Barın, Hüsameddin Deniz2022-09-212022-09-212022-092022-092022-09-20http://hdl.handle.net/11693/110557Cataloged from PDF version of article.Thesis (Master's): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2022.Includes bibliographical references (leaves 76-77).Employee turnover prediction is crucial for the companies in the sense that the precautionary action by the employers can be made in advance. A turnover data provided by a company was examined throughout the thesis. Firstly, the missing data were imputed. Then a hierarchical model aiming to explain the attrition heterogeneity among the employees and preventing separation was fitted to the data set. Finally, the results of the implementation were analyzed along with the benchmark models. Based on the results, the proposed hierarchical model had a higher performance on the target metric and the heterogeneity across the units was inferred through the hierarchical model which outperformed the benchmark models.x, 77 leaves : illustrations ; 30 cm.Englishinfo:eu-repo/semantics/openAccessEmployee turnoverBayesian hierarchical modelsMissing data imputationThesisB161326