employee_Absenteeism.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Name - Employee Absenteeism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# IMPORTING ALL THE LIBRARY\n",
    "import os\n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from fancyimpute import KNN, NuclearNormMinimization, SoftImpute, BiScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the data\n",
    "os.chdir('D:/Data Science/EDWISOR/2_PORTFOLIO/project 1')\n",
    "data = pd.read_excel('Absenteeism_at_work_Project.xls', sheet_name= 'Absenteeism_at_work' ,header=0,\n",
    "                     na_values= ['',' ','NA','na','Na','N/A','N/a','n/a'])\n",
    "####################################################################################################################\n",
    "#Defining feature as categorical\n",
    "Categorical_col = ['Reason for absence', 'Month of absence', 'Day of the week',\n",
    "       'Seasons', 'Disciplinary failure', 'Education', 'Social drinker',\n",
    "       'Social smoker']\n",
    "Non_Categorical_col = [i for i in data.columns if i not in Categorical_col]\n",
    "for i in data.columns:\n",
    "    if i in Categorical_col: data[i] = pd.Categorical(data[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Applying ML but removing all the missing value of an data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Self Defined function\n",
    "####################################################################################################################\n",
    "# Return the percentage of missing data in the original dataset\n",
    "def PerOfMissing(d1,d2):# d1--data by droping the NAN value d2--Original data\n",
    "    percent_of_missing_data = round( 100 - ((len(d1)/len(d2))*100), 2)\n",
    "    percent_of_missing_data = str(percent_of_missing_data) + '% of data has Missing value'\n",
    "    return percent_of_missing_data\n",
    "####################################################################################################################\n",
    "# Return MAE, MRSE, R², Adjusted R²\n",
    "def reg_acc(y_true, y_pre):\n",
    "    from math import sqrt\n",
    "    from sklearn.metrics import mean_absolute_error\n",
    "    from sklearn.metrics import mean_squared_error\n",
    "    from sklearn.metrics import r2_score\n",
    "    print (\"MSE of data: \", mean_squared_error(y_true,y_pre))\n",
    "    print (\"***RMSE of data: \", sqrt(mean_squared_error(y_true,y_pre)),'***')\n",
    "    print ('Other Parameters:')\n",
    "    print (\"R² : \", r2_score(y_true,y_pre))\n",
    "    print ('MAE:',mean_absolute_error(y_true,y_pre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.65% of data has Missing value\n"
     ]
    }
   ],
   "source": [
    "# droping all the NAN value from the data and saving the data in data_without_NAN\n",
    "data_without_NAN = data.dropna()\n",
    "print (PerOfMissing(data_without_NAN,data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Applied by removing the missing value and preprocessing  the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####____MODEL_CHECK____#########\n",
      "KNN model\n",
      "MSE of data:  66.68781887755101\n",
      "***RMSE of data:  8.16626100963905 ***\n",
      "Other Parameters:\n",
      "R² :  -0.11571613071126441\n",
      "MAE: 5.046875\n",
      "###########\n",
      "###########\n",
      "Decision Tree\n",
      "MSE of data:  736.7879774305555\n",
      "***RMSE of data:  27.143838664244885 ***\n",
      "Other Parameters:\n",
      "R² :  0.04957067331946796\n",
      "MAE: 10.108072916666668\n",
      "###########\n",
      "###########\n",
      "Random Forest\n",
      "MSE of data:  149.56804135742186\n",
      "***RMSE of data:  12.229801362140837 ***\n",
      "Other Parameters:\n",
      "R² :  0.028691006943876052\n",
      "MAE: 6.293561197916667\n",
      "###########\n",
      "###########\n",
      "MSE of data:  43.5766653210526\n",
      "***RMSE of data:  6.601262403590135 ***\n",
      "Other Parameters:\n",
      "R² :  -0.2416276176800649\n",
      "MAE: 5.016390603178143\n",
      "Linear Regression\n",
      "Intercept : 23.39943367103103\n",
      "Coefficient\n",
      "ID --> -0.17634088405346032\n",
      "Reason for absence --> -0.5507704011683515\n",
      "Month of absence --> 0.06829573735259103\n",
      "Day of the week --> -0.7244787660593269\n",
      "Seasons --> -0.1945388458495434\n",
      "Transportation expense --> 1.6727617638601746\n",
      "Distance from Residence to Work --> -8.571132077428267\n",
      "Service time --> 3.077904116914585\n",
      "Age --> 0.2057680149159055\n",
      "Work load Average/day  --> -1.4372215321393904\n",
