Wine data

import pandas as pd

df_wine=pd.read_csv('https://archive.ics.uci.edu/ml/'

'machine-learning-databases/wine/wine.data',

header=None)

df_wine.columns=['Class label', 'Alcohol' ,'Malic acid', 'Ash', 'Alcalinity of ash',

'Magnesium', 'Total phenols', 'Flavanoids', 'Noneflavanoid phenols', 'Proanthocyanins',

'Color intensity', 'Hue', '0D280/0D315 of diluted wines', 'Proline']

from sklearn.model_selection import train_test_split

X, y=df_wine.iloc[:,1:].values, df_wine.iloc[:,0].values

X_train, X_test, y_train, y_test=\

train_test_split(X, y, test_size=0.3, random_state=0, stratify=y)

Wine data std

import pandas as pd

df_wine=pd.read_csv('https://archive.ics.uci.edu/ml/'

'machine-learning-databases/wine/wine.data', header=None)

from sklearn.model_selection import train_test_split

X, y=df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values

X_train, X_test, y_train, y_test=\

train_test_split(X,y,test_size=0.3, stratify=y, random_state=0)

from sklearn.preprocessing import StandardScaler

sc=StandardScaler()

X_train_std=sc.fit_transform(X_train)

X_test_std=sc.fit_transform(X_test)