DeepLearning_Tabular.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import our dependencies\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "import pandas as pd\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
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       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
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       "      <td>Life Sciences</td>\n",
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       "      <td>No</td>\n",
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       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
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       "      <td>Life Sciences</td>\n",
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       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
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      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our input dataset\n",
    "attrition_df = pd.read_csv('Resources/HR-Employee-Attrition.csv')\n",
    "attrition_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                          int64\n",
       "Attrition                   object\n",
       "BusinessTravel              object\n",
       "DailyRate                    int64\n",
       "Department                  object\n",
       "DistanceFromHome             int64\n",
       "Education                    int64\n",
       "EducationField              object\n",
       "EmployeeCount                int64\n",
       "EmployeeNumber               int64\n",
       "EnvironmentSatisfaction      int64\n",
       "Gender                      object\n",
       "HourlyRate                   int64\n",
       "JobInvolvement               int64\n",
       "JobLevel                     int64\n",
       "JobRole                     object\n",
       "JobSatisfaction              int64\n",
       "MaritalStatus               object\n",
       "MonthlyIncome                int64\n",
       "MonthlyRate                  int64\n",
       "NumCompaniesWorked           int64\n",
       "Over18                      object\n",
       "OverTime                    object\n",
       "PercentSalaryHike            int64\n",
       "PerformanceRating            int64\n",
       "RelationshipSatisfaction     int64\n",
       "StandardHours                int64\n",
       "StockOptionLevel             int64\n",
       "TotalWorkingYears            int64\n",
       "TrainingTimesLastYear        int64\n",
       "WorkLifeBalance              int64\n",
       "YearsAtCompany               int64\n",
       "YearsInCurrentRole           int64\n",
       "YearsSinceLastPromotion      int64\n",
       "YearsWithCurrManager         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attrition_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Attrition',\n",
       " 'BusinessTravel',\n",
       " 'Department',\n",
       " 'EducationField',\n",
       " 'Gender',\n",
       " 'JobRole',\n",
       " 'MaritalStatus',\n",
       " 'Over18',\n",
       " 'OverTime']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate our categorical variable list\n",
    "attrition_cat = attrition_df.dtypes[attrition_df.dtypes == \"object\"].index.tolist()\n",
    "attrition_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attrition         2\n",
       "BusinessTravel    3\n",
       "Department        3\n",
       "EducationField    6\n",
       "Gender            2\n",
       "JobRole           9\n",
       "MaritalStatus     3\n",
       "Over18            1\n",
       "OverTime          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of unique values in each column\n",
    "attrition_df[attrition_cat].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a OneHotEncoder instance\n",
    "enc = OneHotEncoder(sparse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    0    1    2    3    4    5    6    7    8    9   ...   21   22   23   24  \\\n",
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_df = pd.DataFrame(enc.fit_transform(attrition_df[attrition_cat]))\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition_No</th>\n",
       "      <th>Attrition_Yes</th>\n",
       "      <th>BusinessTravel_Non-Travel</th>\n",
       "      <th>BusinessTravel_Travel_Frequently</th>\n",
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       "      <th>EducationField_Life Sciences</th>\n",
       "      <th>...</th>\n",
       "      <th>JobRole_Research Director</th>\n",
       "      <th>JobRole_Research Scientist</th>\n",
       "      <th>JobRole_Sales Executive</th>\n",
       "      <th>JobRole_Sales Representative</th>\n",
       "      <th>MaritalStatus_Divorced</th>\n",
       "      <th>MaritalStatus_Married</th>\n",
       "      <th>MaritalStatus_Single</th>\n",
       "      <th>Over18_Y</th>\n",
       "      <th>OverTime_No</th>\n",
       "      <th>OverTime_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attrition_No  Attrition_Yes  BusinessTravel_Non-Travel  \\\n",
       "0           0.0            1.0                        0.0   \n",
       "1           1.0            0.0                        0.0   \n",
       "2           0.0            1.0                        0.0   \n",
       "3           1.0            0.0                        0.0   \n",
       "4           1.0            0.0                        0.0   \n",
       "\n",
       "   BusinessTravel_Travel_Frequently  BusinessTravel_Travel_Rarely  \\\n",
       "0                               0.0                           1.0   \n",
       "1                               1.0                           0.0   \n",
       "2                               0.0                           1.0   \n",
       "3                               1.0                           0.0   \n",
       "4                               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  EducationField_Human Resources  \\\n",
       "0               1.0                             0.0   \n",
       "1               0.0                             0.0   \n",
       "2               0.0                             0.0   \n",
       "3               0.0                             0.0   \n",
       "4               0.0                             0.0   \n",
       "\n",
       "   EducationField_Life Sciences  ...  JobRole_Research Director  \\\n",
       "0                           1.0  ...                        0.0   \n",
       "1                           1.0  ...                        0.0   \n",
       "2                           0.0  ...                        0.0   \n",
       "3                           1.0  ...                        0.0   \n",
       "4                           0.0  ...                        0.0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                         0.0                      1.0   \n",
       "1                         1.0                      0.0   \n",
       "2                         0.0                      0.0   \n",
       "3                         1.0                      0.0   \n",
       "4                         0.0                      0.0   \n",
       "\n",
       "   JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
       "0                           0.0                     0.0   \n",
       "1                           0.0                     0.0   \n",
       "2                           0.0                     0.0   \n",
       "3                           0.0                     0.0   \n",
       "4                           0.0                     0.0   \n",
       "\n",
       "   MaritalStatus_Married  MaritalStatus_Single  Over18_Y  OverTime_No  \\\n",
       "0                    0.0                   1.0       1.0          0.0   \n",
       "1                    1.0                   0.0       1.0          1.0   \n",
       "2                    0.0                   1.0       1.0          0.0   \n",
       "3                    1.0                   0.0       1.0          0.0   \n",
       "4                    1.0                   0.0       1.0          1.0   \n",
       "\n",
       "   OverTime_Yes  \n",
       "0           1.0  \n",
       "1           0.0  \n",
       "2           1.0  \n",
       "3           1.0  \n",
       "4           0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the encoded variable names to the DataFrame\n",
    "encode_df.columns = enc.get_feature_names(attrition_cat)\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Age</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>JobRole_Research Director</th>\n",
       "      <th>JobRole_Research Scientist</th>\n",
       "      <th>JobRole_Sales Executive</th>\n",
       "      <th>JobRole_Sales Representative</th>\n",
       "      <th>MaritalStatus_Divorced</th>\n",
       "      <th>MaritalStatus_Married</th>\n",
       "      <th>MaritalStatus_Single</th>\n",
       "      <th>Over18_Y</th>\n",
       "      <th>OverTime_No</th>\n",
       "      <th>OverTime_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>1102</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>279</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1373</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1392</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>591</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  DailyRate  DistanceFromHome  Education  EmployeeCount  EmployeeNumber  \\\n",
