# search for an optimal value of K for KNN
k_range = list(range(1, 31))
k_scores = []
for k in k_range:
   knn = KNeighborsClassifier(n_neighbors=k)
   scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')
   k_scores.append(scores.mean())
# plot the value of K for KNN (x-axis) versus the cross-validated accuracy (y-axis)
plt.plot(k_range, k_scores)
plt.xlabel('Value of K for KNN')
plt.ylabel('Cross-Validated Accuracy')