Enhancing Talent Recruitment in Business Intelligence Systems: A Comparative Analysis of Machine Learning Models

Abstract

In the competitive field of business intelligence, optimizing talent recruitment through data-driven methodologies is crucial for better decision-making. This study compares the effectiveness of various machine learning models to improve recruitment accuracy and efficiency. Using the recruitment data from a major Yemeni organization (2019–2022), we evaluated models including K-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, Decision Trees, Random Forest, Gradient Boosting Classifier, AdaBoost Classifier, and Neural Networks. Hyperparameter tuning and cross-validation were used for optimization. The Random Forest model achieved the highest accuracy (92.8%), followed by Neural Networks (92.6%) and Gradient Boosting Classifier (92.5%). These results suggest that advanced machine learning models, particularly Random Forest and Neural Networks, can significantly enhance the recruitment processes in business intelligence systems. This study provides valuable insights for recruiters, advocating for the integration of sophisticated machine learning techniques in talent acquisition strategies.