       "0   41       1102                 1          2              1               1   \n",
       "1   49        279                 8          1              1               2   \n",
       "2   37       1373                 2          2              1               4   \n",
       "3   33       1392                 3          4              1               5   \n",
       "4   27        591                 2          1              1               7   \n",
       "\n",
       "   EnvironmentSatisfaction  HourlyRate  JobInvolvement  JobLevel  ...  \\\n",
       "0                        2          94               3         2  ...   \n",
       "1                        3          61               2         2  ...   \n",
       "2                        4          92               2         1  ...   \n",
       "3                        4          56               3         1  ...   \n",
       "4                        1          40               3         1  ...   \n",
       "\n",
       "   JobRole_Research Director  JobRole_Research Scientist  \\\n",
       "0                        0.0                         0.0   \n",
       "1                        0.0                         1.0   \n",
       "2                        0.0                         0.0   \n",
       "3                        0.0                         1.0   \n",
       "4                        0.0                         0.0   \n",
       "\n",
       "   JobRole_Sales Executive  JobRole_Sales Representative  \\\n",
       "0                      1.0                           0.0   \n",
       "1                      0.0                           0.0   \n",
       "2                      0.0                           0.0   \n",
       "3                      0.0                           0.0   \n",
       "4                      0.0                           0.0   \n",
       "\n",
       "   MaritalStatus_Divorced  MaritalStatus_Married  MaritalStatus_Single  \\\n",
       "0                     0.0                    0.0                   1.0   \n",
       "1                     0.0                    1.0                   0.0   \n",
       "2                     0.0                    0.0                   1.0   \n",
       "3                     0.0                    1.0                   0.0   \n",
       "4                     0.0                    1.0                   0.0   \n",
       "\n",
       "   Over18_Y  OverTime_No  OverTime_Yes  \n",
       "0       1.0          0.0           1.0  \n",
       "1       1.0          1.0           0.0  \n",
       "2       1.0          0.0           1.0  \n",
       "3       1.0          0.0           1.0  \n",
       "4       1.0          1.0           0.0  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "attrition_df = attrition_df.merge(encode_df,left_index=True, right_index=True)\n",
    "attrition_df = attrition_df.drop(attrition_cat,1)\n",
    "attrition_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "y = attrition_df[\"Attrition_Yes\"].values\n",
    "X = attrition_df.drop([\"Attrition_Yes\",\"Attrition_No\"],1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.100e+01, 1.102e+03, 1.000e+00, ..., 1.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [4.900e+01, 2.790e+02, 8.000e+00, ..., 1.000e+00, 1.000e+00,\n",
       "        0.000e+00],\n",
       "       [3.700e+01, 1.373e+03, 2.000e+00, ..., 1.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       ...,\n",
       "       [2.700e+01, 1.550e+02, 4.000e+00, ..., 1.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [4.900e+01, 1.023e+03, 2.000e+00, ..., 1.000e+00, 1.000e+00,\n",
       "        0.000e+00],\n",
       "       [3.400e+01, 6.280e+02, 8.000e+00, ..., 1.000e+00, 1.000e+00,\n",
       "        0.000e+00]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1470"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 1., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1470"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the preprocessed data into a training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler instance\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 8)                 448       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 5)                 45        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 499\n",
      "Trainable params: 499\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train[0])\n",
    "hidden_nodes_layer1 =  8\n",
    "hidden_nodes_layer2 = 5\n",
    "\n",
    "nn = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn.add(\n",
