import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler wine = pd.read_csv('winequality-red.csv') X_train,X_rem,y_train,y_rem = train_test_split(wine.iloc[:,:-1],wine.iloc[:,-1], test_size=0.2, random_state=1,stratify=wine["quality"]) X_valid, X_test, y_valid, y_test = train_test_split(X_rem,y_rem, test_size=0.5) print("Wielkosc danych: train, test, valid:") print(X_train.shape) print(X_valid.shape) print(X_test.shape) print("wine describe:") print(wine.describe()) norm = MinMaxScaler() norm_fit = norm.fit(X_train) norm_X_train = norm_fit.transform(X_train) norm_X_test = norm_fit.transform(X_test) norm_X_valid = norm_fit.transform(X_valid)