# -*- coding: utf-8 -*-
"""
Created on Sat Nov  7 16:11:55 2020

@author: jonat
"""


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv("dataprepro.csv")

X = dataset.iloc[:,:-1].values
y = dataset.iloc[:,3].values

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])

from sklearn.preprocessing import LabelEncoder,OneHotEncoder
labelencoder_X = LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:,0])

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)

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) 

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

