployeeNumber'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33854a18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T16:57:11.244098Z",
     "start_time": "2022-10-16T16:57:11.214148Z"
    }
   },
   "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></th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">No</th>\n",
       "      <th>1</th>\n",
       "      <td>110</td>\n",
       "      <td>6679.672727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>572</td>\n",
       "      <td>6772.849650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1532</td>\n",
       "      <td>6782.622715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>252</td>\n",
       "      <td>7340.174603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Yes</th>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>4143.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116</td>\n",
       "      <td>4928.051724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>254</td>\n",
       "      <td>5022.007874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>3975.185185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           EmployeeNumber  MonthlyIncome\n",
       "Attrition WorkLifeBalance                               \n",
       "No        1                           110    6679.672727\n",
       "          2                           572    6772.849650\n",
       "          3                          1532    6782.622715\n",
       "          4                           252    7340.174603\n",
       "Yes       1                            50    4143.560000\n",
       "          2                           116    4928.051724\n",
       "          3                           254    5022.007874\n",
       "          4                            54    3975.185185"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(['Attrition', 'WorkLifeBalance']).agg({'EmployeeNumber':'count', 'MonthlyIncome':'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0acc70e9",
   "metadata": {},
   "source": [
    "## Observation\n",
    "1. Majority of the employees rated 2-3 star for WLB \n",
    "2. More number of employees for those who are working rated 2-3 star in WLB \n",
    "3. 4 star rating is comparitively lesser for those left the organization \n",
    "5. Salary has no impact in work life balance \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4ac4da9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T16:36:16.045152Z",
     "start_time": "2022-10-16T16:36:15.919408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOQAAAFpCAYAAACMDoQ/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAekUlEQVR4nO3df7xVdZ3v8dc7EgkQkPH4IwEFFQ2Ogno0nAwT752wmVScUvRqzuSN5KKTV53MW5BllHdGpxkrNTRHbVRgJH/UrVHEmsjrpMcEBBMB8cdJB1DSwyFSgc/8sRbHDWcfPMLZe3335v18PPbj7P1dP/hsPe+z1vquH19FBGaWhvcVXYCZvcOBNEuIA2mWEAfSLCEOpFlCHEizhLy/6AIqZa+99ooDDzyw6DLMOnjiiSdejYiGctPqNpAHHnggzc3NRZdh1oGkFzqb5l1Ws4Q4kDth5syZfOhDH6JPnz4cdNBBzJ8/H4B58+Zx2GGH0bt3b0488UReeKHTP4isXbuWCRMm0KdPHw444ADuvPPO9mkvvfQSY8aMYeDAgVx66aVbLTd+/HjvAdSjiKjL19FHHx2V9OCDD8aQIUPi0UcfjU2bNkVLS0u0tLTEmjVrol+/fjF79uzYsGFDXHbZZfHhD3+40/VMnDgxzjjjjFi3bl3Mnz8/+vXrF4sXL46IiMmTJ8f1118fr7/+egwbNiwef/zxiIiYOXNmTJ48uaLfzyoHaI5Ofm8LD06lXpUO5HHHHRc333xzh/bvf//7cdxxx7V/bmtri169esVvf/vbDvO2tbXFbrvtFkuXLm1vO+ecc+Lyyy+PiIjx48fHM888ExERZ555ZsyaNSveeOONGD16dPz+97/v5m9k1bK9QHqXdQds2rSJ5uZm1qxZw8EHH8ygQYO48MIL2bBhA0uWLGHUqFHt827ZnV2yZEmH9Tz77LP06NGD4cOHt7eNGjWqfd7Gxkbmzp3L66+/TnNzMyNGjGDq1KlcfPHFDBgwoOLf06rPgdwBq1at4u233+buu+9m/vz5LFiwgCeffJJvfOMbtLW10b9//63m79+/P+vWreuwnneb94orrmD+/PmccMIJTJkyhbfffptFixbxyU9+krPPPpuxY8fy3e9+t3Jf1Kqubk97VNIHPvABAC666CL2228/AC655BK+8Y1vMHbsWFpbW7eav7W1lT322KPDevr27bvdeQcOHMisWbMA2Lx5M2PHjuXGG2/k6quvprGxkVtvvZWjjjqKcePGMWLEiG7/nlZ93kLugD333JNBgwYhqcO0kSNHsnDhwvbP69evZ8WKFYwcObLDvMOHD2fjxo0sW7asvW3hwoVl550xYwZjxoyhsbGRp556iqamJnr27Mnhhx/O4sWLu+mbWeE6O7is9VelO3WmTp0aTU1NsWrVqli7dm0cf/zx8ZWvfCVWr14d/fr1i7vvvjs2bNgQX/ziF7fby3rmmWfGxIkTo62tLX71q19t1cu6xapVq6KxsTHWrVsXEVnv65e+9KVYt25dHHzwwe29r1YbKKKXFbgFWA0sLmmbBSzIX88DC/L2A4ENJdNuLFnmaOApYDlwHaCu/PuVDuRbb70VkydPjv79+8c+++wTF110UWzYsCEiIubOnRuHHnpo9OrVK0444YRYuXJl+3LTp0+P8ePHt39+7bXX4tRTT43evXvH4MGD44477ujwb5177rkxe/bs9s8vvvhiHHvssTFgwIC45JJLKvclrSK2F0hl07ufpLFAG3B7RDSWmX4t8EZEfF3SgcBPOpnvMeALwH8APwWui4ifvdu/39TUFD5xbimS9ERENJWbVrFOnYj4ZR60cgUJOAMYt711SNoP6BcRj+afbwdOA941kO/myb+5aGdXUTeOvO47RZdguaI6dT4KrIqIZSVtQyU9KenfJX00b9sfaCmZpyVvM6tLRZ32OAu4q+TzK8CQiHhN0tHAvZJGAh27MaHTfWxJk4BJAEOGDOnGcs2qo+pbSEnvB04n6+ABICLejIjX8vdPACuA4WRbxEEliw8CXu5s3RExIyKaIqKpoaHs7WZmSStil/W/Ac9ERPuuqKQGST3y98OAQ4DnIuIVYJ2kMflx52eA+wqo2awqKhZISXcBjwKHSmqRdH4+aSJb764CjAUWSVoI3A1cEBFr82mTgZvJTnusoBs6dMxSVcle1rM6af+rMm1zgDmdzN8MdDgdYlaPfOmcWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRJSydGvbpG0WtLikrYrJf1O0oL89YmSaVdIWi5pqaSPl7QfLempfNp1+bB0ZnWpklvIW4HxZdq/HRGj89dPASSNIBumbmS+zPVbxosEbiAbFfmQ/FVunWZ1oWKBjIhfAmvfdcbMqcDMfCTllWRjQR4raT+gX0Q8GhEB3A6cVpGCzRJQxDHkhZIW5bu0e+Zt+wMvlczTkrftn7/ftr0sSZMkNUtqXrNmTXfXbVZx1Q7kDcBBwGjgFeDavL3ccWFsp72siJgREU0R0dTQ0LCTpZpVX1UDGRGrImJTRGwGbgKOzSe1AINLZh0EvJy3DyrTblaXqhrI/JhwiwnAlh7Y+4GJknaXNJSs8+axiHgFWCdpTN67+hngvmrWbFZN76/UiiXdBXwM2EtSC/BV4GOSRpPtdj4PfB4gIpZImg08DWwEpkTEpnxVk8l6bD8A/Cx/mdWligUyIs4q0/yD7cw/HZhepr0ZaOzG0syS5St1zBLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZaQigUyHyF5taTFJW1/L+mZfATleyQNyNsPlLRB0oL8dWPJMkdLekrScknX5cPSmdWlSm4hbwXGb9M2F2iMiCOAZ4ErSqatiIjR+euCkvYbgElkY0YeUmadZnWjYoGMiF8Ca7dpezAiNuYf/4OtR0fuIB/gtV9EPBoRAdwOnFaBcs2SUOQx5GfZevDVoZKelPTvkj6at+1PNqz5Fi15W1mSJklqltS8Zs2a7q/YrMIKCaSkL5ONlHxH3vQKMCQijgQuAe6U1A8od7wYna03ImZERFNENDU0NHR32VYFy5Yto1evXpxzzjntbfPmzeOwww6jd+/enHjiibzwwgudLr927VomTJhAnz59OOCAA7jzzjvbp7300kuMGTOGgQMHcumll2613Pjx42lubu7+L/QeVT2Qks4D/gL4H/luKBHxZkS8lr9/AlgBDCfbIpbu1g4CXq5uxVZNU6ZM4Zhjjmn//Oqrr3L66adz1VVXsXbtWpqamjjzzDO3u3zPnj1ZtWoVd9xxB5MnT2bJkiUAfOtb3+K8885j5cqV3Hvvve0BnDVrFsOGDaOpqamyX64LqhpISeOBy4FTIuIPJe0Nknrk74eRdd48FxGvAOskjcl7Vz8D3FfNmq16Zs6cyYABAzjppJPa2370ox8xcuRIPv3pT9OrVy+uvPJKFi5cyDPPPNNh+fXr1zNnzhyuuuoq+vbty/HHH88pp5zCD3/4QwBWrlzJuHHj6N+/P8cccwzPPfccra2tXH311Xzzm9+s2vfcnkqe9rgLeBQ4VFKLpPOB7wJ7AHO3Ob0xFlgkaSFwN3BBRGzpEJoM3AwsJ9tylh53Wp1obW1l2rRpXHvttVu1L1myhFGjRrV/7tOnDwcddFD7Vq/Us88+S48ePRg+fHh726hRo9rnbWxsZO7cubz++us0NzczYsQIpk6dysUXX8yAAQMq88Xeo/dXasURcVaZ5h90Mu8cYE4n05qBxm4szRI0depUzj//fAYPHrxVe1tbG9v2B/Tv359169Z1WEdbWxv9+/fvdN4rrriCyZMnc9NNNzFlyhTefvttFi1axFe/+lXOPvtsWlpaOOOMM7jwwgu7+dt1XcUCadZVCxYs4KGHHuLJJ5/sMK1v3760trZu1dba2soee+zxnucdOHAgs2bNAmDz5s2MHTuWG2+8kauvvprGxkZuvfVWjjrqKMaNG8eIESO66+u9J750zgr3i1/8gueff54hQ4aw7777cs011zBnzhyOOuooRo4cycKFC9vnXb9+PStWrGDkyJEd1jN8+HA2btzIsmXL2tsWLlxYdt4ZM2YwZswYGhsbeeqpp2hqaqJnz54cfvjhLF68uMP81eJAWuEmTZrEihUrWLBgAQsWLOCCCy7gz//8z3nggQeYMGECixcvZs6cOfzxj3/k61//OkcccQSHHXZYh/X06dOH008/nWnTprF+/XoeeeQR7rvvPs4999yt5lu9ejXf+973uPLKKwEYOnQoP//5z2lra6O5uZlhw4ZV42uX5UBa4Xr37s2+++7b/urbty+9evWioaGBhoYG5syZw5e//GX23HNPfv3rXzNz5sz2Zb/5zW9y8sknt3++/vrr2bBhA3vvvTdnnXUWN9xwQ4ct5GWXXca0adPo27cvkB1bPvzwwwwePJhTTjml0NMfyk8F1p2mpqbY3oneJ//moipWk7Yjr/tO0SXsUiQ9ERFlU+9OHesW/3DSF4ouIRmXzPunHV7Wu6xmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZaQSg62c4uk1ZIWl7QNlDRX0rL8554l066QtFzSUkkfL2k/WtJT+bTr8lGwzOpSJbeQtwLjt2n7EjAvIg4B5uWfkTQCmAiMzJe5fsvwdMANwCSyIeoOKbNOs7pRsUBGxC+Btds0nwrclr+/DTitpH1mPnDrSrKh546VtB/QLyIezQd3vb1kGbO606VASprXlbYu2CcfhJX85955+/7ASyXzteRt++fvt203q0vbfXK5pF5Ab2Cv/Hhvy/FbP+CD3VhHuePC2E57+ZVIk8h2bxkyZEj3VGZWRe82lMDngYvJwvcE7wSkFfjeDvx7qyTtFxGv5Lujq/P2FqB0pM5BwMt5+6Ay7WVFxAxgBmRje+xAfWaF2u4ua0T8U0QMBS6LiGERMTR/jYqI7+7Av3c/cF7+/jzgvpL2iZJ2lzSUrPPmsXy3dp2kMXnv6mdKljGrO10abCciviPpT4EDS5eJiNs7W0bSXcDHyHZ3W4CvAlcDsyWdD7wIfDpfzxJJs4GngY3AlIjYlK9qMlmP7QeAn+Uvs7rUpUBK+iFwELAA2BKULb2eZUXEWZ1MOqmT+acD08u0NwONXanTrNZ1dTi6JmBE1OtgkmaJ6Op5yMXAvpUsxMy6voXcC3ha0mPAm1saI+KUilRltovqaiCvrGQRZpbpai/rv1e6EDPrei/rOt65QqYnsBuwPiL6Vaows11RV7eQe5R+lnQacGwlCjLble3Q3R4RcS8wrntLMbOu7rKeXvLxfWTnJX1O0qybdbWX9ZMl7zcCz5Pdw2hm3airx5B/XelCzKzrNygPknRP/oycVZLmSBr07kua2XvR1U6dfya7ReqDZHfs/zhvM7Nu1NVANkTEP0fExvx1K9BQwbrMdkldDeSrks6R1CN/nQO8VsnCzHZFXQ3kZ4EzgP8EXgE+Bbijx6ybdfW0x1XAeRHxe8geeAxcQxZUM+smXd1CHrEljAARsRY4sjIlme26uhrI923z2P+BdH3ramZd1NVQXQv8f0l3k10ydwZlnn9jZjunq1fq3C6pmeyCcgGnR8TTFa3MbBfU5d3OPIAOoVkFeXxIs4Q4kGYJcSDNEuJAmiXEgTRLiANplpCqB1LSoZIWlLxaJV0s6UpJvytp/0TJMldIWi5pqaSPV7tms2qp+uVvEbEUGA0gqQfwO+AesrtHvh0R15TOL2kEMBEYSXaD9EOShpcMV2dWN4reZT0JWBERL2xnnlOBmRHxZkSsBJbjZ8JanSo6kBOBu0o+XyhpkaRbSi5m3x94qWSelrytA0mTJDVLal6zZk1lKjaroMICKakncArwr3nTDWSDwo4muwn62i2zllm87DNhI2JGRDRFRFNDg58wYrWnyC3kycBvImIVQESsiohNEbEZuIl3dktbgMElyw0CXq5qpWZVUmQgz6Jkd1XSfiXTJpANEgvZ0+4mStpd0lDgEOCxqlVpVkWF3GQsqTfw34HPlzT/naTRZLujz2+ZFhFLJM0mu9NkIzDFPaxWrwoJZET8AfiTbdrO3c780/EN0bYLKLqX1cxKOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolpJBASnpe0lOSFkhqztsGSporaVn+c8+S+a+QtFzSUkkfL6Jms2oocgt5YkSMjoim/POXgHkRcQgwL/+MpBFkQ5+PBMYD10vqUUTBZpWW0i7rqcBt+fvbgNNK2mdGxJsRsRJYzjujK5vVlaICGcCDkp6QNClv2yciXgHIf+6dt+8PvFSybEve1oGkSZKaJTWvWbOmQqWbVU4hA7YCH4mIlyXtDcyV9Mx25lWZtig3Y0TMAGYANDU1lZ3HLGWFbCEj4uX852rgHrJd0FWS9gPIf67OZ28BBpcsPgh4uXrVmlVP1QMpqY+kPba8B/4MWAzcD5yXz3YecF/+/n5goqTdJQ0FDgEeq27VZtVRxC7rPsA9krb8+3dGxL9JehyYLel84EXg0wARsUTSbOBpYCMwJSI2FVC3WcVVPZAR8Rwwqkz7a8BJnSwzHZhe4dLMCpfSaQ+zXZ4DaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIQ6kWUIcSLOEOJBmCXEgzRLiQJolxIE0S4gDaZYQB9IsIUWMfjVY0s8l/VbSEklfyNuvlPQ7SQvy1ydKlrlC0nJJSyV9vNo1m1VLEaNfbQQujYjf5MPSPSFpbj7t2xFxTenMkkYAE4GRwAeBhyQN9whYVo+qvoWMiFci4jf5+3XAb+lkiPLcqcDMiHgzIlYCy8kGeDWrO4UeQ0o6EDgS+HXedKGkRZJukbRn3rY/8FLJYi1sP8BmNauwQErqC8wBLo6IVuAG4CBgNPAKcO2WWcssHp2sc5KkZknNa9as6f6izSqskEBK2o0sjHdExI8AImJVRGyKiM3ATbyzW9oCDC5ZfBDwcrn1RsSMiGiKiKaGhobKfQGzCimil1XAD4DfRsQ/lLTvVzLbBGBx/v5+YKKk3SUNBQ4BHqtWvWbVVEQv60eAc4GnJC3I2/4PcJak0WS7o88DnweIiCWSZgNPk/XQTnEPq9WrqgcyIn5F+ePCn25nmenA9IoVZZYIX6ljlhAH0iwhDqRZQhxIs4Q4kGYJcSDNEuJAmiXEgTRLiANplhAH0iwhDqRZQhxIs4Q4kGYJcSDNEuJAmiXEgTRLiANplhAH0iwhDqRZQhxIs4Q4kGYJcSDNEuJAmiXEgTRLiANplhAH0iwhDqRZQhxIs4TUTCAljZe0VNJySV8quh6zSqiJQErqAXwPOBkYQTZ03YhiqzLrfjURSLLRlJdHxHMR8RYwEzi14JrMup0iouga3pWkTwHjI+J/5p/PBT4cERduM98kYFL+8VBgaVUL3TF7Aa8WXUSdqJX/lgdEREO5CUWMoLwjyg3w2uEvSUTMAGZUvpzuI6k5IpqKrqMe1MN/y1rZZW0BBpd8HgS8XFAtZhVTK4F8HDhE0lBJPYGJwP0F12TW7WpilzUiNkq6EHgA6AHcEhFLCi6ru9TULnbiav6/ZU106pjtKmpll9Vsl+BAmiXEgTRLiANplhAH0iwhDqTVPEknS5oqaYakIXnbWEkfLLq298qBLICkIyTNkrRC0puSjsrbp0s6uej6aoWkfST9GvgxcB5wPtn1rAB/DUwtqrYd5UBWWR64J4B9gduB3UomvwlcVERdNeo7QF/gsPxVes3zQ8BJRRS1MxzI6vsWcGtEnABM32baAmB0tQuqYeOBr0TEcjrebNAC7F/9knaOA1l9hwGz8vfb/hK1AgOrW07N29RJ+17AhmoW0h0cyOpbDQzrZNpI4MUq1lLr5gMX5U+U2GLLH7nPAg9Xv6SdUxMXl9eZmcDXJT0NPJq3haThwOXADwqrrPZcDvwKWAzcQxbGz0lqBBqBMQXWtkN8cXmVSdodmEP2fKD/BPYjO97ZF3gQmBARbxdXYW2RdBBwJVkHzl7AWmAecGVELCuwtB3iQBZE0kls80sUEXOLrcqK5kCaJcTHkFUgqfd7mT8i/lCpWmqdpNnvYfaIiDMrVkwFOJDV0UaZh3JtR493n2WXVfZpbfXCgayOz/LeAmmdiIgTi66hknwMaZYQbyGtpknag+wp9sOBXttOj4gvVr2oneAtZAEknQl8js5/ifauelE1KD8H+QjQG+gDrCG79PD9wO+BNyKis6uikuRL56pM0tnAbcBysgc+3w/8hOz/RSvw3eKqqznfBpqBfcju9PgE8AHgHLKOtJrqYQUHsgh/C1wFTMk/Xx8RnwWGko1L4VMeXXcscCPZbWsAPSNiU0TcCVwL/FNhle0gB7L6DgEeiYhNZHcq9AOIiHXA/wUu3M6ytrVeQGtEbCa72qn0CQGLgVGFVLUTHMjqewPYPX//O+BDJdME/EnVK6pdzwIH5O+fBC6Q1EvSbmRPD6i58V/cy1p9zcARZMMi3A9Mk7QReAuYBvy6wNpqzUyyG7p/SPa4jgfIjsM3k/1u/1VRhe0o97JWmaQxZOMDzpI0gKyD5xNkV+c8DpwdESsKLLFmSRpMdhdNL+DhiFhccEnvmQOZgPyWrN0jorXoWqxYDmQVSJr2HmaPiLiqYsXUIUmHkj0/p9w53Z9Wv6Id50BWgaTNZM93WU/50aBLhS8M6BpJhwN3kXWMlR1lOyJq6kJ9d+pUx3PAELLHP84E7vHuabe4BXgb+AuyCy3eKracnectZJVIaiIb+fkMsqcE/BvZX/efRETNPR0tBZLagL+MiAeKrqW7+DxklUREc0RcFhFDyJ4n+p9kl8mtlnSHpLHFVliTHiPb86gb3kIWSFJPsocl/2/g/og4veCSaoqkg8n2Mv4R+Dnw+rbz1NrTF3wMWQBJHyHbff0UsAdwN3BDoUXVpleB58mGZOiMO3Wso3xAnYlkdyDsQ3YMuWXLWFN/xRPyL8BxwDW4U8e6StJSsrs5HibrZf2Re1l3nqT1wOfyuzvqggNZBfl5yD+SnYd81//gPg/ZNZKWAF+OiHuLrqW7eJe1Or5WdAF16m+Br0laEBHPF11Md/AW0mqWpMfJTnvsSda58/q280TEsdWtaud4C2m1bHH+qhveQpolxFfqWM1TZrCkP5XUp+h6doYDaTVN0v8iexTKC2QDuB6at/9I0sUFlrZDHEirWZL+FvgH4CZgHFvfgvULavAxkO7UsVo2BZgWEX+3zbDmAEvJHkRdU7yFtFq2L9k9puVspswTBFLnQFotWw6c0Mm0scDTVaylW3iX1WrZPwLXS3qL7I4ZgL0lnQ9cQjZ+Sk3xeUiraXnHzjSyAXe2dOr8AfhaRPx9YYXtIAfSaoqkB4GLImJpSdsnycLYl2xIgUcj4o2CStwpDqTVlPzOmTER8Vj+uQfZfZDHRMRvCi2uG7hTx+rBuz1as2Y4kGYJcSCtFpU7zqqLYy8fQ1pNyY8hXwc2ljTvVaYNqL2nL/g8pNWaun76greQZgnxMaRZQhxIs4Q4kHVO0j6S7pT0nKQnJD0qaUI3rPdjkn7SHTXaOxzIOiZJwL3ALyNiWEQcTfb09EEF1OIOxC5wIOvbOOCtiLhxS0NEvBAR35HUQ9LfS3pc0iJJn4f2Ld8vJN0t6Zl8ZC7l08bnbb8C2gcGktRH0i35up6UdGre/leS/lXSj4EHq/rNa5T/atW3kUBn13eeD7wREcdI2h14JL9wG+DIfNmXgUeAj0hq5p1HZSwHZpWs68vAwxHxWUkDgMckPZRPOw44IiLWduP3qlsO5C5E0veA48kuxn4BOELSp/LJ/YFD8mmPRURLvswC4ECgDVgZEcvy9n8BJuXL/hlwiqTL8s+9eGfcxrkOY9c5kPVtCfCXWz5ExBRJewHNwItktzFtNfqwpI8Bb5Y0beKd35POTlqLbCTjpVs1Sh8mG8/EusjHkPXtYaCXpMklbb3znw8AkyXtBiBp+Ls80/QZYKikg/LPZ5VMewC4qORY88huqX4X5EDWscguwzoNOEHSSkmPAbcBlwM3kz1z5jeSFgPfZzt7TBHxR7Jd1P+Xd+q8UDL5KmA3YFG+rqsq8HV2Cb50ziwh3kKaJcSBNEuIA2mWEAfSLCEOpFlCHEizhDiQZglxIM0S8l93OdON6LfkqwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(dataset, 'Gender', perc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ba6e1ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T17:02:03.391163Z",