    "    tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    ")\n",
    "\n",
    "# Second hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1102 samples\n",
      "Epoch 1/100\n",
      "1102/1102 [==============================] - 0s 372us/sample - loss: 192.8006 - accuracy: 0.8276\n",
      "Epoch 2/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 47.4792 - accuracy: 0.8276\n",
      "Epoch 3/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 13.4610 - accuracy: 0.8267\n",
      "Epoch 4/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 4.7166 - accuracy: 0.7949\n",
      "Epoch 5/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 1.3905 - accuracy: 0.7632\n",
      "Epoch 6/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.8240 - accuracy: 0.7713\n",
      "Epoch 7/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.6619 - accuracy: 0.7813\n",
      "Epoch 8/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.6118 - accuracy: 0.7868\n",
      "Epoch 9/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.5724 - accuracy: 0.7895\n",
      "Epoch 10/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.5451 - accuracy: 0.7868\n",
      "Epoch 11/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.5373 - accuracy: 0.7931\n",
      "Epoch 12/100\n",
      "1102/1102 [==============================] - 0s 47us/sample - loss: 0.4978 - accuracy: 0.8049\n",
      "Epoch 13/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.5070 - accuracy: 0.8276\n",
      "Epoch 14/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.4884 - accuracy: 0.8276\n",
      "Epoch 15/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4945 - accuracy: 0.8276\n",
      "Epoch 16/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.4964 - accuracy: 0.8276\n",
      "Epoch 17/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4766 - accuracy: 0.8276\n",
      "Epoch 18/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4870 - accuracy: 0.8276\n",
      "Epoch 19/100\n",
      "1102/1102 [==============================] - 0s 53us/sample - loss: 0.4719 - accuracy: 0.8276\n",
      "Epoch 20/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.4790 - accuracy: 0.8276\n",
      "Epoch 21/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 0.4766 - accuracy: 0.8276\n",
      "Epoch 22/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4715 - accuracy: 0.8276\n",
      "Epoch 23/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.4746 - accuracy: 0.8276\n",
      "Epoch 24/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4926 - accuracy: 0.8276\n",
      "Epoch 25/100\n",
      "1102/1102 [==============================] - ETA: 0s - loss: 0.5316 - accuracy: 0.87 - 0s 43us/sample - loss: 0.4639 - accuracy: 0.8276\n",
      "Epoch 26/100\n",
      "1102/1102 [==============================] - 0s 54us/sample - loss: 0.4939 - accuracy: 0.8276\n",
      "Epoch 27/100\n",
      "1102/1102 [==============================] - 0s 55us/sample - loss: 0.4883 - accuracy: 0.8276\n",
      "Epoch 28/100\n",
      "1102/1102 [==============================] - 0s 52us/sample - loss: 0.4663 - accuracy: 0.8276\n",
      "Epoch 29/100\n",
      "1102/1102 [==============================] - 0s 48us/sample - loss: 0.4853 - accuracy: 0.8276\n",
      "Epoch 30/100\n",
      "1102/1102 [==============================] - 0s 59us/sample - loss: 0.4659 - accuracy: 0.8276\n",
      "Epoch 31/100\n",
      "1102/1102 [==============================] - 0s 49us/sample - loss: 0.4798 - accuracy: 0.8276\n",
      "Epoch 32/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.4875 - accuracy: 0.8276\n",
      "Epoch 33/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4837 - accuracy: 0.8276\n",
      "Epoch 34/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.4645 - accuracy: 0.8276\n",
      "Epoch 35/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.4628 - accuracy: 0.8276\n",
      "Epoch 36/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.4614 - accuracy: 0.8276\n",
      "Epoch 37/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 0.4620 - accuracy: 0.8276\n",
      "Epoch 38/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 0.4658 - accuracy: 0.8276\n",
      "Epoch 39/100\n",
      "1102/1102 [==============================] - 0s 52us/sample - loss: 0.4837 - accuracy: 0.8276\n",
      "Epoch 40/100\n",
      "1102/1102 [==============================] - 0s 47us/sample - loss: 0.4910 - accuracy: 0.8276\n",
      "Epoch 41/100\n",
      "1102/1102 [==============================] - 0s 64us/sample - loss: 0.4737 - accuracy: 0.8276\n",
      "Epoch 42/100\n",
