ibm-hr-attrition -ann.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IBM HR Analytics Employee Attrition & Performance.          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Please star/upvote it if you find it helpful.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"image-logo.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"image-hr.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONTENTS :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 1 ) Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 2 ) Corelation b/w Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3 ) Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 4 ) Preparing Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 5 ) Making Predictions Using an Artificial Neural Network (ANN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 6 ) Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 7 ) Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 ) Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 ) Importing Various Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Ignore  the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('always')\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# data visualisation and manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "\n",
    "#configure\n",
    "# sets matplotlib to inline and displays graphs below the corressponding cell.\n",
    "% matplotlib inline  \n",
    "style.use('fivethirtyeight')\n",
    "sns.set(style='whitegrid',color_codes=True)\n",
    "\n",
    "#import the necessary modelling algos.\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "#model selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,confusion_matrix,roc_curve,roc_auc_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "#preprocess.\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler,Imputer,LabelEncoder,OneHotEncoder\n",
    "\n",
    "# ann and dl libraraies\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam,SGD,Adagrad,Adadelta,RMSprop\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "import tensorflow as tf\n",
    "import random as rn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 ) Reading the data from a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'WA_Fn-UseC_-HR-Employee-Attrition.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  \\\n",
       "0          ...                                  1            80   \n",
       "1          ...                                  4            80   \n",
       "2          ...                                  2            80   \n",
       "3          ...                                  3            80   \n",
       "4          ...                                  4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 35)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 ) Missing Values Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      "Age                         1470 non-null int64\n",
      "Attrition                   1470 non-null object\n",
      "BusinessTravel              1470 non-null object\n",
      "DailyRate                   1470 non-null int64\n",
      "Department                  1470 non-null object\n",
      "DistanceFromHome            1470 non-null int64\n",
      "Education                   1470 non-null int64\n",
      "EducationField              1470 non-null object\n",
      "EmployeeCount               1470 non-null int64\n",
      "EmployeeNumber              1470 non-null int64\n",
      "EnvironmentSatisfaction     1470 non-null int64\n",
      "Gender                      1470 non-null object\n",
      "HourlyRate                  1470 non-null int64\n",
      "JobInvolvement              1470 non-null int64\n",
      "JobLevel                    1470 non-null int64\n",
      "JobRole                     1470 non-null object\n",
      "JobSatisfaction             1470 non-null int64\n",
      "MaritalStatus               1470 non-null object\n",
      "MonthlyIncome               1470 non-null int64\n",
      "MonthlyRate                 1470 non-null int64\n",
      "NumCompaniesWorked          1470 non-null int64\n",
      "Over18                      1470 non-null object\n",
      "OverTime                    1470 non-null object\n",
      "PercentSalaryHike           1470 non-null int64\n",
      "PerformanceRating           1470 non-null int64\n",
      "RelationshipSatisfaction    1470 non-null int64\n",
      "StandardHours               1470 non-null int64\n",
      "StockOptionLevel            1470 non-null int64\n",
      "TotalWorkingYears           1470 non-null int64\n",
      "TrainingTimesLastYear       1470 non-null int64\n",
      "WorkLifeBalance             1470 non-null int64\n",
      "YearsAtCompany              1470 non-null int64\n",
      "YearsInCurrentRole          1470 non-null int64\n",
      "YearsSinceLastPromotion     1470 non-null int64\n",
      "YearsWithCurrManager        1470 non-null int64\n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 402.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()  # no null or Nan values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         0\n",
       "Attrition                   0\n",
       "BusinessTravel              0\n",
       "DailyRate                   0\n",
       "Department                  0\n",
       "DistanceFromHome            0\n",
       "Education                   0\n",
       "EducationField              0\n",
       "EmployeeCount               0\n",
       "EmployeeNumber              0\n",
       "EnvironmentSatisfaction     0\n",
       "Gender                      0\n",
       "HourlyRate                  0\n",
       "JobInvolvement              0\n",
       "JobLevel                    0\n",
       "JobRole                     0\n",
       "JobSatisfaction             0\n",
       "MaritalStatus               0\n",
       "MonthlyIncome               0\n",
       "MonthlyRate                 0\n",
       "NumCompaniesWorked          0\n",
       "Over18                      0\n",
       "OverTime                    0\n",
       "PercentSalaryHike           0\n",
       "PerformanceRating           0\n",
       "RelationshipSatisfaction    0\n",
       "StandardHours               0\n",
       "StockOptionLevel            0\n",
       "TotalWorkingYears           0\n",
       "TrainingTimesLastYear       0\n",
       "WorkLifeBalance             0\n",
       "YearsAtCompany              0\n",
       "YearsInCurrentRole          0\n",
       "YearsSinceLastPromotion     0\n",
       "YearsWithCurrManager        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x249a858e978>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df) # just to visualize."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 ) The Features and the 'Target'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  \\\n",
       "0          ...                                  1            80   \n",
       "1          ...                                  4            80   \n",
       "2          ...                                  2            80   \n",
       "3          ...                                  3            80   \n",
       "4          ...                                  4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " In all we have 34 features consisting of both the categorical as well as the numerical features. The target variable is the \n",
    " 'Attrition' of the employee which can be either a Yes or a No."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######  Hence this is a Binary Classification problem. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 ) Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section I have done the univariate analysis i.e. I have analysed the range or distribution of the values that various features take. To better analyze the results I have plotted various graphs and visualizations wherever necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>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>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.923810</td>\n",
       "      <td>802.485714</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1024.865306</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>65.891156</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>...</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.793878</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>4.123129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.135373</td>\n",
       "      <td>403.509100</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>602.024335</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>20.329428</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>...</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.852077</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>1.289271</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "      <td>3.568136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>491.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1020.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1555.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2068.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
       "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
       "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
       "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
       "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
       "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
       "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
       "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
       "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
       "\n",
       "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
       "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
       "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
       "std        602.024335                 1.093082    20.329428        0.711561   \n",
       "min          1.000000                 1.000000    30.000000        1.000000   \n",
       "25%        491.250000                 2.000000    48.000000        2.000000   \n",
       "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
       "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
       "max       2068.000000                 4.000000   100.000000        4.000000   \n",
       "\n",
       "          JobLevel          ...           RelationshipSatisfaction  \\\n",
       "count  1470.000000          ...                        1470.000000   \n",
       "mean      2.063946          ...                           2.712245   \n",
       "std       1.106940          ...                           1.081209   \n",
       "min       1.000000          ...                           1.000000   \n",
       "25%       1.000000          ...                           2.000000   \n",
       "50%       2.000000          ...                           3.000000   \n",
