IBM_attrition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "564394d8-6c7d-42ee-9e0a-759039c55f7a",
   "metadata": {},
   "source": [
    "### Predicting Employee Attrition at IBM\n",
    "\n",
    "#### By Kevin Ba Ross\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d95cfb4-fa1d-409c-a108-ac305366756f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from pathlib import Path\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.models import Sequential\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import mba263\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0d05fde-50fc-4f84-a6c0-f6e8ad389aba",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition_binary</th>\n",
       "      <th>Age</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>Department</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>children</th>\n",
       "      <th>Manager_relation_satisfaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmployeeID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Sales</td>\n",
       "      <td>5993</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2090</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3068</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>9526</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4193</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Attrition_binary  Age  JobSatisfaction  Education EducationField  \\\n",
       "EmployeeID                                                                     \n",
       "1                          1   41                4          2  Life Sciences   \n",
       "2                          1   37                3          2          Other   \n",
       "3                          0   32                4          2  Life Sciences   \n",
       "4                          0   38                3          3  Life Sciences   \n",
       "5                          0   29                3          2  Life Sciences   \n",
       "\n",
       "            EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  \\\n",
       "EmployeeID                                                                \n",
       "1                                 2  Female          94               3   \n",
       "2                                 4    Male          92               2   \n",
       "3                                 4    Male          79               3   \n",
       "4                                 4    Male          44               2   \n",
       "5                                 4  Female          49               2   \n",
       "\n",
       "           MaritalStatus  ...  WorkLifeBalance  YearsAtCompany  \\\n",
       "EmployeeID                ...                                    \n",
       "1                 Single  ...                1               6   \n",
       "2                 Single  ...                3               0   \n",
       "3                 Single  ...                2               7   \n",
       "4                 Single  ...                3               9   \n",
       "5                 Single  ...                3               9   \n",
       "\n",
       "            YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \\\n",
       "EmployeeID                                                                      \n",
       "1                            4                        0                     5   \n",
       "2                            0                        0                     0   \n",
       "3                            7                        3                     6   \n",
       "4                            7                        1                     8   \n",
       "5                            5                        0                     8   \n",
       "\n",
       "                        Department  MonthlyIncome  PercentSalaryHike  \\\n",
       "EmployeeID                                                             \n",
       "1                            Sales           5993                 11   \n",
       "2           Research & Development           2090                 15   \n",
       "3           Research & Development           3068                 13   \n",
       "4           Research & Development           9526                 21   \n",
       "5           Research & Development           4193                 12   \n",
       "\n",
       "            children Manager_relation_satisfaction  \n",
       "EmployeeID                                          \n",
       "1                  0                             1  \n",
       "2                  0                             2  \n",
       "3                  0                             3  \n",
       "4                  0                             2  \n",
       "5                  0                             4  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file from the Resources folder into a Pandas DataFrame\n",
    "data = pd.read_csv(\n",
    "    Path(\"IBM_attrition_data_set.csv\"),\n",
    "    index_col='EmployeeID', \n",
    ")\n",
    "\n",
    "# Review the DataFrame\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35e5f25-519f-47b8-93ca-a419a2763edb",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a5af567-984d-409d-8434-f421675e424f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attrition_binary                  int64\n",
       "Age                               int64\n",
       "JobSatisfaction                   int64\n",
       "Education                         int64\n",
       "EducationField                   object\n",
       "EnvironmentSatisfaction           int64\n",
       "Gender                           object\n",
       "HourlyRate                        int64\n",
       "JobInvolvement                    int64\n",
       "MaritalStatus                    object\n",
       "NumCompaniesWorked                int64\n",
       "PerformanceRating                 int64\n",
       "TotalWorkingYears                 int64\n",
       "TrainingTimesLastYear             int64\n",
       "WorkLifeBalance                   int64\n",
       "YearsAtCompany                    int64\n",
       "YearsInCurrentRole                int64\n",
       "YearsSinceLastPromotion           int64\n",
       "YearsWithCurrManager              int64\n",
       "Department                       object\n",
       "MonthlyIncome                     int64\n",
       "PercentSalaryHike                 int64\n",
       "children                          int64\n",
       "Manager_relation_satisfaction     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the data types associated with the columns\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b557431a-82c2-4f6a-983d-3365165c9f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EducationField', 'Gender', 'MaritalStatus', 'Department']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of categorical variables \n",
    "categorical_variables = list(data.dtypes[data.dtypes == \"object\"].index)\n",
    "\n",
    "# Display the categorical variables list\n",
    "categorical_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94d1fb42-8be8-4474-b05e-8ab815a394ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a OneHotEncoder instance\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "\n",
    "# Encode the categorcal variables using OneHotEncoder\n",
    "encoded_data = enc.fit_transform(data[categorical_variables])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e0c9eaad-0db1-40f4-a3b0-d0d9942f8ea5",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>EducationField_Human Resources</th>\n",
       "      <th>EducationField_Life Sciences</th>\n",
       "      <th>EducationField_Marketing</th>\n",
       "      <th>EducationField_Medical</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "   EducationField_Human Resources  EducationField_Life Sciences  \\\n",
       "0                             0.0                           1.0   \n",
       "1                             0.0                           0.0   \n",
       "2                             0.0                           1.0   \n",
       "3                             0.0                           1.0   \n",
       "4                             0.0                           1.0   \n",
       "\n",
       "   EducationField_Marketing  EducationField_Medical  EducationField_Other  \\\n",
       "0                       0.0                     0.0                   0.0   \n",
       "1                       0.0                     0.0                   1.0   \n",
