Evaluating Machine Learning Methods for Solving Class Imbalance in Banking Customer Data: A Comparative Study
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Abstract
The goal of this research was to address an imbalance problem that affects churn prediction for bank customers. In this study, we examined two sampling techniques Synthetic Minority Over-sampling Technique (SMOTE) and random under-sampling along with three predictive models: the decision tree classifier, Naïve Bayes classifier, and support vector machine classifier. The results indicated that the support vector machine classifier, when combined with SMOTE, was the most effective, achieving a recall of 92.99%, an F-score of 91.37%, an area under the curve (AUC) of 96.4%, and a false negative rate of 7.01%.
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