Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Comparative Study of Machine Learning Techniques for Credit Card Customer Churn Prediction Subject The topic of the resource Banking industry; CatBoost; Churn prediction; Machine learning; Random forest classifier; SMOTE Description An account of the resource A customer is a churner when a customer moves from one service provider to another. Nowadays, with an increasing number of severe competition with inside the market, essential banks pay extra interest on customer courting management. A robust and real-time credit card holders churn evaluation is vital and valuable for bankers to preserve credit cardholders. Much research has been observed that retaining an old customer is more than five times easier compared to gaining a new customer. Hence, this paper proposes a method to predict churns based on a bank dataset. In this work, Synthetic Minority Oversampling Technique (SMOTE) has been used for handling the imbalanced dataset. Credit card customer churn is predicted using random forest, k-nearest neighbor, and two boosting algorithms, XGBoost and CatBoost. Hyperparameter tuning using grid search has been used to increase the accuracy. The experimental result shows Catboost has achieved an accuracy of 97.85% and tends to do better than the other models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Bose A.; Thomas K.T. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-141, pp. 295-307. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-19-3035-5_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-3035-5_23</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140245291&amp;doi=10.1007%2F978-981-19-3035-5_23&amp;partnerID=40&amp;md5=fbb45336c6080f7e4657c9484338642a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140245291&amp;doi=10.1007%2f978-981-19-3035-5_23&amp;partnerID=40&amp;md5=fbb45336c6080f7e4657c9484338642a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bose A., Department of Data Science, Christ University, Bangalore, India; Thomas K.T., Department of Data Science, Christ University, Bangalore, India