Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering

DOI: http://dx.doi.org/10.15439/2024R78

Citation: Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering, Vijender Kumar Solanki, Tran Duc Tan, Pradeep Kumar, Manuel Cardona (eds). ACSIS, Vol. 42, pages 131–137 (2024)

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Abstract. To complement this approach, gradient boosting (XGBoost) is utilized to uncover hidden or non-linear relationships within the data. This enables more accurate predictions of workforce trends, including career development patterns and employee turnover rates. By integrating these techniques, the proposed framework provides a dual benefit. First, it enhances talent management and workforce planning by offering actionable insights into employee engagement and retention. Second, it equips marketing and human resources teams with strategies tailored to boost employee satisfaction and loyalty. The results demonstrate the immense potential of machine learning in refining labor market analytics. Organizations can use these insights to make strategic, data-informed decisions that improve workforce efficiency while aligning with broader business goals. This integration of machine learning into labor market analysis not only strengthens employee management processes but also positions organizations to adapt effectively to evolving workforce demands, ultimately fostering a more robust and sustainable labor network.

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