      "Hit target --> 2.6159532548163047\n",
      "Disciplinary failure --> -15.409096698609082\n",
      "Education --> -2.206077256620801\n",
      "Son --> 1.0082542169753292\n",
      "Social drinker --> 3.178133289913451\n",
      "Social smoker --> -1.4034448298656532\n",
      "Pet --> -0.013216647891405779\n",
      "Body mass index --> -11.340367030101078\n"
     ]
    }
   ],
   "source": [
    "data_without_NAN = data_without_NAN.drop(['Height','Weight'],axis=1)\n",
    "scaling_col = ['Transportation expense', 'Distance from Residence to Work','Service time',\n",
    "              'Work load Average/day ', 'Hit target','Body mass index']\n",
    "\n",
    "for i in scaling_col:\n",
    "    data_without_NAN[i]=(data_without_NAN[i]-min(data_without_NAN[i]))/(max(data_without_NAN[i])-min(data_without_NAN[i]))\n",
    "    #print(i,': Scaling Done')\n",
    "\n",
    "\n",
    "\n",
    "# Checking the outcome without preprocessing of the data set\n",
    "#imputing the training and testing data with test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = data_without_NAN.drop(['Absenteeism time in hours'], axis=1)\n",
    "Y = data_without_NAN['Absenteeism time in hours']\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y, test_size=0.2)\n",
    "\n",
    "print('#####____MODEL_CHECK____#########')\n",
    "## KNN model\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "KNN_model = KNeighborsRegressor(n_neighbors=7).fit(x_train,y_train)\n",
    "y_KNN_predict = KNN_model.predict(x_test)\n",
    "#accuracy_KNN =  KNN_model.score(y_KNN_predict,y_test)\n",
    "##print (\"accuracy\", accuracy_KNN*100)\n",
    "print('KNN model')\n",
    "reg_acc(y_KNN_predict,y_test)\n",
    "print('###########')\n",
    "print('###########')\n",
    "# Decision Tree\n",
    "from sklearn import tree\n",
    "dec_model   = tree.DecisionTreeRegressor().fit(x_train,y_train)\n",
    "y_dec_model = dec_model.predict(x_test)\n",
    "#accuracy_c50 =  c50_model.score(x_test,y_test)\n",
    "print('Decision Tree')\n",
    "reg_acc(y_dec_model,y_test)\n",
    "print('###########')\n",
    "print('###########')\n",
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "RF_model = RandomForestRegressor(n_estimators=20).fit(x_train,y_train)\n",
    "y_rf_predict = RF_model.predict(x_test)\n",
    "print('Random Forest')\n",
    "reg_acc(y_rf_predict,y_test)\n",
    "print('###########')\n",
    "print('###########')\n",
    "\n",
    "#Linear Reg\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lReg = LinearRegression().fit(x_train,y_train)\n",
    "l_predict = lReg.predict(x_test)\n",
    "reg_acc(l_predict,y_test)\n",
    "print('Linear Regression')\n",
    "print('Intercept :',lReg.intercept_)\n",
    "print('Coefficient')\n",
    "for i,coef in zip(data_without_NAN.columns,lReg.coef_):\n",
    "    print (i,\"-->\",coef)\n",
    "\n",
    "\n",
    "\n",
    "#Naye Base\n",
    "#from sklearn.naive_bayes import GaussianNB\n",
    "#NB_model = GaussianNB().fit(x_train, y_train)\n",
    "#y_nb_predict = NB_model.predict(x_test)\n",
    "#print('Naye Base')\n",
    "#reg_acc(y_nb_predict,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-Processing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Applying missing value analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking which missing value analysis is good for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Variables</th>\n",
       "      <th>Missing_Val</th>\n",
       "      <th>Missing_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Body mass index</td>\n",
       "      <td>31</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Absenteeism time in hours</td>\n",
       "      <td>22</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Height</td>\n",
       "      <td>14</td>\n",
       "      <td>1.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Work load Average/day</td>\n",
       "      <td>10</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Education</td>\n",
       "      <td>10</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Transportation expense</td>\n",
       "      <td>7</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Son</td>\n",
       "      <td>6</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Disciplinary failure</td>\n",
       "      <td>6</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hit target</td>\n",
       "      <td>6</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Social smoker</td>\n",
       "      <td>4</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Age</td>\n",
       "      <td>3</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Reason for absence</td>\n",
