from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
# Load the iris dataset
iris = load_iris()
# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)
# Create a KNeighborsClassifier object
knn = KNeighborsClassifier(n_neighbors=5)
# Fit the model to the training data
knn.fit(X_train, y_train)
# Make predictions on the test data
y_pred = knn.predict(X_test)
# Evaluate the model's accuracy
print("Accuracy:", knn.score(X_test, y_test))