       "75%       3.000000          ...                           4.000000   \n",
       "max       5.000000          ...                           4.000000   \n",
       "\n",
       "       StandardHours  StockOptionLevel  TotalWorkingYears  \\\n",
       "count         1470.0       1470.000000        1470.000000   \n",
       "mean            80.0          0.793878          11.279592   \n",
       "std              0.0          0.852077           7.780782   \n",
       "min             80.0          0.000000           0.000000   \n",
       "25%             80.0          0.000000           6.000000   \n",
       "50%             80.0          1.000000          10.000000   \n",
       "75%             80.0          1.000000          15.000000   \n",
       "max             80.0          3.000000          40.000000   \n",
       "\n",
       "       TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\n",
       "count            1470.000000      1470.000000     1470.000000   \n",
       "mean                2.799320         2.761224        7.008163   \n",
       "std                 1.289271         0.706476        6.126525   \n",
       "min                 0.000000         1.000000        0.000000   \n",
       "25%                 2.000000         2.000000        3.000000   \n",
       "50%                 3.000000         3.000000        5.000000   \n",
       "75%                 3.000000         3.000000        9.000000   \n",
       "max                 6.000000         4.000000       40.000000   \n",
       "\n",
       "       YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count         1470.000000              1470.000000           1470.000000  \n",
       "mean             4.229252                 2.187755              4.123129  \n",
       "std              3.623137                 3.222430              3.568136  \n",
       "min              0.000000                 0.000000              0.000000  \n",
       "25%              2.000000                 0.000000              2.000000  \n",
       "50%              3.000000                 1.000000              3.000000  \n",
       "75%              7.000000                 3.000000              7.000000  \n",
       "max             18.000000                15.000000             17.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let us first analyze the various numeric features. To do this we can actually plot a boxplot showing all the numeric features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x249a8401160>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(data=df,kind='box',size=10,aspect=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that all the features have pretty different scales and so plotting a boxplot is not a good idea. Instead what we can do is plot histograms of various continuously distributed features.\n",
    "\n",
    "We can also plot a kdeplot showing the distribution of the feature. Below I have plotted a kdeplot for the 'Age' feature.\n",
    "Similarly we plot for other numeric features also. We can also use a distplot from seaborn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x249a8b549e8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df['Age'],shade=True,color='#ff4125')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x249a8bb5da0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly we can do this for all the numerical features. Below I have plotted the subplots for the other features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('always')\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "fig,ax = plt.subplots(5,2, figsize=(9,9))                \n",
    "sns.distplot(df['TotalWorkingYears'], ax = ax[0,0]) \n",
    "sns.distplot(df['MonthlyIncome'], ax = ax[0,1]) \n",
    "sns.distplot(df['YearsAtCompany'], ax = ax[1,0]) \n",
    "sns.distplot(df['DistanceFromHome'], ax = ax[1,1]) \n",
    "sns.distplot(df['YearsInCurrentRole'], ax = ax[2,0]) \n",
    "sns.distplot(df['YearsWithCurrManager'], ax = ax[2,1]) \n",
    "sns.distplot(df['YearsSinceLastPromotion'], ax = ax[3,0]) \n",
    "sns.distplot(df['PercentSalaryHike'], ax = ax[3,1]) \n",
    "sns.distplot(df['YearsSinceLastPromotion'], ax = ax[4,0]) \n",
    "sns.distplot(df['TrainingTimesLastYear'], ax = ax[4,1]) \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now analyze the various categorical features. Note that in these cases the best way is to use a count plot to show the relative count of observations of different categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_df=df.select_dtypes(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Attrition', 'BusinessTravel', 'Department', 'EducationField', 'Gender',\n",
       "       'JobRole', 'MaritalStatus', 'Over18', 'OverTime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cat(attr,labels=None):\n",
    "    if(attr=='JobRole'):\n",
    "        sns.factorplot(data=df,kind='count',size=5,aspect=3,x=attr)\n",
    "        return\n",
    "    \n",
    "    sns.factorplot(data=df,kind='count',size=5,aspect=1.5,x=attr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have made a function that accepts the name of a string. In our case this string will be the name of the column or attribute which we want to analyze. The function then plots the countplot for that feature which makes it easier to visualize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cat('Attrition')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######  Note that the number of observations belonging to the 'No'  category is way greater than that belonging to 'Yes' category. Hence we have skewed classes and this is a typical example of the 'Imbalanced Classification Problem'. To handle such types of problems we need to use the over-sampling or under-sampling techniques. I shall come back to this point later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now similalry analyze other categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cat('BusinessTravel')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot clearly shows that most of the people belong to the 'Travel_Rarely' class. This indicates that most of the people did not have a job which asked them for frequent travelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cat('OverTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cat('Department')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cat('EducationField')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cat('Gender') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that males are presnt in higher number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAMBsCT4AAACA2RJ8AAAAALMl+AAAAABmS/ABAAAAzJbgAwAAAJgtwQcAAAAwW4IPAAAAYLYOW+bMq+qmSZ7S3cdW1XckOTHJniTvS/Kw7j6/qh6X5I5JzkvyyO4+ZZllAgAAAA4dSws+qurXk9w3yRemSU9L8pjufnNV/VmSO1fVfye5VZKbJrlGklcmufGyygQAMHc//op/2O4iHFL+7m532u4iALAPy7zV5UNJ7rrweFeSt0x/vzbJ7ZIck+QN3b2nu89IclhVHbXEMgEAAACHkKWN+OjuV1bVtRYm7ejuPdPfn0ty5SRHJjl74TUr08/c1/x37969oXIcvaFXsVk2Wi8H4vClzZm1LLMu2Vrqcj7U5Xyoy/lQlwAHj127dq05fanf8bHK+Qt/H5HknCTnTn+vnr5P663QameecvoGi8dm2Gi9HIhTffvLllpmXZ723qXNmjUssy5zxvJmzcUttS7/+13LmzcXs9S6/IhbXbbSUusSgE2xlb/q8p6qOnb6+7gkJyV5e5I7VNXOqjo6yc7uPmsLywQAAADM2FaO+Pj/kjy3qi6b5PQkr+jur1XVSUlOzghhHraF5QEAAABmbqnBR3d/NMnNpr8/kPELLqtfc3yS45dZDgAAAODQtJW3ugAAAABsKcEHAAAAMFuCDwAAAGC2BB8AAADAbAk+AAAAgNkSfAAAAACzJfgAAAAAZkvwAQAAAMyW4AMAAACYLcEHAAAAMFuHbXcBAACAi/ulV39su4twSDnhJ66x3UUAlsSIDwAAAGC2BB8AAADAbLnVBQAAYIne/udnbncRDim3uN9R210EDjJGfAAAAACzJfgAAAAAZkvwAQAAAMyW4AMAAACYLcEHAAAAMFuCDwAAAGC2BB8AAADAbAk+AAAAgNkSfAAAAACzJfgAAAAAZkvwAQAAAMyW4AMAAACYLcEHAAAAMFuCDwAAAGC2BB8AAADAbAk+AAAAgNkSfAAAAACzJfgAAAAAZkvwAQAAAMyW4AMAAACYLcEHAAAAMFuCDwAAAGC2BB8AAADAbB221Qusqvck+ez08CNJnp3k6UnOS/KG7n78VpcJAAAAmKctDT6q6vJJ0t3HLkz79yQ/meTDSV5TVTfs7ndvZbkAAABgXz75R6dudxEOKd/4y9fblPls9YiP6ye5YlW9YVr28Uku190fSpKqen2S2yYRfAAAAACX2FYHH19M8odJnpfkO5O8Nsk5C89/Lsm1NzKj3bt3b2iBR+9f+biENlovB+Lwpc2ZtSyzLtla6nI+1OV8qMv5WG5dXn2J82a15dals5KttMy6/DZnJVtqf+ty165da07f6uDjA0n+q7v3JPlAVX02ydcvPH9ELhqErGu9FVrtzFNO398ycglstF4OxKmnLG3WrGGZdXnae5c2a9awzLrMGcubNRe31Lr873ctb95czFLr8iP/sLx5czHLrMsXnfGxpc2bi1tmXb79P89c2ry5uGXW5Sff6laXrbRZdbnVv+rywCRPTZKq+pYkV0zyhaq6TlXtSHKHJCdtcZkAAACAmdrqER/PT3JiVb0tyZ6MIOT8JC9OcpmMX3VxyQkAAADYFFsafHT3V5Lca42nbraV5QAAAAAODVt9qwsAAADAlhF8AAAAALMl+AAAAABmS/ABAAAAzJbgAwAAAJgtwQcAAAAwW4IPAAAAYLYEHwAAAMBsCT4AAACA2RJ8AAAAALMl+AAAAABmS/ABAAAAzJbgAwAAAJgtwQcAAAAwW4IPAAAAYLYEHwAAAMBsCT4AAACA2RJ8AAAAALMl+AAAAABmS/ABAAAAzJbgAwAAAJgtwQcAAAAwW4IPAAAAYLYEHwAAAMBsCT4AAACA2RJ8AAAAALMl+AAAAABmS/ABAAAAzJbgAwAAAJgtwQcAAAAwW4IPAAAAYLYEHwAAAMBsCT4AAACA2RJ8AAAAALMl+AAAAABmS/ABAAAAzJbgAwAAAJitw7a7AElSVTuTPDPJ9ZN8OcnPdvd/bW+pAAAAgEu7g2XEx12SXL67fzDJbyR56jaXBwAAAJiBgyX4OCbJ65Kku9+Z5EbbWxwAAABgDnbs2bNnu8uQqnpekld292unx2ckuXZ3n7fW63fv3r39hQYAAAAOKrt27dqxetpB8R0fSc5NcsTC453rhR7J2isCAAAAsNrBcqvL25P8aJJU1c2SvHd7iwMAAADMwcEy4uPVSW5fVe9IsiPJA7a5PAAAAMAMHBTf8QEAAACwDAfLrS4AAAAAm07wAQAAAMyW4AMAAACYrYPly00PGlX1G0lul+T8JHuS/FZ3717ntddK8tfdfbMDXNb9kzwhyYcXJr+3u3/xQOa3geVdN8lVu/utVfXXSe7X3V9ZxrK2QlUdm+RlSU7LqKsjMz7Le2/Xem1km6iq70jy9Iz977Ak/5bkN7v7/DVee4MkP97dT9jPcvxQknO6+9SqelV333Wd1x2d5Prd/ff7M/8DMdXXQ7r7npv52v1Y/oOTvLC7v7oJ83pxkm9Ncq0kX0ny8eznvltV/5PkWnv76e6F1z4jye919/+u8dyDknyiu1+z0WUfqKle3pTknt390oXppyZ5d3fff5OW86Qkd0jyq9395g2+Z7+25ar64yRP6+4zDrCM98+F7ffOjDbo8d39xqr6kSRHd/dzDmTe0/wf3t1/cgnef2xW7UNV9XtJ3t/dJ+7HfK6VqU1b1a58oru/6UDLtz+mMpya5N3TpMsn+XySu3f3Z7aiDPujqn4iybu6++PrPP/1SX6ku18y9Tne2N2nbGkhN9FWtQuHqm3ul14lydu7+2EHMr9l21c7eWnp916K+7NvTnLFJF9McniSjyR5RHefvQnH2C3rn26WzTrublJZrpVL0XFzqwg+FlTV9yb58SS36O4900nni5Jcf4mLfUl3/8YS57/oJ5N8IslbN/OEcpu9cVUD85KMOnzF9hVpn343yTO6+3VVtSPJq5LcOePXjS6iu/89yb8fwDIemOSvk5y6XugxuU2S705yqTmwXAK/leTPk1zi4KO7750kVXV8RujwZ5d0nvtY3rqBSnc/f5nLXsP7k/x0kpcmF3Qsv26Tl3GPJD/Q3Z/bj/fs17bc3Y88kIKtckH7XVXfmOStVXWr7n7dJsz7MUkOOPhYkgvalW1Y9mndfezKg6p6cpIHJfnDbSjLvjwiyUMyAtG1XC/jOPWS7v69LSvVcm1Fu3DI2e5+aVXtTHJSVd2ou/9tics8UPtqJy9N/d5LY382GWHS+5Okqu6d5DlJfnITjrGHUv90WS5Nx80tIfi4qE8lOTrJA6vqdd3971V1kySpqlsledz0uismuV/Gld4sPP+kJF9L8qEkP5/k25OcmHGidV5G43CxK7arVdWRSd6Z0fn/WkZH85gkP7DGMg5L8sIk18xIW38xSSX57u7+jaq6fEaH5BZJ7p/kK1X17oxk+bpJ3pORqH6hqn5tKucrMhquyyf5UpIHd/fHNvohbpequmySb07ymenxk5P8UMbV2Kd198ur6heS/EzGlZO3dfevVdU1ssb6Tu+/UZIjkpze3Q+YTnRvnuRKGY3HTya5S0Y9PCvJ65McVVV/M5Xl1O7+uVVF/e8k96+qzyU5JclPJTlv6mCckOQmSS6bsb19NlN6XFV3T/IrGfX/tql+j8/Yzq6esQ38cpKzkvxIkhtW1WlJTunub1q97kl+Y/p3xap6R3f/3SX5/A9UVd0tycMyfso6Se42/f+dVfX6JFdL8qzufn5V/UCSZ2R8Bl9K8nMZ9fv3Sc5O8o9J3pWL76u3TPJNGfvSXarqqRn7VDI6eU+vqhOnZV0tyX8keV93/2lVXTXJP3f3rg2sy2WTPDvJtZNcJmMkz0lVdeeMDtqOJP+a5Bemtzynqq6TUSd3SXL3JLfP2L6uneR3u/svquptGfvv5zI6vUdO779vxs9/fzSjrXlOkm9LctUkr+nu46vqLzNS/m+fPoP7dfd/7Gtd9uI/knxXVV2lu89Jcp8kL85oO1NVD09y14z26LPT3/dK8qMZ9XGdJE/p7hOnq0UP6e73V9VDpvKdP63Da6rquIzRUdfIqJfXdvdjq+o7kzwvYz/54jT/C7bljP1k9XxPzEW3kx/NODm9Z1btQ939+qq6U8aVz89mtCmndvfx630o3f3JqnplkjtV1XkZHbY/W7XM12bs4zumaQ/MqNPV+/33J/n6qnpmxkn0C6bP7TIZbdlLp8/uzIy6vkN3f23v1XZR67SP6x7nqmpXLtquXG7qmB89rcvdprKcmHGVeMf0/v/LaBsvn1GHT+juv6mq9yX5QJIvZ9TD86fnk+SXuvu9eyn7joxt4r+mx7+YsQ3sybhCecK0P6+87kpTWb6UfdfHZTNO3ndmbMMP6e737mUZX84Y+fXNGfvoNye5QZI/r6pjkjw+