An AI Intelligent Workforce Analytics Framework for Employee Performance Prediction Using Ensemble Learning
DOI:
https://doi.org/10.18486/ijcsnt/14.3.016Keywords:
Artificial Intelligence, Workforce Analytics, Employee Performance Prediction, XGBoost, Gradient Boosting, Machine LearningAbstract
AI has changed workforce analytics by allowing companies to make decisions based on data and predictions instead of just descriptive insights. In this research, we introduce an AI-driven system that use ensemble learning techniques, specifically XGBoost and Gradient Boosting, to forecast an individual's job performance. To accurately predict employee outcomes, workforce variables, including performance determinants, behavioral indicators, and productivity metrics, are meticulously analyzed. The proposed methodology seeks to enhance forecast accuracy while ensuring interpretability, hence aiding managers in decision-making. The experimental results indicate that both models had the capability to capture intricate nonlinear connections within labor data. XGBoost got 51.2% accuracy with balanced precision and recall metrics, which shows that it can make predictions that are consistent but not very good. The Gradient Boosting model did better than XGBoost on all evaluation criteria, with an F1-score of 62.16%, a recall of 63.89%, an accuracy of 55.2%, and a precision of 60.53%. The findings show that Gradient Boosting is the greatest way to predict how well workers will do since it makes them more resilient and better at generalizing. The results show that AI models that use ensembles function well for workforce analytics and can help HR strategy. The results of this study show how useful machine learning-powered performance prediction systems can be for improving the effectiveness of organizations and making decisions based on facts.
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Copyright (c) 2025 Subhash Kodiyil Raman, Nithya Sambamoorthy, Sreenivasa Babu Nidamanuri, Shanmuga Pria, Farhana Sultana, Rajalingam Arumuganainar
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