Yabaş, UtkuCankaya, Hakki Candanİnce, Türker2023-06-162023-06-162012978-0-7695-4736-70730-3157https://doi.org/10.1109/COMPSAC.2012.54https://hdl.handle.net/20.500.14365/193536th Annual IEEE International Computer Software and Applications Conference (COMPSAC) -- JUL 16-20, 2012 -- Izmir Inst Technol (IZTECH), Izmir, TURKEYCustomer churn is a big concern for telecom service providers due to its associated costs. This short paper briefly explains our ongoing work on customer churn prediction for telecom services. We are working on data mining methods to accurately predict customers who will change and turn to another provider for the same or similar service. Sample dataset we use for our experiments has been compiled by Orange Telecom from real data. They posted the sample dataset for 2009 Knowledge Discovery and Data Mining Competition. IBM has scored the highest on this dataset requiring significant amount of computational resources. We are aiming to find alternative methods that can match or improve the recorded highest score with more efficient use of resources. Dataset has very large number of features, examples and incomplete values. As the first step, we employ some methods to preprocess the dataset for its imperfections. Then, we compare and contrast various ensemble and single classifiers. We conclude the paper with future directions for the study.eninfo:eu-repo/semantics/closedAccesschurn predictionmachine learningdata miningpattern recognitionConference Object10.1109/COMPSAC.2012.542-s2.0-84870849027