       "2                       0.0                     0.0                   0.0   \n",
       "3                       0.0                     0.0                   0.0   \n",
       "4                       0.0                     0.0                   0.0   \n",
       "\n",
       "   EducationField_Technical Degree  Gender_Female  Gender_Male  \\\n",
       "0                              0.0            1.0          0.0   \n",
       "1                              0.0            0.0          1.0   \n",
       "2                              0.0            0.0          1.0   \n",
       "3                              0.0            0.0          1.0   \n",
       "4                              0.0            1.0          0.0   \n",
       "\n",
       "   MaritalStatus_Divorced  MaritalStatus_Married  MaritalStatus_Single  \\\n",
       "0                     0.0                    0.0                   1.0   \n",
       "1                     0.0                    0.0                   1.0   \n",
       "2                     0.0                    0.0                   1.0   \n",
       "3                     0.0                    0.0                   1.0   \n",
       "4                     0.0                    0.0                   1.0   \n",
       "\n",
       "   Department_Human Resources  Department_Research & Development  \\\n",
       "0                         0.0                                0.0   \n",
       "1                         0.0                                1.0   \n",
       "2                         0.0                                1.0   \n",
       "3                         0.0                                1.0   \n",
       "4                         0.0                                1.0   \n",
       "\n",
       "   Department_Sales  \n",
       "0               1.0  \n",
       "1               0.0  \n",
       "2               0.0  \n",
       "3               0.0  \n",
       "4               0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame with the encoded variables\n",
    "encoded_df = pd.DataFrame(\n",
    "    encoded_data,\n",
    "    columns = enc.get_feature_names(categorical_variables) \n",
    ")\n",
    "\n",
    "# Review the DataFrame\n",
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aeb67baf-9946-44d4-af68-54ebe851b57b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Attrition_binary</th>\n",
       "      <th>Age</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>children</th>\n",
       "      <th>Manager_relation_satisfaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmployeeID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5993</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2090</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3068</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9526</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4193</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Attrition_binary  Age  JobSatisfaction  Education  \\\n",
       "EmployeeID                                                      \n",
       "1                          1   41                4          2   \n",
       "2                          1   37                3          2   \n",
       "3                          0   32                4          2   \n",
       "4                          0   38                3          3   \n",
       "5                          0   29                3          2   \n",
       "\n",
       "            EnvironmentSatisfaction  HourlyRate  JobInvolvement  \\\n",
       "EmployeeID                                                        \n",
       "1                                 2          94               3   \n",
       "2                                 4          92               2   \n",
       "3                                 4          79               3   \n",
       "4                                 4          44               2   \n",
       "5                                 4          49               2   \n",
       "\n",
       "            NumCompaniesWorked  PerformanceRating  TotalWorkingYears  \\\n",
       "EmployeeID                                                             \n",
       "1                            8                  3                  8   \n",
       "2                            6                  3                  7   \n",
       "3                            0                  3                  8   \n",
       "4                            0                  4                 10   \n",
       "5                            0                  3                 10   \n",
       "\n",
       "            TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\n",
       "EmployeeID                                                           \n",
       "1                               0                1               6   \n",
       "2                               3                3               0   \n",
       "3                               2                2               7   \n",
       "4                               2                3               9   \n",
       "5                               3                3               9   \n",
       "\n",
       "            YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \\\n",
       "EmployeeID                                                                      \n",
       "1                            4                        0                     5   \n",
       "2                            0                        0                     0   \n",
       "3                            7                        3                     6   \n",
       "4                            7                        1                     8   \n",
       "5                            5                        0                     8   \n",
       "\n",
       "            MonthlyIncome  PercentSalaryHike  children  \\\n",
       "EmployeeID                                               \n",
       "1                    5993                 11         0   \n",
       "2                    2090                 15         0   \n",
       "3                    3068                 13         0   \n",
       "4                    9526                 21         0   \n",
       "5                    4193                 12         0   \n",
       "\n",
       "            Manager_relation_satisfaction  \n",
       "EmployeeID                                 \n",
       "1                                       1  \n",
       "2                                       2  \n",
       "3                                       3  \n",
       "4                                       2  \n",
       "5                                       4  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame with the columnns containing numerical variables from the original dataset\n",
    "numerical_variables_df = data.drop(columns = categorical_variables)\n",
    "\n",
    "# Review the DataFrame\n",
    "numerical_variables_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34074a83-78e8-4197-ad09-dbd76f74a41e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition_binary</th>\n",
       "      <th>Age</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>...</th>\n",
       "      <th>EducationField_Other</th>\n",
       "      <th>EducationField_Technical Degree</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>MaritalStatus_Divorced</th>\n",
       "      <th>MaritalStatus_Married</th>\n",
       "      <th>MaritalStatus_Single</th>\n",
       "      <th>Department_Human Resources</th>\n",
       "      <th>Department_Research &amp; Development</th>\n",
       "      <th>Department_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1469 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition_binary   Age  JobSatisfaction  Education  \\\n",
       "1                  1.0  41.0              4.0        2.0   \n",
       "2                  1.0  37.0              3.0        2.0   \n",
       "3                  0.0  32.0              4.0        2.0   \n",
       "4                  0.0  38.0              3.0        3.0   \n",
       "5                  0.0  29.0              3.0        2.0   \n",
       "...                ...   ...              ...        ...   \n",
       "1465               0.0  39.0              4.0        1.0   \n",
       "1466               0.0  36.0              4.0        2.0   \n",
       "1467               0.0  39.0              1.0        1.0   \n",
       "1468               0.0  27.0              2.0        3.0   \n",
       "1469               0.0  49.0              2.0        3.0   \n",
