diff --git a/dataset/cal_housing_py3.pkz b/dataset/cal_housing_py3.pkz
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diff --git a/knn_regression.py b/knn_regression.py
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+# 导入必要的库
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+import matplotlib.pyplot as plt
+
+# 1. 加载数据集（使用加州房价数据集）
+data = fetch_california_housing(data_home="/home/admin-root/haotian/ML/dataset")
+X = data.data  # 特征数据
+y = data.target  # 目标数据（房价）
+
+# 2. 划分数据集（80%训练，20%测试）
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# 3. 特征标准化（KNN对特征尺度敏感）
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# 4. 构建KNN回归模型（这里K=5）
+knn = KNeighborsRegressor(n_neighbors=5)
+
+# 5. 训练模型
+knn.fit(X_train, y_train)
+
+# 6. 预测
+y_pred = knn.predict(X_test)
+
+# 7. 评估模型性能
+mae = mean_absolute_error(y_test, y_pred)
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+
+# 打印评估指标
+print(f"Mean Absolute Error: {mae:.4f}")
+print(f"Mean Squared Error: {mse:.4f}")
+print(f"R^2 Score: {r2:.4f}")
+
+# 可视化预测结果 vs 实际值（仅在二维数据时更直观）
+plt.scatter(y_test, y_pred, c='blue', edgecolors='k', alpha=0.7)
+plt.xlabel("True Values")
+plt.ylabel("Predictions")
+plt.title("KNN Regression - True vs Predicted")
+plt.savefig("./output/knn_r.png")
diff --git a/output/knn_r.png b/output/knn_r.png
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