Predicting Employee Attrition: A Comparative Analysis of Machine Learning Models Using the IBM Human Resource Analytics Dataset

In employee turnover research and workforce management, addressing the impacts of suboptimal employee performance is crucial for organizations of all sizes and industries. Utilizing advanced machine learning classification models to predict potential employee resignations can enhance human resource departments’ intervention strategies, effectively mitigating attrition challenges. This research investigates the performance of various machine learning algorithms in classification tasks, focusing on their accuracy in predicting outcomes from a given dataset. Five models were evaluated: Random Forest, Support Vector Machine (SVM), Decision Tree, Gradient Boosting, and a Hybrid Model that integrates multiple algorithms. The goal was to identify which model yields the highest accuracy and to understand the strengths and weaknesses of each approach. Results showed that the Hybrid Model achieved the highest accuracy at 95.0%, suggesting that combining different algorithms effectively harnesses their strengths while mitigating individual weaknesses. The SVM accurately classified the instance with 88.6%, demonstrating its capability to manage complex decision boundaries in high-dimensional spaces. Both Random Forest and Gradient Boosting attained an accuracy of 87.3%, reflecting their ensemble techniques that enhance predictive performance by reducing overfitting and optimizing error reduction. In comparison, the Decision Tree classifier exhibited the least accuracy at 80.5%, highlighting its susceptibility to overfitting and limited generalizability. The superior performance of the Hybrid Model indicates a promising direction for future research, where integrating diverse algorithms could lead to more robust predictions. Overall, this study provides valuable insights for practitioners and researchers seeking to optimize model selection and improve predictive accuracy in their domains.