Research on the Construction of Commodity Sales Forecasting Model of E-commerce Platform Based on Machine Learning

Abstract

With the continuous growth of the global e-commerce market, e-commerce platforms at all levels have introduced corresponding commodity sales forecasting mechanisms to improve inventory management efficiency, optimize supply chain deployment and improve marketing strategies. However, the traditional forecasting methods adopted at this stage show obvious limitations in the face of complex market environment and massive data information, and it is difficult to meet the requirements of e-commerce platform for real-time and accuracy of forecasting results. In this regard, based on the current situation, this paper will deeply explore the application feasibility of machine learning technology in this field, and design and construct a brand-new commodity sales forecasting model, aiming at further improving the commodity sales forecasting mechanism and providing powerful decision support tools for e-commerce platforms and merchants. Practice has proved that the whole model is a combined framework, in which random forest (RF) can handle high-dimensional data and has strong robustness to outliers and missing values. XGBoost can capture complex nonlinear relationships in data. Through the combination of the two, we can more accurately capture the complex relationship between commodity sales and various characteristics, thus reducing the prediction error and improving the accuracy of prediction.