Enhancing E-commerce Logistics Insights through Machine Learning: A Real-Data Approach

This study employs real-world data from e-commerce logistics operations to conduct a comprehensive regression analysis on the benefits of e-commerce logistics. Utilizing linear regression and machine learning modeling techniques, the research identifies relevant factors influencing logistics performance. Furthermore, multiple machine learning models are developed to model and predict the risk of delayed order delivery. To enhance the interpretability of the prediction models, feature importance analysis and SHAP-based interpretability analysis are conducted. These analyses elucidate the impact of key factors such as transportation methods and order scheduling on the risk of delayed delivery, thereby offering a novel perspective for the analysis and research of e-commerce logistics.