International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Employee attrition poses a significant financial and operational challenge to modern organizations, leading to increased recruitment costs and loss of institutional knowledge. This research proposes a robust predictive framework to identify at-risk employees and determine the underlying drivers of turnover. Utilizing the IBM HR Analytics dataset, we implement a machine learning pipeline centered on the Random Forest Classifier. To address the inherent class imbalance in attrition data, the Synthetic Minority Over-sampling Technique (SMOTE) was employed, significantly improving the model's sensitivity to minority class instances. Experimental results demonstrate that the model achieves an F1-score of [Insert Score, e.g., 0.89] and an AUC-ROC of [Insert Score, e.g., 0.92]. Feature importance analysis identifies Monthly Income, Overtime, and Age as the primary predictors of turnover. The study concludes with the deployment of a web-based dashboard, providing HR practitioners with an actionable tool for proactive intervention and data-driven retention strategies.

Keywords: Machine Learning, Employee Attrition, Random Forest, SMOTE, Predictive Analytics, HR Management.

Downloads: | DOI: 10.17148/IJARCCE.2026.15149