Machine Learning Solutions for Talent Loss: Predictive Insights and Strategic Countermeasures

In the context of global economic globalization, intense market competition across various industries emphasizes the crucial role of talent as core enterprise resources and competitive factors.This paper discusses the widespread issue of talent loss, which incurs significant operational challenges, including increased employment costs, diminished service quality, and reduced customer satisfaction.Utilizing a human resources dataset from the Kaggle platform, we employ a random forest algorithm to develop a talent loss prediction model.This model is designed to forecast potential resignations, allowing enterprises to implement timely retention strategies.Our analysis identifies key factors contributing to talent turnover and assesses the effectiveness of various countermeasures.The proposed strategies include integrating a talent-focused approach into corporate strategies, enhancing performance management systems, creating competitive compensation frameworks, developing internal training programs, and fostering a people-oriented culture.By predicting and understanding the dynamics of talent loss, this model provides a valuable tool for enterprise managers to mitigate its impacts effectively.The findings offer theoretical insights into preventing talent loss and reducing its adverse effects on company performance.