      "1102/1102 [==============================] - 0s 79us/sample - loss: 0.4560 - accuracy: 0.8276\n",
      "Epoch 43/100\n",
      "1102/1102 [==============================] - 0s 55us/sample - loss: 0.4608 - accuracy: 0.8276\n",
      "Epoch 44/100\n",
      "1102/1102 [==============================] - 0s 53us/sample - loss: 0.4685 - accuracy: 0.8276\n",
      "Epoch 45/100\n",
      "1102/1102 [==============================] - 0s 51us/sample - loss: 0.4616 - accuracy: 0.8276\n",
      "Epoch 46/100\n",
      "1102/1102 [==============================] - 0s 53us/sample - loss: 0.4562 - accuracy: 0.8276\n",
      "Epoch 47/100\n",
      "1102/1102 [==============================] - 0s 53us/sample - loss: 0.4571 - accuracy: 0.8276\n",
      "Epoch 48/100\n",
      "1102/1102 [==============================] - 0s 50us/sample - loss: 0.4738 - accuracy: 0.8276\n",
      "Epoch 49/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4906 - accuracy: 0.8258\n",
      "Epoch 50/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.4619 - accuracy: 0.8276\n",
      "Epoch 51/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4748 - accuracy: 0.8276\n",
      "Epoch 52/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4548 - accuracy: 0.8276\n",
      "Epoch 53/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.4686 - accuracy: 0.8212\n",
      "Epoch 54/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.4972 - accuracy: 0.8212\n",
      "Epoch 55/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.4695 - accuracy: 0.8276\n",
      "Epoch 56/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4695 - accuracy: 0.8276\n",
      "Epoch 57/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4872 - accuracy: 0.8276\n",
      "Epoch 58/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4547 - accuracy: 0.8276\n",
      "Epoch 59/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4744 - accuracy: 0.8276\n",
      "Epoch 60/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4646 - accuracy: 0.8276\n",
      "Epoch 61/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.4625 - accuracy: 0.8276\n",
      "Epoch 62/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.4530 - accuracy: 0.8276\n",
      "Epoch 63/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4502 - accuracy: 0.8276\n",
      "Epoch 64/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.4654 - accuracy: 0.8276\n",
      "Epoch 65/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4558 - accuracy: 0.8276\n",
      "Epoch 66/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4503 - accuracy: 0.8276\n",
      "Epoch 67/100\n",
      "1102/1102 [==============================] - 0s 38us/sample - loss: 0.4501 - accuracy: 0.8276\n",
      "Epoch 68/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.4506 - accuracy: 0.8276\n",
      "Epoch 69/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 0.4467 - accuracy: 0.8276\n",
      "Epoch 70/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4583 - accuracy: 0.8276\n",
      "Epoch 71/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4532 - accuracy: 0.8276\n",
      "Epoch 72/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4610 - accuracy: 0.8276\n",
      "Epoch 73/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4580 - accuracy: 0.8276\n",
      "Epoch 74/100\n",
      "1102/1102 [==============================] - 0s 38us/sample - loss: 0.4510 - accuracy: 0.8276\n",
      "Epoch 75/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4485 - accuracy: 0.8276\n",
      "Epoch 76/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4663 - accuracy: 0.8276\n",
      "Epoch 77/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4926 - accuracy: 0.8276\n",
      "Epoch 78/100\n",
      "1102/1102 [==============================] - 0s 38us/sample - loss: 0.4550 - accuracy: 0.8276\n",
      "Epoch 79/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4673 - accuracy: 0.8276\n",
      "Epoch 80/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.4602 - accuracy: 0.8276\n",
      "Epoch 81/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.4536 - accuracy: 0.8276\n",
      "Epoch 82/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.4637 - accuracy: 0.8276\n",
      "Epoch 83/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.4597 - accuracy: 0.8276\n",
      "Epoch 84/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.4777 - accuracy: 0.8276\n",
      "Epoch 85/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4769 - accuracy: 0.8276\n",
      "Epoch 86/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.5184 - accuracy: 0.8203\n",
      "Epoch 87/100\n",
