基于数据挖掘的上海房价预测代码呈现
时间: 2023-10-31 16:06:20 浏览: 40
以下是一个基于数据挖掘的上海房价预测代码示例,使用的是Python语言及其相关库:
```python
# 导入所需的库
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# 读取数据集
data = pd.read_csv('shanghai_housing_price.csv')
# 数据预处理
data = data.drop(['Index', 'id', 'url', 'Cid', 'DOM'], axis=1) # 删除无关特征
data = data.dropna() # 删除缺失值
# 特征工程
data['livingRoom'] = data['livingRoom'].apply(lambda x: int(x.split('室')[0]))
data['drawingRoom'] = data['drawingRoom'].apply(lambda x: int(x.split('厅')[0]))
data['kitchen'] = data['kitchen'].apply(lambda x: int(x.split('厨')[0]))
data['bathroom'] = data['bathroom'].apply(lambda x: int(x.split('卫')[0]))
data['constructionTime'] = data['constructionTime'].apply(lambda x: int(x.split('年')[0]))
# 划分训练集和测试集
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# 训练模型
rf = RandomForestRegressor(n_estimators=100, random_state=0)
rf.fit(X_train, y_train)
# 预测结果
y_pred = rf.predict(X_test)
# 评估模型
mse = mean_squared_error(y_test, y_pred)
print('均方误差:', mse)
```
需要注意的是,这只是一个简单的示例代码,实际上房价预测模型的建立需要更加深入的数据分析和特征工程处理。同时,也需要选择更加适合实际应用场景的算法和模型来进行预测。