       "\n",
       "      EnvironmentSatisfaction  HourlyRate  JobInvolvement  NumCompaniesWorked  \\\n",
       "1                         2.0        94.0             3.0                 8.0   \n",
       "2                         4.0        92.0             2.0                 6.0   \n",
       "3                         4.0        79.0             3.0                 0.0   \n",
       "4                         4.0        44.0             2.0                 0.0   \n",
       "5                         4.0        49.0             2.0                 0.0   \n",
       "...                       ...         ...             ...                 ...   \n",
       "1465                      2.0        60.0             2.0                 0.0   \n",
       "1466                      3.0        41.0             4.0                 4.0   \n",
       "1467                      4.0        42.0             2.0                 4.0   \n",
       "1468                      2.0        87.0             4.0                 1.0   \n",
       "1469                      4.0        63.0             2.0                 2.0   \n",
       "\n",
       "      PerformanceRating  TotalWorkingYears  ...  EducationField_Other  \\\n",
       "1                   3.0                8.0  ...                   1.0   \n",
       "2                   3.0                7.0  ...                   0.0   \n",
       "3                   3.0                8.0  ...                   0.0   \n",
       "4                   4.0               10.0  ...                   0.0   \n",
       "5                   3.0               10.0  ...                   0.0   \n",
       "...                 ...                ...  ...                   ...   \n",
       "1465                3.0               21.0  ...                   0.0   \n",
       "1466                3.0               17.0  ...                   0.0   \n",
       "1467                3.0                9.0  ...                   0.0   \n",
       "1468                4.0                6.0  ...                   0.0   \n",
       "1469                3.0               17.0  ...                   0.0   \n",
       "\n",
       "      EducationField_Technical Degree  Gender_Female  Gender_Male  \\\n",
       "1                                 0.0            0.0          1.0   \n",
       "2                                 0.0            0.0          1.0   \n",
       "3                                 0.0            0.0          1.0   \n",
       "4                                 0.0            1.0          0.0   \n",
       "5                                 0.0            0.0          1.0   \n",
       "...                               ...            ...          ...   \n",
       "1465                              0.0            0.0          1.0   \n",
       "1466                              0.0            0.0          1.0   \n",
       "1467                              0.0            0.0          1.0   \n",
       "1468                              0.0            0.0          1.0   \n",
       "1469                              0.0            0.0          1.0   \n",
       "\n",
       "      MaritalStatus_Divorced  MaritalStatus_Married  MaritalStatus_Single  \\\n",
       "1                        0.0                    0.0                   1.0   \n",
       "2                        0.0                    0.0                   1.0   \n",
       "3                        0.0                    0.0                   1.0   \n",
       "4                        0.0                    0.0                   1.0   \n",
       "5                        0.0                    0.0                   1.0   \n",
       "...                      ...                    ...                   ...   \n",
       "1465                     0.0                    1.0                   0.0   \n",
       "1466                     0.0                    1.0                   0.0   \n",
       "1467                     0.0                    1.0                   0.0   \n",
       "1468                     0.0                    1.0                   0.0   \n",
       "1469                     0.0                    1.0                   0.0   \n",
       "\n",
       "      Department_Human Resources  Department_Research & Development  \\\n",
       "1                            0.0                                1.0   \n",
       "2                            0.0                                1.0   \n",
       "3                            0.0                                1.0   \n",
       "4                            0.0                                1.0   \n",
       "5                            0.0                                1.0   \n",
       "...                          ...                                ...   \n",
       "1465                         0.0                                1.0   \n",
       "1466                         0.0                                1.0   \n",
       "1467                         0.0                                1.0   \n",
       "1468                         0.0                                0.0   \n",
       "1469                         0.0                                1.0   \n",
       "\n",
       "      Department_Sales  \n",
       "1                  0.0  \n",
       "2                  0.0  \n",
       "3                  0.0  \n",
       "4                  0.0  \n",
       "5                  0.0  \n",
       "...                ...  \n",
       "1465               0.0  \n",
       "1466               0.0  \n",
       "1467               0.0  \n",
       "1468               1.0  \n",
       "1469               0.0  \n",
       "\n",
       "[1469 rows x 34 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the numerical variables from the original DataFrame to the one-hot encoding DataFrame\n",
    "combined_data = pd.concat(\n",
    "    [\n",
    "        numerical_variables_df, \n",
    "        encoded_df\n",
    "    ],\n",
    "    axis=1 )\n",
    "\n",
    "# Drop NaN value in the Dataframe\n",
    "clean_data = combined_data.dropna()\n",
    "\n",
    "# Display the data\n",
    "clean_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "18d8fa33-0b75-4b28-b49f-5c20f8e4a540",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Monthly Income into per 1000\n",
    "\n",
    "clean_data['MonthlyIncome'] = clean_data['MonthlyIncome']/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5556a0ba-e3d4-4d82-b7e7-544bcc395bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the data into labels and features\n",
    "\n",
    "# Separate the y variable, the labels\n",
    "y = clean_data[\"Attrition_binary\"]\n",
    "\n",
    "# Separate the X variable, the features\n",
    "X = clean_data.drop(columns=\"Attrition_binary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d930b14e-e7a4-47d3-90a7-da2df84c2a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1232\n",
       "1.0     237\n",
       "Name: Attrition_binary, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d18ac87d-3a31-424b-8cdf-112daea0cbb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition out of 1469 = 237, which is equivalent to 16.1%.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Attrition out of {1232+237} = {237}, which is equivalent to {round(237/(1232+237)*100,1)}%.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21142137-c6a0-469c-a3f0-8c62698efc00",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Create a Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40709a15-d8d4-4cd8-b412-fef190bdad3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the train_test_learn module\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data using train_test_split\n",
    "# Assign a random_state of 1 to the function\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31726f0e-3ba2-457a-b31f-a55f468f80b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the LogisticRegression module from SKLearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Instantiate the Logistic Regression model\n",
    "# Assign a random_state parameter of 1 to the model\n",
    "lr_model = LogisticRegression(random_state=1)\n",
    "\n",
    "# Fit the model using training data\n",
    "lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22589783-dfea-4ebd-8c1a-eb082433d5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a prediction using the testing data\n",
    "testing_predictions = lr_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07cc4419-7e34-4cab-b2e6-372558e93a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.578556263269639"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the balanced_accuracy score of the model\n",