q44jSR6d5PrTKLSbZ7RLD07y9O5+S1XdOKO9uFvGNvSdU3kes9ERUNtg3XZhP9uEi22D3f2Bqnpskp/I2OavmOSxGf2Xi203VfXfGf2e0zcp4NxO290vPSJjf/5sVR2eNbbHqT04Kcn3Jfl0RgB294z9aedUxq/Pxfsvt0jy1Kksn0ly74x9dK1lnJrkLRmh4Z6Mi0UPz4Xt5G9kHBOukuQbkjw3yd/lUtrvvRT1Zy+iu19cVU+azj1elwuPsYvLuV0u3pbu65j+seyj79fdv7//n/TWWT0aZGXU5HQc+WpG/+NyGceDH8vY7++c0b97di7eDzoxq44/3f3urGODx827JnnUVJ6PZrQpRyb5y+n/wzL2yTdW1UczzjW/tDKqZXrPUzLaoedkbL8rbdR7MraHW2aTzpcPhO/4WNDdZ2VK1pOcXFXvT3Kn6envS3Kf7r5NRmN695X3TRvTc5PctbtvleR/Mxrb2yfZnbGTPymjU7javarqzQv/7tvd507vf25GqHG/jM7xWst4SJKPdvcPTo9vus66/W/GRvW0hSG1X03yyozGLhmN059nJIEndPetp78P5itSt5k+t9MyhnO9urv/ZTph+vbuvkWSWyd5dFVdJeMk8RHT5/Xhqjosa6zvFD59prtvn9Fg36yqvnVa5undffOMA8txGZ/5zZN8b0YH+shpOT+Y5LZVdfVVZX5MRrD15IxOzQuTXDmjgfuG7r5JxgnGjVfeUGNo9OOT3La7j0nyrVV1++npL3f3cRknSL88DYF9XZJfXzXE8CLrPpX19zJO/Lcl9Jh8V5I7Tql0Z9zikIxO8o9lNJKPqqqjMvaBh0/7wDOTPG167Tcl+eHpwHexfXUaFfGJJPecTmi/PcnNMsKPe01XJ5NxxeXmSf4gY79LxoHhxRtcl59P8vGpfHdJ8qypA/P0JMd1942SfDLjNpkkec7C/nzbadoR3X3HjJODR62a/28necVUxt/OwjaScZB8W3f/cEYHaXFo8oe7+w4ZHcq9dlw26FVJfmJq+26S5B3JBVcHr5bkdt19y4w6XCnjlbv7Thlt7Lqj3Hrc1vWJJD+c5Kgk75zKfkySh04v+8MkT5625WdnXP3cyLa8uJ0susg+VFWXyTghPm5qF/5vXx/I5JMZne71lvncJA+btvV/TPLrWWO/7+4nJfl0d/9CxjZ11lTnt0vyxKpaWcZLuvt2ewk9brN4fMnYlrOX9nHd49wa7cqVMobcH5PRfv1Axgn9301lfXTGtvHdSZ46taUPz4Xb5ZWS/E53/3TGaKx/mT7rB2d0ulf73mk9Ts0ITP4ryYtqXBG/R8b2cUxGsFnTez40rcvxSVbqfF/1cZOMk/PjkvxSkiP3sYz/nrbPZ2ScZLwmY5Te/TKOEWsdR56U0dYs3gr13IyTmOTC4//PZtT9D2VsJ3+6xudyMFmrXdjfNuFi22BVXT+jPm6c0a5+8/Ta9babayS51wxCj+3sl76lqj6Q5I1JntTdH8z62+MVk7x4agven9FmJWPbPybjhGet/stdMraZW2WEu1fdyzKOTPJXC+ty3Kp28jsyTt5+ePp8fuVS2O+9NPZn1/KZjABq0cpydmTttnRfx/SN9P0OFmsed/fho9O2e3pGXf9oxnb6Yxnt2Vr9oGTV8WeN+e7vcfOnk/zRtJ++IaP+H5Pkn6Z98u5Jnj/19dZz+amt/6vCBGqmAAANUklEQVSM0Vh37O4bJ/mfaV0OtF3aFEZ8LKjx3QvndvcDp8c3SvKPVfWmjMo5oao+n3HS8vaFtx6VcSB+2dQXukLGBvOkjBOX12V0pH5rjcWueatLd59SVeck+UqPhP/q6yzjqIyrVunu9yV5X417NFfsyN49L+Pk7P1JPtDjvrzrJvmtqnrU9P6D7n7IBW/sMRriakn+KeP+wmSk+rumRicZna1rZjTgv1pVT0lycsb6rbW+/5fk6lX1VxlXy680zSMZJ+fJGFlzynTS8cUkj6hxT92He7p/rqo+ldEpWHTr7v7jJH9cVVfKaPAfm3El6+Qk6e5PJHnMlBAn46B+VMb2mIzU/trTc++Z/v9YxsFrPWut+8HgUxkN8eczTpJOnqa/s6d7W6eOwLWSfEuP23+S5K25sHPykb7wPti97atJ8j1JTuruPUm+WlXvzDjIJ1PddveHq+pz08Hh3hkdz424bkan4hbT48tmjCQ4c+rApqdRA1M9rtyn/YlcuJ3srT4r46Cf7v7naT5PnJ47O8kPVtVtM9qbyy68b3Ge+xy5sgEvyTjJ+HDGlb5MZTq/qr6S5K+mz//bcuF+s1Jv622na22Pn05y46q6dZJzM66GJONzWNlXXpZccG/6Whbnu7idLFr9mR+VcSz45DT9pIwO1r5cM6PDunhsXVzm9yR55lT3h2d0RBbX5RMZnYxF35Pkn6fnPzftC9eZnuvs3eqh0yv7y3rt4772nUWf7u6PTn+vbL+VcQKT7n7jtMzvy2jLHpRxZenwhXmslP+6GZ3Fe0yP1+r0nNbdx1bVFTKu8n2yu8+rqu+fyv4vC+/9jpX1n/5/R5I/mv7eV328NuNq899mnCQ9MWMEznrLWNx2Vvb7FXs7jqz2+iR/MIXct8wIXZ6R5JZVtXJB47Cqulp3n73OPLbbWu3C+RnH1I22CWttg9+TC4+1/1dVK7dcrLfdnHUQf0b7ZTv7pVX17dPrPjBNv27W2B6TfLW73zpNe0fGCfTJuXD/Xq//8rsZAem/TOvyrr0sI9n7sfETSR5Z44r1uVl/P0sO3n7vpbE/exFT4PZNGf26RSvLWa8t3dcxfSN9v4PFesfd1Rb7JisjNc7JCA+TESBdPuv3g5K9H3+S/T9u/kqS36yqh2aEMH+T0f6+OBkX0avq3Iz9eb11Wanrb8gI3D41vfcJezmX3Ui7tCmM+Lio62U0hisN6gcyKuBrGQ3lA3p8SdfHc9FKPisjybrzdOXoSRlf9HXnjBOs2yZ5eS5+9XZdNYb/fz7jFoi77WUZp2e6elJV164x9PhLufCKyA0XZnt+VtX5lOLvSPJrGSlcMna6R03L+fkc/PcXZurk3CfJ86rqmzPW4U3TOtwmY4jjhzOudj9kShp/ICPZXmt9j0tyjYWrkVfIhXW+8iWk788Y9r2zqg6vqn/KaJD27KO4vz9d7Uh3fz4XDvderMsr17jNY8VHMhq220/lfEZGJyHrLO9idb3Ouq/1ui1TVVfOuBJ0z4wrPf+XCz/nH6iqw6rq6zIa3g8l+XhVXW96/la5sEO2+MWw6+2rK+t6eqbbXGoM3b15kg+uMZ/nZpyE/s9KaLEB70/yl1Md/WjGdvfJJN8wXaFJVf1pjdsGkrXrbm/bz+I2cpuq+t2F5x6U5FM9vn/k6bno/fX72ib3S3d/eJr/L2UMgcxUpusluUt33yPjtrudufDzX6sM67VVK+6f8WWa984YEn3FqWO1+Dncu8aQzcVteW9t4FpWl+1TSY6oMcooGaOD9mpqd+6cMXJg0eIyO2MI57EZowtek/X3+5XP7fSME+FU1REZHduVDvF667Mv67WPezvOrSxv5TNeqz4X1+WHpg757yT58+6+b8Yxa3Gei23pH03l+ansZYRVd/9fRhj52zVGAnSS/8wIlI/NuMK7cpvMyn52i+k1i8tM1q6PY5P8v+nq2xMzTs72toy9tb/rHUfWOhafn9FPeFaSv5lOQN6fcZX72GleL8809P1gtE67cGT2r01Yaxv8z4yO/86qulzG8StZf7s50P3iYLRt/dLu/kjGCK2XV9UVs/72ePi0LyZr72vr9V/uneTEaXTCf2Zcsd7bNr/W9rKyzr+a5OTuvs/0ntXH/cX1Oqj7vZey/uxqD8oYhbV6H1x5vF5buq9j+kb6fge7C/olVXXNjNu/Vuztc75/1u4H7et9F9iP4+aDkxw/bVM7Mm4vXOyDfGtGSHL2yvpMZbnBwuJW6uRTSa4yhfmpqhMyLmBu6vny/jLiY0F3v6qqvifJu6YEfWeSX+vuz1bVX0zTP5NxIvMtC+87v6oekXFP+s6MRO5+Gan2X9a43/v8jO9fWO1eVbXYqf5sRqfhdzI2tJ0ZV07+NWMY9uplvCPJC6rqLRn3fz8yYyjTQ2t8L8Du6bWZ/v6Dqjp9VRmePy3vTdPjX82FB9orTMs96HX3adOOdUJGJ+jYqjopI91+9XS19L1J/rWqzsyFVxjWWt+PJHlsjdEAX844yHzLquX9e1W9LuMqy86MDuuXN1DUe2RcpXlyRhr/4Yyha59Pcrup3g7LCARWlnVmVT0tyVtqDMP/aMbBbz3vyhji+JGFaWut+7kZwybf3d1/vYGyX1I/vHC1LhkN8dsz0u4vZHRwviXj8/9SxtXXq2Q0xJ+uqp9L8idTQ3texkF2tfX21ZMyTkpvnbFtnJwxKuJl3f3uumDk+gVenTFM7z77sX7PSvLcaX88MuNLbL86HcRfW1Vfy9gP170Pcx+emLG//0zGAe+BGYFRMkYFvKTGKKEvZAx9/cYDXM5GvDTJfXvcf78y+ui/knxhquMvJ/l/WbXfrHJCkj+tqo9lbJOr/UuSv66qW2as0wen+f1akmdX1WMyrk7dJ+PqxaNr3Mu9r/nu1dSmPzzjCuVnM/bvD67x0pX2+2sZnYQHTNvperN+aMZ3P1xmevygab5r7fen1fh+lgdmbFNvy2ifHt/dn9rLMjbi77N2+7jucW6yVruy6Hczts/7ZGyfD8oYOn1CVX0i4+Rn9a1Ayej8PL/Gd14cmXFryrp6fJ/Kr2YMib55xnbytumk+JRcWOfH1fh+nctkdB5XW6s+zk7y0qp6ZEa9PqG7/6Oq1lvGWt6RMXz+x7P2ceRDSa47LWPRC6bXfOf0+Nm5aHvyzDVOKA42q9uF87J/bcLFtsEe39vxjxm3iJ6VMRLnq9nP7ebSaJv6pYvL/+eq+ueMdukxWWN7nNqiR9X4FY4zptf99MI81uu/XC4Xjvb8SsZJ18f3soy1rLSTz8/ox907Yx8+b9pXL5X93ktRfzYZbegXpr//Nxe9zXb1eq3Xlu7rmL6Rvt/B7t+SnFNV78oIE9Y7jq62Xj9ov2zwuHlKkn+qqrMzvmLhHzL6Cy+ocRH+Chm3dJ5XVb+f0af+aNYI5Kf99hcy2qCvZYxOWe9cdr/apUtix549m3ohEGAWalzhekuSm14KTjbYZFX1mxn3hn956li/obv/fLvLxcbU+OK3v+7N+YUdtlGN4dF36+5nTp30/0xymz7An8lkc9XClxxuc1EA9sqID4BVqurmGan4o4Ueh6zPJXlnVX0x44rGS7e3OHDIOivjVpd/zRhJ9DyhBwD7y4gPAAAAYLZ8uSkAAAAwW4IPAAAAYLYEHwAAAMBs+XJTAGBbVNX9kxzb3fdf5/kTk7y5u09cNf3YjJ/a+69p0mUyfurxKd39Z3tZ3rEZP4997CUqOABwqSL4AAAujf5tMcCoqhsk+deqekl3n7t9xQIADjaCDwBgW1XVdyV5TpKvT/KFJL/U3f86PX2nqvrFJJdN8jvd/bJ1ZnOt6b1frqqdSf44yW0zfgL1L7r7KauW+R1JnpXkakm+mOQXu/s9m7piAMBBQfABAGy3v0zye939qqq6WZJXTGFIklwxyU2TXD3J7qp66zT9RlX170m+LslVk7w5ye27+8tV9QtJrpHkekkul+TNVfW+jGBkxYuSPLy731NV35vk1UlqqWsJAGwLX24KAGynKyX5ju5+VZJ09zuTfDoXhhAv6u7zuvvjSU7OCEGScavLDZJ8f5I3JjlnYZTIbZKc2N1f6+4vJnlxxuiPJElVXSnJjZO8cApPXpLkSlV1tWWuKACwPQQfAMCWqapbVtW3TA93JFnr+zh25MJRqectTN+Z5KuLL+zuLyf5uSQ/VlU/tfC69eaXjC9D/VJ332DlX0ag8un9XR8A4OAn+AAAttIDk9xl+vt6Gb/M8uGqumuSTLe6fFOS902v+emq2lFV10xyoySnrJ5hd382yeOS/GFVXSFjBMjPVNVlquqKSe6d5E2rXv/BqrrPtMzbJ3nr6vkCAPMg+AAAttKTM0KJ0zNuU3lWkvsk+aWqem+SP0ly1+7+yvT6zyfZnfHztT/f3WetM9/nTa/9lSTPTvI/Sf4jyXuS/H13v3rV6++d5Ger6tSpTPfo7j2btI4AwEFkx549jvEAAADAPBnxAQAAAMyW4AMAAACYLcEHAAAAMFuCDwAAAGC2BB8AAADAbAk+AAAAgNkSfAAAAACz9f8DCHpgfIrVKTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cat('JobRole')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######  Similarly we can continue for other categorical features. \n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Note that the same function can also be used to better analyze the numeric discrete features like 'Education' ,'JobSatisfaction' etc...  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just uncomment the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_disc=['Education','EnvironmentSatisfaction','JobInvolvement','JobSatisfaction','WorkLifeBalance','RelationshipSatisfaction','PerformanceRating']\n",
    "# for i in num_disc:\n",
    "#     plot_cat(i)\n",
    "\n",
    "# similarly we can intrepret these graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 ) Corelation b/w Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x249ab7db908>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#corelation matrix.\n",
    "cor_mat= df.corr()\n",
    "mask = np.array(cor_mat)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(30,12)\n",
    "sns.heatmap(data=cor_mat,mask=mask,square=True,annot=True,cbar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### BREAKING IT DOWN\n",
    "Firstly calling .corr() method on a pandas data frame returns a corelation data frame containing the corelation values b/w the various attributes.\n",
    "now we obtain a numpy array from the corelation data frame using the np.array method.\n",
    "nextly using the np.tril_indices.from() method we set the values of the lower half of the mask numpy array to False. this is bcoz on passing the mask to heatmap function of the seaborn it plots only those squares whose mask is False. therefore if we don't do this then as the mask is by default True then no square will appear. Hence in a nutshell we obtain a numpy array from the corelation data frame and set the lower values to False so that we can visualise the corelation. In order for a full square just use the [:] operator in mask in place of tril_ind... function.\n",
    "in next step we get the refernce to the current figure using the gcf() function of the matplotlib library and set the figure size.\n",
    "in last step we finally pass the necessary parameters to the heatmap function.\n",
    "\n",
    "DATA=the corelation data frame containing the 'CORELATION' values.\n",
    "\n",
    "MASK= explained earlier.\n",
    "\n",
    "vmin,vmax= range of values on side bar\n",
    "\n",
    "SQUARE= to show each individual unit as a square.\n",
    "\n",
    "ANNOT- whether to dispaly values on top of square or not. In order to dispaly pass it either True or the cor_mat.\n",
    "\n",
    "CBAR= whether to view the side bar or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### SOME INFERENCES FROM THE ABOVE HEATMAP\n",
    "\n",
    "1. Self relation ie of a feature to itself is equal to 1 as expected.\n",
    "\n",
    "2. JobLevel is highly related to Age as expected as aged employees will generally tend to occupy higher positions in the company.\n",
    "\n",
    "3. MonthlyIncome is very strongly related to joblevel as expected as senior employees will definately earn more.\n",
    "\n",
    "4. PerformanceRating is highly related to PercentSalaryHike which is quite obvious.\n",
    "\n",
    "5. Also note that TotalWorkingYears is highly related to JobLevel which is expected as senior employees must have worked for a larger span of time.\n",
    "\n",
    "6. YearsWithCurrManager is highly related to YearsAtCompany.\n",
    "\n",
    "7. YearsAtCompany is related to YearsInCurrentRole.\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we can drop some highly corelated features as they add redundancy to the model but since the corelation is very less in genral let us keep all the features for now. In case of highly corelated features we can use something like Principal Component Analysis(PCA) to reduce our feature space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 ) Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 ) Plotting the Features against the 'Target' variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  3.1.1 ) Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that Age is a continuous quantity and therefore we can plot it against the Attrition using a boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x249ab81e940>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(data=df,y='Age',x='Attrition',size=5,aspect=1,kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the median as well the maximum age of the peole with 'No' attrition is higher than that of the 'Yes' category. This shows that peole with higher age have lesser tendency to leave the organisation which makes sense as they may have settled in the organisation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.2 ) Department"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that both Attrition(Target) as well as the Deaprtment are categorical. In such cases a cross-tabulation is the most reasonable way to analyze the trends; which shows clearly the number of observaftions for each class which makes it easier to analyze the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Research & Development    961\n",