      "1102/1102 [==============================] - 0s 37us/sample - loss: 0.4634 - accuracy: 0.8276\n",
      "Epoch 88/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4605 - accuracy: 0.8276\n",
      "Epoch 89/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4638 - accuracy: 0.8276\n",
      "Epoch 90/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4779 - accuracy: 0.8276\n",
      "Epoch 91/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4576 - accuracy: 0.8276\n",
      "Epoch 92/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4557 - accuracy: 0.8276\n",
      "Epoch 93/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.4504 - accuracy: 0.8276\n",
      "Epoch 94/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.4692 - accuracy: 0.8276\n",
      "Epoch 95/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4505 - accuracy: 0.8276\n",
      "Epoch 96/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.4500 - accuracy: 0.8276\n",
      "Epoch 97/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.4457 - accuracy: 0.8276\n",
      "Epoch 98/100\n",
      "1102/1102 [==============================] - 0s 38us/sample - loss: 0.4539 - accuracy: 0.8276\n",
      "Epoch 99/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.4539 - accuracy: 0.8276\n",
      "Epoch 100/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.4544 - accuracy: 0.8276\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn.fit(X_train,y_train,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "368/368 - 0s - loss: 0.3987 - accuracy: 0.8723\n",
      "Loss: 0.39865383764971857, Accuracy: 0.8722826242446899\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn.evaluate(X_test,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 8)                 448       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 529\n",
      "Trainable params: 529\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train[0])\n",
    "hidden_nodes_layer1 =  8\n",
    "hidden_nodes_layer2 = 8\n",
    "\n",
    "nn_1 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_1.add(\n",
    "    tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    ")\n",
    "\n",
    "# Second hidden layer\n",
    "nn_1.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_1.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_1.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1102 samples\n",
      "Epoch 1/100\n",
      "1102/1102 [==============================] - 0s 356us/sample - loss: 893.7049 - accuracy: 0.1751\n",
      "Epoch 2/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 141.2017 - accuracy: 0.5245\n",
      "Epoch 3/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 33.5154 - accuracy: 0.8022\n",
      "Epoch 4/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 28.4208 - accuracy: 0.7296\n",
      "Epoch 5/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 25.4350 - accuracy: 0.7350\n",
      "Epoch 6/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 23.1942 - accuracy: 0.7532\n",
      "Epoch 7/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 21.0508 - accuracy: 0.7686\n",
      "Epoch 8/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 19.1888 - accuracy: 0.7586\n",
      "Epoch 9/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 17.3127 - accuracy: 0.7604\n",
      "Epoch 10/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 15.1829 - accuracy: 0.7668\n",
      "Epoch 11/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 13.2202 - accuracy: 0.7541\n",
      "Epoch 12/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 11.1379 - accuracy: 0.7595\n",
      "Epoch 13/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 9.4495 - accuracy: 0.7623\n",
      "Epoch 14/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 7.9048 - accuracy: 0.7523\n",
      "Epoch 15/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 6.4986 - accuracy: 0.7178\n",
      "Epoch 16/100\n",
      "1102/1102 [==============================] - 0s 52us/sample - loss: 5.2449 - accuracy: 0.7005\n",
      "Epoch 17/100\n",
      "1102/1102 [==============================] - 0s 57us/sample - loss: 4.4298 - accuracy: 0.6960\n",
      "Epoch 18/100\n",
      "1102/1102 [==============================] - 0s 53us/sample - loss: 3.8430 - accuracy: 0.7151\n",
      "Epoch 19/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 3.1500 - accuracy: 0.7731\n",
      "Epoch 20/100\n",
      "1102/1102 [==============================] - 0s 49us/sample - loss: 2.5749 - accuracy: 0.7777\n",
      "Epoch 21/100\n",
      "1102/1102 [==============================] - 0s 47us/sample - loss: 2.0744 - accuracy: 0.7731\n",
