Customer Churn Prediction in Banking Industry Using K-Means and Support Vector Machine Algorithms

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dc.contributor.authorAbdulsalam, Sulaiman Olaniyi
dc.contributor.authorArowolo, Micheal Olaolu
dc.contributor.authorJimada-Ojuolape, Bilkisu
dc.contributor.authorSaheed, Yakub Kayode
dc.date.accessioned2024-08-09T14:34:07Z
dc.date.available2024-08-09T14:34:07Z
dc.date.issued2020
dc.description.abstractThis study proposes a customer churn mining structure based on data mining methods in a banking sector. This study predicts the behavior of customers by using clustering technique to analyze customer’s competence and continuity with the sector using k-means clustering algorithm. The data is clustered into 3 labels, on the basis of the transaction in and outflow. The clustering results were classified using Support Vector Machine (SVM), an Accuracy of 97% was achieved. This study enables the banking administrators to mine the conduct of their customers and may prompt proper strategies as per engaging quality and improve proper conducts of administrator capacities in customer relationship.
dc.identifier.citationOlaniyi, A. S., Olaolu, A. M., Jimada-Ojuolape, B., & Kayode, S. Y. (2020). Customer churn prediction in banking industry using K-means and support vector machine algorithms. International Journal of Multidisciplinary Sciences and Advanced Technology, 1(1), 48-54.
dc.identifier.issn2708-0587
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/2231
dc.language.isoen
dc.publisherInternational Journal of Multidisciplinary Sciences and Advanced Technology
dc.relation.ispartofseries1; 1
dc.titleCustomer Churn Prediction in Banking Industry Using K-Means and Support Vector Machine Algorithms
dc.typeArticle
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