# 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
df = pd.DataFrame(X)

y = dataset.iloc[:,3].values
df = pd.DataFrame(y)

## 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])   # In MATLAB upperbound included
##X[:,1:3] = imputer.transform(X[:,1:3])
#X[:,1:3] = imputer.fit_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 train and split 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)"""
