Machine Learning-Driven Reliability Engineering for E-Commerce Sites: A Study on Browsing, Cart, and Checkout Phases

Authors

• Divya Soundarapandian

Abstract

This study explores reliability engineering in e-commerce systems by analyzing critical user journey stages – browsing, cart, and checkout – using advanced machine learning models. The research uses random forest regression (RFR) and support vector regression (SVR) to assess and predict system performance, stability, and potential failure points. Data was obtained from simulated e-commerce transactions that included key parameters such as response time, server load, transaction success rate, and error frequency. The models were evaluated using statistical indicators including coefficient of determination (R²), mean square error (MSE), and mean absolute error (MAE) to measure predictive accuracy. The results indicated that both models achieved high reliability and accuracy, with SVR showing slightly higher generalization ability. The findings highlight that  predictive modeling can effectively identify performance bottlenecks, reduce downtime, and improve the overall robustness of e-commerce operations. By integrating reliability engineering principles with machine learning techniques, this study contributes to improving user experience, enhancing system resilience, and ensuring scalable and stable online transaction environments. This hybrid analytics approach provides a foundation for developing proactive monitoring strategies in modern e-commerce infrastructures.