Credit Card Default Prediction: A Comparative Analysis of Machine Learning Models and Ensemble Techniques

Natarajsivam, Ajaypradeep; Hemasree, K.; Divija, M.; Priyanka, D. Celeena; Gowthami, A.

Credit Card Default Prediction: A Comparative Analysis of Machine Learning Models and Ensemble Techniques

Natarajsivam, Ajaypradeep and Hemasree, K. and Divija, M. and Priyanka, D. Celeena and Gowthami, A. (2025) Credit Card Default Prediction: A Comparative Analysis of Machine Learning Models and Ensemble Techniques. In: Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part II.

PDF
79544.pdf
Download (275kB)

Abstract

CCDP is crucial for financial institutions to mitigate risks. While previous studies have primarily explored DT and AdaBoost models, limited research has assessed ensemble learning and DL techniques in this domain. Existing work often lacks transparency in feature selection, class imbalance handling

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Date Deposited:	04 Mar 2026 20:19
Last Modified:	16 Apr 2026 15:53
URI:	http://eprints.eai.eu/id/eprint/60189

Actions (login required)

: View Item