Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent

Efficient and accurate hiring processes are critical for organizational success, yet traditional recruitment methods often face challenges such as inefficiencies and delays. This study explores the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance predictive hiring models. A hybrid framework is proposed, integrating neural networks with Stochastic Gradient Descent (SGD) optimization and feature selection methods, including Logistic Regression (LR) and Discriminant Analysis (DA). The approach demonstrates a marked improvement in prediction accuracy and efficiency, with Logistic Regression emerging as a more effective feature selection method for neural networks in this context. By leveraging these techniques, human resource teams can streamline candidate evaluations, enhance decision-making processes, and modernize recruitment workflows. This research underscores the transformative potential of AI in addressing the limitations of traditional hiring practices.