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Predictive Modeling of Bank Marketing Campaign Responses Using Machine Learning

Author

Listed:
  • Komal Kothawade

    (Department of Statistics, Dr. D. Y. Patil Arts, Commerce and Science College, Pimpri, Pune-18, Maharashtra, India)

  • Mayuri Babar

    (Department of Statistics, Dr. D. Y. Patil Arts, Commerce and Science College, Pimpri, Pune-18, Maharashtra, India)

  • Deepali Akolkar

    (Department of Statistics, Dr. D. Y. Patil Arts, Commerce and Science College, Pimpri, Pune-18, Maharashtra, India)

  • Neha Chothe

    (Department of Statistics, Dr. D. Y. Patil Arts, Commerce and Science College, Pimpri, Pune-18, Maharashtra, India)

Abstract

This study aims to develop a predictive model to assess client responses to bank marketing campaigns. Using an open-source dataset derived from a Portuguese bank’s marketing efforts and hosted on Kaggle, we apply various classification algorithms including Logistic Regression, Random Forest, and LightGBM. The study involves thorough preprocessing, feature engineering, and model evaluation using ROC-AUC and F1 metrics. The best performing model achieved an ROC-AUC of approximately 0.80 using LightGBM, with SHAP analysis revealing the most influential factors.

Suggested Citation

  • Komal Kothawade & Mayuri Babar & Deepali Akolkar & Neha Chothe, 2025. "Predictive Modeling of Bank Marketing Campaign Responses Using Machine Learning," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 14(13), pages 213-214, October.
    • Handle: RePEc:bjf:ijltem:v:14:y:2025:i:13:a:950
    DOI: 10.51583/IJLTEMAS.2025.1413SP042
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