Evaluating Data-Driven Models to Enhance Employee Retention and Performance

Employee attrition presents considerable challenges for organizations, affecting productivity, employee morale, and financial health. This research seeks to create and evaluate data-driven models to more accurately forecast employee turnover. By analyzing various employee data, such as demographic details, job-related factors, and performance metrics, machine learning techniques are used to uncover the primary drivers of attrition. The goal is to identify the most effective model for prediction, of-fering practical insights to shape employee retention strategies. In conclusion, this study contributes to the development of proactive measures aimed at reducing attrition and enhancing workforce management. This study employs Logistic Regression and Ran-dom Forest, each bringing distinct advantages to HR analytics over conventional approaches. Logistic Regression is appreciated for its ease of interpretation, making it highly applicable for HR insights. Conversely, the ensemble nature of Random Forest enhances prediction accuracy by effectively identifying non-linear trends commonly found in employee behavior. By combining these models, the approach provides both robust predictive capabilities and valuable insights into major attrition factors, which are essential for crafting targeted retention strategies.