       "      <td>3</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Service time</td>\n",
       "      <td>3</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Distance from Residence to Work</td>\n",
       "      <td>3</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Social drinker</td>\n",
       "      <td>3</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pet</td>\n",
       "      <td>2</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Weight</td>\n",
       "      <td>1</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Month of absence</td>\n",
       "      <td>1</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Seasons</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Day of the week</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ID</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Variables  Missing_Val  Missing_per\n",
       "0                   Body mass index           31         4.19\n",
       "1         Absenteeism time in hours           22         2.97\n",
       "2                            Height           14         1.89\n",
       "3            Work load Average/day            10         1.35\n",
       "4                         Education           10         1.35\n",
       "5            Transportation expense            7         0.95\n",
       "6                               Son            6         0.81\n",
       "7              Disciplinary failure            6         0.81\n",
       "8                        Hit target            6         0.81\n",
       "9                     Social smoker            4         0.54\n",
       "10                              Age            3         0.41\n",
       "11               Reason for absence            3         0.41\n",
       "12                     Service time            3         0.41\n",
       "13  Distance from Residence to Work            3         0.41\n",
       "14                   Social drinker            3         0.41\n",
       "15                              Pet            2         0.27\n",
       "16                           Weight            1         0.14\n",
       "17                 Month of absence            1         0.14\n",
       "18                          Seasons            0         0.00\n",
       "19                  Day of the week            0         0.00\n",
       "20                               ID            0         0.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating list of columns name on basis of NAN value\n",
    "col_with_nan, col_without_nan = [],[]\n",
    "for i in data.columns:\n",
    "    if data[i].isnull().sum() > 0:\n",
    "        col_with_nan.append(i)\n",
    "    else :col_without_nan.append(i)\n",
    "\n",
    "#Creating the table which columns has how much missing value\n",
    "missing_val = pd.DataFrame(data.isnull().sum())\n",
    "missing_val = missing_val.reset_index()\n",
    "missing_val = missing_val.rename(columns={'index': 'Variables', 0: 'Missing_Val'})\n",
    "missing_val['Missing_per'] = round((missing_val['Missing_Val']/len(data))*100,2)\n",
    "missing_val = missing_val.sort_values('Missing_per', ascending= False).reset_index(drop = True)\n",
    "missing_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP-Omen\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "#random Number\n",
    "index_NO_nan = data.dropna().index\n",
    "random_index = []\n",
    "for i in range(len(col_with_nan)):\n",
    "    random_index.append(random.choice(index_NO_nan))\n",
    "####################################################################################################################\n",
    "\n",
    "#replacing data with nan\n",
    "dum = data.copy()\n",
    "for i in range(len(col_with_nan)):\n",
    "    dum[col_with_nan[i]].loc[random_index[i]] = np.nan\n",
    "####################################################################################################################\n",
    "\n",
    "#created table\n",
    "actV,meanV,medianV = [],[],[]\n",
    "for i in range(len(col_with_nan)):\n",
    "    actV.append(data[col_with_nan[i]].loc[random_index[i]])\n",
    "        \n",
    "for i in range(len(col_with_nan)):\n",
    "    if col_with_nan[i] in Categorical_col:\n",
    "        meanV.append(np.nan)#Categorical data has no mean(Error occur )\n",
    "        medianV.append(np.nan)#Categorical data has no median\n",
    "    else:\n",
    "        meanV.append(data[col_with_nan[i]].mean())\n",
    "        medianV.append(data[col_with_nan[i]].mean())\n",
    "\n",
    "frame ={'Actual Value': actV,'Mean': meanV, 'Median': medianV}\n",
