Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WorkExo Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle as pk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing datasets\n",
    "data = pd.read_csv(\"./datasets/Workexo.csv\", usecols = ['JobInvolvement', 'Department','HourlyRate', 'StandardHours','PerformanceRating'])\n",
    "data=data.reindex(columns=['Department','JobInvolvement', 'HourlyRate', 'StandardHours','PerformanceRating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 5 columns):\n",
      "Department           1470 non-null object\n",
      "JobInvolvement       1470 non-null int64\n",
      "HourlyRate           1470 non-null int64\n",
      "StandardHours        1470 non-null int64\n",
      "PerformanceRating    1470 non-null int64\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 57.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "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>Department</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>PerformanceRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sales</td>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Department  JobInvolvement  HourlyRate  StandardHours  \\\n",
       "0                   Sales               3          94             80   \n",
       "1  Research & Development               2          61             80   \n",
       "2  Research & Development               2          92             80   \n",
       "3  Research & Development               3          56             80   \n",
       "4  Research & Development               3          40             80   \n",
       "\n",
       "   PerformanceRating  \n",
       "0                  3  \n",
       "1                  4  \n",
       "2                  3  \n",
       "3                  3  \n",
       "4                  3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Research & Development    961\n",
       "Sales                     446\n",
       "Human Resources            63\n",
       "Name: Department, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(data.Department)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#distributing independent and dependent variables\n",
    "X = data.iloc[:,:-1].values\n",
    "y = data.iloc[:,-1].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Sales' 3 94 80]\n",
      " ['Research & Development' 2 61 80]\n",
      " ['Research & Development' 2 92 80]\n",
      " ...\n",
      " ['Research & Development' 4 87 80]\n",
      " ['Sales' 2 63 80]\n",
      " ['Research & Development' 4 82 80]]\n"
     ]
    }
   ],
   "source": [
    "#independent variable\n",
    "print(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Integer Encoding of Department type\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "X[:,0] = label_encoder.fit_transform(X[:,0])\n",
    "X = X.astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  3 94 80]\n",
      " [ 1  2 61 80]\n",
      " [ 1  2 92 80]\n",
      " ...\n",
      " [ 1  4 87 80]\n",
      " [ 2  2 63 80]\n",
      " [ 1  4 82 80]]\n"
     ]
    }
   ],
   "source": [
    "#Independent variable after Integer Encoding\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the dataset into Training and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Decision Tree Classification to the Training set\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the Test Set Results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8641304347826086\n"
     ]
    }
   ],
   "source": [
    "#Accuracy/100\n",
    "print(classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[306   1]\n",
      " [ 49  12]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating and printing the accuracy\n",
    "# accuracy = (cm[0][0] + cm [1][1])/(cm[0][0] + cm [0][1]+cm[1][0] + cm [1][1])\n",
    "# print(accuracy*100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pk.dump(classifier, open( \"model.sav\", \"wb\" ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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