boston房价预测python
时间: 2023-06-30 14:19:19 浏览: 79
要预测Boston房价,可以使用机器学习算法来实现。以下是一个使用Python实现的简单的线性回归模型:
1. 首先,导入必要的库,包括pandas,numpy,matplotlib和sklearn:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
```
2. 加载数据集,可以使用sklearn自带的Boston房价数据集:
```python
from sklearn.datasets import load_boston
boston = load_boston()
X = pd.DataFrame(boston.data, columns=boston.feature_names)
y = pd.DataFrame(boston.target, columns=['MEDV'])
```
3. 探索性数据分析,查看数据的基本信息和特征之间的关系:
```python
plt.scatter(X['RM'], y['MEDV'])
plt.xlabel('Average number of rooms per dwelling')
plt.ylabel('Median value of owner-occupied homes in $1000s')
plt.show()
```
4. 划分训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
```
5. 建立线性回归模型并训练:
```python
lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)
```
6. 预测测试集:
```python
y_pred = lin_reg.predict(X_test)
```
7. 评估模型表现:
```python
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
print('RMSE:', rmse)
```
这里只是一个简单的线性回归模型,还可以使用其他机器学习算法来预测Boston房价,如决策树、随机森林等。