     "start_time": "2022-10-16T17:02:03.370924Z"
    }
   },
   "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></th>\n",
       "      <th>EmployeeNumber</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">No</th>\n",
       "      <th>Female</th>\n",
       "      <td>1002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yes</th>\n",
       "      <th>Female</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  EmployeeNumber\n",
       "Attrition Gender                \n",
       "No        Female            1002\n",
       "          Male              1464\n",
       "Yes       Female             174\n",
       "          Male               300"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(['Attrition', 'Gender']).agg({'EmployeeNumber':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "555094b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T17:04:08.337031Z",
     "start_time": "2022-10-16T17:04:08.112938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(dataset, 'JobRole', perc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d0679778",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T17:06:47.681410Z",
     "start_time": "2022-10-16T17:06:47.672968Z"
    }
   },
   "outputs": [],
   "source": [
    "explainer = dataset.groupby(['Attrition', 'JobRole'])['EmployeeNumber'].count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "60192f04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T17:14:36.634781Z",
     "start_time": "2022-10-16T17:14:36.618344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Manager</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>Research Director</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Manager</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Research Director</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attrition                    JobRole  EmployeeNumber\n",
       "0         No  Healthcare Representative             244\n",
       "1         No            Human Resources              80\n",
       "2         No      Laboratory Technician             394\n",
       "3         No                    Manager             194\n",
       "4         No     Manufacturing Director             270\n",
       "5         No          Research Director             156\n",
       "6         No         Research Scientist             490\n",
       "7         No            Sales Executive             538\n",
       "8         No       Sales Representative             100\n",
       "9        Yes  Healthcare Representative              18\n",
       "10       Yes            Human Resources              24\n",
       "11       Yes      Laboratory Technician             124\n",
       "12       Yes                    Manager              10\n",
       "13       Yes     Manufacturing Director              20\n",
       "14       Yes          Research Director               4\n",
       "15       Yes         Research Scientist              94\n",
       "16       Yes            Sales Executive             114\n",
       "17       Yes       Sales Representative              66"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "afd15b0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T17:08:20.562851Z",
     "start_time": "2022-10-16T17:08:20.538029Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">EmployeeNumber</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobRole</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Healthcare Representative</th>\n",
       "      <td>244</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>80</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laboratory Technician</th>\n",
       "      <td>394</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manager</th>\n",
       "      <td>194</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing Director</th>\n",
       "      <td>270</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Director</th>\n",
       "      <td>156</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Scientist</th>\n",
