# Data Preprocessing Template

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

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

# Taking care of missing data
# sklearn.preprocessing library
# Crl + I for help
from sklearn.preprocessing import Imputer

# Create imputer object
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
# imputing the number columns
imputer = imputer.fit(X[:,1:3])
X[:,1:3] = imputer.transform(X[:,1:3])

# Encoding Categorical
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
label_encoderX = LabelEncoder()
X[:, 0] = label_encoderX.fit_transform(X[:, 0])
# Dummy variables for categorical variables so that machine learning 
# doesn't attribute order to categories
onehotencoder = OneHotEncoder(categorical_features=[0])
X = onehotencoder.fit_transform(X).toarray()

label_encoder_y = LabelEncoder()
y = label_encoder_y.fit_transform(y)

# Splitting the dataset into the 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 (Example: Eucledian distance)
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
# fit and transform
X_train = sc_X.fit_transform(X_train)
# sc_X already fit to X_train
X_test = sc_X.transform(X_test)