Predictive Hybrid Ensemble Approaches for Forecasting Employee Attrition on HR Data

Modern organizations are becoming increasingly concerned about employee attrition, which results in higher training and recruitment expenses, disruptions to workflows, and the loss of skilled and experienced workers. Early detection of possible resignations enables businesses to take prompt action and put in place efficient retention plans. Using the IBM HR Analytics dataset, this study forecasts employee attrition using sophisticated machine learning techniques, particularly ensemble learning approaches like Random Forest, XGBoost, and LightGBM. Data preprocessing, feature selection and engineering, model development, hyperparameter tuning, and evaluation using classification performance metrics like accuracy, precision, recall, and F1-score are all methodical steps in the study. The experimental findings demonstrate that ensemble learning models, which provide strong generalization and high accuracy, perform noticeably better than baseline models. These results show how useful and successful machine learning (ML)-based attrition prediction is in assisting HR departments in creating strategic policies that lower employee turnover and foster long-term organizational success.