# 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

# 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
"""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)"""

#impoortind dataset
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
'''from sklearn.impute import SimpleImputer
imputer=SimpleImputer(missing_values=np.nan,strategy="mean")
imputer=imputer.fit(x[:,1:3])
x[:,1:3]=imputer.transform(x[:,1:3])'''

#encoding categorial data
'''from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([("Country", OneHotEncoder(), [0])], remainder = 'passthrough')
x= ct.fit_transform(x)


labelencoder_y=LabelEncoder()
y=labelencoder_y.fit_transform(y)'''

#splitting the data set into trainig set 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)

#features scaling
'''from sklearn.preprocessing import StandardScaler
sc_x=StandardScaler()
x_train=sc_x.fit_transform(x_train)
x_test=sc_x.fit_transform(x_test)'''








