Detection of Banks' Customers Loyalty Using Naive Bayes and Support Vector Machine Classifiers: A Machine Learning Approach

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dc.contributor.authorSulaiman Olaniyi Abdulsalam
dc.contributor.authorIsmaila Yusuf
dc.contributor.authorSamson Olutunji Ojerinde
dc.contributor.authorAbdullahi Yahaya
dc.date.accessioned2025-01-29T04:38:14Z
dc.date.available2025-01-29T04:38:14Z
dc.date.issued2024
dc.description.abstractThis 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.
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/3663
dc.language.isoen
dc.publisherUniversity of Ibadan Journal of Science and Logics in ICT Research
dc.titleDetection of Banks' Customers Loyalty Using Naive Bayes and Support Vector Machine Classifiers: A Machine Learning Approach
dc.typeArticle
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