       "      <td>490</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Executive</th>\n",
       "      <td>538</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Representative</th>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          EmployeeNumber     \n",
       "Attrition                             No  Yes\n",
       "JobRole                                      \n",
       "Healthcare Representative            244   18\n",
       "Human Resources                       80   24\n",
       "Laboratory Technician                394  124\n",
       "Manager                              194   10\n",
       "Manufacturing Director               270   20\n",
       "Research Director                    156    4\n",
       "Research Scientist                   490   94\n",
       "Sales Executive                      538  114\n",
       "Sales Representative                 100   66"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(explainer, index=['JobRole'], values = ['EmployeeNumber'], columns =['Attrition'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b501cf0",
   "metadata": {},
   "source": [
    "## Observations:\n",
    "\n",
    "1. Sales Representative/Executive, Lab Technicians, HR and research scientist are highest contributor of Attrition \n",
    "2. Minimal Attrition for leadership Team \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "26369dd0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T17:17:08.711990Z",
     "start_time": "2022-10-16T17:17:08.558897Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SGBwRdbdQnMvay+KPpw8zq0KNvUrVRdJvJS2Q9Jak0ZK6bOg9EXFqRHSKiO0ioktE/Dwi3omIwyJi9/TnooL9r4uI3SJiz4h4vKC9NiJ6p9vOy2sMzhlnnEGnTp3Ycccd2WOPPbjnnnvWbBs7dix77bUXLVu25JBDDuH111/fpM+ZM2cO/fv3p3379lx44YXrvG/AgAHU1taW/ouZZaixp1S/IDnt+RjJVaLfp21bjUsvvZRZs2axZMkSHn30US6//HLGjx/P22+/zQknnMC1117LokWLqKmp4eSTT27y5wDccMMNDBw4kJkzZ/K73/1uTcCMGjWKHj16UFNTk8l3NSuXxgZOx4j4RUSsTB/3AVvVZaBevXrRvHlzACQhienTp/Pwww/Tq1cvvvKVr9CiRQuuuuoqJk6cyD//+c8mfQ7AzJkzOfTQQ2nTpg19+/ZlxowZLFmyhOHDh3P99ddn80XNyqixgfO2pDMkNUsfZwDvlLOwSvStb32Lli1bstdee9GpUyeOPPJIJk+ezCc/+ck1+3zkIx9ht912Y/LkyU36HIDevXszZswYFi9eTG1tLT179uSKK65g6NChtG3bttxfz6zsGhs4XwdOAt4E5gMnAk3uSK52d9xxB0uXLmXcuHGccMIJNG/enGXLltGmTZt19mvTpg1Lly5t0udAcro1btw4DjroIAYPHsyKFSt45ZVXOProoznttNM48MADue2228r6Hc3KqbGBcy0wMCI6RsROJAF0VdmqqmDNmjXjgAMOYO7cudx55520atWKJUvWvctjyZIltG7duoFPKP45AO3bt2fUqFFMnDiRIUOG8O1vf5tbb72V4cOH07t3b5588knuuusupkyZUrbvZ1ZOjQ2cfevu6gZIry59qjwlVYeVK1cyffp0evXqxcSJE9e0/+c//1nT3pTPqW/EiBH079+f3r178+qrr1JTU8P222/PPvvsw6RJk0r2Pcyy1NjA2SZdJgZYM81EY6e2qHoLFixg5MiRLFu2jFWrVvHEE0/wwAMPcOihh3L88cczadIkRo8ezQcffMA111zDvvvuy1577dWkz6m/3+23385VV10FQPfu3XnqqadYtmwZtbW19OjRI4uvbVZyjQ2cm4C/SbpW0jXA34Aby1dWZZHEnXfeSZcuXWjXrh0XXXQRN998M8ceeywdO3Zk9OjRXHbZZbRr146///3vjBw5cs17r7/+eo444oiNfk6hiy66iGHDhtGqVSsg6dv585//TNeuXTnmmGN8edyqlho7ji6ds+ZQkhsqx0ZERXck1NTUhAfKbb5HTxiSdwmZOebhn+ZdwhZB0viIKPqvYqNPi9KAqeiQaazJw4bmXUKmel1zc94lmAGbOD2FmdmmcOCYWWYcOGaWGQeOmWXGgWNmmXHgmFlmHDhmlhkHjpllxoFjZplx4JhZZhw4ZpYZB46ZZcaBY2aZceCYWWYcOGaWmcwDR9KekiYUPJZIGirpKklvFLQfWfCeSyVNkzRV0hezrtnMSiPzeYkjYirQB0BSM+AN4Lcky878JCJ+VLh/OtPgKUAvkpU/n5S0R8Ha42ZWJfI+pToMmB4RDS/GDccCIyNieUTMBKYB/TKpzsxKKu/AOQV4oOD1eZJekXRvwSoRnYE5BfvMTdvWI2mQpFpJtQsXLixPxWa2yXILHEnbA8cA/5s23QnsRnK6NZ9kpQhIJm2vr+jM7xExIiJqIqKmY8etaulzs6qQ5xHOEcBLEfEWQES8FRGrImI1cDdrT5vmAl0L3tcFmJdppWZWEnkGzqkUnE5J6lSw7XigbnnJR4FTJDWX1B3YHXghsyrNrGRyWT1TUkvgcOCbBc03SupDcro0q25bREyW9CDJEjUrgcG+QmVWnXIJnIh4D/hovbavbmD/64Dryl2XmZVX3lepzGwr4sAxs8w4cMwsMw4cM8uMA8fMMuPAMbPMOHDMLDMOHDPLjAPHzDLjwDGzzDhwzCwzDhwzy4wDx8wy48Axs8w4cMwsMw4cM8uMA8fMMuPAMbPMOHDMLDMOHDPLjAPHzDLjwDGzzDhwzCwzDhwzy0wugSNplqRXJU2QVJu2tZc0RtJr6c92BftfKmmapKmSvphHzWa2+fI8wjkkIvpERE36+nvA2IjYHRibvkZST+AUoBcwALhDUrM8CjazzVNJp1THAr9Mn/8SOK6gfWRELI+ImcA0oF/25ZnZ5sorcAL4k6TxkgalbTtHxHyA9OdOaXtnYE7Be+embWZWZbbN6ffuHxHzJO0EjJH0zw3sqyJtUXTHJLwGAXTr1m3zqzSzksrlCCci5qU/FwC/JTlFektSJ4D054J097lA14K3dwHmNfC5IyKiJiJqOnbsWK7yzWwTZR44kj4iqXXdc+ALwCTgUWBguttA4JH0+aPAKZKaS+oO7A68kG3VZlYKeZxS7Qz8VlLd7/9NRPxR0ovAg5LOBmYDXwGIiMmSHgSmACuBwRGxKoe6zWwzZR44ETED+GSR9neAwxp4z3XAdWUuzczKrJIui5vZFs6BY2aZceCYWWYcOGaWGQeOmWXGgWNmmXHgmFlmHDhmlhkHjpllxoFjZplx4JhZZhw4ZpYZB46ZZcaBY2aZceCYWWYcOGaWGQeOmWXGgWNmmXHgmFlmHDhmlhkHjpllxoFjZplx4JhZZhw4ZpYZB46ZZSaPtcW7SnpK0j8kTZY0JG2/StIbkiakjyML3nOppGmSpkr6YtY1m1lp5LG2+Ergwoh4SVJrYLykMem2n0TEjwp3ltQTOAXoBXwMeFLSHl5f3Kz6ZH6EExHzI+Kl9PlS4B9A5w285VhgZEQsj4iZwDSgX/krNbNSy7UPR9KuwKeAv6dN50l6RdK9ktqlbZ2BOQVvm0sDASVpkKRaSbULFy4sV9lmtolyCxxJrYDRwNCIWALcCewG9AHmAzfV7Vrk7VHsMyNiRETURERNx44dS1+0mW2WXAJH0nYkYXN/RDwMEBFvRcSqiFgN3M3a06a5QNeCt3cB5mVZr5mVRh5XqQT8HPhHRPy4oL1TwW7HA5PS548Cp0hqLqk7sDvwQlb1mlnp5HGVan/gq8Crkiakbd8HTpXUh+R0aRbwTYCImCzpQWAKyRWuwb5CZVadMg+ciHiW4v0yj23gPdcB15WtKDPLhEcam1lmHDhmFeS1116jRYsWnHHGGUW3jxw5kj333JM2bdqw0047MXDgQJYsWbJm+9ChQ2nXrh2f+cxneOONN9a033///QwZMqTs9W+MA8esggwePJi+ffs2uH3//ffnr3/9K++++y4zZsxg5cqVXH755QC88MILjB8/njfffJMDDjiAG264AYB3332XH/3oR1xzzTWZfIcNceCYVYiRI0fStm1bDjvssAb36dq1Kx06dFjzulmzZkybNg2AmTNncsABB9C8eXMOO+wwZsyYAcBll13GxRdfTJs2bcr7BRrBgWNWAZYsWcKwYcO46aabNrrvs88+S5s2bWjdujWjR49m6NChAPTq1Ytx48bx/vvvM3bsWHr16kVtbS1Tp07ltNNOK/M3aBwHjlkFuOKKKzj77LPp2rXrRvc94IADePfdd5k7dy4XX3wxu+66KwC9e/fmy1/+Mv3792f27NlccsklDBkyhFtuuYVbbrmFAw88kNNPP53FixeX98tsgAPHLGcTJkzgySef5IILLmjS+zp37syAAQM45ZRT1rRdcMEFTJw4kVGjRjFq1Cg+97nPsXr1akaMGMHYsWPZe++9GT58eKm/QqPlMfDPzAo8/fTTzJo1i27dugGwbNkyVq1axZQpU3jppZc2+N6VK1cyffr09drfeustfvazn/H888/z+9//nn333ZftttuOvn378tOf/rQs36MxfIRjlrNBgwYxffp0JkyYwIQJEzjnnHM46qijeOKJJ9bb9/7772f27NlEBK+//jqXXXZZ0U7m73znO1x99dW0bNmS7t278+KLL7Js2TKefvppevTokcXXKsqBY5azli1bsssuu6x5tGrVihYtWtCxY0dmz55Nq1atmD17NgBTpkzhs5/9LK1atWL//fdnzz335O67717n85566ikWL17M8ccfD0C/fv046qij6Nq1K0899RTf+973Mv+OdRRRdKaHqldTUxO1tbVFt00eNjTbYnLW65qbN/m9j56Q/2CxrBzzcH6nGlsSSeMjoqbYNvfhmJXA5Qecl3cJmfnBs7dt8nt9SmVmmXHgmFlmHDhmlhkHjpllxoFjZplx4JhZZhw4ZpYZB46ZZcaBY2aZceCYWWYcOGaWGQeOmWXGgWNmmamawJE0QNJUSdMk5Tehh5ltsqoIHEnNgNuBI4CeJOuQ98y3KjNrqqoIHKAfMC0iZkTEh8BI4NicazKzJqqKGf8knQgMiIj/l77+KvBfEXFevf0GAYPSl3sCUzMtdOM6AG/nXUQV8N+p8Srxb/XxiOhYbEO1zPinIm3rJWVEjABGlL+cTSOptqGpF20t/50ar9r+VtVySjUXKFwhrAswL6dazGwTVUvgvAjsLqm7pO2BU4BHc67JzJqoKk6pImKlpPOAJ4BmwL0RMTnnsjZFxZ7uVRj/nRqvqv5WVdFpbGZbhmo5pTKzLYADx8wy48CxXEk6U9JHG9jWXtKZWddUTSS1zbuGpnDgWN5+AezWwLbu6fatnqRzJX234HUfSXOBdySNl9Qlx/IazYGTIUl7STpO0sfyrqWCFBvUWeejwJKsCqlw32bdv8UtJGPRTif5/3h4HkU1VVVcFq9Gkn4GRESck74+Gfgfksv6yyQNiIi/5VljXiQdy7r3wl0haWG93VoAnyMZg2XQjfRWHUkdgf2BwyLiaUkfApu+4HeGfIRTPgOAZwpeXws8AHyMZDzRtXkUVSF2AvZJH5CcUu1T7/Fx4E/AN/MosAItB7ZPnx8CvAeMS18vAtrmUFOT+QinfHYC5gBI2h34BHBCRLwpaQQwKs/i8hQRdwN3A0h6Cjg3Iv6Zb1UV7wVgcNpvcz7wx4hYlW7rQZXc6uPAKZ9FwM7p888Db0bEpPS1SE6ttnoRcUjeNVSJC0lu53mV5B+yrxdsOxn4ax5FNZUDp3weB66RtDPwXeDBgm29gVl5FFWJ0k70L5HclNui3uaIiEuyr6qyRMQU4BPpEIJFse4tAhcBb+ZTWdP41oYykdQG+AnQF5gAnBcR76bbxgF/8/9IIOl4kr6tZsAC4MN6u0RE9Mi8sAolSSTB3BWYGBH/ybmkJnHgWK4k/QN4DfhaRCzKu55KJulbwOXALiTzQfWNiJckPQw8ExE351lfY/gqVZlJ6inpq5K+L2mXtO0TklrnXVuF6Arc4rDZMEkXAz8m6Ww/lHXHLz1N0o9T8dyHUyaSWgH3AicCK0j+1n8kOde+HphNcu69tfsbyXSwT+ZdSIUbDAyLiBvTRQUKTQX2yKGmJvMRTvn8GPgscBjQmnX/RXqMZJyOwXeAQZIGSvqYpJb1H3kXWCF2AcY3sG0163e2VyQf4ZTPCcCQiHiqyL9Ir5MMbDN4Jf35C4rMU53yEAKYBhwEjC2y7UBgSrblbBoHTvnsALzTwLbWwKoGtm1tvk7DQWNr3Qzckd7G8FDatpOks0mOEr+RV2FN4atUZSLpaWBeRJyWHuGsAGrSqwq/AjpExJG5FmlVJe04Hga0ZO0p+nvA1RHxw9wKawIHTplIOoCkI/RZ4H+BO4ArSTpITwQOjAjfmJhKV1Ldj+Sq1b3pLSCfAN6KiKX5Vlc50qubnyFZj2oR8Fzd+K5q4MApI0n7k0wb0J+kHyKA54HvRkRVDEUvtwau5tWNL3kQmB0Rvpq3hfBVqjKKiL9GxOeAHUlGh7aOiP0dNuvw1bxGkHRdOuVJsW13SaqK2QccOBmIiPcjYl5EvJd3LRXoBOCSiHiK9TvSfTVvrVNZOx1FfeOA0zKsZZP5KlUJSbqxCbv7psSEr+Y1zseANxrYNi/dXvEcOKX1lSbsG4ADJ5nR70ySUdj1nUgyEtmSEeqfBp4qsu3TQP0ZEyuSA6eEIqJ73jVUocuBJyU9SXI1L4AjJV1AejUvz+IqyIPAMEn/jIg/1DVKOhK4gipZgdNXqSx3vpq3cZJakEzA9XmSU9D5QCegPclUrMdFxPL8KmwcB04ZpXOX7E9yY91697pExB2ZF1XBJO0AtAMWu4O9OElfJJnT+KMkwTM2IsbkW1XjOXDKJJ3pbyzQk+Rf7LrLvWv+4BHhe4Rso9Kjm1eA8yOiWF9X1XAfTvncBLxLMnJ2DvBfwFvAGSSdpEflV1plkdQPOB7oTPEpRqtirpdyiYgP0hU2V+ddy+Zy4JTPQcAQknNtSI4mZwPXS9qG5FaHL+ZVXKVIO4dvIgnjGaw/xagl7gfOIumvqVoOnPJpCyyMiNWSlpAsG1Pnb/iSeJ0LgZ8C3wmf32/IbOAkSbUkI7DfYt277CMi7sylsiZw4JTPTJKrCACTSZZk/b/09dEkN94ZNAf+4LDZqJvSn51Ixt3UF0DFB45vbSifPwBfSJ//APiypLmSZpIsZHZrbpVVlvtIbm+wDYiIbTbyqIoLEL5KlRFJfUk6RlsAYyLi8ZxLqghpf9ZtJEMH/gwsrrdLVZwqWOM4cCxXkj4PjCa5b6qYqJZ/vcspHVG8QRHxWBa1bA4HTgbSicDPBvYiuSfmVxHxer5VVQZJ/yJZhXQIMC0iVuRbUWWStJp1x3PVqapxXQ6cEpJ0E3B0ROxR0Naa5AbF3YF/A22A/wD9IuJfuRRaQSQtIxmW72ViNkBSsWk62pP0E34NOCsins+0qE3gTuPSOgT4n3ptF5H0T3wjIjqQTCMwi+SGO0umYf1k3kVUuoh4vcjj5Yj4b+DnwPfzrrExfIRTQpIWAV+tdzfvJICI6F3Q9lWSia+3+jWzJR0K3AX8iuKdxkREVSyBkhdJhwGPRESrvGvZGI/DKa1tgQ/qXkhqD+wN3F5vv1kkC5vZ2hU3rwGurrdNJH0UFd83kRdJ25OcUs3