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

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International Journal of Communications, Network and System Sciences 2024年第6期17卷 81-103页

作者： Abraham Jallah Balyemah Sonkarlay J. Y. Weamie Jiang Bin karmue vasco jarnda Felix Jwakdak Joshua 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

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.

关键词： E-Commerce Platform Purchasing Behavior Prediction Logistic Regression Algorithm

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