       "Sales                     446\n",
       "Human Resources            63\n",
       "Name: Department, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Department.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x249ac28d400>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(data=df,kind='count',x='Attrition',col='Department')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research &amp; Development</th>\n",
       "      <td>0.861602</td>\n",
       "      <td>0.138398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>0.793722</td>\n",
       "      <td>0.206278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.838776</td>\n",
       "      <td>0.161224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attrition                     No       Yes\n",
       "Department                                \n",
       "Human Resources         0.809524  0.190476\n",
       "Research & Development  0.861602  0.138398\n",
       "Sales                   0.793722  0.206278\n",
       "All                     0.838776  0.161224"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(columns=[df.Attrition],index=[df.Department],margins=True,normalize='index') # set normalize=index to view rowwise %."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that most of the observations corresspond to 'No' as we saw previously also. About 81 % of the people in HR dont want to leave the organisation and only 19 % want to leave. Similar conclusions can be drawn for other departments too from the above cross-tabulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.3 ) Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.852041</td>\n",
       "      <td>0.147959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.829932</td>\n",
       "      <td>0.170068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.838776</td>\n",
       "      <td>0.161224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attrition        No       Yes\n",
       "Gender                       \n",
       "Female     0.852041  0.147959\n",
       "Male       0.829932  0.170068\n",
       "All        0.838776  0.161224"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(columns=[df.Attrition],index=[df.Gender],margins=True,normalize='index') # set normalize=index to view rowwise %."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 85 % of females want to stay in the organisation while only 15 % want to leave the organisation. All in all 83 % of employees want to be in the organisation with only being 16% wanting to leave the organisation or the company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.4 ) Job Level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.736648</td>\n",
       "      <td>0.263352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.902622</td>\n",
       "      <td>0.097378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.853211</td>\n",
       "      <td>0.146789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.952830</td>\n",
       "      <td>0.047170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.927536</td>\n",
       "      <td>0.072464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.838776</td>\n",
       "      <td>0.161224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attrition        No       Yes\n",
       "JobLevel                     \n",
       "1          0.736648  0.263352\n",
       "2          0.902622  0.097378\n",
       "3          0.853211  0.146789\n",
       "4          0.952830  0.047170\n",
       "5          0.927536  0.072464\n",
       "All        0.838776  0.161224"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(columns=[df.Attrition],index=[df.JobLevel],margins=True,normalize='index') # set normalize=index to view rowwise %."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People in Joblevel 4 have a very high percent for a 'No' and a low percent for a 'Yes'. Similar inferences can be made for other job levels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.5 ) Monthly Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x249ac2ac198>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAAEYCAYAAACHjumMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFhdJREFUeJzt3Xu01WWdx/H3OVzE8EBadLER736sZgbzOOElBIeUvEw21Kg12YBpWjRGw6TpMAmuxpqWupakjoYlkV0o1IKKZKxURNE8KWrpl7SSVdNNXFxC9ARnzx+/38HNce9zfhvOc/beh89rLRb79+xn7/Pda3M+PL/b87SUSiXMzFJorXcBZjZ4OWDMLBkHjJkl44Axs2QcMGaWzNB6F9BfOjo6Su3t7fUuw2x31VKp0SMYM0vGAWNmyThgzCwZB4yZJeOAMbNkHDBmlowDxsySccCYWTIOGDNLxgFjZsk4YMwSmzdvHpMnT2bevHn1LmXAJbsXSdI0YFq+OQI4ApgEXANsBZZHxFxJrcD1wDjgReDciHhK0tE9+6aq1SyVLVu2sGTJEgCWLl3Keeedx5577lnnqgZOshFMRCyIiEkRMQnoAC4EbgDeB7wNGC/pSOBdwIiIOAb4JHBV/haV+po1lc7OTrqnpe3q6qKzs7POFQ2s5HdTSzoKeDNwCfDxiHg6b78DmAy8HvgBQESsknSUpFHAHhX6/rS3n9XR0ZHsc5jtjM2bN++wvXr1akaOHFmnatKpNpPBQEzXcCkwFxgFbCxr3wQclLdvKGvf1kvfXnm6Bms0GzZs2GF73LhxjB49uk7VDLykB3klvRI4PCJ+TBYYbWVPtwHrK7S39tLXzJpI6rNIxwN3AkTERqBT0sGSWoApwApgJXAKQH5g97Fe+ppZE0m9iyTgl2XbFwBfBYaQnRl6QNJPgBMl3Uc2K9b0an0T12pm/axlsCy85ikzrRFt2LCBqVOnbt++7bbbBusxmIpTZg6aOXlt9zHt5o/Vu4TCtr24dYftj379Uobs0fi/dgumX9Mv7+Mrec0sGQeMmSXjgDGzZBwwZpaMA8bMknHAmFkyDhgzS8YBY5ZQS2vZ9WctPbZ3Aw4Ys4Rahw1hr8P2AWCvQ/ehddiQOlc0sBr/kkKzJrf3W/dl77fuW+8y6sIjGDNLxgFjZsk4YMwsGQeMmSXjgDGzZBwwZpaMA8bMknHAmFkyDhgzS8YBY2bJOGDMLBkHjJkl44Axs2SS3k0t6RLgncBw4HrgbmABUAIeB2ZERJeky4BTga3AzIh4UNIhlfqmrNfM+leyEYykScCxwHHARGA/4GpgdkRMIFsJ7nRJR+bPjwfOAq7L3+JlfVPVamZppBzBTAEeA24HRgGfAM4jG8UALANOAoJs7ekSsFbSUEljgPYKfW/v7Qd2dHT092cw2y3V+rtUbdnmlAHzamB/4DTgQGAJ0JoHCcAmYDRZ+Kwre113e0uFvr3y2tS7iUcX1ruCQa+/fpdSBsw64MmI6ARC0gtku0nd2oD1wMb8cc/2rgptZtZEUp5Fuhd4h6QWSfsCI4Ef5sdmAE4GVgArgSmSWiWNJRvlPAs8XKGvmTWRZCOYiPiupOOBB8mCbAbwK2C+pOHAE8DiiNgmaQVwf1k/gFk9+6aq1czSSHqaOiIuqtA8sUK/OcCcHm1rKvU1s+bhC+3MLBkHjJkl44Axs2QcMGaWjAPGzJJxwJhZMg4YM0vGAWNmyThgzCwZB4yZJeOAMbNkHDBmlowDxsySccCYWTIOGDNLxgFjZsk4YJrAvHnzmDx5MvPmzat3KWY1ccA0uC1btrBkyRIAli5dypYtW+pckVlxDpgG19nZSamUrd7S1dVFZ2dnnSsyK84BY2bJOGDMLBkHjJkl44Axs2SSrosk6WFgQ775K+BG4BpgK9mC93MltQLXA+OAF4FzI+IpSUf37JuyVjPrf8kCRtIIgIiYVNb2CPBu4JfA9yQdCRwAjIiIY/JQuQo4HbihZ9+I+Gmqes2s/6UcwYwDXiFpef5z5gB7RMTTAJLuACYDrwd+ABARqyQdJWlUlb4OGLMmkjJgngeuBG4CDgWWAevLnt8EHASM4qXdKIBtedvGCn171dHRsWsVN6DNmzfvsL169WpGjhxZp2psd1Hr71J7e3vF9pQBswZ4KiJKwBpJG4B9yp5vIwucV+SPu7WShUtbhb69qvYhm9mGDRt22B43bhyjR4+uUzUN4tGF9a5g0Ouv36WUZ5HOITuegqR9yYJks6SDJbUAU4AVwErglLzf0cBjEbER6KzQ18yaSMoRzBeBBZLuBUpkgdMFfBUYQnZm6AFJPwFOlHQf0AJMz19/Qc++CWs1swSSBUxEdALvq/DU0T36dZGFSc/Xr+rZ18yaiy+0M7NkCo9gJB0AvJnslPLYiPhVqqLMbHAoFDCSzgRmkx2oPQa4X9K/R8QtKYtL6X0XfbXeJRTStfWFHbbPn7uY1qEj6lRNbb72uX+udwlWZ0V3kS4GjgU2RsQfgbcAlySryswGhaIBsy0iNnVvRMTvyM4ImZlVVfQYzM8kfRQYJukI4CPAI+nKMrPBoOgIZgbwBmAL8CWyK20/kqooMxscCo1gImIz2TEXH3cxs8KKnkWaCXwK6L4JpgUoRcSQVIWZWfMregxmJnBERKxNWYyZDS5Fj8E8AfwhZSFmNvgUHcFcAzwmaRXZFJYARMQ5Saoys0GhaMB8FrgFeCZhLWY2yBQNmBcj4vKklZjZoFM0YO6VdBXZtJfb1y6NiHuSVGVmg0LRgDmyx9+QTSL19/1bjr1MS/mVAC09ts0aW9EL7U4AkNQGDImIPufHtf7ROmQYe455I1v+9AR7jjmc1iHD6l2SWWFFL7Q7CPgGcDDQIukZ4IyI+EXK4iwzauwxjBp7TL3LMKtZ0etgbgQ+FxGvioh9gM8A89OVZWaDQdGAeXVELO7eiIhvsuMSJGZmL1M0YF7Ml3kFQFI72cJqZmZV1XIv0q2SniO70XEf4MxkVZnZoFD0LNIqSYcBh5GNen5dPsOdmVklhXaRJJ0B/DQifka2a/RzSacnrczMml7RXaTZwNsBIuLp/BjMcuA7vb1I0muADuBEspskF5BdoPc4MCMiuiRdBpyaPz8zIh6UdEilvrV9NDOrt6IHeYdHxPbpGvKVBVp6e4GkYWSnt7fkTVcDsyNiQv7a0/MDxxOB8cBZwHXV+has08waSC33In2dbK3oEtkB3vv7eM2VwA28NM1mO3B3/ngZcBIQZOtOl4C1koZKGlOl7+0FazWzBlE0YGYAFwLnA38B7gGur9ZZ0jTgTxFxh6TugGnJgwRgE9n0m6OAdWUv7W6v1LdPHR0dhT6MDQx/H82r1u+uvb29YnvRs0gvSrqR7HaB7l2j1wHVptA8ByhJejtwBLAQeE3Z823AerLVCdoqtHdVaOtTtQ9Z0aIni/e1nVLT91GLRxemeV/brr++u6JnkS4FfkM2crmLbPflrmr9I+L4iJgYEZPI1k/6ALBM0qS8y8nACmAlMEVSq6SxQGtEPAs8XKGvmTWZortIHwQOjog/7cLPmgXMlzScbI7fxRGxTdIKsuM5rWS7YhX77sLPNbM6KRowa4HnduYH5KOYbhMrPD8HmNOjbU2lvmbWXIoGzC/IziT9GHihu9HTaJpZb4oGzG/zP9DH9S9mZt2KnkWam7oQMxt8eg2YfJeoVO35iPCcvGZWVV8jmDkDUYSZDU69BkxE3A0g6RPAVyLi9wNSlZkNCkUP8r4CuEvS02R3OX87Iv6SrCozGxQKXckbEXMj4nCyyb5PAFZLulbSEUmrM7OmVnS6BiSNBA4EDiK7V+g54BpJn0lUm5k1uaLrIt1CNuHU94BPR8S9efsewO94aUoGM7Ptih6D+RFwfkRsLm/M77J+U/+XZWaDQV/XwXyqbHOWpB2ej4jLfWbJzKrpawTj2wLMbKf1dR2MbxEws51W9CDvucAVwKvyphagFBFDUhVmZs2v6EHe/wBOyNdFMjMrpOh1MH90uJhZrfo6i/SB/OEzkr5DttDa1u7nI8KzL5tZVX3tIp2Q/705/zOh7LkS2WoBZmYV9XUWaTqApBMj4n/Ln5M0NWVhZtb8+tpFOhPYA7i8x0V3Q4FLgdsS1mZmTa6vXaQ24Lj87xPK2reSnVkyM6uqr12km4CbJE2OiB8OUE1mNkgUvQ7mOUnfAvah7PYBz8lrZr0pGjALgRuBx+llEvBykoYA8wEB24DpZOG0IH+Px4EZEdEl6TLgVLJdr5kR8aCkQyr1LVivmTWAogHzfERcW+N7/wNARByXrzN9NVnAzI6IuyTdAJwu6RmyVRzHA/sBtwJ/l/ffoS9we401mFkdFQ2YOyT9K3AHO67suLbaCyLi25K+m2/uD/yBbJRyd962DDgJCGB5RJSAtZKGShoDtFfo64AxayJFA+bs/O9/K2srkU2fWVVEbJX0ZeAfgfcAp+VBArAJGA2MAtaVvay7vaVC3151dHT01cUGkL+P5lXrd9fe3l6xvejKjgfW9NN2fO2/SLoYeADYs+ypNmA9sDF/3LO9q0Jbr6p9yIoWPVm8r+2Umr6PWjzqC8hT66/vrtDNjpLGSFok6VlJ6yXdJum1fbzmbEndc/U+TxYYD+XHYwBOBlYAK4EpkloljQVaI+JZ4OEKfc2siRTdRboRuA84jyyUPgR8ETitl9fcBtws6R5gGDATeAKYL2l4/nhxRGyTtAK4P3/vGfnrZ/XsW8sHM7P6KxowB0VE+b1Hn5N0dtXeQD5B+BkVnppYoe8ceixTGxFrKvU1s+ZRdD6YkqT9ujfyXRmv7GhmvSo6gpkN3C/pAbJrWcaT7SaZmVVVdMIpgGvIDta2Aqt4aX5eM7OK+hrBLAD+CNwJdLLjMiaH4wmnzKwXfQXMkcCZwInAauAbwJ2+J8jMiuhruoZHgEeASyQdRRY2V0h6CPhGRNyVvkQza1ZFD/ISEQ+RXSg3Afgs8H5gr1SFmVnz6zNgJLUAxwP/RHZF7SPA54GlaUszs2bX11mk/wHeATwMfBO4KCKeH4jCzKz59TWCOZ/sTue35H+ukLT9yYjo9W5qM9u99RUwO30XtZlZX2eRnhmoQsxs8Cl6L5KZWc0cMGaWjAPGzJJxwJhZMg4YM0vGAWNmyThgzCwZB4yZJeOAMbNkHDBmlowDxsySccCYWTKFZ7SrlaRhwJeAA4A9gE8DPyebSLwEPA7MiIguSZcBpwJbgZkR8aCkQyr1TVWvmfW/lCOY9wPrImIC2Ux41wJXA7PzthbgdElHkq3gOB44C7guf/3L+ias1cwSSDaCAb7FjutJbwXagbvz7WXASUAAyyOiBKyVNFTSmCp9b+/tB3Z0dPRf9bbL/H00r1q/u/b29ortyQImIv4MIKmNLGhmA1fmQQKwCRgNjCKbNY8e7S0V+vaq2oesaNGTxfvaTqnp+6jFo16OK7X++u6SHuTN17P+MfCViPgaUH4MpQ1YD2zMH/dsr9TXzJpIsoCR9FpgOXBxRHwpb35Y0qT88cnACmAlMEVSq6SxQGtEPFulr5k1kZTHYC4F9gb+U9J/5m0fA+ZJGg48ASyOiG2SVgD3kwXejLzvLGB+ed+EtZpZAimPwXyMLFB6mlih7xxgTo+2NZX6mlnz8IV2ZpaMA8bMknHAmFkyDhgzS8YBY2bJOGDMLBkHjJkl44Axs2QcMGaWjAPGzJJxwJhZMg4YM0vGAWNmyThgzCwZB4yZJeOAMbNkHDBmlowDxsySccCYWTIOGDNLxgFjZsk4YMwsGQeMmSWTcuE1JI0H/jsiJkk6BFgAlIDHgRkR0SXpMuBUYCswMyIerNY3Za1m1v9SLh17EXATMCJvuhqYHRETgBbgdElHki2uNh44C7iuWt9UdZpZOil3kZ4GppZttwN354+XAW8H3gYsj4hSRKwFhkoaU6WvmTWZlEvH3irpgLKmlogo5Y83AaOBUcC6sj7d7ZX69qmjo2OXarb+5e+jedX63bW3t1dsT3oMpofyYyhtwHpgY/64Z3ulvn2q9iErWvRk8b62U2r6Pmrx6MI072vb9dd3N5BnkR6WNCl/fDKwAlgJTJHUKmks0BoRz1bpa2ZNZiBHMLOA+ZKGA08AiyNim6QVwP1kYTejWt8BrNPM+knSgImIXwNH54/XkJ0x6tlnDjCnR1vFvmbWXHyhnZkl44Axs2QcMGaWjAPGzJJxwJhZMg4YM0vGAWNmyThgzCwZB4yZJeOAMbNkHDBmlowDxsySccCYWTIOGDNLxgFjZsk4YMwsGQeMmSXjgDGzZBwwZpaMA8bMknHAmFkyDhgzS8YBY2bJOGDMLJmBXNmxJpJageuBccCLwLkR8VR9qzKzWjTyCOZdwIiIOAb4JHBVnesxsxo1csC8DfgBQESsAo6qbzlmVquWUqlU7xoqknQTcGtELMu31wIHRcTWSv07Ojoa84OY7Sba29tberY17DEYYCPQVrbdWi1coPKHM7P6auRdpJXAKQCSjgYeq285ZlarRh7B3A6cKOk+oAWYXud6zKxGDXsMxsyaXyPvIplZk3PAmFkyDhgzS8YB00AkLZb0ybLtvSSFpHH1rMuKkzRJ0npJ+5W1fVbStDqWVTcOmMZyAfBhSW/Kt68EvhARq+tYk9WuE7hZ0m5/bVYjn6be7UTEs5I+Ctwk6RLgYLLA+RtgHtnp+nXAOcBwYBHZfxLDgAsiwtcKNYYfkX0vM4BruxslzQLOArYC90TExfUpb+B4BNNgImIp8CSwAJgWESVgPjAjIiYB3wcuAt4KbABOBi4ERtWjXqvqw8DHJR2ab7cBZwDH5n8OlXRavYobKA6YxrQQeCAifptvvxG4XtJdZKOXfYFlwN3Ad4DLga461GlVRMQ6YCbZfxStwAhgVUT8Jf9PYwXw5vpVODAcMM0hgA/kI5iLgO8Bk4DfRcRJwKeBK+pWnVWUj0YDmAa8AIyXNDQ/NnM8sKaO5Q0IH4NpDh8GFkoakm9/kOxYzCJJM4FtZKMYazwzgcnAJuCbZPfYtQL3At+uY10DwrcKmFky3kUys2QcMGaWjAPGzJJxwJhZMg4YM0vGAWP9QtJfSypJendZ21xJE/LH50l6b5XXfl/SvpIOlPTFvO2ofOJ3a2IOGOsv5wDfAs4va5sIdF+7cxywR6UXRsQpEfF/wP5k918REQ9FxLnpyrWB4OtgbJdJGgb8BpgA3AeMJwuU64HfA9cAc4E/A+cB7wVeBRxCdmXy58muTF4CHAR8mSys5kTEJEmHAV8A9gE2AxdGxE8kLSC7H6sdeANweUTcnP4TW1EewVh/OBV4JiLWkF2d+qGIWAg8RLbk7+fJwuNTEXFH/pp1EfHG/HL6bhcCD0XEjB7vfwswLyL+Fvg4sFhS92hoP7JgeyfZ9BbWQBww1h+mA1/PHy8Cpksa3sdrHijyxpL2Ag6JiNtg+yqfzwHKuyzPbx58nGyEYw3EAWO7RNJryKaMmCXp18BNwN7A1D5euqXgj6j0b7SFl+6jewEgDxlrMA4Y21VnAz+MiL+KiAMiYn/gv8hm59vKS0FQ/rial/WJiI3ALyVNhe2L8L2ObMRiDc4BY7tqGtnB3HLXkU2IdRdwg6RjgTuBSyW9p5f3egJ4paSv9Gh/P3ChpMfIZoibGhGd/VC7JeazSGaWjEcwZpaMA8bMknHAmFkyDhgzS8YBY2bJOGDMLBkHjJkl8/86s4DzUu1+MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(data=df,kind='bar',x='Attrition',y='MonthlyIncome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Note