# -*- coding: utf-8 -*-
"""
Spyder Editor
"""
#Importing Libraries
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

#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])

#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 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 (to put all data on same scale)
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train=sc_X.fit_transform(X_train)
X_test=sc_X.transform(X_test)

