Efficient Optimization of Neural Networks for Predictive Hiring: An In-Depth Approach to Stochastic Gradient Descent

Identifying optimal candidates swiftly for specific job roles remains a critical challenge for recruiters and companies, given the substantial costs and time constraints associated with the recruitment process. The quality of the prediction using ML models is largely influenced by discriminant variables and is pivotal in determining candidate suitability. This study aims to advance job performance prediction systems by harnessing the synergy of hybrid neural networks and Gradient Descent (SGD). Our objective is to streamline and enhance recruitment screening through a meticulous analysis of historical employee performance data. The research methodology progresses through four pivotal stages: data acquisition, processing (cleaning and handling values), construction and building of model, and optimization, culminating in model quality evaluation. Emphasizing the importance of Gradient Descent in fortifying model performance, we introduce a novel training algorithm that seamlessly integrates Stochastic Gradient Descent (SGD) to bolster model convergence and accuracy. A comprehensive study is conducted to assess the effectiveness of each hybrid model, with a focus on model performance metrics such as accuracy and efficiency.