    "data_frame = pd.DataFrame(data = frame, index = col_with_nan, dtype =int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputing row 1/740 with 0 missing, elapsed time: 0.231\n",
      "Imputing row 101/740 with 1 missing, elapsed time: 0.234\n",
      "Imputing row 201/740 with 0 missing, elapsed time: 0.238\n",
      "Imputing row 301/740 with 0 missing, elapsed time: 0.239\n",
      "Imputing row 401/740 with 0 missing, elapsed time: 0.241\n",
      "Imputing row 501/740 with 0 missing, elapsed time: 0.243\n",
      "Imputing row 601/740 with 0 missing, elapsed time: 0.245\n",
      "Imputing row 701/740 with 0 missing, elapsed time: 0.245\n",
      "##########################DONE##########################\n",
      "Imputing row 1/740 with 0 missing, elapsed time: 0.260\n",
      "Imputing row 101/740 with 1 missing, elapsed time: 0.264\n",
      "Imputing row 201/740 with 0 missing, elapsed time: 0.267\n",
      "Imputing row 301/740 with 0 missing, elapsed time: 0.269\n",
      "Imputing row 401/740 with 0 missing, elapsed time: 0.271\n",
      "Imputing row 501/740 with 0 missing, elapsed time: 0.272\n",
      "Imputing row 601/740 with 0 missing, elapsed time: 0.274\n",
      "Imputing row 701/740 with 0 missing, elapsed time: 0.274\n",
      "##########################DONE##########################\n",
      "Imputing row 1/740 with 0 missing, elapsed time: 0.241\n",
      "Imputing row 101/740 with 1 missing, elapsed time: 0.245\n",
      "Imputing row 201/740 with 0 missing, elapsed time: 0.248\n",
      "Imputing row 301/740 with 0 missing, elapsed time: 0.250\n",
      "Imputing row 401/740 with 0 missing, elapsed time: 0.253\n",
      "Imputing row 501/740 with 0 missing, elapsed time: 0.254\n",
      "Imputing row 601/740 with 0 missing, elapsed time: 0.256\n",
      "Imputing row 701/740 with 0 missing, elapsed time: 0.258\n",
      "##########################DONE##########################\n"
     ]
    }
   ],
   "source": [
    "###########################\"KNN with 3\"###########################\n",
    "data_KNN = KNN(k=3).complete(dum)\n",
    "data_KNN = pd.DataFrame(data = data_KNN, columns=data.columns)\n",
    "knn_array = []\n",
    "for i in range(len(col_with_nan)):\n",
    "    knn_array.append(int(data_KNN[col_with_nan[i]].loc[random_index[i]]))\n",
    "    \n",
    "data_frame['KNN_3'] = knn_array\n",
    "print('##########################DONE##########################')\n",
    "###########################\"KNN with 5\"###########################\n",
    "data_KNN5 = KNN(k=5).complete(dum)\n",
    "data_KNN5 = pd.DataFrame(data = data_KNN, columns=data.columns)\n",
    "knn_array = []\n",
    "for i in range(len(col_with_nan)):\n",
    "    knn_array.append(int(data_KNN5[col_with_nan[i]].loc[random_index[i]]))\n",
    "    \n",
    "data_frame['KNN_5'] = knn_array\n",
    "print('##########################DONE##########################')\n",
    "###########################\"KNN with 7\"###########################\n",
    "data_KNN7 = KNN(k=7).complete(dum)\n",
    "data_KNN7 = pd.DataFrame(data = data_KNN, columns=data.columns)\n",
    "knn_array = []\n",
    "for i in range(len(col_with_nan)):\n",
    "    knn_array.append(int(data_KNN7[col_with_nan[i]].loc[random_index[i]]))\n",
    "    \n",
    "data_frame['KNN_7'] = knn_array\n",
    "print('##########################DONE##########################')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAMPLE TESTED ARRAY IS [469, 203, 365, 649, 126, 649, 163, 409, 256, 428, 245, 583, 126, 713, 337, 182, 507, 537]\n"
     ]
    },
    {
     "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>Actual Value</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>KNN_3</th>\n",
       "      <th>KNN_5</th>\n",
       "      <th>KNN_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Reason for absence</th>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month of absence</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation expense</th>\n",
       "      <td>235</td>\n",
       "      <td>221.035</td>\n",
       "      <td>221.035</td>\n",
       "      <td>270</td>\n",
       "      <td>270</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance from Residence to Work</th>\n",
       "      <td>51</td>\n",
       "      <td>29.6676</td>\n",
       "      <td>29.6676</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Service time</th>\n",
       "      <td>10</td>\n",
       "      <td>12.5658</td>\n",
       "      <td>12.5658</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>38</td>\n",
       "      <td>36.4491</td>\n",
       "      <td>36.4491</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Work load Average/day</th>\n",
       "      <td>343253</td>\n",
       "      <td>271189</td>\n",
       "      <td>271189</td>\n",
       "      <td>261338</td>\n",
       "      <td>261338</td>\n",
       "      <td>261338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hit target</th>\n",
       "      <td>98</td>\n",
       "      <td>94.5872</td>\n",
       "      <td>94.5872</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disciplinary failure</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Son</th>\n",
       "      <td>0</td>\n",
       "      <td>1.01771</td>\n",
       "      <td>1.01771</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social drinker</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social smoker</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pet</th>\n",
       "      <td>0</td>\n",
       "      <td>0.746612</td>\n",
       "      <td>0.746612</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>68</td>\n",
       "      <td>79.0636</td>\n",
       "      <td>79.0636</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>165</td>\n",
       "      <td>172.153</td>\n",
       "      <td>172.153</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Body mass index</th>\n",
       "      <td>36</td>\n",
       "      <td>26.6841</td>\n",
       "      <td>26.6841</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Absenteeism time in hours</th>\n",
       "      <td>8</td>\n",
       "      <td>6.97772</td>\n",
       "      <td>6.97772</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Actual Value      Mean    Median   KNN_3  \\\n",
       "Reason for absence                         23       NaN       NaN      26   \n",
       "Month of absence                            4       NaN       NaN       4   \n",
       "Transportation expense                    235   221.035   221.035     270   \n",
       "Distance from Residence to Work            51   29.6676   29.6676      51   \n",
       "Service time                               10   12.5658   12.5658      10   \n",
       "Age                                        38   36.4491   36.4491      38   \n",
       "Work load Average/day                  343253    271189    271189  261338   \n",
       "Hit target                                 98   94.5872   94.5872      93   \n",
       "Disciplinary failure                        0       NaN       NaN       0   \n",
       "Education                                   3       NaN       NaN       2   \n",
       "Son                                         0   1.01771   1.01771       1   \n",
       "Social drinker                              1       NaN       NaN       0   \n",
       "Social smoker                               0       NaN       NaN       0   \n",
       "Pet                                         0  0.746612  0.746612       0   \n",
       "Weight                                     68   79.0636   79.0636      70   \n",
       "Height                                    165   172.153   172.153     165   \n",
       "Body mass index                            36   26.6841   26.6841      35   \n",
       "Absenteeism time in hours                   8   6.97772   6.97772       7   \n",
       "\n",
       "                                  KNN_5   KNN_7  \n",
       "Reason for absence                   26      26  \n",
       "Month of absence                      4       4  \n",
       "Transportation expense              270     270  \n",
       "Distance from Residence to Work      51      51  \n",
       "Service time                         10      10  \n",
       "Age                                  38      38  \n",
       "Work load Average/day            261338  261338  \n",
       "Hit target                           93      93  \n",
       "Disciplinary failure                  0       0  \n",
       "Education                             2       2  \n",
       "Son                                   1       1  \n",
       "Social drinker                        0       0  \n",
       "Social smoker                         0       0  \n",
       "Pet                                   0       0  \n",
       "Weight                               70      70  \n",
       "Height                              165     165  \n",
       "Body mass index                      35      35  \n",
       "Absenteeism time in hours             7       7  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"SAMPLE TESTED ARRAY IS\", random_index)\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN turn's out to produce good output relevent to the other missing value analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputing row 1/740 with 0 missing, elapsed time: 0.235\n",
