import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, mean_squared_error, r2_score

iris = datasets.load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

model = KNeighborsClassifier()
model.fit(X_train, y_train)

predictions = model.predict(X_test)

accuracy = accuracy_score(y_test, predictions)
confusion = confusion_matrix(y_test, predictions)
report = classification_report(y_test, predictions)

print("KNN Classification Performance Metrics:")
print("Accuracy:", accuracy)
print("Confusion Matrix:\n", confusion)
print("Classification Report:\n", report)

housing = datasets.fetch_california_housing()
X = housing.data
y = housing.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

model = KNeighborsRegressor()
model.fit(X_train, y_train)

predictions = model.predict(X_test)

mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print("\nKNN Regression Performance Metrics:")
print("Mean Squared Error:", mse)
print("R^2 Score:", r2)