from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
iris = load_iris()
x = iris.data
y = iris.target
knn = KNeighborsClassifier()
k_range = range(1, 31)
k_score = []
for k in k_range:
knn = KNeighborsClassifier(n_neighbors=k)
score = cross_val_score(knn, x, y, cv=10, scoring="accuracy")
k_score.append(score.mean())
plt.plot(k_range, k_score)
plt.xlabel("Value of K for KNN")
plt.ylabel("Cross-Validated Accuracy")
plt.show()