Predicting Customers’ Churn in a Bank Using Firefly Feature Selection and RBF-Support Vector Machine Classification Algorithms

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dc.contributor.authorAbdulsalam, S.O.
dc.contributor.authorYusuf, I.
dc.contributor.authorOgunwuyi, M.R.
dc.contributor.authorEdafeajiroke, M.F.
dc.date.accessioned2025-10-29T11:52:33Z
dc.date.available2025-10-29T11:52:33Z
dc.date.issued2023
dc.description.abstractCustomer churn prediction helps businesses identify factors contributing to customer attrition, allowing them to take preventive actions. This project develops a churn prediction model for a bank using Firefly Feature Selection and RBF-Support Vector Machine (SVM) Classification Algorithms. The dataset consists of 900 instances and 10 features, representing customer churn rates. Firefly Feature Selection was used to find the most pertinent features after preprocessing techniques like Label Encoding and Oversampling were used. After that, the RBF-SVM model was used to make predictions. F1-score, recall, accuracy, and precision were used to assess the model’s performance. The strong predictive capability was demonstrated by the model’s 84% accuracy, 84% precision, 100% recall, and 91% F1 score. This methodology assists companies in identifying consumers who are at risk and in taking preventative action to lower churn rates. Future research could focus on improving feature selection techniques, model robustness, and applying the model to other industries for comparison. Keywords: Customer churn prediction, Firefly Feature Selection, RBF-Support Vector Machine (SVM), Classification algorithms, Model evaluation
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/6272
dc.language.isoen
dc.publisherFaculty of Information and Communication Technology, Kwara State University, Malete, Nigeria - KWASU Journal of Information, Communication and Technology, 2(1), 17 – 25
dc.titlePredicting Customers’ Churn in a Bank Using Firefly Feature Selection and RBF-Support Vector Machine Classification Algorithms
dc.typeArticle
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