Early Detection of Employee Turnover Risks Using Machine Learning Approaches

Lakshmi Satwika Neelisetty, Naga Preethika Reddy Bonthu, Amar Jukuntla

2025

Abstract

Employee turnover poses a significant challenge for organizations, resulting in productivity losses and increased costs associated with recruitment, training, and knowledge transfer. Predicting attrition in advance allows organizations to implement proactive retention strategies, thereby improving workforce stability. This study proposes a machine learning-based predictive model to identify employees at risk of leaving by analyzing key factors such as demographic attributes, job roles, performance metrics, and organizational influences. By leveraging advanced data-driven techniques, the model estimates the likelihood of attrition, providing actionable insights for HR decision-making. The proposed approach aims to enhance employee retention efforts by enabling organizations to address underlying factors contributing to turnover, ultimately fostering a more engaged and stable workforce.

Paper Citation

in Harvard Style

Neelisetty L., Bonthu N. and Jukuntla A. (2025). Early Detection of Employee Turnover Risks Using Machine Learning Approaches. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 119-128. DOI: 10.5220/0013893000004919

in Bibtex Style

@conference{icrdicct`2525,
author={Lakshmi Neelisetty and Naga Bonthu and Amar Jukuntla},
title={Early Detection of Employee Turnover Risks Using Machine Learning Approaches},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={119-128},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013893000004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Early Detection of Employee Turnover Risks Using Machine Learning Approaches
SN - 978-989-758-777-1
AU - Neelisetty L.
AU - Bonthu N.
AU - Jukuntla A.
PY - 2025
SP - 119
EP - 128
DO - 10.5220/0013893000004919
PB - SciTePress