    "balanced_accuracy_score(y_test, testing_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "857e7481-e403-4b79-ad86-8b089439d6de",
   "metadata": {},
   "source": [
    "**A Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b44b796-99d2-4a76-accc-7bfeb1a3e36d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[311   3]\n",
      " [ 45   9]]\n"
     ]
    }
   ],
   "source": [
    "# Generate a confusion matrix for the model\n",
    "print(confusion_matrix(y_test, testing_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03647b6-7817-4ee3-a1ff-248dc1f83d93",
   "metadata": {},
   "source": [
    "**A Classification Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c75c09a-b7ac-4b3b-8db6-e88ff600edb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "        0.0       0.87      0.99      0.17      0.93      0.41      0.18       314\n",
      "        1.0       0.75      0.17      0.99      0.27      0.41      0.15        54\n",
      "\n",
      "avg / total       0.86      0.87      0.29      0.83      0.41      0.17       368\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "print(classification_report_imbalanced(y_test, testing_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceabd982-cdd9-42b2-ac56-64025fbde1ae",
   "metadata": {},
   "source": [
    "---\n",
    "#### Calculating odds ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "731033f3-50a2-4482-b4b0-52a96121b72e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.363699\n",
      "         Iterations 7\n"
     ]
    }
   ],
   "source": [
    "result_logit_model = mba263.logit(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a6f0a80c-46aa-43fb-b326-ee94e5ab81b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data['predicted_logit_model'] = result_logit_model.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f6c7d20f-9873-44e7-8b62-db7bfc7a35bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.487279\n",
       "2    0.387083\n",
       "3    0.089036\n",
       "4    0.060051\n",
       "5    0.074857\n",
       "Name: predicted_logit_model, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data['predicted_logit_model'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a92ebc9a-9c73-4c58-924b-ee06db286c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# result_logit_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "64e58ef1-8142-4a6c-a665-4f71c85ca700",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mba263.odds_ratios(result_logit_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc54c80-c60f-4faf-b924-dea2b1b01353",
   "metadata": {},
   "source": [
    "### Odds Ratios Output from R\n",
    "#### The odds ratios below are the output created by Professor Guilbeault"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0300e44-7b5f-42d5-a770-ca11eca44ff7",
   "metadata": {},
   "source": [
    "!['Odds Ratio Output'](IBM_odds_ratios_output.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94b1a66-fdc0-4c4b-9066-65fb0413e650",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Are there gender differences in the likelihood of attrition (or in employee job satisfaction)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a338f4c-0cdc-4ab5-805c-670865aa0384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition_binary</th>\n",
       "      <th>Age</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>...</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>Department</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>children</th>\n",
       "      <th>Manager_relation_satisfaction</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmployeeID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Sales</td>\n",
       "      <td>5993</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2090</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3068</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>9526</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
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       "      <td>8</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4193</td>\n",
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       "      <td>4</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Attrition_binary  Age  JobSatisfaction  Education EducationField  \\\n",
       "EmployeeID                                                                     \n",
       "1                          1   41                4          2  Life Sciences   \n",
       "2                          1   37                3          2          Other   \n",
       "3                          0   32                4          2  Life Sciences   \n",
       "4                          0   38                3          3  Life Sciences   \n",
       "5                          0   29                3          2  Life Sciences   \n",
       "\n",
       "            EnvironmentSatisfaction  HourlyRate  JobInvolvement MaritalStatus  \\\n",
       "EmployeeID                                                                      \n",
       "1                                 2          94               3        Single   \n",
       "2                                 4          92               2        Single   \n",
       "3                                 4          79               3        Single   \n",
       "4                                 4          44               2        Single   \n",
       "5                                 4          49               2        Single   \n",
       "\n",
       "            NumCompaniesWorked  ...  YearsAtCompany  YearsInCurrentRole  \\\n",
       "EmployeeID                      ...                                       \n",
       "1                            8  ...               6                   4   \n",
       "2                            6  ...               0                   0   \n",
       "3                            0  ...               7                   7   \n",
       "4                            0  ...               9                   7   \n",
       "5                            0  ...               9                   5   \n",
       "\n",
       "            YearsSinceLastPromotion  YearsWithCurrManager  \\\n",
       "EmployeeID                                                  \n",
       "1                                 0                     5   \n",
       "2                                 0                     0   \n",
       "3                                 3                     6   \n",
       "4                                 1                     8   \n",
       "5                                 0                     8   \n",
       "\n",
       "                        Department  MonthlyIncome  PercentSalaryHike  \\\n",
       "EmployeeID                                                             \n",
       "1                            Sales           5993                 11   \n",
       "2           Research & Development           2090                 15   \n",
       "3           Research & Development           3068                 13   \n",
       "4           Research & Development           9526                 21   \n",
       "5           Research & Development           4193                 12   \n",
       "\n",
       "            children Manager_relation_satisfaction  Gender_Male  \n",
       "EmployeeID                                                       \n",
       "1                  0                             1            0  \n",
       "2                  0                             2            1  \n",
       "3                  0                             3            1  \n",
       "4                  0                             2            1  \n",
       "5                  0                             4            0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert gender to dummy variable\n",
    "data_gender_dummy = pd.get_dummies(data, columns=['Gender'], drop_first=True)\n",
    "data_gender_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4c514dd-79cf-4b3d-a6b4-d51a1ef01037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender_Male\n",
       "0    0.147959\n",
       "1    0.170068\n",
       "Name: Attrition_binary, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on gender - Male=1 Female=0\n",
    "data_gender_attrition = data_gender_dummy.groupby('Gender_Male')['Attrition_binary'].mean()\n",
