Predicting Purchase Frequency in e-Commerce: Hybrid Machine Learning Approach

Nilay İşeri, Mustafa Keskin, Onur Arda Raştak

2025

Abstract

This paper addresses the problem of predicting customer purchase frequency. We developed machine learning models to forecast the number of purchases a user will make next month, categorizing them into three classes. We compared multiclass classification, regression, and hybrid approaches. Our analysis shows that the most effective method is a hybrid approach that uses a binary classifier to target the 4+ purchases and a regression model for the remaining classes. This two-stage model provided a significant performance increase over single models, proving to be a robust solution for imbalanced, ordinal prediction tasks.

Paper Citation

in Harvard Style

İşeri N., Keskin M. and Raştak O. (2025). Predicting Purchase Frequency in e-Commerce: Hybrid Machine Learning Approach. In Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS; ISBN 978-989-758-783-2, SciTePress, pages 64-68. DOI: 10.5220/0014305900004848

in Bibtex Style

@conference{iceeecs25,
author={Nilay İşeri and Mustafa Keskin and Onur Arda Raştak},
title={Predicting Purchase Frequency in e-Commerce: Hybrid Machine Learning Approach},
booktitle={Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS},
year={2025},
pages={64-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014305900004848},
isbn={978-989-758-783-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS
TI - Predicting Purchase Frequency in e-Commerce: Hybrid Machine Learning Approach
SN - 978-989-758-783-2
AU - İşeri N.
AU - Keskin M.
AU - Raştak O.
PY - 2025
SP - 64
EP - 68
DO - 10.5220/0014305900004848
PB - SciTePress