fyK4VwYFTWv8CDmbtYmVfSn8+UW+/nfDAvzqH5F1ANZD0Iuuv37U9sCvJFb6zsq5pUzhwSus24G5JbUiGnp9PMuK4/v0vXwAmZVxbRYqIv+RdQ5WYzPqB8wHJpGW/i4jJ2ZfUdA6cEoqI+yR1Ill4vi3wEjC48FKvpI7Asax/+rBVk7Qn0Jdk6P58oDYi/plvVZUjIr6Wdw2l4E5jy5WkHYG7gS+TXDVdBrQimYrhYeD/RcSS/CqsLGmfzT4kl8QXAa9GRNXcYe/L4pa3O0hOMc8EWkbEjiRL2Q4EDk+3GyDpuySn6i+Q9Au+CLyVLgFcFXxKZXk7FrggIn5T1xARHwD3pyO0f5xbZRVE0lDgBpIhBKNIgmdn4GTgBknLI+KW/CpsHAeO5W0ZDV/SnUcyKtuSfsHhEXFZQdtU4BlJi0kuUFR84PiUyvJ2O3BROoH6GunRzUX4lKpOV4qvSQXwNMlS0hXPRziWtzYk95nNkTQGWEAyTulw4H2gtmBF0615tdLZJH1dxe45OzzdXvF8lcpylU5I1lixtd4OIuk8klOme4GHSPpwdiJZ7fVrJCs6VPzRoAPHrEpI+gZwJckNwHVTVcwDroqIe/KsrbEcOGZVJF1csQtrB0jOrab5oB04ljtJPYCLgQNYO6BtHPCjiJiRZ22VTlLbiFicdx2N5atUlitJ+wETSEYav0gyTcWL6euXJRVboWCrI+ncdOBf3es+kuYC70gaL6kqrlL5CMdyJekpkn/4jihcTzy9LP4YsDoiDs2rvkohaQpwS0Tclb5+hmRC/h+TrHE2OSLOyLHERvFlcctbP+CkwrABiIj3JP2IZFStQTeSgX51NwDvDxwWEU9L+pBkpoKK51Mqy9v7wEcb2NaeggnNtnLLSea/gWQOofdI+rkg6fNqm0NNTebAsbz9ARgu6YDCxvT1DcDvc6mq8rwADJbUi+Q2hj9GxKp0Ww+Sy+MVz304litJHwUeAT4DLGTtgLadSNZgPy4i3smvwsogqSfwKEm4zAEOr1v1Q9ITwJsRMTDHEhvFgWMVQdIA1p2A6+8RUX+mxK1eGtCLCsfeSNqHJHAW5ldZ4zhwzKpIwcC/rsDEiKiqu+ndh2OZk9SyKY+8660Ukr4FvAG8TtJhvGfa/nA6X07Fc+BYHpYBS5vw2Oqls/r9mGQ61kNZd8nfp0km4qp4Hodjefg6669AYBs2GBgWETdKqr9O11SS1V0rngPHMhcR9+VdQxXaBRjfwLbVJKOOK55PqawiSOop6auSvi9pl7TtE5Ja511bhZgGHNTAtgOBqlgO2Uc4litJrUgmlToRWEHy3+QfgTeB60lmsrsotwIrx83AHeltDA+lbTtJOhv4DvCNvAprCh/hWN5+DHwWOIxkydrCztDHgAF5FFVp0gm2LiO9UTNtfgz4KckEXL9p6L2VxONwLFeS3gaGRMT9aWfoCqAmIl6SdAjwaET4tCqVnmJ+BuhAcg/VcxHxbr5VNZ5PqSxvOwAN3brQGljVwLatUkQspd5a9WkwfzcijsinqsbzKZXl7UWSVTeLOZHkfqqtlqS2kk6RdLGkEyVtV7DtK5JqgbFA9/yqbDwf4VjeLgeelPQk8L8k43OOlHQBSeAcmGdxeUrvkfoTyQqbdV6S9GXgNySnVpOB06mSeYN8hGO5iohnSTqMm5NMIiXgapK7oj8fES/mWF7ergeWkARLS2Bvkn6bF4HewJkRsU9EPBARq/Mrs/HcaWwVI119sx2wuP4MgFsjSfNJOtQfLGjbDXgNGFQtS8MU8hGO5UrSWZLaAkTE+xExz2Gzxs7ArHptda8nZlpJiThwLG93AW9KelTSaZI+kndBFaahU5CVmVZRIj6lslylRzcnACeR3AW9AngceAD4Q0RstXMaS1oNLGb9cOlQrD0idsqksM3gwLGKkc5mdyJJ+BxEMlH4o9Ww/Ek5SLqyKftHxNXlqqVUHDhWkSQdBYwAdomI+tMxWJXyOByrGOm4k5PTRw9gOsmlYdtCOHAsV5L2JjmFOplkysw5wIPAyIh4Kc/arPR8SmW5SjtG55OMMh4VEc/lXJKVkY9wLG+HAn8J/8u3VfARjlUESe1Ihut3BR6PiH9LagF8WC3D9m3jPPDPciWpmaQbgbnAX4Bfs/bO59FAky4NW2Vz4FjerieZHvM8kitThTP+PQIcnUdRlUbSdpIukvQ3SbMlLaj/yLvGxnAfjuXtTOB7EfGLIsufTCcJIYOfAN8E/g94Cvgw33I2jQPH8taWJFiK2R7woL/EV0iC+aa8C9kcPqWyvE0Cjm1g2xGAx+IkBLySdxGby0c4lrcfAKPTuXDqZvzrI+l4klOIY/IsroLcDZwKjMm7kM3hy+KWO0knATcC3Qqa3wAuLJx8amsm6XyS9admkoTO4nq7RETcmXVdTeXAsYohaU/goyTTaE71YMC10hHZGxLVcJOrA8fMMuNOY8uNpAMl/UbSDEn/SR8zJP2PpAPyrs9Kz0c4lgtJV5CszjAP+DPJSGMBnUnur+oEXBkRP8ityAokqQuwB9Ci/raIeCz7iprGgWOZS49engGuBa6uf69UOgDwSpK1tD8XEVv1YniwZonfB4Ev1DWlP9f8D+w+HLMiJP0P0D4ijtzIfo8B70TEV7OprHJJug04hOQ2kGeB44F/A2eQHBGeWg1reLkPx/LQHxjZiP1GkiwCZ3AkcB3w9/T1vIh4JiIGkdxzdnFulTWBA8fysAvJeJKNmUnSl2PJGlVzImIV8B+gfcG2x1h7qlXRHDiWh5bA8kbs9yFFOke3UnNIloeBZOXNLxVs+y+gKpbT8a0NlpfPSuqwkX32yqSS6jAG+DzwW5I7x38paT+S4D4QqIqbOt1pbJlrxKjZQlUxgrbcJLUEWkbE2+nr40nW8NqBJIx+Vg0zIzpwLHOSPt6U/SPi9XLVYtly4JhVGUnbkswVtI6IeC+HcprEncZmVUBSG0l3SJpP0kG8tMij4rnT2Kw63Eey3vrdwDSqdIpRn1KZVQFJS4BvRsQDedeyOXxKZVYdZgMV30ezMQ4cq2iStPG9tgrfBS6X1G2je1Yw9+FYxUgXxPtTRDyZvt4BeAA4Ls+6KkFEPCbp88A0SbNYf4pRIqJf1nU1lQPHKslRwPmSpgGXkkxPsWe+JVUGST8ChgIv4k5js9KQ1IpkYq6hwJvAgRHR0LpVWw1Ji4H/jogb8q5lc7gPxzIn6UhJwxrY3Ak4AZgMtCO5S9qSDuPxeRexuRw4lodhQPP6jZL6AX8jWYlzf+Au4PvZllaxfgoMqvZOdPfhWB56AlcUNkg6ChgFPA6cHhEfSnoSOCuH+ipRB5JpKKZKepri61JdknVRTeXAsTwsAXYjXUVS0jeBW4GfA98qWI+qGUWOhLZSJwIrge2Aw4tsD6DiA8edxpY5ScOBb5Nc8v4EyXwu04DeEfFhwX6jgc4R0T+XQq3kfIRjebgcWEaybvgqYAjJ6UKtpFvTbSel20/Kq0grPR/hWEVIp1y4DhhMMgXpO8DlEfGzXAurEJK+tbF9IuKOLGrZHA4cqyhp8HQAFlTDDHZZ2cgsiQHVsS6VL4tbRYmIlRHxpsNmXRGxTf0HycoNpwITSa78VTwf4ZhVOUmDgNMi4uC8a9kYH+GYVb+ZQE3eRTSGA8esiknqBFxI4xYWzJ0vi5tVAUkLSTuHC2wPtCaZ4/iEzIvaBA4cs+pwO+sHzgfAXOCPEfFO9iU1nTuNLXeSTiT5F7oLRZb2rYaJpaxxfIRjuZJ0Fcnd4xOBKVTpxFLWOD7CsVxJmgP8OiI8DUU9kl5k/dOoBlXDkaCPcCxvrYGxeRdRoSazbuAIOBP4P5JbP6qOj3AsV5LuApZGxMV511Lp0ts+PgRqIuKlvOvZFD7CscxJOrLg5ZPAjZI6kMyPs7j+/hHxWEalVbqqPzrwEY5lLr0RMUhOETYmquGmxCxIagaswEc4Zk3SPe8CLB8OHMtcRLyedw1VrmpPS3xKZbmSdArQNSJ+WGTbRcDsiHgw+8ry18DtDB1I+rlW1t8/InbKoKzN4iMcy9ulwD0NbHsv3b5VBg7Fb2eoag4cy9sngEkNbPsHsHuGtVSUiLgq7xpKzdNTWN7eI7mHqpiuwPIMa7Eyc+BY3p4ErpC0Tv+DpI7AZcCfcqnKysKdxpYrSd2A50lucfgjMJ9kffEvknSO7h8Rc3Ir0ErKgWO5S49mvgMcAnyU5D6hscBPIuLtPGuz0nLgWG4ktSBd4jcins+7His/9+FYbiLiA+AUiky6ZVsmB47l7c8kp1K2FfA4HMvb7cA9kj4CPAa8Rb3BbhExJY/CrPTch2O5KrKEbf0Jp3y3+BbERziWN59ObUV8hGNmmfERjlUMSdtQfJmY93Iox8rAV6ksV0pcImkayWx2S4s8bAvhwLG8nQ98D/g5SSfxdcA1wL+AWcCg3CqzknMfjuVK0iRgBMnl8TXz9aanV78HXo2I7+VZo5WOj3Asb92BCRGxiiRw2gJExGrgDmBgfqVZqTlwLG/vAK3S57OBTxVsawfskHlFVja+SmV5+yvQl2SU8W+AqyS1J1nwbTBelXOL4j4cy5WkPYHOEfFnSc2BG4ETSY5sxgDfjogFedZopePAMbPM+JTKKoKkdkBvknmMH4+If6fz5XyYdiDbFsCdxpYrSdtKuhGYC/wF+DVrV+YcDVyZV21Weg4cy9t1wDeA84AerLve+CPA0XkUZeXhUyrL25nA9yLiF5LqT0MxnSSEbAvhIxzLW1uSYClme8Bz4WxBHDiWt0nAsQ1sOwJ4KcNarMx8SmV5+wEwWtIOwP+SzPjXR9LxwDeBY/IszkrL43Asd5JOIhnw162g+Q3gwoh4MJ+qrBwcOFYxJO0BdAAWAVPD/3FucRw4VpEktY2IxXnXYaXlTmPLlaRzJX234HUfSXOBdySNl9Qlx/KsxBw4lrdvA0sKXt8CzANOJ/nvc3geRVl5+CqV5a0bMBVAUkdgf+CwiHha0ofAbXkWZ6XlIxzL23KSAX6QrFH1HjAufb2IdAZA2zL4CMfy9gIwOO23OR/4YzrdKCS3NczLrTIrOV+lslxJ6gk8ShIuc4DDI+Jf6bYngDcjwvMabyEcOFYRJH0UWFQ49kbSPiSBszC/yqyUHDhWESQJ6EIyAdfEiPhPziVZGbjT2HIn6VsktzK8TtJhvGfa/rCkoTmWZiXmwLFcSboY+DFwN3Ao607A9TRwcg5lWZn4KpXlbTAwLCJuLDIB11RgjxxqsjLxEY7lbRdgfAPbVgMtMqzFysyBY3mbBhzUwLYDgSkZ1mJl5lMqy9vNwB3pbQwPpW07STob+A7JBOu2hfBlcctd2nE8DGjJ2k7j94CrI+KHuRVmJefAsYogqTXwGdZOwPVcRLybb1VWag4cq1iSDgG+GxFH5F2LlYb7cCwXktoCA0hGFs8EHomIFem2rwCXAJ8G/pVXjVZ6DhzLXHqP1J+AnQuaX5L0ZeA3JKdWk0km4RqVfYVWLr4sbnm4nmSWv8+QdBTvTdJv8yLQGzgzIvaJiAciYnV+ZVqpuQ/HMidpPjCkcAkYSbsBrwGDIuKe3IqzsvIRjuVhZ2BWvba61xMzrcQy5cCxvDR0aL0y0yosUz6lssxJWg0sZv1w6VCsPSJ2yqQwKztfpbI8XJ13AZYPH+GYWWbch2NmmXHgmFlmHDhmlhkHjm2QpFWSJkiaLGmipO9IKvt/N5K+JuljW8rvsYQDxzbm/YjoExG9gMOBI4Ery/kL07mNvwZkEQRZ/R7DgWNNEBELgEHAeUo0k/RDSS9KekXSNwEkHSzpGUm/lTRF0l11R0WS7pRUmx4xrbk8LmmWpGGSngVOBWqA+9Ojqx3S7ddLei59/6clPSFpuqRzCj7n4oJ6rk7bdpX0D0l3p7/3T+lnnlj/92T2x9xaRYQffjT4AJYVafs3ye0Jg4DL07bmQC3QHTgY+IBk+d5mwBjgxHS/9unPZiTLwOybvp5FMvdN3e94GqgpeD0LODd9/hPgFaA10BFYkLZ/ARhBMmvgNsD/kcyLvCvJYMI+6X4PAmcU+z1+lPfhgX+2KeqmAf0CsG96pADQBtgd+BB4ISJmAEh6ADiAZM7ikyQNIhl02gnoSRIesPGpKB5Nf74KtIqIpcBSSR+k8+t8IX28nO7XKq1nNjAzIiak7eNJQsgy5sCxJpHUA1gFLCAJnm9HxBP19jmY9e+VCkndgYuAvhHxb0n3se4yMBtb3nd5+nN1wfO619um9dwQET+rV8+u9fZfBfj0KQfuw7FGk9QRuAu4LZLzkSeAcyVtl27fQ9JH0t37Seqe9t2cDDwL7EgSKu9K2hnY0NShS0lOmZriCeDrklql9XSWtLH7sDbl99gm8hGObcwOkiYA25H0g/yaZGlegHtITk1ekiRgIXBcuu05YDiwD/AM8NuIWC3pZZLZ/GYAf93A770PuEvS+yQTdW1URPxJ0t7Ac0k5LAPOIDmiadTviYj3G/O7bNP4XiorufSU6qKI+FLOpViF8SmVmWXGRzhmlhkf4ZhZZhw4ZpYZB46ZZcaBY2aZceCYWWYcOGaWmf8PQBn4xlTb3sAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(dataset, \n",
    "                            'Department', perc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ed65ddb5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T17:18:15.834654Z",
     "start_time": "2022-10-16T17:18:15.695372Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(dataset[dataset['Attrition'] == 'Yes'], \n",
    "                            'Department', perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d32f19d",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "- More number of records will have more impact hence in Department we see for 2 categories more number of employees are present and more impact of attrition for those 2 categories.\n",
    "- so technically Departments has no relation with attrition "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aeae68c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T17:22:49.767323Z",
     "start_time": "2022-10-16T17:22:49.569338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(dataset, 'EducationField', perc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "330ec9cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-16T17:23:10.552215Z",
     "start_time": "2022-10-16T17:23:10.366512Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(dataset[dataset['Attrition'] == 'Yes'], 'EducationField', perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd03151",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "1. Marketing and Technical degree as a part of Education field has more impact \n",
    "2. lifescience contains more number of employees hence it is ovios to have more attritation due to population majority effect "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b85e22e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "ecd35c037bd360a9223f97d1b9b8f2c86e12889559e066ee9e282756f5cb5240"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}