Abstract
The size of companies has seen an exponential growth over the years. Corporations recruit anywhere from a few hundred to a few thousand employees every year. With such rates, human resource management in companies is proving to be more and more significant every day. Done manually, HRM is a laborious task, given the sheer quantity of employees. Luckily, over the years, data analytics in HR is emerging as an integral part in corporate operation. Yet, there remain a few tasks that involve human involvement, one of them being selecting candidates that are eligible for a promotion. This paper proposes a solution using decision tree-based machine learning algorithms to learn from past employee records to aid this decision-making process. It explores the usage of two machine learning algorithms, random forest, and XGBoost to predict whether an employee is eligible to receive a promotion or not and determine what factors are responsible for that prediction.
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Vishal Balaji, D., Arunnehru, J. (2022). Predictive Analysis on HRM Data: Determining Employee Promotion Factors Using Random Forest and XGBoost. In: Manogaran, G., Shanthini, A., Vadivu, G. (eds) Proceedings of International Conference on Deep Learning, Computing and Intelligence. Advances in Intelligent Systems and Computing, vol 1396. Springer, Singapore. https://doi.org/10.1007/978-981-16-5652-1_15
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DOI: https://doi.org/10.1007/978-981-16-5652-1_15
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