      "Imputing row 101/740 with 1 missing, elapsed time: 0.241\n",
      "Imputing row 201/740 with 0 missing, elapsed time: 0.245\n",
      "Imputing row 301/740 with 0 missing, elapsed time: 0.247\n",
      "Imputing row 401/740 with 0 missing, elapsed time: 0.249\n",
      "Imputing row 501/740 with 0 missing, elapsed time: 0.250\n",
      "Imputing row 601/740 with 0 missing, elapsed time: 0.252\n",
      "Imputing row 701/740 with 0 missing, elapsed time: 0.253\n"
     ]
    }
   ],
   "source": [
    "# Applying the KNN imputation over the data (k = 3)\n",
    "process_data = KNN(k=3).complete(data)\n",
    "process_data = pd.DataFrame(data = process_data, columns=data.columns)\n",
    "##########################################################################################\n",
    "# So, now on we will proccess further in KNN_pre_data\n",
    "##########################################################################################\n",
    "# converting all the non categorical value in integer\n",
    "\n",
    "for i in process_data.columns:\n",
    "    process_data[i]= process_data[i].astype('int')\n",
    "\n",
    "for i in Categorical_col:\n",
    "    process_data[i] = pd.Categorical(process_data[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generating the correlation heatmap\n",
    "plt.figure(figsize = (15, 10))\n",
    "heat = sns.heatmap(process_data.drop(['ID'],axis=1).corr(), annot = True)\n",
    "heat.figure.savefig('Heatmap.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As per the heat map Weight is highly correlated with Body mass index. Moreover, weight divides with square of an height\n",
    "#  is equal body mass index so weight and height column will be drop from the data set\n",
    "process_data = process_data.drop(['Height','Weight'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Normalisation and Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All the numerical data will be scaled\n",
    "scaling_col = ['Transportation expense', 'Distance from Residence to Work','Service time',\n",
    "              'Work load Average/day ', 'Hit target','Body mass index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transportation expense : Scaling Done\n",
      "Distance from Residence to Work : Scaling Done\n",
      "Service time : Scaling Done\n",
      "Work load Average/day  : Scaling Done\n",
      "Hit target : Scaling Done\n",
      "Body mass index : Scaling Done\n"
     ]
    }
   ],
   "source": [
    "for i in scaling_col:\n",
    "    process_data[i]=(process_data[i]-min(process_data[i]))/(max(process_data[i])-min(process_data[i]))\n",
    "    print(i,': Scaling Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving of the processed data set\n",
    "process_data.to_excel('Process data_new.xls', index = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting the data set in train-test\n",
    "from sklearn.model_selection import train_test_split\n",
    "Pro_X = process_data.drop(['Absenteeism time in hours'], axis=1)\n",
    "Pro_Y = process_data['Absenteeism time in hours']\n",
    "X_TRAIN,X_TEST,Y_TRAIN,Y_TEST = train_test_split(Pro_X,Pro_Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of data:  292.2972972972973\n",
      "***RMSE of data:  17.09670428174089 ***\n",
      "Other Parameters:\n",
      "R² :  -0.15988070175311475\n",
      "MAE: 7.360360360360361\n"
     ]
    }
   ],
   "source": [
    "# Applying KNN Machine learnig algorithm\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "Pro_KNN_model = KNeighborsRegressor(n_neighbors=3).fit(X_TRAIN,Y_TRAIN)\n",
    "Final_KNN_predict = Pro_KNN_model.predict(X_TEST)\n",
    "reg_acc(Y_TEST,Final_KNN_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the train-test data into excel sheet\n",
    "#write = pd.ExcelWriter('Train_Test_data.xlsx', engine='xlsxwriter')\n",
    "#X_TRAIN.to_excel(write,sheet_name='TrainX',index=False)\n",
    "#Y_TRAIN.to_excel(write,sheet_name='TrainY',index=False)\n",
    "#X_TEST.to_excel(write,sheet_name='TestX',index=False)\n",
    "#Y_TEST.to_excel(write,sheet_name='TestY',index=False)\n",
    "#write.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of data:  234.78154897008716\n",
      "***RMSE of data:  15.322582973183312 ***\n",
      "Other Parameters:\n",
      "R² :  0.06835064745353725\n",
      "MAE: 6.44050414909134\n",
      "Intercept : 10.382840681365666\n",
      "Coefficient\n",
      "ID --> -0.1785224414710312\n",
      "Reason for absence --> -0.38356380969241666\n",
      "Month of absence --> 0.19603574229512963\n",
      "Day of the week --> -0.4067291076554663\n",
