Leveraging Machine Learning Methods for Predicting Employee Turnover Within the Framework of Human Resources Analytics

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dc.contributor.authorTaner, Zeynep
dc.contributor.authorAreta, Ouranıa
dc.contributor.authorHızıroğlu, Kadir
dc.date.accessioned2025-03-20T09:41:22Z
dc.date.available2025-03-20T09:41:22Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractEmployee turnover is a critical challenge for organizations, leading to significant costs and disruptions. This study aims to leverage Machine Learning (ML) techniques within the framework of Human Resources Analytics (HRA) to predict employee turnover effectively. The research evaluates and compares the performance of six widely used models: Decision Trees, Support Vector Machines (SVM), Logistic Regression, Random Forest, XGBoost, and Artificial Neural Networks. These models were implemented using the R programming language on an open-source dataset from IBM. The methodology involved data preprocessing, splitting into training, validation and testing sets, model training, and performance evaluation using metrics such as accuracy, sensitivity, specificity, precision, F1-score, and ROC-AUC. The results indicate that the Logistic Regression model outperformed the other models, achieving high accuracy and a good F1-score. The study concludes by emphasizing the importance of HRA and ML techniques in predicting and managing employee turnover, while discussing limitations such as class imbalance and the need for more rigorous performance evaluation. Future research directions include exploring alternative models, feature selection techniques, and addressing class imbalance.
dc.identifier.doi10.38016/jista.1440879
dc.identifier.endpage158
dc.identifier.issn2651-3927
dc.identifier.issue2
dc.identifier.startpage145
dc.identifier.trdizinid1267101
dc.identifier.urihttps://doi.org/10.38016/jista.1440879
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1267101
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1930
dc.identifier.volume7
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofZeki sistemler teori ve uygulamaları dergisi (Online)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20250319
dc.subjectHuman Resources Analytics
dc.subjectEmployee Turnover Prediction
dc.subjectMachine Learning Models
dc.titleLeveraging Machine Learning Methods for Predicting Employee Turnover Within the Framework of Human Resources Analytics
dc.typeArticle

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