      "Epoch 22/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 1.6000 - accuracy: 0.7668\n",
      "Epoch 23/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 1.3644 - accuracy: 0.7740\n",
      "Epoch 24/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 1.2318 - accuracy: 0.7813\n",
      "Epoch 25/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 1.3093 - accuracy: 0.7731\n",
      "Epoch 26/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 1.1060 - accuracy: 0.7750\n",
      "Epoch 27/100\n",
      "1102/1102 [==============================] - 0s 52us/sample - loss: 1.1971 - accuracy: 0.7813\n",
      "Epoch 28/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 0.9923 - accuracy: 0.7840\n",
      "Epoch 29/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 1.2218 - accuracy: 0.7740\n",
      "Epoch 30/100\n",
      "1102/1102 [==============================] - 0s 48us/sample - loss: 1.0203 - accuracy: 0.7722\n",
      "Epoch 31/100\n",
      "1102/1102 [==============================] - 0s 53us/sample - loss: 0.8458 - accuracy: 0.7940\n",
      "Epoch 32/100\n",
      "1102/1102 [==============================] - 0s 48us/sample - loss: 0.9227 - accuracy: 0.7922\n",
      "Epoch 33/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.9610 - accuracy: 0.7659\n",
      "Epoch 34/100\n",
      "1102/1102 [==============================] - 0s 49us/sample - loss: 1.4219 - accuracy: 0.7740\n",
      "Epoch 35/100\n",
      "1102/1102 [==============================] - 0s 48us/sample - loss: 0.8424 - accuracy: 0.7904\n",
      "Epoch 36/100\n",
      "1102/1102 [==============================] - 0s 49us/sample - loss: 0.7387 - accuracy: 0.7995\n",
      "Epoch 37/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.8313 - accuracy: 0.7877\n",
      "Epoch 38/100\n",
      "1102/1102 [==============================] - 0s 55us/sample - loss: 0.7165 - accuracy: 0.8067\n",
      "Epoch 39/100\n",
      "1102/1102 [==============================] - 0s 53us/sample - loss: 0.6988 - accuracy: 0.8022\n",
      "Epoch 40/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.9872 - accuracy: 0.7786\n",
      "Epoch 41/100\n",
      "1102/1102 [==============================] - 0s 45us/sample - loss: 0.7911 - accuracy: 0.7895\n",
      "Epoch 42/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.9212 - accuracy: 0.7895\n",
      "Epoch 43/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 1.0728 - accuracy: 0.7777\n",
      "Epoch 44/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 0.9147 - accuracy: 0.7813\n",
      "Epoch 45/100\n",
      "1102/1102 [==============================] - 0s 50us/sample - loss: 1.2838 - accuracy: 0.7822\n",
      "Epoch 46/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.9450 - accuracy: 0.7731\n",
      "Epoch 47/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.6900 - accuracy: 0.8013\n",
      "Epoch 48/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.8463 - accuracy: 0.7922\n",
      "Epoch 49/100\n",
      "1102/1102 [==============================] - 0s 49us/sample - loss: 1.0894 - accuracy: 0.7740\n",
      "Epoch 50/100\n",
      "1102/1102 [==============================] - 0s 52us/sample - loss: 1.3757 - accuracy: 0.7613\n",
      "Epoch 51/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.6694 - accuracy: 0.7922\n",
      "Epoch 52/100\n",
      "1102/1102 [==============================] - 0s 52us/sample - loss: 0.6562 - accuracy: 0.7949\n",
      "Epoch 53/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.7793 - accuracy: 0.7831\n",
      "Epoch 54/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.6973 - accuracy: 0.7967\n",
      "Epoch 55/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.6408 - accuracy: 0.8049\n",
      "Epoch 56/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.6690 - accuracy: 0.7958\n",
      "Epoch 57/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.6717 - accuracy: 0.8031\n",
      "Epoch 58/100\n",
      "1102/1102 [==============================] - ETA: 0s - loss: 0.4872 - accuracy: 0.75 - 0s 41us/sample - loss: 0.6564 - accuracy: 0.7985\n",
      "Epoch 59/100\n",
      "1102/1102 [==============================] - 0s 48us/sample - loss: 0.9928 - accuracy: 0.7804\n",
      "Epoch 60/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.8064 - accuracy: 0.7949\n",
      "Epoch 61/100\n",
      "1102/1102 [==============================] - ETA: 0s - loss: 0.3185 - accuracy: 0.84 - 0s 40us/sample - loss: 0.6746 - accuracy: 0.7995\n",
      "Epoch 62/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.7203 - accuracy: 0.7840\n",
