Detection of Banks' Customers Loyalty Using Naive Bayes and Support Vector Machine Classifiers: A Machine Learning Approach
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
University of Ibadan Journal of Science and Logics in ICT Research
Abstract
This research paper presents a machine learning approach for detecting and predicting customer loyalty in the
banking sector. The study utilizes Naive Bayes and Support Vector Machine (SVM) classifiers to analyze
customer data, including demographic information, transaction history, and customer feedback. The dataset is
divided into training and testing sets for model development and evaluation. The Naive Bayes classifier
leverages the assumption of feature independence, while the SVM classifier constructs optimal hyperplanes for
class separation. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the
models. Both classifiers demonstrate high accuracy in identifying loyal customers, indicating their potential for
real-world application. The study also analyzes the influence of factors like age, income level, and transaction
frequency on customer loyalty through feature importance analysis. The proposed machine learning approach
offers valuable insights for banks to identify and target loyal customers, enabling effective customer relationship
management and improved business performance. The research underscores the importance of feature
engineering and model selection in developing accurate customer loyalty prediction models.