import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

def house_price_prediction():
    df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv")
    X = df.drop("medv", axis=1)
    y = df["medv"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = LinearRegression()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    print("🏠 房价预测")
    print("MSE:", mean_squared_error(y_test, y_pred))
    print("R2 Score:", r2_score(y_test, y_pred))