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Ms. Manjeet Kaur is an Assistant Professor in the Faculty of Agriculture at Jagannath University, Jaipur, Rajasthan, India. Her academic role is associated with agricultural education. Her contribution to this volume addresses machine learning approaches to human resource management and intelligent workforce-related decision-making.
Machine learning (ML) is revolutionising human resource management (HRM) by facilitating data-informed workforce planning, sophisticated recruitment, employee performance evaluation, talent enhancement, and employee retention strategies. Conventional HR methodologies frequently depend on manual procedures, subjective evaluations, and disjointed information systems, which may hinder the velocity, uniformity, and scalability of organisational decision-making Supervised learning algorithms such as logistic regression, decision trees, Random Forest, gradient boosting, and support vector machines facilitate classification and prediction tasks, whereas clustering and anomaly detection methods assist in recognising employee segments and atypical workforce behaviours. Natural language processing can facilitate résumé evaluation, job-description alignment, employee feedback assessment, and competence recognition. The section highlights that machine learning ought to enhance human decision-making instead of autonomously making significant job choices. Transparent protocols, significant human supervision, equity evaluation, and robust data governance are crucial for the creation of reliable and efficient intelligent HR systems.