data normalization

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

iris = datasets.load_iris()

X = iris.data
y = iris.target

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size= 0.2,random_state=666)

standardScaler = StandardScaler()
standardScaler.fit(X_train)

standardScaler.mean_ #mean 
standardScaler.scale_ #variance X_train = standardScaler.transform(X_train) #normalization X_test_standerd 
= 
standardScaler.transform(X_test) #test dataset normalization



from sklearn.neighbors import KNeighborsClassifier
KNN_classifier = KNeighborsClassifier(n_neighbors=3)
KNN_classifier.fit(X_train,y_train)
KNN_classifier.score(X_test_standerd,y_test)