基于Python技术对农产品价格预测及系统开发
Agricultural Products Price Prediction and System Development Based on Python Technology

随着信息技术和大数据的发展，农产品价格预测对市场分析和决策起着越来越重要。本文爬取了农产品从2022年1月1日至2024年6月23日的数据，并基于这些数据建立了线性回归模型，ARIMA模型，以及随机森林模型三种不同的模型进行预测。研究结果表明，将时间与地点作为自变量预测价格时，随机森林模型预测的效果优于其他两个模型，能有效地捕捉价格变化趋势，为市场参与者提供决策支持。本文基于随机森林模型，利用FLASK框架构建了WEB端，使用者只需要在该网页选择产地及时间便可直接看到当日所预测的价格。
With the development of information technology and big data, agricultural product price forecasting plays an increasingly important role in market analysis and decision-making. This paper crawls the data of agricultural products from 1 January 2022 to 23 June 2024, and based on these data, three different models, linear regression model, ARIMA model, and random forest model, are established for prediction. The results of the study show that when time and place are used as independent variables to predict prices, the Random Forest Model predicts better than the other two models, effectively capturing price trends and providing decision support for market participants. Based on the Random Forest model, this paper constructs a web page using the FLASK framework, in which users only need to select the origin and time to see the predicted price on the same day directly.