“Machine Learning Approaches to Enhance Candidate Selection: A Comparative Study in HR Recruitment"

Through a comparison of machine learning algorithms, this abstract provides a thorough investigation of the use of predictive analytics in HR recruitment. Organisations are increasingly using advanced analytics to improve their recruitment operations in the ever-changing talent acquisition landscape. The goal of this study is to compare how well different machine learning algorithms predict the results of successful candidates. Using a comparative methodology, the paper examines the effectiveness of well-known machine learning algorithms, including Random Forest, Support Vector Machines, Neural Networks, and Gradient Boosting. Model training and assessment are based on a heterogeneous dataset that includes past recruiting data, including candidate traits, interview performance, and subsequent job success measures.