Jacquese Starling

M.S. Data Science

Unraveling the Complexities of Employee Retention: A Comparative Analysis of Logistic Regression and Random Forest Models

Employee attrition is a critical challenge facing organizations, with significant impacts on productivity, morale, and the bottom line. This study employed a rigorous, data-driven approach to uncover the key drivers of employee turnover within a specific organizational context. Using a combination of Logistic Regression and Random Forest models, we analyzed a comprehensive dataset of employee records and demographic information. The findings revealed a multifaceted set of factors contributing to attrition, including work-life balance, compensation, career development opportunities, and manager-employee relationships. By leveraging these data-driven insights, the study provides a roadmap for targeted interventions and talent management strategies. The results underscore the power of integrating people analytics and business acumen to foster work environments that prioritize employee engagement, well-being, and long-term retention. This research offers valuable guidance for organizational leaders seeking to transform their approach to talent management, ultimately driving sustainable success and positively impacting the lives of their workforce. The study’s methodology and findings contribute to the growing body of knowledge on evidence-based human capital management practices.