ID Ever_Married Graduated Gender Profession Spending_Score Segmentation Family_Size Age Work_Experience0 462809 0 0 1 5 2 3 3 4 11 462643 1 1 0 2 0 0 2 18 152 466315 1 1 0 2 2 1 0 44 13 461735 1 1 1 7 1 1 1 44 04 462669 1 1 0 3 1 0 5 20 15... ... ... ... ... ... ... ... ... ... ...8063 464018 0 0 1 9 2 3 6 4 08064 464685 0 0 1 4 2 3 3 15 38065 465406 0 1 0 5 2 3 0 14 18066 467299 0 1 0 5 2 1 3 8 18067 461879 1 1 1 4 0 1 2 17 08068 rows × 10 columns

` data1=data.drop(["ID","Segmentation"],axis=1)

from sklearn.model_selection import train_test_split     x_train,x_test,y_train,y_test=train_test_split(data1,data.Segmentation,test_size=0.20,random_state=50) from sklearn.neighbors import KNeighborsClassifier knn=KNeighborsClassifier(n_neighbors=17) knn.fit(x_train,y_train) tahmin=knn.predict(x_test) knn.score(x_test,y_test) #0.4838909541511772 knn.predict([[1,1,0,2,0,2,18,15]])

UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names

#warnings.warn(#your textarray([1])`

When I make a prediction, I was not expecting this warning.