# -*- coding: utf-8 -*-
"""
Created on Mon Jan 27 20:47:11 2020

@author: shaguna awasthi
"""
#importing library
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


#importing dataset
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 3].values 
#for missing data take mean of the colm values 
#from sklearn.preprocessing import Imputer
#imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis=0)
#imputer.fit(X[:,1:3])
#X[:, 1:3] = imputer.transform(X[:, 1:3])

#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)

#splitting dataset into training 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
#age and salary variable are not on the same scale
"""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
#we first need to fit it and then tranform it to the training set
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)"""

