%0 Journal Article
%T Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing
%A Abraham Jallah Balyemah
%A Sonkarlay J. Y. Weamie
%A Jiang Bin
%A Karmue Vasco Jarnda
%A Felix Jwakdak Joshua
%J Int'l J. of Communications, Network and System Sciences
%P 81-103
%@ 1913-3723
%D 2024
%I Scientific Research Publishing
%R 10.4236/ijcns.2024.176006
%X 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.
%K E-Commerce Platform
%K Purchasing Behavior Prediction
%K Logistic Regression Algorithm
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=134276