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

dataset = pd.read_csv('machine_learning/1_Data_Preprocessing/Section 3 - Data Preprocessing in Python/Python/Data.csv')

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

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

#encode categorical data
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder

ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')
x = np.array(ct.fit_transform(x))

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)

#split dataset
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 = 1) #same data

#feature scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
#don't standardize dummy variables
x_train[:,3:] = sc.fit_transform(x_train[:,3:])
x_test[:,3:] = sc.transform(x_test[:,3:])














