Predictive Modeling for Customer Churn

A tétel rövid nézete
dc.contributor.advisorHarangi, Balázs
dc.contributor.authorAlmanasrah, Wafa Mahmood Mohamed
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2026-02-12T18:56:54Z
dc.date.available2026-02-12T18:56:54Z
dc.date.created2025
dc.description.abstractThis thesis focuses on predicting customer churn in the telecommunications industry using machine learning techniques. The goal was to identify the main factors that drive customers to leave and provide data-based insights for improving retention strategies. Several models were developed and compared, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Extra Trees, and XGBoost. Visual and statistical evaluations confirmed that the models captured clear behavioral patterns behind churn. Overall, the study demonstrates how predictive analytics can support telecom companies in anticipating customer loss and designing more effective retention actions.
dc.description.courseGazdaságinformatikus
dc.description.degreeBSc/BA
dc.format.extent48
dc.identifier.urihttps://hdl.handle.net/2437/404448
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectChurn
dc.subjectTelecommunication
dc.subjectMachine learning
dc.subject.dspaceInformatics
dc.titlePredictive Modeling for Customer Churn
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