python请使用线性回归算法对波士顿房价进行预测

好的，以下是实现步骤：

1. 导入所需的库和数据集：

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

boston = load_boston()
X = boston.data
y = boston.target

2. 分割数据集，将数据集分为训练集和测试集：

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

3. 构建线性回归模型，并对训练集进行训练：

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

4. 对测试集进行预测：

y_pred = model.predict(X_test)

5. 计算模型的均方误差：

mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)

完整代码如下：

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

boston = load_boston()
X = boston.data
y = boston.target

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

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

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)

希望对你有帮助！