Python PM2.5 预测模型：结合温度和湿度

建立 PM2.5 预测模型：

1. 导入相关库

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

2. 读取数据，并进行预处理

data = pd.read_csv('data.csv')
data = data.dropna() # 删除含有空值的行
X = data[['temperature', 'humidity']]
y = data['PM2.5']

3. 划分训练集和测试集

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

4. 建立线性回归模型并训练

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

5. 预测 PM2.5 值并评估模型

y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
print('RMSE:', rmse)

建立温度和湿度预测模型与上述步骤相似，只需改变 y 值为温度或湿度即可。