Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing

作     者：Abraham Jallah Balyemah Sonkarlay J. Y. Weamie Jiang Bin Karmue Vasco Jarnda Felix Jwakdak Joshua Abraham Jallah Balyemah;Sonkarlay J. Y. Weamie;Jiang Bin;Karmue Vasco Jarnda;Felix Jwakdak Joshua

作者机构：College of Computer Science and Engineering Hunan University Changsha China Department of Health Inspection and Quarantine Xiangya School of Public Health Central South University Changsha China 

出 版 物：《International Journal of Communications, Network and System Sciences》 (通讯、网络与系统学国际期刊（英文）)

年 卷 期：2024年第17卷第6期

页      面：81-103页

学科分类：120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 12[管理学] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 020205[经济学-产业经济学] 

主　　题：E-Commerce Platform Purchasing Behavior Prediction Logistic Regression Algorithm 

摘      要：This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms.