Decision tree prediction based on preceding feature engineering

This research employed feature engineering techniques to preprocess an original stock dataset, followed by the introduction of a decision tree prediction model for forecasting the dataset. Experimental results demonstrate an enhancement in predictive performance, offering a more effective analytical tool for forecasting stock market trends. This approach also serves as an inspiration in fields such as optoelectronic signal processing, optical image recognition, and optical computation and processing.