# Data Preprocessing Template

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

# Take 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
label_encoder_X = LabelEncoder()
X[:, 0] = label_encoder_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)

# Split the dataset into the Training 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)

# 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)