# -*- coding: utf-8 -*-




"""
Created on Wed Jun 13 00:06:28 2018

@author: shibajyotidebbarma
"""



#Taking care of Missing Data
#from sklearn.preprocessing import Imputer
#imputer = Imputer(missing_values = 'NaN', strategy = 'mean' , axis=0)
#imputer = imputer.fit(X[:,1:3])
#X[:,1:3] = imputer.transform(X[:,1:3])



#BEFORE SPLITTING THE DATASET
#Encoding categorical data
#from sklearn.preprocessing import LabelEncoder, OneHotEncoder
#labelencoder_X = LabelEncoder()
#X[:,0 ] = labelencoder_X.fit_transform(X[: , 0])
#onehotencoder = OneHotEncoder(categorical_features = [0])
#X = onehotencoder.fit_transform(X).toarray()
#labelencoder_y = LabelEncoder()
#y = labelencoder_y.fit_transform(y)











#Importing the libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing the dataset
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:,3].values

#Splitting the dataset into Training set and Test Set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=0)


#Feature Scaling
"""
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
"""