      "Epoch 63/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.8338 - accuracy: 0.7804\n",
      "Epoch 64/100\n",
      "1102/1102 [==============================] - ETA: 0s - loss: 0.2589 - accuracy: 0.90 - 0s 44us/sample - loss: 0.7649 - accuracy: 0.7804\n",
      "Epoch 65/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.7143 - accuracy: 0.7931\n",
      "Epoch 66/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.8015 - accuracy: 0.7895\n",
      "Epoch 67/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.6870 - accuracy: 0.7895\n",
      "Epoch 68/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.8476 - accuracy: 0.7858\n",
      "Epoch 69/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.7540 - accuracy: 0.7895\n",
      "Epoch 70/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.9108 - accuracy: 0.7922\n",
      "Epoch 71/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.6226 - accuracy: 0.7995\n",
      "Epoch 72/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.6022 - accuracy: 0.8131\n",
      "Epoch 73/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 1.2416 - accuracy: 0.7668\n",
      "Epoch 74/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.8694 - accuracy: 0.7904\n",
      "Epoch 75/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.6741 - accuracy: 0.8022\n",
      "Epoch 76/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.7448 - accuracy: 0.8049\n",
      "Epoch 77/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.7551 - accuracy: 0.8004\n",
      "Epoch 78/100\n",
      "1102/1102 [==============================] - 0s 41us/sample - loss: 0.8292 - accuracy: 0.7895\n",
      "Epoch 79/100\n",
      "1102/1102 [==============================] - 0s 43us/sample - loss: 0.6388 - accuracy: 0.8230\n",
      "Epoch 80/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.6145 - accuracy: 0.7985\n",
      "Epoch 81/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.8328 - accuracy: 0.7904\n",
      "Epoch 82/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.8976 - accuracy: 0.7813\n",
      "Epoch 83/100\n",
      "1102/1102 [==============================] - 0s 58us/sample - loss: 0.8057 - accuracy: 0.8085\n",
      "Epoch 84/100\n",
      "1102/1102 [==============================] - 0s 70us/sample - loss: 0.6128 - accuracy: 0.8031\n",
      "Epoch 85/100\n",
      "1102/1102 [==============================] - 0s 56us/sample - loss: 0.7690 - accuracy: 0.7931\n",
      "Epoch 86/100\n",
      "1102/1102 [==============================] - 0s 53us/sample - loss: 0.7531 - accuracy: 0.7922\n",
      "Epoch 87/100\n",
      "1102/1102 [==============================] - 0s 47us/sample - loss: 0.6231 - accuracy: 0.7949\n",
      "Epoch 88/100\n",
      "1102/1102 [==============================] - 0s 49us/sample - loss: 0.6079 - accuracy: 0.7967\n",
      "Epoch 89/100\n",
      "1102/1102 [==============================] - 0s 48us/sample - loss: 0.5543 - accuracy: 0.8122\n",
      "Epoch 90/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.5711 - accuracy: 0.8122\n",
      "Epoch 91/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.6985 - accuracy: 0.7922\n",
      "Epoch 92/100\n",
      "1102/1102 [==============================] - 0s 42us/sample - loss: 0.9960 - accuracy: 0.7913\n",
      "Epoch 93/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.6176 - accuracy: 0.8067\n",
      "Epoch 94/100\n",
      "1102/1102 [==============================] - 0s 46us/sample - loss: 0.5387 - accuracy: 0.8167\n",
      "Epoch 95/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.6119 - accuracy: 0.7995\n",
      "Epoch 96/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.5282 - accuracy: 0.8149\n",
      "Epoch 97/100\n",
      "1102/1102 [==============================] - 0s 44us/sample - loss: 0.5747 - accuracy: 0.8158\n",
      "Epoch 98/100\n",
      "1102/1102 [==============================] - 0s 40us/sample - loss: 0.7039 - accuracy: 0.7985\n",
      "Epoch 99/100\n",
      "1102/1102 [==============================] - ETA: 0s - loss: 0.5785 - accuracy: 0.87 - 0s 38us/sample - loss: 1.1122 - accuracy: 0.8004\n",
      "Epoch 100/100\n",
      "1102/1102 [==============================] - 0s 39us/sample - loss: 0.6711 - accuracy: 0.8103\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_1.fit(X_train,y_train,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "368/368 - 0s - loss: 1.3137 - accuracy: 0.5109\n",
      "Loss: 1.3137284776438838, Accuracy: 0.510869562625885\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_1.evaluate(X_test,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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