Keywords:
employee performance
forecasting
workforce development
red-tailed hawk mutated intelligent decision

  • Abstract

    Employee performance prediction is amongst the most vital tasks that streamline human resource enhancement by facilitating organizations to base evidence-driven decisions about talent management, retention practices, and overall organizational performance. Empirical forecasting schemes allow organizations to detect best-performing employees, reduce the threats of employee departures, and streamline productivity. This paper suggests a novel evidence-based employee performance prediction model using the Red-tailed Hawk Mutated Intelligent Decision Tree (RTH-IDT).The data set, which is downloaded from Kaggle, has employee performance data and demographic data. Preprocessing data involves Z-score normalization to feature standardization that provides consistency and minimizes bias. Principal Component Analysis (PCA) employs dimensionality reduction to improve computational efficacy and predictive effectiveness. The suggested RTH-IDT hybrid model improves prediction quality through the incorporation of the Red-tailed Hawk Optimization algorithm with an Intelligent Decision Tree, maximizing classification accuracy. Operating on Python, the model performs higher with accuracy at 98.66%, precision at 99.10%, and recall at 99.82%. The findings are representative of the suitability of the model in accurately forecasting 310 employee performance records and identifying attrition risks. Compared to conventional forecasting methods, the RTH-IDT process yields greater reliability and accuracy and is a valuable tool for workforce planning. By providing actionable employee performance insight, it allows organizations to make more informed data-driven decisions, so they can get the most from workforce planning, improve training and development programs, and streamline general human resource practice. Coupling advanced machine learning algorithms, the prediction model is made stronger and more reactive, allowing businesses to better realize staff and organizational potential.

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How to cite

Hota, H. N., Sunil, J., Biswal, S. K., Khambra, V., Raj, N., & Gupta, S. (2025). Optimizing employee performance forecasting: A data-driven approach to workforce development. Multidisciplinary Science Journal, 7, 2025ss0208. https://doi.org/10.31893/multiscience.2025ss0208
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