that the average income for 'No' class is quite higher and it is obvious as those earning well will certainly not be willing to exit the organisation. Similarly those employees who are probably not earning well will certainly want to change the company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.6 ) Job Satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x249abab04e0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(data=df,kind='count',x='Attrition',col='JobSatisfaction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.771626</td>\n",
       "      <td>0.228374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.835714</td>\n",
       "      <td>0.164286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.834842</td>\n",
       "      <td>0.165158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.886710</td>\n",
       "      <td>0.113290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.838776</td>\n",
       "      <td>0.161224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attrition              No       Yes\n",
       "JobSatisfaction                    \n",
       "1                0.771626  0.228374\n",
       "2                0.835714  0.164286\n",
       "3                0.834842  0.165158\n",
       "4                0.886710  0.113290\n",
       "All              0.838776  0.161224"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(columns=[df.Attrition],index=[df.JobSatisfaction],margins=True,normalize='index') # set normalize=index to view rowwise %."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note this shows an interesting trend. Note that for higher values of job satisfaction( ie more a person is satisfied with his job) lesser percent of them say a 'Yes' which is quite obvious as highly contented workers will obvioulsy not like to leave the organisation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.7 ) Environment Satisfaction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.746479</td>\n",
       "      <td>0.253521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.850174</td>\n",
       "      <td>0.149826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.863135</td>\n",
       "      <td>0.136865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.865471</td>\n",
       "      <td>0.134529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.838776</td>\n",
       "      <td>0.161224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attrition                      No       Yes\n",
       "EnvironmentSatisfaction                    \n",
       "1                        0.746479  0.253521\n",
       "2                        0.850174  0.149826\n",
       "3                        0.863135  0.136865\n",
       "4                        0.865471  0.134529\n",
       "All                      0.838776  0.161224"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(columns=[df.Attrition],index=[df.EnvironmentSatisfaction],margins=True,normalize='index') # set normalize=index to view rowwise %."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we can notice that the relative percent of 'No' in people with higher grade of environment satisfacftion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.8 ) Job Involvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.662651</td>\n",
       "      <td>0.337349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.189333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.855991</td>\n",
       "      <td>0.144009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.909722</td>\n",
       "      <td>0.090278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.838776</td>\n",
       "      <td>0.161224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attrition             No       Yes\n",
       "JobInvolvement                    \n",
       "1               0.662651  0.337349\n",
       "2               0.810667  0.189333\n",
       "3               0.855991  0.144009\n",
       "4               0.909722  0.090278\n",
       "All             0.838776  0.161224"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(columns=[df.Attrition],index=[df.JobInvolvement],margins=True,normalize='index') # set normalize=index to view rowwise %."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.9 ) Work Life Balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.831395</td>\n",
       "      <td>0.168605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.857783</td>\n",
       "      <td>0.142217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.838776</td>\n",
       "      <td>0.161224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attrition              No       Yes\n",
       "WorkLifeBalance                    \n",
       "1                0.687500  0.312500\n",
       "2                0.831395  0.168605\n",
       "3                0.857783  0.142217\n",
       "4                0.823529  0.176471\n",
       "All              0.838776  0.161224"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(columns=[df.Attrition],index=[df.WorkLifeBalance],margins=True,normalize='index') # set normalize=index to view rowwise %."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we notice a similar trend as people with better work life balance dont want to leave the organisation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.10 ) RelationshipSatisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.793478</td>\n",
       "      <td>0.206522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.851485</td>\n",
       "      <td>0.148515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.845316</td>\n",
       "      <td>0.154684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.148148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.838776</td>\n",
       "      <td>0.161224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attrition                       No       Yes\n",
       "RelationshipSatisfaction                    \n",
       "1                         0.793478  0.206522\n",
       "2                         0.851485  0.148515\n",
       "3                         0.845316  0.154684\n",
       "4                         0.851852  0.148148\n",
       "All                       0.838776  0.161224"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(columns=[df.Attrition],index=[df.RelationshipSatisfaction],margins=True,normalize='index') # set normalize=index to view rowwise %."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Notice that I have plotted just some of the important features against out 'Target' variable i.e. Attrition in our case. Similarly we can plot other features against the 'Target' variable and analye the trends i.e. how the feature effects the 'Target' variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 ) Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature Selection is one of the main steps of the preprocessing phase as the features which we choose directly effects the model performance. While some of the features seem to be less useful in terms of the context; others seem to equally useful. The better features we use the better our model will perform.\n",
    "\n",
    "We can also use the Recusrive Feature Elimination technique (a wrapper method) to choose the desired number of most important features.\n",
    "The Recursive Feature Elimination (or RFE) works by recursively removing attributes and building a model on those attributes that remain.\n",
    "\n",
    "It uses the model accuracy to identify which attributes (and combination of attributes) contribute the most to predicting the target attribute.\n",
    "\n",
    "We can use it directly from the scikit library by importing the RFE module or function provided by the scikit. But note that since it tries different combinations or the subset of features;it is quite computationally expensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['BusinessTravel','DailyRate','EmployeeCount','EmployeeNumber','HourlyRate','MonthlyRate'\n",
    "           ,'NumCompaniesWorked','Over18','StandardHours', 'StockOptionLevel','TrainingTimesLastYear'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  4 ) Preparing Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before feeding our data into a ML model we first need to prepare the data. This includes encoding all the categorical features (either LabelEncoding or the OneHotEncoding) as the model expects the features to be in numerical form. Also for better performance we will do the feature scaling ie bringing all the features onto the same scale by using the StandardScaler provided in the scikit library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 ) Feature Encoding "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have used the Label Encoder from the scikit library to encode all the categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform(feature):\n",
    "    le=LabelEncoder()\n",
    "    df[feature]=le.fit_transform(df[feature])\n",
    "    print(le.classes_)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Attrition', 'Department', 'EducationField', 'Gender', 'JobRole',\n",
       "       'MaritalStatus', 'OverTime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df=df.select_dtypes(include='object')\n",
    "cat_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No' 'Yes']\n",
      "['Human Resources' 'Research & Development' 'Sales']\n",
      "['Human Resources' 'Life Sciences' 'Marketing' 'Medical' 'Other'\n",
      " 'Technical Degree']\n",
      "['Female' 'Male']\n",
      "['Healthcare Representative' 'Human Resources' 'Laboratory Technician'\n",
