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 Faculty Publications Conference Paper Faculty Publications- Conference Papers 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/. Creator An entity primarily responsible for making the resource Singh, Garima; Joshi, Hemlata Title A name given to the resource Customer churn behaviour prediction in telecommunication using classification algorithms and modelling Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived AIP Conference Proceedings;Volume;3191;Issue;1;Article No.;40006; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0248958" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0248958</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218679790?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218679790?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh G., Department of Statistics, CHRIST (Deemed to Be University), Bengaluru, India; Joshi H., Department of Statistics, CHRIST (Deemed to Be University), Bengaluru, India Description An account of the resource The cost of obtaining a high-quality client is usually five times more than the cost of keeping an existing customer. This is why it is very important that businesses keep their customers at home. To retain and improve their customers' satisfaction, researchers in various fields such as marketing, information technology, and business intelligence studied various ways to deliver the best possible services. Despite the good performance of the work done before, there is still a considerable gap in their prediction of the churners. In most cases, the training dataset is too large, and the high dimensionality of it causes the classification algorithms to fail. In the present paper, an attempt was made to estimate customer churn with greater accuracy in the membership of cellular wireless services using a call details records dataset consisting of 3333 clients having 21 attributes each. With the advancement of Machine Learning (ML) and artificial intelligence, most popular approaches such as logistic regression, CART, and C5 algorithms have been used with and without using the data balancing technique SMOTE. The performance evaluation of these predictive models is done using the model accuracy, confusion matrix, AUC value, ROC curve, and Cohen's Kappa statistics. The study results indicate that the C5 algorithm could estimate customer churn with an accuracy of more than 92% for both balanced and imbalanced datasets. 2025 Author(s). Subject The topic of the resource Churn Prediction; Classification Techniques; Customer Churn; Machine Learning; SMOTE; Telecommunication Publisher An entity responsible for making the resource available American Institute of Physics Relation A related resource ISSN: 0094243X; ISBN: 978-073545110-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online