KFold Cross Validation # The code is as below. 
import pandas as pd  
import numpy as np  
import matplotlib.pyplot as plt

bankdata = pd.read_csv("bill_authentication.csv")
X = bankdata.iloc[:, 0:4].values
y = bankdata.iloc[:, 4].values



from sklearn.model_selection import KFold
kf = KFold(n_splits=10, shuffle=True,random_state=50)
for train_index, test_index in kf.split(X):
   print("TRAIN:", train_index, "TEST:", test_index)
   X_train, X_test = X[train_index], X[test_index]
   y_train, y_test = y[train_index], y[test_index]




from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)




from sklearn.neighbors import KNeighborsClassifier  
classifier = KNeighborsClassifier(n_neighbors=5)  
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
Predicted=pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})   

from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
Confusion_matrix = confusion_matrix(y_test,y_pred)  
ClassificationReport = classification_report(y_test,y_pred)
AccuracyScore = accuracy_score(y_test, y_pred)


NewData = pd.read_csv("PredectionData.csv")
NewPredictededtion=classifier.predict(NewData)
NewPredictededtion=pd.DataFrame({'Variance':NewData.Variance, 'Skewness':NewData.Skewness, 'Curtosis':NewData.Curtosis, 'Entropy':NewData.Entropy, 'Predicted':NewPredictededtion})


I have problem 


  when I run the code I have the prediction result for only one fold


how I edit code to show the predicted result of all folds
It's really hard to understand your code but it seems that you are only seeing the last fold's y_pred . Create a list and in each iteration append the prediction to it.