Stock price prediction research in steel and food industries using random forest algorithm

The application of computer technology in the financial field and the utilization of computer algorithms to enhance the accuracy of stock price prediction have drawn the attention of numerous researchers. Against this backdrop, this study aims to precisely predict the stock prices in the steel and food industries by leveraging the random forest algorithm in the computer domain, so as to provide valuable reference for investors. This study selected the historical trading data of six listed A-shares as the basic data source. Firstly, the dataset was divided into the training set and the testing set according to a scientific proportion. Subsequently, the parameters of the random forest model were reasonably set, and thus the random forest model was constructed for subsequent analysis. Through the practical application of this model to predict the daily closing prices of stocks, it was found that the model demonstrated excellent capabilities and adaptability, with relatively small deviations between the predicted values and the actual values. This indicates that the model has relatively high reliability and practical value in predicting the stock prices of the steel and food industries. In conclusion, the application of the random forest algorithm in predicting the stock prices of the steel and food industries is an effective method, which has a promising application prospect and is worthy of further in-depth research and promotion.