Employee turnover probability prediction

buir.advisorDayanık, Savaş
dc.contributor.authorBarın, Hüsameddin Deniz
dc.date.accessioned2022-09-21T10:37:50Z
dc.date.available2022-09-21T10:37:50Z
dc.date.copyright2022-09
dc.date.issued2022-09
dc.date.submitted2022-09-20
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2022.en_US
dc.descriptionIncludes bibliographical references (leaves 76-77).en_US
dc.description.abstractEmployee 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.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2022-09-21T10:37:50Z No. of bitstreams: 1 B161326.pdf: 953432 bytes, checksum: 9a99a2bfe76c7c01915a3722e77646ae (MD5)en
dc.description.provenanceMade available in DSpace on 2022-09-21T10:37:50Z (GMT). No. of bitstreams: 1 B161326.pdf: 953432 bytes, checksum: 9a99a2bfe76c7c01915a3722e77646ae (MD5) Previous issue date: 2022-09en
dc.description.statementofresponsibilityby Hüsameddin Deniz Barınen_US
dc.format.extentx, 77 leaves : illustrations ; 30 cm.en_US
dc.identifier.itemidB161326
dc.identifier.urihttp://hdl.handle.net/11693/110557
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEmployee turnoveren_US
dc.subjectBayesian hierarchical modelsen_US
dc.subjectMissing data imputationen_US
dc.titleEmployee turnover probability predictionen_US
dc.title.alternativePersonel kaybı olasılığı tahminlemeen_US
dc.typeThesisen_US
thesis.degree.disciplineIndustrial Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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