kfold.py

# 5-fold cross validation
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
fold5 = KFold(n_splits=5,shuffle=False,random_state=1)
scores = []
df = pd.read_csv('iris.csv',header=None)
X = df.drop(columns=[4])
Y = df[4]
knn = KNeighborsClassifier(n_neighbors = 9)
for train_index,test_index in fold5.split(X):
    X_train,X_test,Y_train,Y_test = X.iloc[train_index],X.iloc[test_index],Y.iloc[train_index],Y.iloc[test_index]
    knn.fit(X_train,Y_train)
    pred = knn.predict(X_test)
    print(metrics.accuracy_score(Y_test,pred) * 100)
    scores.append(round(knn.score(X_test,Y_test)*100,2))
print(scores)    
print(np.mean(scores))