      " 'Manager' 'Manufacturing Director' 'Research Director'\n",
      " 'Research Scientist' 'Sales Executive' 'Sales Representative']\n",
      "['Divorced' 'Married' 'Single']\n",
      "['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "for col in cat_df.columns:\n",
    "    transform(col)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>YearsAtCompany</th>\n",
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       "      <th>YearsSinceLastPromotion</th>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Attrition  Department  DistanceFromHome  Education  EducationField  \\\n",
       "0   41          1           2                 1          2               1   \n",
       "1   49          0           1                 8          1               1   \n",
       "2   37          1           1                 2          2               4   \n",
       "3   33          0           1                 3          4               1   \n",
       "4   27          0           1                 2          1               3   \n",
       "\n",
       "   EnvironmentSatisfaction  Gender  JobInvolvement  JobLevel  \\\n",
       "0                        2       0               3         2   \n",
       "1                        3       1               2         2   \n",
       "2                        4       1               2         1   \n",
       "3                        4       0               3         1   \n",
       "4                        1       1               3         1   \n",
       "\n",
       "           ...           OverTime  PercentSalaryHike  PerformanceRating  \\\n",
       "0          ...                  1                 11                  3   \n",
       "1          ...                  0                 23                  4   \n",
       "2          ...                  1                 15                  3   \n",
       "3          ...                  1                 11                  3   \n",
       "4          ...                  0                 12                  3   \n",
       "\n",
       "   RelationshipSatisfaction  TotalWorkingYears  WorkLifeBalance  \\\n",
       "0                         1                  8                1   \n",
       "1                         4                 10                3   \n",
       "2                         2                  7                3   \n",
       "3                         3                  8                3   \n",
       "4                         4                  6                3   \n",
       "\n",
       "   YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                   4                        0   \n",
       "1              10                   7                        1   \n",
       "2               0                   0                        0   \n",
       "3               8                   7                        3   \n",
       "4               2                   2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # just to verify."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 ) Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scikit library provides various types of scalers including MinMax Scaler and the StandardScaler. Below I have used the StandardScaler to scale the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the neural networks are quite sensitive towards the scale of the features. Hence it is always good to perform feature scaling on the data before feeding it into an Artificial Neural Network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "scaled_df=scaler.fit_transform(df.drop('Attrition',axis=1))\n",
    "X=scaled_df\n",
    "Y=df['Attrition'].as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 ) One Hot Encoding the Target "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that there are two main things to watch out before feeding data into an ANN. \n",
    "\n",
    "The first is that our data needs to be in the form of numpy arrays (ndarray).\n",
    "\n",
    "The second that the target variable should be one hot encoded eg 2--> 0010 (assuming 0 based indexing) and so on.. \n",
    "In this way for a 'n' class classification problems our target variable will have n classes and hence after one hot encoding we shall have n labels with each label corressponding to a particular target class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       ...,\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=to_categorical(Y)\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 ) Splitting the data into training and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.25,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 )  Making Predictions Using an Artificial Neural Network (ANN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 ) Handling the Imbalanced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we have a imbalanced dataset with majority of observations being of one type ('NO') in our case. In this dataset for example we have about 84 % of observations having 'No' and only 16 % of 'Yes' and hence this is an imbalanced dataset.\n",
    "\n",
    "To deal with such a imbalanced dataset we have to take certain measures, otherwise the performance of our model can be significantly affected. In this section I have discussed two approaches to curb such datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1.1 ) Oversampling the Minority or Undersampling the Majority Class\n",
    " \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In an imbalanced dataset the main problem is that the data is highly skewed ie the number of observations of certain class is more than that of the other. Therefore what we do in this approach is to either increase the number of observations corressponding  to the minority class (oversampling) or decrease the number of observations for the majority class (undersampling).\n",
    "\n",
    "Note that in our case the number of observations is already pretty low and so oversampling will be more appropriate.\n",
    "\n",
    "Below I have used an oversampling technique known as the SMOTE(Synthetic Minority Oversampling Technique) which randomly creates some 'Synthetic' instances of the minority class so that the net observations of both the class get balanced out.\n",
    "\n",
    "One thing more to take of is to use the SMOTE before the cross validation step; just to ensure that our model does not overfit the data; just as in the case of feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# oversampler=SMOTE(random_state=42)\n",
    "# x_train_smote,  y_train_smote = oversampler.fit_sample(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1.2 ) Using the Right Evaluation Metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another important point while dealing with the imbalanced classes is the choice of right evaluation metrics. \n",
    "\n",
    "Note that accuracy is not a good choice. This is because since the data is skewed even an algorithm classifying the target as that belonging to the majority class at all times will achieve a very high accuracy. \n",
    "For  eg if we have 20 observations of one type 980 of another ; a classifier predicting the majority class at all times will also attain a accuracy of 98 % but doesnt convey any useful information.\n",
    "\n",
    "Hence in these type of cases we may use other metrics such as -->\n",
    "\n",
    "\n",
    "'Precision'-- (true positives)/(true positives+false positives)\n",
    "\n",
    "'Recall'-- (true positives)/(true positives+false negatives)\n",
    "\n",
    "'F1 Score'-- The harmonic mean of 'precision' and 'recall'\n",
    "\n",
    "'AUC ROC'-- ROC curve is a plot between 'senstivity' (Recall) and '1-specificity' (Specificity=Precision)\n",
    "\n",
    "'Confusion Matrix'-- Plot the entire confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2) Setting the random seeds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in order to get exactly same results after training an artificial neural network at different instances of time we need to specify the random seed for the Keras backend engine which is TensorFlow in my case. Also I have specified the seeds for the python random module as well as for the numpy.\n",
    "\n",
    "In order to adjust the weights oof an ANN ; the BackProp algorithm starts with a random weights and hence after a given no of epochs the results can be different if the random initialisation of weights is different in starting. \n",
    "\n",
    "Hence to obtain the same results it is necessary to specify the random seed to get the reproducible results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "rn.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.set_random_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 ) Building the Keras model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(input_dim=23,units=8,activation='relu'))\n",
    "model.add(Dense(units=16,activation='relu'))\n",
    "model.add(Dense(units=2,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  BREAKING IT DOWN\n",
    "\n",
    "1. First we need to build a model. For this we use the Sequential model provided by the Keras which is nothing but a linear stack of layers.\n",
    "\n",
    "\n",
    "2. Next we need to add the layers to our Sequential model. For this we use the model.add() function.\n",
    "\n",
    "\n",
    "3. Note that for each layer we need to specify the number of units ( or the number of neurons) and also the activation function used by the neurons.\n",
    "\n",
    "  Note that activation  function is used to model complex non-linear relationships and their are many choices. But generally it is preferred to use 'relu' function for the hidden layers and the 'sigmoid' or the 'logistic' function for the output layer. For a multi-class classification problem we can use the 'softmax' function as the activation function for the output layer.\n",
    "  \n",
    "\n",
    "4. Note that the first layer and ONLY the first layer expects the input dimensions in order to know the shape of the input numpy array.\n",
    "\n",
    "\n",
    "5. Finally note that the number of units or neurons in the final layer is equal to the number of classes of the target variable. In other words for a 'n' class classification problem we shall have 'n' neurons in the output layer. \n",
    " \n",
    " Each neuron represents a specific target class. The output of each neuron in the final layer thus represents the probability of given observation being classified to that target class. The observation is classified to the target class; the neuron corressponding to which has the highest value. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 ) Compiling the Keras model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(lr=0.01),loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BREAKING IT DOWN\n",
    "\n",
    "1. Now we need to compile the model. We have to specify the optimizer used by the model We have many choices like Adam, RMSprop etc.. Rfer to Keras doc for a comprehensive list of the optimizers available.\n",