    "data_gender_attrition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce99b6e-a170-437f-94e9-92b53a1a1a9e",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Do levels of attrition (job satisfaction) vary by department?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1fbcaf7f-b656-4dc5-af89-f44fadcf5a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition for each department'}, ylabel='Department'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on department employees are working in\n",
    "data_department_attrition_count = data.groupby('Department')['Attrition_binary'].count()\n",
    "data_department_attrition_mean = data.groupby('Department')['Attrition_binary'].mean()\n",
    "data_department_attrition_mean.plot(kind='barh', figsize=(5,2), title=\"Likelihood of employee attrition for each department\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02abbfc2-f9c3-4736-87b3-8b6f2da659bd",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Do levels of attrition (job satisfaction) vary by education level or education field?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23d55b59-6f50-4bc4-94b2-9706b59bdb6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition for each education level'}, ylabel='Education'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on education level\n",
    "data_edu_level_attrition = data.groupby('Education')['Attrition_binary'].mean()\n",
    "# Education 1 = Lowest 5 = Highest\n",
    "data_edu_level_attrition.plot(kind='barh', figsize=(8,4), title=\"Likelihood of employee attrition for each education level\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1bfed699-aaff-4df7-8528-201ab324094c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition for each educational field'}, ylabel='EducationField'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on educational field\n",
    "data_edu_field_attrition = data.groupby('EducationField')['Attrition_binary'].mean()\n",
    "data_edu_field_attrition.plot(kind='barh', title=\"Likelihood of employee attrition for each educational field\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3447077-730b-4489-b833-f253fc774b40",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Do levels of attrition (job satisfaction) vary as a function of an employee's marital status or the number of children they have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c99a9bf0-d8a2-4e34-9f1d-6efae8ebdd60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition for each marital status'}, ylabel='MaritalStatus'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on marital status\n",
    "data_marriage_attrition = data.groupby('MaritalStatus')['Attrition_binary'].mean()\n",
    "data_marriage_attrition.plot(kind='barh', figsize=(6,2), title=\"Likelihood of employee attrition for each marital status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d0357699-7db2-4ad5-bc00-88351fdf60cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition for the number of children each employee has'}, ylabel='children'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAekAAAEICAYAAAB71gywAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcrElEQVR4nO3de7wtdV3/8dfbw+FO3DU7IMe7Qmryw0ulvwhNuaiUpWLeswi7qP2spPRnpFJkallWpmgqGkqJSoIpmWSKgAd/CCFyMcHDXW4HjLxAn98fMxuG5V57r733Onvm7P16Ph7rcWbmO5fPfOc781nzndnrpKqQJEnDc6++A5AkSbMzSUuSNFAmaUmSBsokLUnSQJmkJUkaKJO0JEkDtegkneSJSS7ujF+e5MmLWM9dyyX5/STHt8Prk1SSrRYb4wJiOCPJLy9y2ZcluS7Jt5PsPu3YFhDHi5N8vq/tL6e2rh8wR/mFSQ7cDNu9T5LPJbktyVumvf52G+9N8sbNse7NaSnn0BS2vV2Sf0qyKck/LHDZSvKgMWXPS/LpCefd4s+/ufavT322rdks97GeNwEmuRz45ar6l+70qvp34KHTDKaq/mia69vckqwF3go8vqq+0nc8W5IkxwAPqqrnd6adAXygqo6fa9mq2rGzzHuBK6vqtZ3y/aYdb+tI4Abgh2oKPzCQ5MU059YTlrquVe4XgPsAu1fVHdNaaVV9EPjgtNYnLYbd3UtzH2Bb4MK+A1kNlqNXZR77AF9dTIIeQOxbhDQWel3aB7hkmgl6mjz2WoqldHcfmOTKMWUPS/KNJEe0409Lcl6SW5KcmeSRY5Y7JskHRiY/L8k3k9yQ5DWdebdJ8udJrm4/f55km075ryS5LMlNSU5J8iOdsp9J8rW2e+ztQObYz1m3k+QhwEx3/y1J/nXM8o9v9/mWJF/pdsO23ThvbMu/3XbZ7Z7kg0luTfKlJOs781eSlyf5z7Y+/nTcBS3JT7TLb2r//Yl2+rOSnDsy76uSfKyzv29u6/y6JO9Isl1n3omOZTvv25JsbPfl3CRPbKcfDPw+8Jx2v7+S5FjgicDb22lv7+zzrye5FLi0M+1BSY4Engf87kz9teXdRyhj28lMG273//ok1yR5yZh9eS/wos62njzhul+d5Frg70bW93DgHcCPt+u7pVO8a5JT03Srn53kgZ3lHpbk9LZdX5zk2XPU/xlJ3pDkC+26Pp1kj258I/N36+2YJP+Q5APtshckeUiS32vramOSp4xs8oFJzmnb3MeT7NZZ93znwbFJvgDcDvzAo4wkD2/nuyXN44xntNP/EHgdd7ell86y7Jo0j9K+3u7LuUn27szy5CSXJrk5yV8lSbvc2G7NNOfpKW3bPgd44Ej5bO127LnT1v1vJzm/rb8PJ9l2tm238/9SkovamD+VZJ9O2dsyy3m3lLqYZfv3SnJ0u54bk5w0c7xz96PKl7Rx3JzkqCSPaffvlrTnd6eev5DkL9t9/1qSJ82x3dcmuaJth+9PsnNbdmqS3xyZ//wkP9sOjz13Ms91b0wsb2737RtJDulMf0l7bG5Lc63+1U7ZHkk+0dbBTUn+PfN9Ka2qOT/A5cCTZ5l+IE034z3mA/YHvgk8rZ2+P3A98DhgDc2F7nJgm9H1A8fQdHcCrAcKeBewHfAo4LvAw9vy1wNnAfcG9gTOBN7Qlh1E0y25P7AN8JfA59qyPYBbabrI1gK/BdxB0+042/7PtZ2ZGLcas+w64EbgUJovRD/Tju/Zlp8BXEZzgu8MfBW4pK3HrYD3A3/XWV8BnwV2A+7XzvvLbdmLgc+3w7sBNwMvaNfz3HZ897Y+bpqpx3b+/wf8fDv858Ap7Tp2Av4J+ONJjuUs+//8dptbAa8CrgW2HT3WnfnPGD0O7T6f3sazXWfag9rh9wJvHNdm5zl+B7bH/vVtWziUJknsOmZ/7rGtCdf9J22dbzfL+u46ZiPbuAl4bFtvHwQ+1JbtAGwEXtKW7U/TzvcbE+8ZwNeBh9CcQ2cAx812/o45F78DPJW72+I3gNe0dfUrwDdGtnUV8KNtnB/h7nN5kvPgm8B+7bbWjsS1luY8+X1ga5rz+zbgoePa0sjyvwNcQPN4LjTXkt07bekTwC4059S3gINnOz7cs919CDip3dcfbfd9dN672i2TXQfPAX6kXeYi4Kgx+/OzbX08vK2v1wJnTnjeLaouZonhlTRtfy+a9v23wIkj18V30PQ0PoWmLX2M5lxZ19bFT3Xq+Q6aa/Fa4DnAJmC30esC8Evtvj8A2BE4GTihLXs2cHYnxkfRtLOtmefcYY7r3pjz9vs058Aa4GXA1UDa8sNorukBformmrJ/W/bHbb2sbT9PnFlubPudq3D0xB2ZfiA/mKT/ELgS+OnO9L+hvXB1pl3cOUB3rZ/Zk/ReneXOAY5oh78OHNopeypweTv8buBNnbId20pdD7wQOKtTljbmcUl6ru3MxDguSb96pgF1pn0KeFGn8b2mU/YW4JOd8acD542c+Ad3xn8N+MzoBYUmOZ8zst0vAi/uHJNj2+H9aBL4Nm1d/BfwwM5yP057MZ7vWE7Qlm4GHjV6rDvlZ4weh3afD5pl2qRJeq7jdyDw393jR3PxePyY+O+xrQnW/T3ai+OY9d11zEa2cXxn/FDga+3wc4B/H5n/b4E/GLP+M4DXjrSXf57t/B1zLp4+0ha/Daxpx3dqj8MunW0d15l/33b/1zDZefD6OerpiTSJ5l6daScCx4xrS7O00cPHlBXwhM74ScDRsx2fmXbX7tP3gYd1yv5olnkP6oxPch18fqfsTcA7xsT8SeClnfF70SSCfSY47xZVF7PMexHwpM74fds62Yq7r4vrOuU3As/pjH8EeGWnnu9Kcu20c4AXdNrHTJL+DPBrnfke2tnuzA3Ig9uyNwN/Pd+5wzzXvTHn7WWd8e3b/f3hMfN/DHhFO/x64OO0169JPtN+Jn0UzTe6z3am7QO8qr29vyVNt97eNN8YJ3FtZ/h2moRLu/wVnbIrOuu8R1lVfZumkaxryzZ2yqo7Pou5tjOffYBnjez7E2ga9IzrOsP/Pcv4jtxTN9ZxsYzGPDPvunb4fcAvtl1ZLwBOqqrv0twNbg+c24n3n9vpM/sz8bFM0418UduFdQtNb8Ees807j7mOz3zmO3431j2fZXbb2FLX/a2q+s4CYp0xrs3vAzxupP6fB/zwItY1idG2eENV3dkZZ2R9o21zLc3xnuQ8mO8c3FhV/zOy/nVj5h+1N80XqnEWWkd70iSF0f0d1S2f5NyZNI59gLd11nMTTaJZB/Oed9Oqi32Aj3ZiuAi4k+Y9nRkLubZd1V6LZ0x6bbuC5ljcp72GnQQ8v+1Cfi5wQifecefOfNe92dxVT1V1ezu4I0CSQ5Kc1XZn30LzRXum/v+Upifg021X+NFzbAOY/otjRwH3S/JnnWkbae7adul8tq+qE5e4ratpKn7G/dppP1CWZAea7p+rgGtoGupMWbrjC9zOfDbS3EF0932HqjpuwuVn0411XCyjMc/MexVAVZ1Fc5fzROAXubsh30Bz8uzXiXfnuvtt6omPZfsc7NU0XVC7VtUuNF1YM8+4anSZMdPmmj5fGSzt+M1nvnXPF9t85aM2Av82Uv87VtXLFrgeaO4ctp8ZSbKGuS9Kkxhtm9+naVOTnAdz1cXVwN4jz+7uas8T2MjIM+Ml+hZN9+zo/o7q7tM0r4MbgV8dWdd2VXXmBOfdtOpiI3DISAzbVtWkx2TUupHn35Ne2+5HcyxmvgC8jyb5Pgm4vaq+2Il33Lkz33VvYmneSfkIzV38fdr6P422/qvqtqp6VVU9gKZ36v+Me/4+Y9IkvTbJtp3PuLcVbwMOBv53kpkT8F3AUUkel8YOSQ5LstOE2x7nROC1SfZM8zLM64CZl87+HnhJkh9rK+2PaJ5VXA6cCuyX5Jntfrycue9E5trOfD4APD3JU9O8sLFtmhd29lroznb8TpJd07zs8Qrgw7PMcxrwkCS/mGSrJM+h6X78RGee9wNvB+6oqs8DtHcq7wL+LMm9AZKsS/LUdpmFHMudaE6ebwFbJXkd8EOd8uuA9SMX3uuY5aWhecy3zFKO33yWuu7rgL2SbD3h/J+gOa4vSLK2/TwmzUtoC3UJsG17/NbSPNfcZp5l5vP8JPsm2Z6mW+8f2zvvpZ4HZ9N8qfjddp8PpLnAfWjC5Y8H3pDkwW27fWSW8JsG7T6dDByTZPsk+9I8Y57LNK+D7wB+L8l+AEl2TvKstmy+825adfEO4Ni0L6y158Dhi1jPjHsDL2+P77NonrefNst8JwK/leT+SXakubZ/eKY3rE3K/0Pz6PCEznJjz50JrnsLsTXNefQt4I40L5Td9YJlmpcHH9R+IbmVpvfhzlnX1Jo0SZ9G801j5nPMuBmr6haaF0MOSfKGqtpA84D97TTPRi6j6dNfqjcCG4DzaV6E+HI7jar6DPB/ab7RXEPzzfGItuwG4FnAcTRd4A8GvrCY7cynqjYCh9O88PItmm9zv8PSejA+DpwLnEfzhePds2z3RuBpNC+N3Aj8Ls2LfDd0ZjuB5oWXE0YWfzXNMTorya3Av9D+PfwCj+WnaJ6dXULTJfUd7tn9N/OjEzcm+XI7/DbgF9K8MfkX4ypgxLuBfdN0U31slvJFH78JLHXd/0rz53vXJrlhvpmr6jaaE/4ImjuKa7n7xbQFqapNNM+oj6e5I/0vmnczluIEmmfq19K8MPTydltLOg+q6nvAM4BDaO56/hp4YVV9bcK43krTDfppmgvju2le5lqK36Dp3ryWZp//bq6Zp3kdrKqP0hz3D7Xn6H/Q1A3Mf95Nqy7eRvOi1aeT3EbzEtnjFrGeGWfTXItvAI4FfqG9jo16D007+xzNi4zfAX5zZJ73A4+g84V5gnNn7HVvIdrtvJymjm+m6ak8pTPLg9t1f5vmPaG/rqoz5lrnzNto2gIkKZqXIi6bwrq2o3lJav+qunTJwUnSImTKP+qT5IXAkdNaX9/8MZPV62XAl0zQklaK9lHLrwHv7DuWafGXcFahND/1Gpq/t5SkLV77DPlkmu7kv+85nKmxu1uSpIGyu1uSpIGyu3sR9thjj1q/fn3fYUjSFuXcc8+9oaqW+vf4q4pJehHWr1/Phg0b+g5DkrYoSWb7ZTbNwe5uSZIGyiQtSdJAmaQlSRook7QkSQNlkpYkaaBM0pIkDZRJWpKkgTJJS5I0UCZpSZIGyiQtSdJAmaQlSRook7QkSQPlf7CxCBdctYn1R5/adxiStKwuP+6wvkNYdbyTliRpoEzSkiQNlElakqSBMklLkjRQJmlJkgbKJC1J0kCZpCVJGiiTtCRJA2WSliRpoEzSQJJtk5yT5CtJLkzyh33HJEmSPwva+C5wUFV9O8la4PNJPllVZ/UdmCRp9TJJA1VVwLfb0bXtp/qLSJIku7vvkmRNkvOA64HTq+rsnkOSJK1yJulWVd1ZVT8G7AU8NsmPdsuTHJlkQ5INd96+qZcYJUmri0l6RFXdApwBHDwy/Z1VdUBVHbBm+537CE2StMqYpIEkeybZpR3eDngy8LVeg5IkrXq+ONa4L/C+JGtovricVFWf6DkmSdIqZ5IGqup84NF9xyFJUpfd3ZIkDZRJWpKkgTJJS5I0UCZpSZIGyiQtSdJAmaQlSRook7QkSQNlkpYkaaD8MZNFeMS6ndlw3GF9hyFJWuG8k5YkaaBM0pIkDZRJWpKkgTJJS5I0UCZpSZIGyiQtSdJAmaQlSRook7QkSQNlkpYkaaBM0pIkDZRJWpKkgTJJS5I0UCZpSZIGyiQtSdJAmaQlSRook7QkSQNlkpYkaaBM0pIkDZRJWpKkgTJJS5I0UCZpSZIGyiQtSdJAmaQlSRook7QkSQNlkpYkaaBM0pIkDdRWfQewJbrgqk2sP/rUvsPQKnb5cYf1HYKkZeCdtCRJA2WSliRpoEzSkiQNlElakqSBMklLkjRQJmlJkgbKJC1J0kCZpCVJGiiTtCRJA2WSliRpoEzSQJK9k3w2yUVJLkzyir5jkiTJ3+5u3AG8qqq+nGQn4Nwkp1fVV/sOTJK0enknDVTVNVX15Xb4NuAiYF2/UUmSVjuT9Igk64FHA2ePTD8yyYYkG+68fVMvsUmSVheTdEeSHYGPAK+sqlu7ZVX1zqo6oKoOWLP9zv0EKElaVUzSrSRraRL0B6vq5L7jkSTJJA0kCfBu4KKqemvf8UiSBCbpGT8JvAA4KMl57efQvoOSJK1u/gkWUFWfB9J3HJIkdXknLUnSQJmkJUkaKJO0JEkDZZKWJGmgVtSLY0m2AX4eWE9n36rq9X3FJEnSYq2oJA18HNgEnAt8t+dYJElakpWWpPeqqoP7DkKSpGlYaUn6zCSPqKoLNudGHrFuZzYcd9jm3IQkSSsuST8BeHGSb9B0dweoqnpkv2FJkrRwKy1JH9J3AJIkTcuK+hOsqroC2Bs4qB2+nRW2j5Kk1WNFJbAkfwC8Gvi9dtJa4AP9RSRJ0uKtqCQN/BzwDOC/AKrqamCnXiOSJGmRVlqS/l5VFVAASXboOR5JkhZtpSXpk5L8LbBLkl8B/gV4V88xSZK0KCvm7e4kAT4MPAy4FXgo8LqqOr3XwCRJWqQVk6SrqpJ8rKr+F2BiliRt8VZad/dZSR7TdxCSJE3DirmTbv008KtJrqB5w9tfHJMkbbFWWpL2F8ckSSvGikjSSXZrB2/rNRBJkqZoRSRpmv8/umi6t+8H3NwO7wJ8E7h/b5FJkrRIK+LFsaq6f1U9APgU8PSq2qOqdgeeBpzcb3SSJC3OikjSHY+pqtNmRqrqk8BP9RiPJEmLtlK6u2fckOS1NP+pRgHPB27sNyRJkhZnpd1JPxfYE/go8DHg3u00SZK2OCvqTrqqbgJe0XcckiRNw4pI0kn+ifZ/vppNVT1jGcORJGkqVkSSBt7cdwCSJE3bikjSVfVvfccgSdK0rYgkneSkqnp2kguYpdvb3+6WJG2JVkSS5u6XxZ7WaxSSJE3RikjSVXVN++8VfcciSdK0rKi/k07yzCSXJtmU5NYktyW5te+4JElajBVxJ93xJprf7r6o70AkSVqqlZakr1uOBH3BVZtYf/Spm3sz0tRcftxhfYcgaRFWRJJO8sx2cEOSD9P8JOh3Z8qryv8JS5K0xVkRSRp4evtvAbcDT+mUFf53lZKkLdCKSNJV9RKAJO8DXlFVt7TjuwJv6TE0SZIWbUW93Q08ciZBA1TVzcCj+wtHkqTFW2lJ+l7t3TMASXZjhfQWSJJWn5WWwN4CnJnkH2meRT8bOLbfkCRJWpwVlaSr6v1JNgAHAQGeWVVf7TksSZIWZUUlaYA2KZuYJUlbvJX2TFqSpBXDJA0keU+S65P8R9+xSJI0wyTdeC9wcN9BSJLUZZIGqupzwE19xyFJUpdJekJJjkyyIcmGO2/f1Hc4kqRVwCQ9oap6Z1UdUFUHrNl+577DkSStAiZpSZIGyiQtSdJAmaSBJCcCXwQemuTKJC/tOyZJklbcL44tRlU9t+8YJEka5Z20JEkDZZKWJGmgTNKSJA2USVqSpIEySUuSNFAmaUmSBsokLUnSQJmkJUkaKH/MZBEesW5nNhx3WN9hSJJWOO+kJUkaKJO0JEkDZZKWJGmgTNKSJA2USVqSpIEySUuSNFAmaUmSBsokLUnSQJmkJUkaKJO0JEkDZZKWJGmgTNKSJA2USVqSpIEySUuSNFAmaUmSBsokLUnSQJmkJUkaKJO0JEkDZZKWJGmgTNKSJA2USVqSpIEySUuSNFAmaUmSBsokLUnSQJmkJUkaKJO0JEkDtVXfAWyJLrhqE+uPPrXvMCStUpcfd1jfIWiZeCctSdJAmaQlSRook7QkSQNlkpYkaaBM0pIkDZRJWpKkgTJJS5I0UCZpSZIGyiQtSdJAmaRbSQ5OcnGSy5Ic3Xc8kiSZpIEka4C/Ag4B9gWem2TffqOSJK12JunGY4HLquo/q+p7wIeAw3uOSZK0ypmkG+uAjZ3xK9tpkiT1xiTdyCzT6h4zJEcm2ZBkw523b1qmsCRJq5lJunElsHdnfC/g6u4MVfXOqjqgqg5Ys/3OyxqcJGl1Mkk3vgQ8OMn9k2wNHAGc0nNMkqRVbqu+AxiCqrojyW8AnwLWAO+pqgt7DkuStMqZpFtVdRpwWt9xSJI0w+5uSZIGyiQtSdJAmaQlSRook7QkSQNlkpYkaaBM0pIkDZRJWpKkgTJJS5I0UP6YySI8Yt3ObDjusL7DkCStcN5JS5I0UCZpSZIGyiQtSdJAmaQlSRook7QkSQNlkpYkaaBM0pIkDZRJWpKkgTJJS5I0UCZpSZIGyiQtSdJAmaQlSRook7QkSQOVquo7hi1OktuAi/uOYxZ7ADf0HcQsjGthjGtyQ4wJjGucfapqzx63v8Xxv6pcnIur6oC+gxiVZINxTc64FmaIcQ0xJjAuTY/d3ZIkDZRJWpKkgTJJL847+w5gDONaGONamCHGNcSYwLg0Jb44JknSQHknLUnSQJmkJUkaKJM0kOTgJBcnuSzJ0bOUJ8lftOXnJ9l/vmWT7Jbk9CSXtv/uuhwxJdk7yWeTXJTkwiSv6CxzTJKrkpzXfg5dSExLiastuzzJBe22N3SmL6mulhJXkod26uO8JLcmeWVbthz19bAkX0zy3SS/Pcmyy1Rfs8Y1gPY1V3312b7G1ddma18TxPS8tq2fn+TMJI+ab9lp1JWmrKpW9QdYA3wdeACwNfAVYN+ReQ4FPgkEeDxw9nzLAm8Cjm6Hjwb+ZJliui+wfzu8E3BJJ6ZjgN/uo67assuBPWZZ76LrahpxjaznWpofXFiu+ro38Bjg2O62NlfbmkJcfbevWeMaQPsaG9fmaF8TxvQTwK7t8CFs5uuWn83z8U4aHgtcVlX/WVXfAz4EHD4yz+HA+6txFrBLkvvOs+zhwPva4fcBP7scMVXVNVX1ZYCqug24CFi3gG1vlrjmWe9S6mqacT0J+HpVXbHA7S86rqq6vqq+BHx/Actu9voaF1ff7WuO+ppLb/U1Yprta5KYzqyqm9vRs4C9Jlh2qXWlKTNJNxeYjZ3xK/nBi864eeZa9j5VdQ00Fzaab9rLEdNdkqwHHg2c3Zn8G23313sW0ZW11LgK+HSSc5Mc2ZlnKXU1jbhmHAGcODJtc9fXYpZdjvqaV0/tay59tq9JTLN9LTSml9L0JM237FLrSlNmkm66P0eN/l3auHkmWXYxlhJTU5jsCHwEeGVV3dpO/hvggcCPAdcAb1nmuH6yqvan6Xr79ST/e4Hb31xxkWRr4BnAP3TKl6O+Nseym33dPbavufTZvuZewfTb18QxJflpmiT96oUuq/6ZpJtvkXt3xvcCrp5wnrmWvW6mO7X99/pliokka2kuoB+sqpNnZqiq66rqzqr6H+BdNN1eC7GkuKpq5t/rgY92tr+UulpyXK1DgC9X1XUzE5apvhaz7HLU11g9t6+xem5f85l2+5oopiSPBI4HDq+qGydYdql1pSkzScOXgAcnuX/7bfcI4JSReU4BXpjG44FNbVfQXMueAryoHX4R8PHliClJgHcDF1XVW7sLjDyD/TngPxYQ01Lj2iHJTm0cOwBP6Wx/KXW1pLg65c9lpCtymeprMcsuR33NagDta1xcfbev+Uy7fc0bU5L7AScDL6iqSyZcdql1pWnbnG+lbSkfmjd/L6F54/E17bSjgKPa4QB/1ZZfABww17Lt9N2BzwCXtv/uthwxAU+g6bo6Hziv/Rzalp3Qzns+zcl43+WqK5o3Sb/Sfi6cZl1N4RhuD9wI7DyyzuWorx+mubO5FbilHf6hzdm2lhLXANrXuLj6bl9zHcfN0r4miOl44ObOcdow17LTqis/0/34s6CSJA2U3d2SJA2USVqSpIEySUuSNFAmaUmSBsokLUnSQJmkJUkaKJO0JEkD9f8BCCN/r3P0qxAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on the number of children each employee has\n",
    "data_num_child_attrition = data.groupby('children')['Attrition_binary'].mean()\n",
    "data_num_child_attrition.plot(kind='barh', title=\"Likelihood of employee attrition for the number of children each employee has\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b17e00d8-2bf2-4f61-9ce2-90c362c70fd8",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Is attrition (or job satisfaction) predicted by employee's hourly pay rate, monthly income, or the most recent percent increase in their salary? If yes, is it a good predictor, or are there better predictors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0d92f62f-d13f-4867-b3b3-9c5fbe4f7966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on the most recent pay hike'}, ylabel='PercentSalaryHikeGroup'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the data for easier interpretation\n",
    "bins = [0,13,16,19,22,100]\n",
    "labels = ['10-12%','13-15%','16-18%','19-21%','22% or more']\n",
    "data['PercentSalaryHikeGroup'] = pd.cut(data['PercentSalaryHike'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Explore the likelihood of attrition based on the most recent pay hike\n",
    "data_percent_salary_hike_attrition = data.groupby('PercentSalaryHikeGroup')['Attrition_binary'].mean()\n",
    "data_percent_salary_hike_attrition.plot(kind='barh', title=\"Likelihood of employee attrition based on the most recent pay hike\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "16f49c31-622a-43d3-934c-c1c136e889fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on employee hourly wage'}, ylabel='HourlyRateGroup'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the data for easier interpretation\n",
    "bins = [0,40,60,80,1000]\n",
    "labels = ['$20 - $39 per hour','$40 - $59 per hour','$60 - $79 per hour','$80 or more per hour']\n",
    "data['HourlyRateGroup'] = pd.cut(data['HourlyRate'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Explore the likelihood of attrition based on hourly wage\n",
    "data_hourly_rate_attrition = data.groupby('HourlyRateGroup')['Attrition_binary'].mean()\n",
    "data_hourly_rate_attrition.plot(kind='barh', title=\"Likelihood of employee attrition based on employee hourly wage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a2b08542-f93d-468f-a725-21a8dfff5525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on monthly income'}, ylabel='MonthlyIncomeGroup'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the data for easier interpretation\n",
    "bins = [0,2500,5000,7500,10000,12500,15000,50000]\n",
    "labels = ['Below $2500','$2500 - $4999','$5000-$7499','$7500-$9999','$10000-$12499','$12500-$14999','$15000 or more']\n",
    "data['MonthlyIncomeGroup'] = pd.cut(data['MonthlyIncome'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Explore the likelihood of attrition based on monthly income\n",
    "data_monthly_income_attrition = data.groupby('MonthlyIncomeGroup')['Attrition_binary'].mean()\n",
    "data_monthly_income_attrition.plot(kind='barh', title='Likelihood of employee attrition based on monthly income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e27defd6-dcf1-4d76-b994-62da72fe978b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MonthlyIncomeGroup\n",
       "Below $2500       224\n",
       "$2500 - $4999     525\n",
       "$5000-$7499       310\n",
       "$7500-$9999       130\n",
       "$10000-$12499      91\n",
       "$12500-$14999      57\n",
       "$15000 or more    133\n",
       "Name: MonthlyIncome, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the number of employee in each group\n",
    "count_income_group = data.groupby('MonthlyIncomeGroup').count()\n",
    "count_income_group['MonthlyIncome']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbce9d60-183c-4b8c-8cf8-63232a35a333",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Do levels of attrition (job satisfaction) vary as a function of an employee's performance?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f864a53b-ec71-470d-9af5-7e4027d15236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on performance rating'}, ylabel='PerformanceRating'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on performance rating\n",
    "data_performance_attrition = data.groupby('PerformanceRating')['Attrition_binary'].count()\n",
