Predictive Modeling of Bank Marketing Campaign Responses Using Machine Learning
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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.
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References
Kaggle Dataset: https://www.kaggle.com/datasets/kukuroo3/bank-marketing-response-predict Lundberg, S.M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. NIPS.
Breiman, L. (2001). Random Forests. Machine Learning Journal.
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