Research on Building System of Brand Operation Effect Prediction Model Based on Machine Learning

The paper introduces a novel machine learning model designed to precisely forecast brand operations' performance. It harnesses the capabilities of Long Short-Term Memory (LSTM) networks, decision trees, and Gradient Boosting Machines (GBM) to comprehensively capture and analyze complex characteristics present in both time series and non-time series data. Initially, the system employs the LSTM model to handle brand operation's time series data, effectively capturing long-term dependencies inherent in the time series. The recurrent structure of LSTM allows the model to memorize past events and predict future trends. The decision tree algorithm is involved in feature selection and preliminary prediction, and the non-time series features are processed efficiently to identify the features with the most significant impact on the brand operation effect, so as to provide key input variables for the model, and preliminarily estimate the operation effect, which enhances the interpretability of the model. In the final stage, the time series prediction results of LSTM and the feature prediction results of decision tree are combined and input into the gradient boosting machine for deep ensemble learning. Gradient boosting machine gradually optimizes and improves the overall prediction accuracy by iteratively learning the residuals predicted by the predecessor model. This process not only effectively reduces the prediction error, but also improves the understanding and adaptability of complex operation scenarios through the complementary effect between models, and ensures the stability and generalization ability of the prediction model.