    "data_performance_attrition.plot(kind='barh', title=\"Likelihood of employee attrition based on performance rating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5199adfe-23a3-4140-8975-ff1720ceb61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PerformanceRating\n",
       "3    1244\n",
       "4     226\n",
       "Name: Attrition_binary, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the number of employee in each group\n",
    "data.groupby('PerformanceRating')['Attrition_binary'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "261ca461-1a3a-4c65-b1c1-f7a9d1f0f54d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on job involvement'}, ylabel='JobInvolvement'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on job involvement\n",
    "data_attrition_JobInvolvement = data.groupby('JobInvolvement')['Attrition_binary'].mean()\n",
    "data_attrition_JobInvolvement.plot(kind='barh', title=\"Likelihood of employee attrition based on job involvement\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "47826f6e-db9d-4140-b7f6-4087f6049fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JobInvolvement\n",
       "1     83\n",
       "2    375\n",
       "3    868\n",
       "4    144\n",
       "Name: Attrition_binary, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the number of employee in each group\n",
    "data.groupby('JobInvolvement')['Attrition_binary'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62425f1b-5712-4c15-8f01-f24e08a67a00",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Do levels of attrition (job satisfaction) vary as a function of the quality of an emplyee's work environment, as measured by employee's self-reported \"EnvironmentSatisfaction\", \"WorkLifeBalance\", and overall relationship with their manager (measured by the \"Manager_relation_satisfaction\" variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e818c159-f619-4c2d-878a-9eafc167dd09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on their environment satisfaction rating'}, ylabel='EnvironmentSatisfaction'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on environment satisfaction rating\n",
    "data_attrition_environment_satisfaction = data.groupby('EnvironmentSatisfaction')['Attrition_binary'].mean()\n",
    "data_attrition_environment_satisfaction.plot(kind='barh', title=\"Likelihood of employee attrition based on their environment satisfaction rating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "eab873c3-a02d-4fe2-9100-4da212d4c835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on their manager relation satisfaction rating'}, ylabel='Manager_relation_satisfaction'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M1JqI+C7w28z8VK/1/IKj4fUe4CKTAqm3qL56d9mIWJ3qPvdPTAr0VBARL42I50bEUhGxI1UP9kn9ys3eb17SjInqK0CD6v/IJfX2bqrbR4up/qf9va1GIw3uWVS3H9ek+rf892Tmr/sV8laCJEnq8FaCJEnq8FZCw9Zaa62cN29e22FI0lPGggUL7szMMb/kSdPPxKBh8+bNY/78+W2HIUlPGRHR6xs/Nc28lSBJkjpMDCRJUoeJgSRJ6jAxkCRJHSYGkiSpw8RAkiR1mBhIkqQOEwNJktRhYiBJkjpMDCRJUoeJgSRJ6jAxkCRJHf6IUsMuu/k+5h10StthSAIWHbpL2yFIs449BpIkqcPEQJIkdZgYSJKkDhMDSZLUYWIgSZI6TAwkSVKHiYEkSeowMZAkSR0mBpIkqcPEYBpExJyI+HVE/LTtWCRJmgoTg+lxIHBl20FIkjRVJgZTFBHrA7sAR7UdiyRJU2ViMHWHAx8Bnmg5DkmSpszEYAoi4vXAHZm5oM96+0XE/IiYv/ih+xqKTpKkiTMxmJqXA2+IiEXA/wA7RMTxo1fKzCMyc+vM3HrOinObjlGSpIGZGExBZv6fzFw/M+cBuwG/yMw9Ww5LkqRJMzGQJEkdS7cdwNNFZp4NnN1yGJIkTYk9BpIkqcPEQJIkdZgYSJKkDhMDSZLUYWIgSZI6/K+EIiJeDhwMbERVLwFkZj6nzbgkSWqSiUHX0cAHgQXA4pZjkSSpFSYGXfdl5mltByFJUptMDLrOioh/BU4EHh2ZmZkXT+eLvGi9ucw/dJfp3KQkSdPGxKDrL8rfrWvzEtihhVgkSWqFiUGRma9qOwZJktrmvysWETE3Ig6LiPnl8eWI8DeSJUlDxcSg65vAA8Bby+N+4FutRiRJUsO8ldD13Mx8U2360xGxsK1gJElqgz0GXQ9HxHYjE+ULjx5uMR5Jkhpnj0HXe4Bjy7iCAO4G9mk1IkmSGmZiUGTmQuAlEbFqmb6/3YgkSWre0CcGEbFnZh4fEf84aj4AmXlYK4FJktSCoU8MgJXK31XGWJZNBiJJUtuGPjHIzG+Up2dk5v+rLysDECVJGhr+V0LXfww4T5Kkp62h7zGIiG2BlwFrjxpnsCowp52oJElqx9AnBsCywMpUdVEfZ3A/8OZWIpIkqSVDnxhk5jnAORFxTGZe33Y8kiS1yTEGXUdFxGojExGxekT8vMV4JElqnIlB11qZee/IRGbeAzyjvXAkSWqeiUHXExGx4chERGyE32MgSRoyQz/GoObjwHkRcU6ZfiWwX4vxSJLUOBODIjN/FhFbAdtQ/YjSBzPzzpbDkiSpUSYGS1oM3AEsD2wWEWTmuS3HJElSY0wMioh4F3AgsD6wkKrn4AJghxbDkiSpUQ4+7DoQeClwfWa+CtgS+EO7IUmS1CwTg65HMvMRgIhYLjN/C2zackySJDXKWwldN5UvODoJOD0i7gFuaTUiSZIaNvSJQUQ8OzN/n5lvLLMOjoizgLnAz1oMTZKkxg19YgD8APiziDgzM18Nnd9PmBGX3Xwf8w46ZaY2L2kGLDp0l7ZDkBpjYgBLRcSngOeN+tllADLzsBZikiSpFQ4+hN2AR+j+7PLohyRJQ2Poewwy8yrgCxFxaWae1nY8kiS1yR6DrudFxKpROToiLo6I17YdlCRJTTIx6HpHZt4PvBZYG9gXOLTdkCRJapaJQVeUvzsD38rMS2rzJEkaCiYGXQsi4n+pEoOfR8QqwBMtxyRJUqOGfvBhzTuBLYDrMvOhiFiT6naCJElDY+gTg4h4fvldhC3KrOdEeAdBkjSchj4xAP4R2A/48hjLEn92WZI0RIY+McjM/crTnUZ+XXFERCzfq2xZfi6wHFVd/iAzPzUjgUqS1AAHH3adP+C8ukeBHTLzJVS3InaMiG2mOzBJkpoy9D0GEfEsYD1ghYjYku6/KK4KrNirbGYm8GCZXKY8coZClSRpxg19YgC8DtgHWB+o/2DSA8DH+hWOiDnAAmBj4D8z85djrLMf1TgG5qy69tQjliRphgx9YpCZxwLHRsSbMvOHkyi/GNgiIlYDfhQRL8zMy0etcwRwBMBy62xij4IkadYa+sRgRGb+MCJ2ATYHlq/N/8yA5e+NiLOBHYHL+6wuSdKs5ODDIiK+DrwNeD/VOIO3ABv1KbN26SkgIlYAXgP8dmYjlSRp5pgYdL0sM98O3JOZnwa2BTboU2Yd4KyIuBS4CDg9M386w3FKkjRjvJXQ9XD5+1BErAvcBTy7V4HMvBTYcqYDkySpKSYGXT8ttwX+FbiY6t8Oj2o1IkmSGmZiUGTmZ8vTH0bET4HlM/O+NmOSJKlpjjEoIuIt5aeWAf4J+Fb5wiNJkoaGiUHXJzPzgYjYjupLj44Fvt5yTJIkNcrEoGtx+bsL8LXM/DGwbIvxSJLUOBODrpsj4hvAW4FTI2I5rB9J0pDxja/rrcDPgR0z815gDaqxBgBExOotxSVJUmOi+oFA9RMRF2fmVlPdztZbb53z58+fjpAkaShExILM3LrtOIaFPQaDi/6rSJL01GZiMDi7ViRJT3smBpIkqcPEYHDeSpAkPe35lcg1ETEHeCa1esnMG8rTV7cSlCRJDTIxKCLi/cCngNuBJ8rsBF4MkJl3txSaJEmNMTHoOhDYNDPvajsQSZLa4hiDrhsBf01RkjTU7DHoug44OyJOAR4dmZmZh7UXkiRJzTIx6LqhPJbFH0+SJA0pE4MiMz8NEBGrVJP5YMshSZLUOMcYFBHxwoj4NXA5cEVELIiIzduOS5KkJpkYdB0B/GNmbpSZGwEfAo5sOSZJkhplYtC1UmaeNTKRmWcDK7UXjiRJzXOMQdd1EfFJ4LgyvSfw+xbjkSSpcfYYdL0DWBs4EfhReb5vqxFJktQwewyKzLwHOKDtOCRJatPQJwYRcXhmfiAifkL12whLyMw3tBCWJEmtGPrEgO6Ygi+1GoUkSbPA0CcGmbmgPN0iM/+9viwiDgTOaT4qSZLa4eDDrr3HmLdP00FIktSmoe8xiIjdgT2AZ0fEybVFqwD+BLMkaagMfWIAnA/cCqwFfLk2/wHg0lYikiSpJUOfGGTm9cD1wLZtxyJJUtscY1BExDYRcVFEPBgRj0XE4oi4v+24JElqkolB11eB3YGrgRWAdwH/0WpEkiQ1bOhvJdRl5jURMSczFwPfiojz245JkqQmmRh0PRQRywILI+KLVAMSp/3XFS+7+T7mHXTKdG9W0iQtOnSXtkOQZhVvJXTtBcwB3gf8EdgAeFOrEUmS1DB7DIry3wkADwOfbjMWSZLaMvSJQURcxhg/njQiM1/cYDiSJLVq6BMD4PVtByBJ0mwx9IlB7RYCEbERsElmnhERK2D9SJKGjIMPi4j4e+AHwDfKrPWBk1oLSJKkFpgYdP0D8HLgfoDMvBp4RqsRSZLUMBODrkcz87GRiYhYmh6DEiVJejoyMeg6JyI+BqwQEX8FfB/4ScsxSZLUKBODro8CfwAuA94NnAp8oleBiNggIs6KiCsj4oqIOLCBOCVJmjGOugciYing0sx8IXDkBIo+DnwoMy+OiFWABRFxemb+ZkYClSRphtljAGTmE8AlEbHhBMvdmpkXl+cPAFcC681AiJIkNcIeg651gCsi4ldUv5UAQGa+YZDCETEP2BL45RjL9gP2A5iz6trTEaskSTPCxKBr0r+PEBErAz8EPpCZ949enplHAEcALLfOJv6ngyRp1jIxKDLznF7LI+KCzNx2jPnLUCUF38nME2cqPkmSmuAYg8EtP3pGRARwNHBlZh7WfEiSJE0vE4PBjXUL4OXAXsAOEbGwPHZuOC5JkqaNtxKmIDPPA6LtOCRJmi72GAzOBECS9LRnYgBExJyIOKPPans1EowkSS0yMQAyczHwUETM7bHO5Q2GJElSKxxj0PUIcFlEnM6SX3B0QHshSZLULBODrlPKQ5KkoWViUGTmsRGxArBhZl7VdjySJLXBMQZFRPw1sBD4WZneIiJObjUoSZIaZmLQdTDw58C9AJm5EHh2e+FIktQ8byV0PZ6Z91Xfctwx7T949KL15jL/0F2me7OSJE0LE4OuyyNiD2BORGwCHACc33JMkiQ1ylsJXe8HNgceBU4A7gc+0GZAkiQ1zR6DIjMfAj5eHpIkDSUTgyIifsKTxxTcB8wHvpGZjzQflSRJzfJWQtd1wIPAkeVxP3A78LwyLUnS0549Bl1bZuYra9M/iYhzM/OVEXFFa1FJktQgewy61o6IDUcmyvO1yuRj7YQkSVKz7DHo+hBwXkRcCwTVlxu9NyJWAo5tNTJJkhpiYlBk5qnl+wueT5UY/LY24PDw1gKTJKlBJgZL2gTYFFgeeHFEkJnfbjkmSZIaY2JQRMSngO2BzYBTgZ2A8wATA0nS0HDwYdebgVcDt2XmvsBLgOXaDUmSpGaZGHQ9nJlPAI9HxKrAHcBzWo5JkqRGeSuha35ErEb1ZUYLqL7s6FetRiRJUsNMDIrMfG95+vWI+BmwamZe2mZMkiQ1zcSgJiLWAzai1EtEvDIzz203KkmSmmNiUETEF4C3Ab8BFpfZCZgYSJKGholB198Am2bmo20HIklSW/yvhK7rgGXaDkKSpDbZY9D1ELAwIs4EOr0GmXlAeyFJktQsE4Ouk8tDkqShZWJQZKa/oChJGnomBkX5ZcXPU/1WwvIj8zPTbz+UJA0NBx92fQv4GvA48CqqH086rtWIJElqmIlB1wqZeSYQmXl9Zh4M7NByTJIkNcpbCV2PRMRSwNUR8T7gZuAZLcckSVKjTAy6PgCsCBwAfJaqt2Dv6X6Ry26+j3kHnTLdm5WkWW3Robu0HYIGZGJQZOZF5emDwL5txiJJUluGPjGIiJ7fXZCZb2gqFkmS2jb0iQGwLXAjcALwSyDaDUeSpPaYGMCzgL8Cdgf2AE4BTsjMK1qNSpKkFgz9vytm5uLM/Flm7g1sA1wDnB0R7285NEmSGmePARARywG7UPUazAO+ApzYZkySJLVh6BODiDgWeCFwGvDpzLy85ZAkSWrN0CcGwF7AH4HnAQdEdMYeBpCZuWpbgUmS1LShTwwyc+jHWUiSNMI3xSmIiG9GxB0R4e0HSdLTgonB1BwD7Nh2EJIkTRcTgynIzHOBu9uOQ5Kk6WJiIEmSOkwMGhAR+0XE/IiYv/ih+9oOR5KkcZkYNCAzj8jMrTNz6zkrzm07HEmSxmViIEmSOkwMpiAiTgAuADaNiJsi4p1txyRJ0lQM/RccTUVm7t52DJIkTSd7DCRJUoeJgSRJ6jAxkCRJHSYGkiSpw8RAkiR1mBhIkqQOEwNJktRhYiBJkjr8gqOGvWi9ucw/dJe2w5AkaUz2GEiSpA4TA0mS1GFiIEmSOkwMJElSh4mBJEnqMDGQJEkdJgaSJKnDxECSJHWYGEiSpA4TA0mS1GFiIEmSOkwMJElSh4mBJEnqiMxsO4ahEhEPAFe1HccY1gLubDuIcczW2GZrXGBskzFb44LZG1tTcW2UmWs38DrCn11uw1WZuXXbQYwWEfNnY1wwe2ObrXGBsU3GbI0LZm9sszUuTY23EiRJUoeJgSRJ6jAxaN4RbQcwjtkaF8ze2GZrXGBskzFb44LZG9tsjUtT4OBDSZLUYY+BJEnqMDGQJEkdJgZTEBE7RsRVEXFNRBw0xvKIiK+U5ZdGxFb9ykbEGhFxekRcXf6u3mRsEbFBRJwVEVdGxBURcWCtzMERcXNELCyPnZuKqyxbFBGXldeeX5vfdp1tWquThRFxf0R8oCxros6eHxEXRMSjEfHhQco2WGdjxjbT7WwqsZVlM9bWplBnM9rOBozt70rbvzQizo+Il/QrO11tTQ3KTB+TeABzgGuB5wDLApcAm41aZ2fgNCCAbYBf9isLfBE4qDw/CPhCw7GtA2xVnq8C/K4W28HAh9uos7JsEbDWGNtttc7G2M5tVF/I0lSdPQN4KfC5+mvNknY2Xmwz1s6mGttMtrWpxjVT7WwCsb0MWL0834mGrmk+mn3YYzB5fw5ck5nXZeZjwP8Au45aZ1fg21m5EFgtItbpU3ZX4Njy/Fjgb5qMLTNvzcyLATLzAeBKYL1JxDCtcfXZbqt1NmqdVwPXZub1k4hhUnFl5h2ZeRHwpwmUbaTOxotthtvZlGLrY6r1Nl1xTXc7GzS28zPznjJ5IbD+AGWno62pQSYGk7cecGNt+iaefGEbb51eZZ+ZmbdCdfGk+vTQZGwdETEP2BL4ZW32+0o34jcn0SU41bgS+N+IWBAR+9XWmTV1BuwGnDBq3kzX2WTKNlVnfc1AO5uO2GaqrU1LnTH97Wwysb2TqgetX9npaGtqkInB5MUY80b/7+d46wxSdiqmElu1MGJl4IfABzLz/jL7a8BzgS2AW4EvNxzXyzNzK6ouzH+IiFdO8PVnMjYiYlngDcD3a8ubqLOZKNvI9meonU1HbDPV1qajzmainU0otoh4FVVi8NGJltXsZ2IweTcBG9Sm1wduGXCdXmVvH+meLn/vaDg2ImIZqov1dzLzxJEVMvP2zFycmU8AR1J1HzYWV2aO/L0D+FHt9Vuvs2In4OLMvH1kRkN1NpmyTdXZuGawnU05thlsa1OKq5iJdjZwbBHxYuAoYNfMvGuAstPR1tQgE4PJuwjYJCKeXTL43YCTR61zMvD2qGwD3Fe60nqVPRnYuzzfG/hxk7FFRABHA1dm5mH1AqPup78RuLzBuFaKiFVKHCsBr629fqt1Vlu+O6O6dxuqs8mUbarOxjTD7Wyqsc1kW5vK8RwxE+1soNgiYkPgRGCvzPzdgGWno62pSTM5svHp/qAapf47qtG4Hy/z9gf2L88D+M+y/DJg615ly/w1gTOBq8vfNZqMDdiOqgvwUmBheexclh1X1r2U6mRfp8G4nkM10vkS4IrZVGdl2YrAXcDcUdtsos6eRfWJ7X7g3vJ81VnSzsaMbabb2RRjm9G2NsXjOWPtbMDYjgLuqR2z+b3KTmdb89Hcw69EliRJHd5KkCRJHSYGkiSpw8RAkiR1mBhIkqQOEwNJktRhYiBJkjpMDCRJUsf/B+gnu+sTFeVfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on manager relation satisfaction rating\n",
