Analysis of Agricultural Product Prices Based on Selenium Framework and Random Forest Model
As an important strategic resource in C51hina's market economic system, price fluctuations of agricultural products will not only affect consumers, but also affect the production risks of producers and operators. In the context of big data technology empowering agricultural development, the study constructed a local agricultural product big data platform system based on the Selenium framework+random forest model for real-time collection, visual analysis and sequence prediction. The prediction effects of this prediction system and the decision tree model and support vector machines (SVM) model were compared and analyzed. The study finds that the platform has strong real-time data collection and the prediction effect is better than the decision tree model and SVM model.
agricultural product price forecastSelenium frameworkrandom forest modeldecision tree modelSVM model
万方数据
维普
