# -*- coding: utf-8 -*-
"""
Created on Tue Apr 25 16:37:25 2017

@author: Snehan
"""

#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

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.cross_validation 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)'''

# -*- coding: utf-8 -*-
"""
Created on Tue Apr 25 16:37:25 2017

@author: Snehan
"""

#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

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.cross_validation 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)'''

# -*- coding: utf-8 -*- Created on Tue Apr 25 16:37:25 2017
 
 @author: Snehan #Importing the libraries #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 #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)