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Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 610-617
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 29-05-2026
This project presents a machine learning-based system for analyzing e-commerce user behaviour and predicting purchase intention. The dataset consists of 49,999 interaction events across 12,438 sessions and 10,537 users, with a purchase rate of 3.92%, indicating severe class imbalance. Four models - Logistic Regression, Decision Tree, Random Forest, and Neural Network were trained and evaluated. Logistic Regression achieved the best performance with an F1-score of 97.51% and an AUC of 99.98%. The system includes a Flask-based web application that provides real-time predictions along with business insights such as risk segmentation and recommended actions.
E-commerce Analytics, User Behaviour Analysis, Purchase Intention Prediction, Machine Learning, Logistic Regression, Random Forest, Neural Network, Decision Tree, Predictive Analytics, Customer Segmentation, Class Imbalance, Web Application.
N.Ranogna, N.Sanjana, K.Madhubabu, & B.Chandrashekar. (2026). Design User Behaviour Analysis Using Machine Learning. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 610-617. Article DOI https://doi.org/10.47001/IRJIET/2026.105082
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