Employee retention is a critical challenge in modern organizations, as high rates of turnover can negatively impact organizational productivity and increase operational costs. The rise of data-driven decision-making in human resources management has enabled organizations to leverage advanced analytics and machine learning techniques to better understand the factors influencing employee attrition and to predict which employees are at risk of leaving. This chapter presents an application of various machine learning algorithms—including logistic regression, decision trees, random forests, and gradient boosting—on the open-source IBM HR Analytics Employee Attrition & Performance dataset. The analysis aims not only to achieve accurate predictions of employee turnover but also to provide actionable insights into the underlying determinants of attrition using model interpretability techniques such as SHAP (Shapley Additive Explanations). Key variables including job satisfaction, monthly income, years at company, and work-life balance are explored in detail to identify their relative importance in predicting employee departure. The findings are intended to assist human resource managers in developing proactive strategies to enhance employee engagement and retention. This chapter demonstrates the practical value of machine learning models in human resources analytics, offering both predictive power and interpretability to support evidence-based management decisions.