Sancar, SemanurUzun-Per, MeryemGarcía Márquez, Fausto PedroJamil, AkhtarEken, SüleymanHameed, Alaa Ali2023-04-112023-04-112023Sancar, S., & Uzun-Per, M. (2023). Testing the performance of feature selection methods for customer churn analysis: Case study in B2B business. F.P. García Márquez, A. Jamil, S. Eken, A.A. Hameed, (Eds.), International Conference on Computing, Intelligence and Data Analytics (pp 509-519). Springer: Cham. https://doi.org/10.1007/978-3-031-27099-4_3997830312709949783031270987https://doi.org/10.1007/978-3-031-27099-4_39https://hdl.handle.net/20.500.13055/442Churn analysis has recently become one of the favorite topics of marketing teams with the development of machine learning models. This study aims to discover the most suitable feature selection (FS) model for churn analysis by using the databases of BiletBank, a business-to-business (B2B) company. It was found that some categorical data such as agency type and currency used by customers, along with periodic flight sales data, are also meaningful features for churn analysis in the BiletBank customer portfolio. This feature selection study in the database will be a source for future churn analysis studies.eninfo:eu-repo/semantics/closedAccessCustomer Churn AnalysisFeature SelectionB2BSequential Forward SelectionSequential Backward SelectionClassificationLogistic RegressionSupport Vector MachinesRandom Forest ClassifierExtra Tress ClassifierConference Object10.1007/978-3-031-27099-4_395095192-s2.0-85151063013Q4