Predicting Customers’ Churn in a Bank Using Firefly Feature Selection and RBF-Support Vector Machine Classification Algorithms
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Information and Communication Technology, Kwara State University, Malete, Nigeria - KWASU Journal of Information, Communication and Technology, 2(1), 17 – 25
Abstract
Customer 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