Enhancing Predictive Models in E-Commerce: A Comparative Study Using XGBoost Across Diverse Scenarios - Zhang Jianhao (ITM Web of Conferences)
With the growth of the internet, online shopping has become increasingly popular. However, sudden demand spikes during holidays or special events can disrupt market equilibrium, causing stock shortages and logistical challenges. To address these sudden surges in demand, this study utilizes existing online sales data, transforming it into actionable insights. Our strategy involves continuously feeding historical data into selected models to predict future sales volumes. By identifying patterns in the data, we aim to make the predictions more tangible and assess the validity of our approach through a statistical linear regression model. We employed three different models—Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting MachineLight (GBM), and Extreme Gradient Boosting (XGBoost)—to determine which one is the most efficient. After comparing the performance of these models, the results indicate that XGBoost is the optimal choice. The findings suggest that accurate sales predictions enable e-commerce platforms to increase inventory during peak periods, maximize the utility of goods, ensure customer satisfaction, and stimulate transaction activity. This study underscores the importance of accurately forecasting sales and revenue in e-commerce, helping platforms to stay ahead of demand, optimize resource allocation, and maintain market competitiveess.