*Corresponding Author: Alaa A Elnazer, Department of Marketing, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.

Received: January 27, 2026; Published: May 08, 2026

Abstract

TThis study develops a comprehensive predictive framework by systematically comparing five classification models—Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGBoost)—using the Online Shoppers’ Purchasing Intention dataset. A diverse set of performance metrics, including Accuracy, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R², Correlation Coefficient (CC), Coefficient of Variation (COV), and Error Coefficient (EC), were employed to evaluate and benchmark the models. Descriptive statistics and correlation analysis provided a foundational understanding of the behavioral attributes shaping purchasing outcomes, while inferential analyses, including ANOVA and the Wilcoxon Signed-Rank Test, confirmed statistically significant differences among models and validated the robustness of the comparative framework. The findings suggest that the Random Forest model was the best in most of the evaluation measures as it had the lowest RMSE, the highest correlation with the actual outcomes, and the most stable. Even though Artificial Neural Networks displayed similar levels of accuracy, the Random Forest was more consistent and reduced the number of predictive errors, which highlights why this algorithm can be used to find out the customer behavior in very complex and nonlinear scenarios. The results show that ensemble techniques are significant in prediction of e-commerce and that hybrid methods have the potential to increase the accuracy and generalization. The research has both methodological and practical significance because it provides a strict standard of the classification algorithms and offers practical information to online retailer which needs to optimize its decision making process, customer satisfaction and long term customer loyalty.

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Citation

Citation: Alaa A Elnazer., etal. “A Comparative Study of Statistical and Machine Learning Techniques for Predicting Customers Shopping Behavior". Acta Scientific Nutritional Health 10.5 (2026): 16-37.

Copyright

Copyright: © 2026 Alaa A Elnazer., etal. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.