Karamollaoğlu, HamdullahYücedağ, İbrahimDoğru, İbrahim Alper2023-07-262023-07-2620219.78167E+12https://doi.org/10.1109/UBMK52708.2021.9558876https://hdl.handle.net/20.500.12684/130416th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- 176826Customer churn analysis is the process of predicting customers who tend to cancel the service (subscription) they receive for various reasons, especially in sectors such as telecommunications, finance and insurance, and determining the necessary operational steps to prevent this cancellation. The study used two separate datasets from kaggle.com to identify customers who tend to unsubscribe in the telecommunications industry. The analysis process was carried out by applying machine learning methods such as Logistic Regression, K-Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Multi-Layer Sensors and Naive Bayes methods on the relevant datasets. It was seen that the most successful method in the customer loss analysis performed on both datasets was the Random Forest method. © 2021 IEEEtr10.1109/UBMK52708.2021.9558876info:eu-repo/semantics/closedAccessCustomer churn analysisMachine learningTelecommunicationAdaptive boostingDecision treesLogistic regressionNearest neighbor searchRandom forestsSupport vector machinesTelecommunication industryAnalysis processChurn analysisComparative analyzesCustomer churn analyseCustomer churn predictionCustomer churnsLogistics regressionsMachine learning methodsNearest-neighbourTelecommunications industrySalesConference Object1391442-s2.0-85125880318