Predicting Employee Performance Levels Using Machine Learning Algorithms: Enhancing HR Decision-Making through AI

Abstract

This study presents a machine-learning framework to predict employee performance levels, empowering HR professionals with data-driven insights for eTective talent management. Leveraging a comprehensive dataset encompassing demographics, job roles, engagement metrics, training history, and historical performance ratings, the research explores multiple algorithms, including LightGBM, XGBoost, XGBoost with SMOTE, and Random Forest. To address class imbalance, the Synthetic Minority Over sampling Technique (SMOTE) was implemented, generating synthetic samples to enhance prediction accuracy across all classes. Feature selection and importance analysis identified key performance predictors, such as tenure, engagement scores, work-life balance, and satisfaction levels. Among the evaluated models, Random Forest achieved the highest accuracy (94%) with balanced class performance, making it the preferred choice for deployment. This research underscores the transformative role of machine learning in HR practices, providing actionable insights to design targeted development programs, optimize employee performance, and improve organizational outcomes.