# -*- coding: utf-8 -*-
"""
Created on Wed Aug 28 15:00:33 2019

@author: BrysDom
"""
#Import libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

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


##missing data
#from sklearn.preprocessing import Imputer
##from sklearn.impute import SimpleImputer
#imputer = Imputer(missing_values='NaN',strategy='mean',axis=0)
##missingvalues = SimpleImputer(missing_values = np.nan, strategy = 'mean', verbose = 0)
#imputer = imputer.fit(X[:,1:3])
##missingvalues = missingvalues.fit(X[:, 1:3])
#X[:,1:3]=imputer.transform(X[:,1:3])
##X[:, 1:3]=missingvalues.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 database 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
"""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)"""

