import pandas as pd
+
+from sklearn.model_selection import train_test_split
+
+from sklearn.linear_model import LinearRegression, Ridge, Lasso
+
+# 创建数据
+
+data = [
+
+[7, 26, 6, 60, 78.5],
+
+[1, 29, 15, 52, 74.3],
+
+[11, 56, 8, 20, 104.3],
+
+[11, 31, 8, 47, 87.6],
+
+[7, 52, 6, 33, 95.9],
+
+[11, 55, 9, 22, 109.2],
+
+[3, 71, 17, 6, 102.7],
+
+[1, 31, 22, 44, 72.5],
+
+[2, 54, 18, 22, 93.1],
+
+[21, 47, 4, 26, 115.9],
+
+[1, 40, 23, 34, 83.8],
+
+[11, 66, 9, 12, 113.3],
+
+[10, 68, 8, 12, 109.4]
+
+]
+
+# 将数据转为 pandas DataFrame 格式，设置列名
+
+df = pd.DataFrame(data, columns=['X1', 'X2', 'X3', 'X4', 'Y'])
+
+# 分别提取特征变量 X 和标签 y
+
+X = df.iloc[:, :-1]
+
+y = df.iloc[:, -1]
+
+# 拆分数据集为训练集和测试集
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=18)
+
+# 创建线性回归模型，进行训练和预测，并输出系数和截距
+
+lr = LinearRegression().fit(X_train, y_train)
+
+y_pred_lr = lr.predict(X_test)
+
+print("线性回归模型:")
+
+print("w:", lr.coef_)
+
+print("b:", lr.intercept_)
+
+# 创建岭回归模型，进行训练和预测，并输出系数和截距
+
+ridge = Ridge(alpha=1.0).fit(X_train, y_train)
+
+y_pred_ridge = ridge.predict(X_test)
+
+print("岭回归模型:")
+
+print("w:", ridge.coef_)
+
+print("b:", ridge.intercept_)
+
+# 创建 Lasso 回归模型，进行训练和预测，并输出系数和截距
+
+lasso = Lasso(alpha=0.1).fit(X_train, y_train)
+
+y_pred_lasso = lasso.predict(X_test)
+
+print("Lasso 回归模型:")
+
+print("w:", lasso.coef_)
+
+print("b:", lasso.intercept_)
+
+