Factors Influencing Employee Turnover in the IT Sector

Date

2025-12-1

Author

Tutlübük Pala, Şifa

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Employee turnover is one of the most important problems that organizations face, especially in rapidly developing and competitive sectors such as information technology (IT). This study aims to employ machine learning techniques to identify and predict the factors affecting turnover intention among IT sector employees. For this objective, a data set was gathered with a survey, including questions such as age, gender, marital status, educational status, work experience, job satisfaction, expected monthly income, and work-life balance. After literature research, the six widely used machine learning algorithms are determined and used to examine the dataset and develop predictive models. The models are Random Forest, LightGBM, XGBoost, CatBoost, Support Vector Machine, and Logistic Regression, which also served as the baseline. These models, which have been used in previous similar research context, are applied to formed datasets and are compared in terms of their performance to see how effective they are in predicting turnover intention. According to result of the study, these techniques enable meaningful analyses. Machine learning approaches, particularly community approaches, can assist in the decision-making process of strategic human resource management (HRM). The results help to identify key factors contributing to employee turnover, enabling rapid intervention in IT sector.

Subject Keywords

employee turnover, machine learning, IT sector, random forest, LightGBM, XGBoost, CatBoost

URI

https://hdl.handle.net/11511/118248

Collections

Graduate School of Informatics, Thesis