      "Seasons --> -0.10238094809366391\n",
      "Transportation expense --> -0.7100339762852044\n",
      "Distance from Residence to Work --> -5.5165700764792\n",
      "Service time --> -10.002115352992854\n",
      "Age --> 0.45206587835384143\n",
      "Work load Average/day  --> 1.965275701934068\n",
      "Hit target --> 4.654967983778953\n",
      "Disciplinary failure --> -10.86668326986222\n",
      "Education --> -2.0910419563864324\n",
      "Son --> 0.3700326927068894\n",
      "Social drinker --> 2.0414851493035338\n",
      "Social smoker --> 1.1644489819486064\n",
      "Pet --> -0.493629161147407\n",
      "Body mass index --> -9.09113596801323\n"
     ]
    }
   ],
   "source": [
    "#Applying linear regression machine learning algorithm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linReg = LinearRegression().fit(X_TRAIN,Y_TRAIN)\n",
    "linear_predict = linReg.predict(X_TEST)\n",
    "reg_acc(Y_TEST,linear_predict)\n",
    "print('Intercept :',linReg.intercept_)\n",
    "print('Coefficient')\n",
    "for i,coef in zip(process_data.columns,linReg.coef_):\n",
    "    print (i,\"-->\",coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree ML\n",
      "MSE of data:  345.61054804804803\n",
      "***RMSE of data:  18.59060375695335 ***\n",
      "Other Parameters:\n",
      "R² :  -1.780393438617672\n",
      "MAE: 7.15427927927928\n",
      "######END########\n",
      "Random Forest ML\n",
      "MSE of data:  260.25189356272597\n",
      "***RMSE of data:  16.13232449347353 ***\n",
      "Other Parameters:\n",
      "R² :  -4.7621185023122194\n",
      "MAE: 6.671\n",
      "######END########\n",
      "Naive Bayes\n",
      "MSE of data:  889.8108108108108\n",
      "***RMSE of data:  29.829696793812886 ***\n",
      "Other Parameters:\n",
      "R² :  -0.6783951750053456\n",
      "MAE: 18.93243243243243\n"
     ]
    }
   ],
   "source": [
    "#Applying all the different machine learning algorithm to check which produce the best result\n",
    "#################################################################\n",
    "from sklearn import tree\n",
    "Deci_tree_pre = tree.DecisionTreeRegressor().fit(X_TRAIN,Y_TRAIN).predict(X_TEST)\n",
    "print('Decision Tree ML')\n",
    "reg_acc(Deci_tree_pre,Y_TEST)\n",
    "print('######END########')\n",
    "#################################################################\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "Ran_for_pre = RandomForestRegressor(n_estimators=50).fit(X_TRAIN,Y_TRAIN).predict(X_TEST)\n",
    "print('Random Forest ML')\n",
    "reg_acc(Ran_for_pre,Y_TEST)\n",
    "print('######END########')\n",
    "#################################################################\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "print('Naive Bayes')\n",
    "NB_pre = GaussianNB().fit(X_TRAIN,Y_TRAIN).predict(X_TEST)\n",
    "reg_acc(NB_pre,Y_TEST)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Measuring the number of 'k' suited with this Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_mae,list_mse,list_r2,num=[],[],[],[]\n",
    "for i in range(50):\n",
    "    if i%2!=0:\n",
    "        dummy_KNN = KNeighborsRegressor(n_neighbors=i).fit(X_TRAIN,Y_TRAIN).predict(X_TEST)\n",
    "        num.append(i)\n",
    "        list_mae.append(mean_absolute_error(dummy_KNN,Y_TEST))\n",
    "        list_mse.append(mean_squared_error(dummy_KNN,Y_TEST))\n",
    "        list_r2.append(r2_score(dummy_KNN,Y_TEST))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********** M A E ***********\n",
      "Kth Value: 27\n",
      "Lowest MAE Value: 6.001001001001001\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=num,y=list_mae)\n",
    "print('*********** M A E ***********')\n",
    "print('Kth Value:',num[list_mae.index(min(list_mae))])\n",
    "print('Lowest MAE Value:',min(list_mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********** M S E ***********\n",
      "Kth Value: 37\n",
      "Lowest MSE Value: 231.97302232839118\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=num,y=list_mse)\n",
    "print('*********** M S E ***********')\n",
    "print('Kth Value:',num[list_mse.index(min(list_mse))])\n",
    "print('Lowest MSE Value:',min(list_mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********** R2 Value ***********\n",
      "Kth Value 3\n",
      "Lowest R2 Value: -3.325567767965528\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=num,y=list_r2)\n",
    "print('*********** R2 Value ***********')\n",
    "print('Kth Value',num[list_r2.index(max(list_r2))])\n",
    "print('Lowest R2 Value:',max(list_r2))"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}