波士顿房价预测数据集线性回归模型python

好的，针对波士顿房价预测数据集，我们可以使用线性回归模型进行预测。以下是使用Python实现线性回归模型的步骤：

1. 导入必要的库和数据集

import pandas as pd
import numpy as np
from sklearn.datasets import load_boston

boston = load_boston()
df = pd.DataFrame(boston.data, columns=boston.feature_names)
df['target'] = boston.target

2. 数据预处理

X = df.drop('target', axis=1)
y = df['target']

3. 划分训练集和测试集

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

4. 训练模型

from sklearn.linear_model import LinearRegression

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

5. 预测结果

y_pred = lr.predict(X_test)

6. 评估模型

from sklearn.metrics import mean_squared_error, r2_score

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

print('Mean Squared Error:', mse)
print('R^2 Score:', r2)