Leveraging Machine Learning for Talent Acquisition: Predicting High-Performance Candidates in Human Resource Management

Abstract

This study explores the application of machine learning (ML) in human resource (HR) management to enhance the recruitment process by predicting high-performing candidates. The research addresses gaps in traditional recruitment methods, which are often time-consuming and susceptible to subjective bias. By employing a Random Forest algorithm, this study utilizes a dataset of 10,000 records, encompassing attributes such as education, work experience, psychometric assessments, and interview evaluations. Data were divided into 70% training and 30% testing sets to ensure robust model evaluation. The findings demonstrate that the Random Forest model achieved a prediction accuracy of 87%, outperforming traditional methods and other ML models like Logistic Regression. The model's ability to identify key attributes contributing to candidate performance underscores its potential for data-driven decision-making in HR management. However, challenges such as data bias, algorithmic transparency, and resistance to technological change were identified as barriers to implementation. This research contributes to the theoretical and practical understanding of ML in HR by offering a predictive model that balances accuracy with interpretability. Practical implications include strategies for integrating ML into existing HR systems, emphasizing the importance of explainable AI to foster trust among practitioners. The study concludes that ML-based recruitment can significantly improve efficiency, objectivity, and the quality of hiring decisions, paving the way for more innovative and strategic HR practices.