    "data_attrition_manager_relation_satisfaction = data.groupby('Manager_relation_satisfaction')['Attrition_binary'].mean()\n",
    "data_attrition_manager_relation_satisfaction.plot(kind='barh', title=\"Likelihood of employee attrition based on their manager relation satisfaction rating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "542e9e1a-97a4-414b-8954-4d500f5bd3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on their work-life balance rating'}, ylabel='WorkLifeBalance'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on work-life balance rating\n",
    "data_attrition_work_life_balance = data.groupby('WorkLifeBalance')['Attrition_binary'].mean()\n",
    "data_attrition_work_life_balance.plot(kind='barh', title=\"Likelihood of employee attrition based on their work-life balance rating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "afa6f9db-53e7-46fb-8007-cfccfe91c0f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic regression and odds ratios for this category only\n",
    "# result_logit_happiness = mba263.logit(data['Attrition_binary'],data[['EnvironmentSatisfaction','Manager_relation_satisfaction','WorkLifeBalance']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c2aeb60c-589a-4f17-8691-5647395b3c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['predicted_happiness'] = result_logit_happiness.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dfe998bc-49a6-49c8-a545-acd7656c9fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['predicted_happiness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ae75aeb2-f819-4f41-a78d-1d4eebbee1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# result_logit_happiness.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8e172f67-cf2d-4792-9fad-419e54da15ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Summary of odds ratios\n",
    "# mba263.odds_ratios(result_logit_happiness)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788d6c17-74fa-428e-bab5-1e1280e1bca8",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Does having a recent promotion succeed in reducing attrition (or increasing job satisfaction)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3a7b5cf9-9000-4a8c-81fb-bfa43b2df833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the data for easier interpretation\n",
    "bins = [0,2,4,9,100]\n",
    "labels = ['0-1 year','2-3 years','4-8 years','9 years or more']\n",
    "data['YearPromoGroup'] = pd.cut(data['YearsSinceLastPromotion'], bins=bins, labels=labels, right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c6e2b166-75a1-4161-9609-8c9c731bea82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearPromoGroup\n",
       "0-1 year           938\n",
       "2-3 years          211\n",
       "4-8 years          232\n",
       "9 years or more     89\n",
       "Name: Attrition_binary, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the number of employee in each group\n",
    "data.groupby('YearPromoGroup')['Attrition_binary'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "16aea4b6-a0d4-42dd-a854-f6bd5699aa01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on the recency of last promotion'}, ylabel='YearPromoGroup'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on the recency of last promotion\n",
    "data_attrition_promotion = data.groupby('YearPromoGroup')['Attrition_binary'].mean()\n",
    "data_attrition_promotion.plot(kind='barh', title=\"Likelihood of employee attrition based on the recency of last promotion\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d95087-67c8-49c5-96cc-4d48ca99743e",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### How do levels of attrition (job satisfaction) vary as a function of the number of years that an employee has spent at the company, or of the number of years an employee has worked with their manager?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0560dfb9-51e7-447f-bd26-62b37b8c293d",
   "metadata": {},
   "source": [
    "**Likelihood of employee attrition based on years employee spent working at IBM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7d49a005-a960-47ff-9b09-f5abb5722b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the data for easier interpretation\n",
    "bins = [0,10,20,30,100]\n",
    "labels = ['0-9 years','10-19 years','20-29 years','30 years or more']\n",
    "data['YearsAtCompanyGroup'] = pd.cut(data['YearsAtCompany'], bins=bins, labels=labels, right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "16e30477-ec1a-44bc-afbc-999105a1fdbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsAtCompanyGroup\n",
       "0-9 years           1104\n",
       "10-19 years          273\n",
       "20-29 years           76\n",
       "30 years or more      17\n",
       "Name: Attrition_binary, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the number of employee in each group\n",
    "data.groupby('YearsAtCompanyGroup')['Attrition_binary'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "61e09df5-b7bf-41cf-92c3-04711e5c52f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on the number of years employee spent working at IBM'}, ylabel='YearsAtCompanyGroup'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on the number of years employee spent working at IBM\n",
    "data_attrition_YearsAtCompany = data.groupby('YearsAtCompanyGroup')['Attrition_binary'].mean()\n",
    "data_attrition_YearsAtCompany.plot(kind='barh', title=\"Likelihood of employee attrition based on the number of years employee spent working at IBM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d87dd98-1121-46b3-bd38-4e2659d49088",
   "metadata": {},
   "source": [
    "**Likelihood of employee attrition based on years with current manager at IBM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0b85a743-f0fd-4c27-8765-bd1731a02148",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the data for easier interpretation\n",
    "bins = [0,3,6,9,100]\n",
    "labels = ['0-2 years','3-5 years','6-8 years','9 years or more']\n",
    "data['YearsWithCurrManagerGroup'] = pd.cut(data['YearsWithCurrManager'], bins=bins, labels=labels, right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "56d1317f-ad1f-45bd-96f7-a9101a343053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsWithCurrManagerGroup\n",
       "0-2 years          683\n",
       "3-5 years          271\n",
       "6-8 years          352\n",
       "9 years or more    164\n",
       "Name: Attrition_binary, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the number of employee in each group\n",
    "data.groupby('YearsWithCurrManagerGroup')['Attrition_binary'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "459d363e-b43f-4a39-896c-98c1501b42cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Likelihood of employee attrition based on the number of years with current manager at IBM'}, ylabel='YearsWithCurrManagerGroup'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the likelihood of attrition based on the number of years with current manager at IBM\n",
    "data_attrition_YearsWithCurrManager = data.groupby('YearsWithCurrManagerGroup')['Attrition_binary'].mean()\n",
    "data_attrition_YearsWithCurrManager.plot(kind='barh', title=\"Likelihood of employee attrition based on the number of years with current manager at IBM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6177d32b-e983-4d15-83a0-e7315a0fb8b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}