Analyzing and Predicting Hiring Decisions Using Machine Learning and Deep Learning

Objective The aim of this article is to explore the use of machine learning and deep learning algorithms to predict the outcomes of hiring decisions. Selecting the right human resources is one of the most fundamental elements in any organization or institution, as it directly influences key performance indicators and overall productivity. Since the hiring process is inherently complex and success is difficult to predict, the application of modern techniques—such as machine learning and deep learning—is recommended to enhance the accuracy of selection decisions. This research aims to investigate the effectiveness of these techniques in helping organizations make better hiring choices and avoid costly mistakes. Methods In this study, several data points were collected, including age, education, work experience, technical abilities, and personality traits of job applicants. These data were analyzed using machine learning and deep learning algorithms to predict each applicant’s likelihood of success in various roles. Classification models were employed to simulate hiring behaviors and predict decisions at different levels of specificity as part of the machine learning analysis. Furthermore, deep learning models were used to explore complex and nonlinear relationships between applicant characteristics and hiring outcomes. All models were trained and tested on high-quality data obtained from trusted, peer-reviewed sources, which were rigorously processed to ensure accuracy and consistency. Results The findings indicate that the application of machine learning and deep learning models significantly improves the accuracy of predicting hiring outcomes. Among all the models evaluated, the CatBoost algorithm performed best, achieving an accuracy of 0.9533, a precision of 0.9540, a recall of 0.8925, and an F1 score of 0.9222, outperforming the other algorithms by a notable margin. The Random Forest and XGBoost models also delivered strong performances, with precision scores of 0.9213 and 0.9500, respectively. Feature analysis revealed that technical skills, recruitment strategy, and interview scores were the most influential factors in hiring decisions. Additionally, ensemble learning models-especially CatBoost-were able to identify and model the complex effects of applicants' personality traits, which traditional machine learning models often failed to capture. Conclusion This study demonstrates that machine learning and deep learning algorithms can significantly enhance decision-making in workforce selection. The CatBoost algorithm performed best due to its ability to model complex and nonlinear relationships between applicant characteristics and hiring outcomes. These technologies offer the potential to reduce hiring costs, improve the quality of new hires, and boost organizational productivity. However, to maximize the benefits of these methods, organizations must collect and process applicant data consistently and accurately. They must also regularly retrain and update machine learning models to ensure continued effectiveness and adaptability in the face of evolving labor market dynamics.