import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split

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

imputer = SimpleImputer(missing_values = np.NaN, strategy = "mean")
imputer = imputer.fit(X[ : , 1:3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])

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)

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)