    "\n",
    "\n",
    "2. Next we need to specify the loss function for the neural network which we seek to minimize.\n",
    "\n",
    "  I have used the 'binary_crossentropy' loss function since this is a binary classification problem. For a multi-class classification problems we may use the 'categorical_crossentropy'.\n",
    "\n",
    "\n",
    "3. Next we need to specify the metric to evaluate our models performance. Here I have used accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5 ) Summary of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 8)                 192       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                144       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 34        \n",
      "=================================================================\n",
      "Total params: 370\n",
      "Trainable params: 370\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provides overall description of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.6 ) Fitting the model on the training data and testing on the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1102 samples, validate on 368 samples\n",
      "Epoch 1/10\n",
      "1102/1102 [==============================] - 1s 456us/step - loss: 0.5200 - acc: 0.7772 - val_loss: 0.3944 - val_acc: 0.8696\n",
      "Epoch 2/10\n",
      "1102/1102 [==============================] - 0s 39us/step - loss: 0.3971 - acc: 0.8303 - val_loss: 0.3582 - val_acc: 0.8709\n",
      "Epoch 3/10\n",
      "1102/1102 [==============================] - 0s 50us/step - loss: 0.3580 - acc: 0.8457 - val_loss: 0.3515 - val_acc: 0.8791\n",
      "Epoch 4/10\n",
      "1102/1102 [==============================] - 0s 50us/step - loss: 0.3375 - acc: 0.8525 - val_loss: 0.3479 - val_acc: 0.8818\n",
      "Epoch 5/10\n",
      "1102/1102 [==============================] - 0s 42us/step - loss: 0.3272 - acc: 0.8652 - val_loss: 0.3498 - val_acc: 0.8668\n",
      "Epoch 6/10\n",
      "1102/1102 [==============================] - 0s 43us/step - loss: 0.3154 - acc: 0.8680 - val_loss: 0.3516 - val_acc: 0.8804\n",
      "Epoch 7/10\n",
      "1102/1102 [==============================] - 0s 45us/step - loss: 0.3115 - acc: 0.8707 - val_loss: 0.3541 - val_acc: 0.8777\n",
      "Epoch 8/10\n",
      "1102/1102 [==============================] - 0s 46us/step - loss: 0.3064 - acc: 0.8716 - val_loss: 0.3572 - val_acc: 0.8764\n",
      "Epoch 9/10\n",
      "1102/1102 [==============================] - 0s 49us/step - loss: 0.2978 - acc: 0.8752 - val_loss: 0.3554 - val_acc: 0.8804\n",
      "Epoch 10/10\n",
      "1102/1102 [==============================] - 0s 46us/step - loss: 0.2969 - acc: 0.8798 - val_loss: 0.3541 - val_acc: 0.8845\n"
     ]
    }
   ],
   "source": [
    "History=model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=10,verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BREAKING IT DOWN\n",
    "\n",
    "1. Lastly we need to fit our model onto the training data just as we do for traditional ML algorithms.\n",
    "\n",
    "\n",
    "2. We have to specify the training(x_train ,y_train) and the testing (validation_data) sets.\n",
    "\n",
    "\n",
    "3. We also need to specify the 'number of epochs'. \n",
    "   An 'epoch' is one entire cycle of 'Forward & Backward propagation' through all the training examples.\n",
    "\n",
    "\n",
    "4. Verbose is an optional parameter that just ensures how the output of each epoch is displayed on the screen.\n",
    "\n",
    "\n",
    "5. We have assigned it to a 'History' variable to retrieve the model performance during each epoch in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.7 ) Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_classes(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.79308105e-01, 8.12292695e-02],\n",
       "       [9.71148610e-01, 2.81063337e-02],\n",
       "       [8.03762555e-01, 1.36793584e-01],\n",
       "       [9.51201797e-01, 4.88448068e-02],\n",
       "       [9.95227933e-01, 3.52992350e-03],\n",
       "       [7.21227765e-01, 2.44345397e-01],\n",
       "       [8.74907970e-01, 1.10384263e-01],\n",
       "       [9.63543653e-01, 3.81244868e-02],\n",
       "       [8.74568224e-01, 1.51544943e-01],\n",
       "       [9.97175455e-01, 2.85343896e-03],\n",
       "       [7.44774342e-01, 2.60270119e-01],\n",
       "       [9.98951912e-01, 2.53731292e-03],\n",
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       "       [9.49914217e-01, 4.51161265e-02],\n",
       "       [9.80708957e-01, 2.11764444e-02],\n",
       "       [9.56352532e-01, 5.13661020e-02],\n",
       "       [7.68591821e-01, 2.00479433e-01],\n",
       "       [9.66306269e-01, 3.44362780e-02],\n",
       "       [9.02825356e-01, 8.93362463e-02],\n",
       "       [9.89300251e-01, 1.08886687e-02],\n",
       "       [9.89854634e-01, 1.23248463e-02],\n",
       "       [9.93565381e-01, 7.23429443e-03],\n",
       "       [9.94278312e-01, 5.75818820e-03],\n",
       "       [9.92714584e-01, 6.34493772e-03],\n",
       "       [9.58429933e-01, 3.90964709e-02],\n",
       "       [9.45849538e-01, 5.38173318e-02],\n",
       "       [9.32135105e-01, 9.96183828e-02],\n",
       "       [9.24995244e-01, 8.06017593e-02],\n",
       "       [9.14493322e-01, 8.96459147e-02],\n",
       "       [9.01392162e-01, 9.89915282e-02],\n",
       "       [9.90315259e-01, 1.15637779e-02],\n",
       "       [9.10140455e-01, 9.37903225e-02],\n",
       "       [9.34911430e-01, 5.37120737e-02],\n",
       "       [9.97327924e-01, 2.71083019e-03],\n",
       "       [9.83707726e-01, 1.55034289e-02],\n",
       "       [9.97769475e-01, 2.02112878e-03],\n",
       "       [9.74591672e-01, 2.54948307e-02],\n",
       "       [9.92675602e-01, 7.07650185e-03],\n",
       "       [9.67222869e-01, 3.35838571e-02],\n",
       "       [8.64798367e-01, 1.44274265e-01],\n",
       "       [9.54147577e-01, 4.78065088e-02],\n",
       "       [8.49554300e-01, 1.51500165e-01],\n",
       "       [8.64939928e-01, 1.03064910e-01],\n",
       "       [9.96942222e-01, 2.83189001e-03],\n",
       "       [9.99776185e-01, 4.12548310e-04],\n",
       "       [9.90037322e-01, 6.72798045e-03],\n",
       "       [5.62903762e-01, 4.25482720e-01],\n",
       "       [5.11152029e-01, 4.60984409e-01],\n",
       "       [9.94076133e-01, 1.15511790e-02],\n",
       "       [7.27190912e-01, 2.45050192e-01],\n",
       "       [9.27185893e-01, 5.19022420e-02],\n",
       "       [7.81372428e-01, 1.90896928e-01],\n",
       "       [9.50014830e-01, 5.41922413e-02],\n",
       "       [9.91334379e-01, 1.14256665e-02],\n",
       "       [9.95897651e-01, 4.19255951e-03],\n",
       "       [9.76296723e-01, 3.12537774e-02],\n",
       "       [8.59377384e-01, 1.35847583e-01],\n",
       "       [5.14076412e-01, 4.33043063e-01],\n",
       "       [5.33948660e-01, 4.43178862e-01],\n",
       "       [9.99937773e-01, 6.55717886e-05],\n",
       "       [6.26620233e-01, 3.63654613e-01],\n",
       "       [6.65686548e-01, 2.81887591e-01],\n",
       "       [7.91810095e-01, 1.65846244e-01],\n",
       "       [6.25612140e-01, 3.57640088e-01],\n",
       "       [8.68408918e-01, 1.34231463e-01],\n",
       "       [7.64593303e-01, 2.42920578e-01],\n",
       "       [9.96715188e-01, 3.86464642e-03],\n",
       "       [9.52666879e-01, 4.88453731e-02],\n",
       "       [9.81665671e-01, 1.87470540e-02]], dtype=float32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.8 ) Evaluating the Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "368/368 [==============================] - 0s 22us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.354127524987511, 0.8845108695652174]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bY5YCPwO+bYy5yRjzX0dZ58fAPGPMAuAh4MoY1hdTc3UxsohITMSsDctaGwKuPWzypi6Wu73T62rgwljV1JuGF2QyYnAmJZsqqG9qJT3FE++SRET6BPXVjqG5xUW0BUMsW1se71JERPoMBVcMtd/qRM2FIiI9R8EVQ4NyUhk3PJt1pfuprm2OdzkiIn2CgivG5hYPIRSGRWt0g0kRkZ6g4Iqx2dMKcTp0Z2QRkZ6i4Iqx7Aw/U8bkYXdUs7eqId7liIgkPQVXL2i/waSOukRETp6CqxfMnFyI2+VkoXoXioicNAVXL0hP8XDahEHs2FvH9j218S5HRCSpKbh6ydyO5kIddYmInAwFVy85bUIBKT4XC1eVEQ4n3bjBIiIJQ8HVS3weF2dMGsy+A43YndXxLkdEJGkpuHrRvPYhoFaquVBE5EQpuHrRtLF5ZKR6WbymnGAwFO9yRESSkoKrF7ldTmZPLaSmLsC6rfvjXY6ISFJScPWyeadGmgt1MbKIyIlRcPWy8SNyyM3ys3RtOa1twXiXIyKSdBRcvczpdDCneAgNzW28u7Ei3uWIiCQdBVcc6GJkEZETp+CKg1FFWRTlpbPivX00NrfGuxwRkaSi4IoDh8PBvOIiWlqDvL1hb7zLERFJKgquOJmr3oUiIiek28FljBkcfZ5jjLneGJMSu7L6vqK8dEYNyWKVreBgfSDe5YiIJI1uBZcx5lfA3caYCcAzwKnA47EsrD+YVzyEYCjM0rXl8S5FRCRpdPeI63TgGuAzwG+ttVcDJmZV9RNzphXhcMACNReKiHRbd4PLFV32YuBlY0wqkBazqvqJ3AEpTDhlIO+9X8X+mqZ4lyMikhS6G1xPAnuA7dbat4F3gV/HrKp+ZF5xEeEwLFqtoy4Rke7oVnBZax8CCqy1l0YnzbHW/jx2ZfUfZ00pxOV0sEAXI4uIdEt3O2d8ArjPGJNujNkIWGPMlTGtrJ/ISvdRbPLZuvsgZZX18S5HRCThdbep8DYivQkvB1YAI4AbY1RTv9MxBJRuMCkickzdvo7LWrsGuBB4yVpbD3hiVlU/c8bEArweFwtWlREOh+NdjohIQutucO0zxjwMnAa8Yox5ENgZu7L6l1S/h9MnDKKssp6tZQfjXY6ISELrbnB9DngHmGetbQC2RadJD5lbrCGgRES6o7vBVQ+kA/cbY14A3EBDzKrqh2aMzyfN72bRqt2EQmouFBE5ku4G1wPAR4lcz/V74BzgZ7Eqqj/yuF2cNaWQ/Qeb2bj9QLzLERFJWN0Nro8Cn7TWvmStfRH4dHSa9KD23oUL1LtQROSI3MexnAcIdHofPNoKxhgn8CgwNbreNdba0i6W+SfworX2seiI808D+UAd8N/W2spu1pj0Jo/OY0CGj8VryvnqpZNxu3TXGRGRw3X3L+MfgbeMMTcaY24E3iRyXdfRXAL4rbVnArcAD3axzN1ATqf31wHrrLVziDRL3trN+voEl9PB7KmF1DW2sHpzv8lrEZHj0t0hn+4F7gSGEbn4+B5gyDFWmw28El1/OTCj80xjzKeBEPByV+tEp8/vTn19ybzoDSY1BJSISNe621SItfYVPggVjDF/Ar5+lFUygc4XJQWNMW5rbZsxZhLweSLnyn50hHXqgKzu1FZSUtKdxWK+jZ4QDocZkOZi6ZoyzhoVwutOzObCRNlfyUL7q/u0r45Pf9xf3Q6uLjiOMb8WyOj03mmtbYu+/hJQRKTJcQTQYozZftg6GUBNdwqZPn16two+kpKSkpPeRk86r+I9nntjC3Xk89Hpw+Ndzock2v5KdNpf3ad9dXz68v46WiCfzNf5Y11stAS4AMAYMxNY1z7DWnuztfYMa+3ZwB+Ah6JHdB3rAOcDi06ivqQ1/7RheN1OHv3LGt4q2RXvckREEspRj7iMMW/RdUA5gJRjbPtvwHnGmKXR5a8yxtwElFprXzrCOr8CnjDGLAZaiDQnxkw4HGZL1fsEQi2x/JjjVpiXzp1fO4u7fvc2Dz2zktqGFi6eOyreZYmIJIRjNRXefqIbttaGgGsPm7ypi+Vu7/S6EbjsRD/zeO1vPMCtb/yEHE8WI8ePpiA9r7c++pgmjhzIj6+fzW2/Wcr/vbiemroAX7pgPA7HsVpoRUT6tqMGl7V2QW8VEg+5qTlcZObzd/s6P3jtfv5n9tcYnzcm3mV1GDE4kwdunMuPfr2Uv7y5hYP1Aa7/9FRcur5LRPqxfv0X0OFwcMW0T/GxvNk0tjZx539+zoL3l8e7rEMMyknl/hvmMGpIFq+t2Ml9T7xDoPWo136LiPRp/Tq42k3LGscP5t2I3+Xllyue4E9rXyQUDsW7rA4DMnzce90spozO5e0Ne7ntN8uob2qNd1kiInGh4IqaNGgc98y/mYL0PP628RV+tvT/CLQlTqeNVL+H278yk1lTCtmwrYrv/XIxB2qb412WiEivU3B1UphZwD3zb2ZC3hje3r2K2998iOqmxLmxo8ft4rtXzOD8M0ewfU8t//vIIvbs191lRKR/UXAdJsOXzq3zvsHZp5zJ1uodfP+1+9lenTjXUrmcDq771BQ+91HD3qpGbn54EVt3d+s6bRGRPkHB1QW3y811p13BF6ZcyoGmGn745oO8W7Ym3mV1cDgcfP5j47j20skcbAjwvUeXsK50f7zLEhHpFQquI3A4HFw8/qN8Z9ZXIRzmJ4t/zUubXiMcTpy7E184eyTf/cIMWtuC/Og3y1i6tjzeJYmIxJyC6xhOHzKNOz7yHQakZPL0muf59bt/pC3YduwVe8mc4iJ+dPVM3C4H9z/5Dq8u3x7vkkREYkrB1Q0jc4Zx3/xbOCV7KG9uW8I9Cx+mPpA4nSKKTT73XDeL9FQvjzy3hj+/bhPqyFBEpCcpuLopJ3UAd3zkO5xeNI0NFZv5wRsPsKeuIt5ldRg7LJv7b5hNXnYKT7+8icdfXE8opPASkb5HwXUc/G4fN836CpeM/xh76ir4/uv3s6Fic7zL6jAkP4Of3DiHYQUZ/H3RNh58poTWtsS5kFpEpCcouI6T0+Hk81Mu4eunf4nmtgB3/+fnvLltabzL6jAwK4UfXz+b8SNyWLiqjLt+u5ymQOKckxMROVkKrhN09iln8sN53yTFk8Jj7zzF02ueT5hhojJSvdz5tTOZMX4QqzZXcutjSzhYH4h3WSIiPULBdRIm5I/h3vk3U5gxiJc2vcaDS35Dc1tiBITf6+YHV53OR2YMZfPOGm755WIqqhvjXZaIyElTcJ2kgox87p7/XSYPMrxTtobb3niQqsbqeJcFgNvl5JufLeaSeaPYXVHP/z68iJ17a+NdlojISVFw9YB0bxrfm3sj80fO5v2aXXz/9fvZdmBHvMsCwOl0cPV/TeKqT0xg/8FmbvnlYjbtOBDvskRETpiCq4e4nS6+MuPzfGnap6lpquVHbz7I27tXxbusDp88Zwzf/Ow0GppaufWxpby7cV+8SxIROSEKrh7kcDj4hDmX786+FofDyYNLfsMLG19NmIuB558+nO9feTrhUJi7f/c2/ylJnMGDRUS6S8EVAzOKpnDXR/6HganZPLP2BX614qmEGSbqjEmDufNrZ+H3unjwmZW8tHBrvEsSETkuCq4YGZE9hHvn/y+jcobzn+3LuGvBL6gN1Me7LAAmjhzIfdfPJjvDx+MvrufJf72XMEeFIiLHouCKoeyULO445yZmDj2VjZVb+MHrD1BWuzfeZQFwSmEWD9w4h8G5aTz3xhYefnY1wWBiXIcmInI0Cq4Y87q9fOvMq/nkhPPZV1/JD15/gLV7N8a7LAAKBqbxwA1zGDUki9dW7OTHT75DS2sw3mWJiByVgqsXOB1OLp/8X9xwxpW0BFu5d+EjvFa6KN5lATAgw8e9181iyuhclq/fy22PL6OhqTXeZYmIHJGCqxfNHXEGPzr7W6R5U3m85Bn+sOo5QqH4N8+l+j3cds1MzpoymPVbq/jeo4uprm2Od1kiIl1ScPWycXmjuHf+zQzJHMy/Nr/JA4t/RVNr/EPC63Fx8xWn8fEzR/B+eS03P7KIPfsT555jIiLtFFxxMCg9j7vP/S5TC8azcs96fvjGT9nfEP/RLFxOB1//1BQuP8+wt6qRmx9ZxNbdNfEuS0TkEAquOEn1pnDLnOv56Oi57DxYxvdev58tVe/HuywcDgdf+Pg4vnbpZA7WB/j+r5awrnR/vMsSEemg4Iojl9PFNdM/x1XFn6E2UMftb/2MpTtL4l0WAJ+YPZL/+cJ0WlqD3Pb4MpatK493SSIigIIrIZw/9hxumfN13A4X/9+y/+MvG/6VEBcEzy0ewg+vnonL6eDHT7zDq8sTY+BgEenfFFwJonjwJO4693/ISxvIs+v/zsNv/4G9dRUEQ/G9rupUk889180iLcXLI8+t5tnXNydEqIpI/+WOdwHygWEDirh3/s38ZPGvWbxjBYt3rMDtdDM4PY/CzAKKMgdRmFFAUWYBRRmD8Hv8vVLX2GHZ3H/DbG57fBlPvbyR4fleXBkVTBubh8Ph6JUaRHpLKByisbWJ+kAD9S2N1Lc00NjajNflxuf24XN58bt9eN1e/C5vxzSnU8cBvUXBlWCy/Jn86Jxv8VrpQrbX7Ka8di+76/ayq3bPh5YdmJJNYeYgijIKIs+ZBRRlFJCdktXjgTJ0UAYP3DCHh59bzcpNFfzoN8sYM3QAl507ljMmFuB0KsAksXQEUEtjNITaH43UBeo7Qqm+paEjpOpaGmhoaSTM8bcqeJzRYHN78bm8+NyRgPO5vNGQi86LTmuf73V58R82/fBw9Lq9OB0KxnYKrgTkdXm40Jzb8T4cDlPTXEtZ7V7K6/ZSVruP8rq97K7dy7p9m1i3b9Mh66e4/V0GWkF6Hm7Xif+T5w5I4Y6vnMnfX1vGujIXy9bt4d4/rGBYQQaXfWQMc6YV4XLpl0t6Vigcoqm1mfqWBuo6HQV1Dp26lk7T20OqtbHbzdoup4sMbxoD/JkMySwg3ZdOujeVdG8aGd40/G4fraE2Am0BAsEWmtsCtLS10hwMEGhroSU6LdDWQqAtQH1LA1WN1QSCLT22H7wuTzTYIgHod/loaQrwct0SPC43Hqcn+uzG7XLjdXoizy4Pbmdkusfl+eDZ1cW0w9eNPnuc7oRqXVFwJQGHw0F2ShbZKVlMGmQOmdfc2kx5XUVHoJXV7aWsdi87asrYethdmJ0OJ4PSc6OBFmluLMqMhFu6N63b9RTmeLnovOns3FvLX97cwoJVZTz4zEr++OomPnXOGM49bSget6tHfnZJfuFwmEBbgMbWZhpbm478aGlm177dvLZwefToJ3JU1NDSSCjcvRFmXA4n6b50Mv0ZFGUWkO5Nizx8aR8EkS+tY3qGNzLd5/bF5A9zKByiNRgJvOZgCy1t0YALtnSEYCA6LRJ+0eltLR3zAsH2QGyhORigpa2F2kA9lW1VtARb2dXcOwN3fxB+kZCMBJv7QwHncXnITc3himmfwu2Mzd8BBVeS83v8jMwZxsicYYdMD4VCVDRWUV57aKCV1e7l3bq1UL72kOWzfBnREOscaAXkpmYfsYliWEEmN31+Op//2Dief6uU11bs5Jd/WcOf/m259OzRfHzmcPw+/RdLZuFwmOa2wBGDpusQOnR6U2tzt4MHgLpoAHnTyPSlU5gxiHRvKhne6FFQp+BJ96aS0enoyB+jADpRTocz2jzoJTMG23/33XeZWjyV1mAbraHW6HMbrcHW6HMbbaFWWoJttIXaaAm20had3vXykee2YBstoVbajjY/2EJjS2PH9GCnf2Ofy8ulEz7OAH8sfuoYBpcxxgk8CkwFAsA11trSTvOvB64EwsCd1tp/GGMcwG5gS3SxZdba78Wqxr7M6XRSkJ5HQXoepxZOPmRebaA+Gmh7KavbF2mCrN3LxspS3qvccsiyXpeHwRmDDjk6q2zax6DaPWT5MkjzplIwMI2vf3oqnz1vLC8s2Mory7bz25fW8+zrm7l47kgunD2S9BRPL/70/Vc4HKY11Bb5Zt/pm3rHt/ZO3/C7Cpum1iYaWzpNb2s+oV6kKW4/qZ4UcvxZpGYUkOpNIcWTQmrHw9/p9QePbbaUM6fPTLgASlQOhyPa7OcBUuLT+hmHAAAT0klEQVRaSygUioRYqBWP04PP7Y3ZZ8Xy6/AlgN9ae6YxZibwIHAxgDEmF/g6MA3wA+8ZY/4JjAJWWmsvimFd/V6mL53MvNGMyxt9yPSWYCt76yqiR2f7KKvdQ3ntPsrr9rGjZvchyz5T9k8g8o0yw5tGpj+DTF86WbkZnHtJKuV7WrHbGnjm7Z38dcXbnD11FBefOZ7BOQP69Unm9mA5tDkoQHOn8yQtwdYPzpcED286+qDJ6ZDmp47nwEldruDAQUo0VAam5jD08IDxpnSEUvv7w0Moxe0/4R52VZ59pPRSb1npWU6nE5/Ti4/YBVa7WAbXbOAVAGvtcmPMjPYZ1tr9xpip1to2Y8wIoMZaGzbGTAeKjDFvAU3At621NoY1Sidel4dhA4oYNqDokOmhcIgDjTWU1e2lvHYfm7ZvJi07nYOBOmoD9dQ213GgsZpdBw8bXaMIfNGX/2lYxn9eBweRoMtOySTTn06GL4MsXzT0/Blk+qIPfzpZvgxSPSlx+ebdFmyjORjoCJDIcyRgPnj9wYn6zifmI9MDNLdGgqW2oQ52P9txTqOnroNzO90dJ+lTPX5y/FkdzVI+12Hdtd0efK7OPd58hx79eCOv/W5fv/5iIckhlsGVCRzs9D5ojHFba9sAoqF1A3AH8IvoMnuA+6y1zxljZgNPA6cd64NKSk5+mKSe2EZ/kE8m+QOj30FSOKR1IhgO0hhsjj6aaAo20xBspr61iV01deyta6DNEaDG00J98z5CzrJjfp4TJ6kuf/SRQqrLT4rLT5or5ZDnFJePUDhES6iV1nBbx3NrqPPrTvNCbbSGW2mJPh++XOgEukMfzoUz0tPL6cYTdJPlSsfjjvb+criiz27cjshJba/Tg7vTdI/THX0+dPn26d0OmBDQRee2MAEaCJBo9wDQ7+Lx6Y/7K5bBVQtkdHrvbA+tdtbaR4wxvwFeNsacA7wNtAfbYmNMkTHGYa096l+R6dOnn1ShJSUlJ72N/uRE91drW4gFK3fzlzc3U1bZgNMZ4vQpOcw7I4/0DKgN1EUf9Rxsjr5ujr4P1FHREpsR9H1uH363jzRvauS6m/buxtHXfrev48jF32le59c+lw+/x3fItTrtPar0/6v7tK+OT1/eX0cL5FgG1xLgIuDZ6Dmude0zjDEGuA/4FNBKpPNGCLgNqAIeMMZMBXYeK7QkeXjcTuafPoxzZgxl2bpynn19M8tX17B8dQ2nTyjgsvljmGtyjrh+S7D10DDrFGr1LQ14oyeEO8KmU/i0X9DZOXB8bh9el0dNYyJJJpbB9TfgPGPMUsABXGWMuQkotda+ZIxZAywj0qvwZWvtAmPMWuBpY8yFRI68roxhfRInLqeD2VOLmDWlkJJNFTz7+mZWvLeXFe/tZcroXD5z7limjMn90Lktb/T6kNzUI4ebiPR9MQsua20IuPawyZs6zb+DyPmtzutUAxfGqiZJLA6HgxnjBzF9XD7rt1bx7BubWb25krWl+zHDsvnM/LGcNmGQukWLyCF0dajEncPhYPLoXCaPzmXzzmqee2Mzy9fv5a7fvc2IwZlcdu4YZk0twqXxEEUEBZckmLHDsvnBVWewY09kOKmFq3bzk6dLePqVTXz6I2M4Z/pQPG6dkxLpz/QXQBLS8MGZfOcL03nslvl8bOZwKqubePjZ1Xz13td4adFWmlvajr0REemTFFyS0AbnpnHDZdP4vx/M5+K5o6hrauXxF9ZzzT2v8dwbm2loao13iSLSyxRckhQGZqVwzcWT+O0PzuOz88fS1hbiyX9t5Oq7/81TL2/kYH0g3iWKSC/ROS5JKlnpPr54/nguPXs0/1r6Pi8u3Mqzr2/mhQVbmTRqIBNPGcjEkQMZO2yAbq0i0kcpuCQppaV4uOzcsVw0ZySvvb2Tfy55n5WbKli5qQKIXOw8dlg2E0dGgmzc8GxS/RqhXqQvUHBJUvN73Vw0ZyQXzRlJdW0zG96vYsO2Kt7bdoD3oq8BnE4HI4uyOo7IJpySQ1a67xhbF5FEpOCSPiM708/sqUXMnhoZ3b6+qZVN2w+wfut+3nv/AFt2VVO6q4YXF24FYOigjI4jsomnDCQvO773MxKR7lFwSZ+VnuJhxvhBzBg/CIDmlja27Kxh/bYq3ttWxcYdB9i1rI5Xlm0HID8nlYmn5DBxZC4TR+ZQlJeuUTtEEpCCS/oNv9fdMUIHQFswxLayg6zfWtXRrPhWyW7eKoncNHNAuo8JI3M6jshGFGZp9A6RBKDgkn7L7Yp04Bg7LJtPnjOaUCjMrn11HUdk67dVsXTtHpau3QNAqt/N+BE5Hc2LY4aq56JIPCi4RKKcTgfDB2cyfHAmF846hXA4zL4DjWzYVtXxKNlUQUm056LX7WTs8OyOI7JxI3JI8elXSiTW9FsmcgQOh4OCgWkUDEzj3NOGAXCgtjnSrLi1qqMH4/qtH/RcHFWU1XFENuGUgWSmeeP5I4j0SQoukeOQ00XPxY3vf3BEtmVXDVt21fDCgkjPxeEFGQzOChNO3cekUQPxe/UrJ3Ky9FskchLSUzycNqGA0yYUAJGei5t3VncckW3cXs2OvUGW2+W4XU4mjszhVJNPsclnxOBM9VoUOQEKLpEe5Pe6mTI6jymj8wBobQvywqvLaQgPYJWtZM2W/azZsp/f/+M9sjN8TBubx6kmn2lj8xmQoQuiRbpDwSUSQx63i5EFfqZPn8iVn4Dq2mZWb6lkpa1gta08pPv9yKIsisfmceq4fMaPGKj7jokcgYJLpBdlZ/o5Z/pQzpk+lFAozPY9tayyFay0Fbz3/gG2lR3kr2+V4ve6mDQqN9qsmKeLoUU6UXCJxEn7+Ikji7L41EfG0BxoY/22qo4ge3fjPt7duA+A/OwUiqPnxqaOziU9Vb0Vpf9ScIkkCL/PfcgQVRXVjayylayyFazeUsmry3fw6vIdOB0wdlg2xSafU00+Y4YOwOVSs6L0HwoukQSVn53Kx2YO52MzhxMMhdmyq7ojyOzOajbtqOZP/7akpXiYOiaX4rGRIMvPSY136SIxpeASSQIup4Nxw3MYNzyHz33UUN/UytotlazaHOno0XloqqK8tI5mxcmjcjWah/Q5+h8tkoTSUzycNaWQs6YUEg6H2bO/gZW2glW2krWllfxj8fv8Y/H7uF0Oxo8YSLHJo9jkM7IwC6cGCpYkp+ASSXIOh4PCvHQK89L5xOyRtLaF2LTjQEcnj3Vb97Nu636e/NdGstK9TBuTz7SxuQzKSWNAho/sDB9pKR71WpSkoeAS6WM8bieTR+UyeVQuX7pgAgfrA6zaHO3ksbmCBat2s2DV7kPWcbucDMjwdQTZgHQf2Zl+BqR3mpbhIzvDT6rfrZCTuFJwifRxWek+zj51CGefOoRwOMyOvXW8934VBw42U1MfoLo2QE19M9V1AXbsqaV0V+io2/O4nYcEWUfgpfsYEA277MxI+KX4FHLS8xRcIv2Iw+FgxOBMRgzO7HJ+OBymobmNmrpmauoCVNcFos+d3tcHqKltZlvZQdqCNUf9PJ/XFQmyw4LuQ8GX7sOvTiTSTfqfIiIdHA4H6Ske0lM8DMnPOOqy4XCY+qbWDwdbF+8376ohFAofdXspPhcpHgdDViwhLzuF/OxU8gZEn7NTyB2QgtejG3eKgktETpDD4SAj1UtGqpehg44ecqFQmLrGFmrag60+0OVRXcWBetaW7j/idgZk+MjPTiFvQCTM8qKv87NTyMtOJSNVnUz6AwWXiMSc0+kgK91HVrqP4YOPvFxJSQmTpkyjqqaJiupGKqubqKhuorIm8rqyuoltZbVs3tl1E6Xf64oG2qFHa+1HbwOz/BplpA9QcIlIQvF5XB3d+7sSCoWpqQ90BFvk0Uhlp7Dbta++y3WdDsjJSjks1KJBlx2Znur3xPLHkx6g4BKRpOJ0OsjJ9JOT6Wfc8K6XaWxupbLmg1CriAZcRTTg7I4DbNx+oMt101M8hxyl5WWnMignlVOKMhk8ME1NkQlAwSUifU6q38PwAg/DC7ruPRkMhqg62HzIUVp7qFVWN1K+v4H3y2s/tF6a382oIQMYHX2MGpqlMIsDBZeI9Dsul5P8nFTyc1KZyMAPzQ+Hw9Q1tnY0QZZXNrC1rIatu2tYW7r/kA4k7WE2asgAxijMeoWCS0TkMA6Hg8w0L5lpXkYNGXDIvIamVraVHaR0dw2lu48dZqOHZDF66ACFWQ9ScImIHIe0FA+TR+cyeXRux7TG5la2lh1k6+4atuzqZpgNGUDBwDQNenwCYhZcxhgn8CgwFQgA11hrSzvNvx64EggDd1pr/2GMSQGeBvKBOuC/rbWVsapRRKQnpPo9HeNDtuscZqW7DlK6u1ph1kNiecR1CeC31p5pjJkJPAhcDGCMyQW+DkwD/MB7xph/AtcB66y1txtjLgduBb4ZwxpFRGKie2FWw7qth4ZZqt/NqKIBjB6qMDsSRzh89GFYTpQx5iFghbX2/0Xfl1lrizrNd1tr24wxY4DnrbWTjTHPAw9Ya5cbY7KApdbaiUf7nJKSktj8ACIivaC5NcTe6lbKq1rYc6CF8upWqmrbDlnG53EwONvL4BwPhTleCnM8ZGe4cfbxc2bTp0/v8geM5RFXJnCw0/tge1gBREPrBuAO4BddrFMHZHXng6ZPn35ShZaUlJz0NvoT7a/jo/3VfdpXEY3N7R1ADlK6K9IJZEdlPdsrAh3LpPrd5KQ7yc3OwuVy4HY5cbucH3rtcTlxuZy4o9NdLgdupxO324nb6YjOi853O3E5P1i2e9vr9Nrp6LEOKCUlJUecF8vgqgU6D2DmbA+tdtbaR4wxvwFeNsacc9g6GcDRh54WEemDUv0eJo3KZdJhzYztYbY12qNxd0U9u/cnVjcAt8vBwKwUfvbteWSkemPzGTHZasQS4CLg2eg5rnXtM4wxBrgP+BTQSqTzRii6zgXACuB8YFEM6xMRSRpdhdm7777LtOJTaQuGaAuGCQZDh7xuDYYIBsPRaYe+bou+Dh7+OhSmrS1EWyhEW1uYYOiD+R/exoe3GQyGyEzz4XHHbkzIWAbX34DzjDFLAQdwlTHmJqDUWvuSMWYNsIxIr8KXrbULjDHvAE8YYxYDLcDnY1ifiEhSczg+aMbrT2IWXNbaEHDtYZM3dZp/B5HzW53XaQQui1VNIiKS/PpXTIuISNJTcImISFJRcImISFJRcImISFJRcImISFJRcImISFJRcImISFKJ2SC7vUWD7IqI9E1HGmQ36YNLRET6FzUViohIUlFwiYhIUlFwiYhIUlFwiYhIUlFwiYhIUlFwiYhIUonljSQTmjHGCTwKTCVyB+ZrrLWl8a0qcRljPMDvgBGAD7jbWvtSXItKAsaYfKAEOM9au+lYy/dnxpjvAf8FeIFHrbW/jXNJCSv6+/gEkd/HIPCV/vT/qz8fcV0C+K21ZwK3AA/GuZ5E90Wgylo7BzgfeCTO9SS86B+XXwNN8a4l0RljzgbOAmYB84ChcS0o8V0AuK21ZwF3AvfEuZ5e1Z+DazbwCoC1djkwI77lJLzngB92et8Wr0KSyE+Bx4DyeBeSBD4GrAP+Bvwd+Ed8y0l4mwF3tOUoE2iNcz29qj8HVyZwsNP7oDGm3zadHou1tt5aW2eMyQD+Atwa75oSmTHmSqDSWvtqvGtJErlEvjxeBlwL/NEY0+VwPwJAPZFmwk3A48Av4lpNL+vPwVULZHR677TW6ijiKIwxQ4G3gKestc/Eu54E92XgPGPMf4BpwJPGmIL4lpTQqoBXrbUt1loLNAN5ca4pkX2byP4aS+Q8/RPGGH+ca+o1/fkIYwlwEfCsMWYmkWYKOQJjzCDg38AN1to34l1PorPWzm1/HQ2va621e+NXUcJbDHzTGPMQMBhIIxJm0rVqPmgePAB4AFf8yuld/Tm4/kbkG/FSwAFcFed6Et33gWzgh8aY9nNd51tr1fFATpq19h/GmLnACiItQddba4NxLiuR/Qz4nTFmEZFemN+31jbEuaZeo9HhRUQkqfTnc1wiIpKEFFwiIpJUFFwiIpJUFFwiIpJUFFwiIpJU+nN3eJFeY4wZQWSYnvcOm/W4tfaXPbD9s4HbrbVnn+y2RBKdgkuk95Rba6fFuwiRZKfgEokzY0wF8DyR0dHrgC9Ya7dHR3T5OeAH9gNfs9aWGmOmERl1PpXIqAlfiG4qzxjzL2AUYImM++cD/gS0Dzd1h25HI8lO57hEek+hMWb1YY/JRMbkW2atnQL8P+AXxhhv9PUN1tqpREaZ/1N0O38E7rLWTo4u883o9GHA9cB4IkE1H7gU2G6tnQ5cDczplZ9UJIY0coZIL4ie4/qPtXZEF/OagFRrbdgYk0nkNigziQxmXNxpuWoiA/austbmHLaNs4nc3HN29P0TwJvAUuA/wDvAP4E/W2tre/rnE+lNOuISib+Qtbb9G6STyL3OuvrdbL/NR8e3TWOM3xgzMvq2890NwoDDWrsFGEfkKG0OsCJ6DyeRpKX/wCLxl2qMuSj6+irgZSLnqAYaY04DMMZ8Bthhrd0B7DbGfDS6/BVE7oDbJWPMDUTOaz0HfB3IJ3IvOpGkpc4ZIr2n0Biz+rBpC6PPlxlj7iHSTPjf1tqAMeazwCPGmDQinTA+G132i8CvjDEPEOm0cQVgjvCZTwJ/MsasI3JE9l1rbU3P/UgivU/nuETizBgTttbqbr8i3aSmQhERSSo64hIRkaSiIy4REUkqCi4REUkqCi4REUkqCi4REUkqCi4REUkq/z+LsbLdf3ciQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(History.history['loss'])\n",
    "plt.plot(History.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['train', 'test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(History.history['acc'])\n",
    "plt.plot(History.history['val_acc'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['train', 'test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 ) Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In order to increase the model performance of the ANN we need to tune the hyperparameters. Some of the hyperparameters include"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a) Number of hidden layers.\n",
    "\n",
    "b) Number of neurons in a particular layer.\n",
    "\n",
    "c) The activation function.\n",
    "\n",
    "d) The optimizer used.\n",
    "\n",
    "e) Number of epochs etc..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7 ) Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Hence we have completed the analysis of the data and also made predictions using an Artificial Neural Network.      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('image-hr.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE END. [please star/upvote if u find it helpful.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}