Machine Learning in Employee Attrition

Employee attrition poses a significant organizational challenge, disrupting operations, escalating costs, and diminishing institutional expertise. Recent advancements in machine learning (ML) provide innovative solutions for identifying potential attrition risks and implementing preemptive interventions. This paper examines advanced ML approaches, including Random Forests, Gradient Boosted Trees, and Long Short-Term Memory network, highlighting their ability of processing intricate datasets and temporal trends. Additionally, the study indicates the critical role of Explainable AI (XAI) in ensuring interpretability and fostering ethical, transparent decision-making. By integrating multimodal data sources and utilizing privacy-preserving methods, organizations can derive actionable insights while maintaining compliance with ethical and regulatory standards. Research findings can serve as a roadmap for leveraging ML to enhance workforce retention strategies, optimize HR processes, and foster sustainable organizational growth.