Leveraging Machine Learning for Employee Resignation Prediction in HR Analytics

A major issue in human resource management, employee attrition prediction offers insightful information for corporate decision-making. In this regard, conventional approaches including decision trees have only shown modest success. These techniques usually presume feature independence, though, and struggle to fit the relationships in the data. Using Graph Attention Networks (GAT), we present a novel method for estimating employee attrition that makes use of the linkages and similarities among employees to raise prediction performance. In this study, we represent each employee as a node in a graph, with their personal attributes (such as age, salary, job satisfaction, etc.) serving as node features. We build an adjacency matrix based on employee similarity computed with cosine similarity or Euclidean distance. We develop a GAT model using this graph structure that aggregates surrounding node features based on attention-based criteria therefore enabling the model to weigh the significance of various relationships between employees. The GAT-based model beats conventional logistic regression according to experimental data, therefore greatly enhancing the accuracy of employee attrition prediction. Improving prediction performance depends critically on the model's capacity to replicate dependencies between characteristics and include relational information from adjacent nodes. This paper shows the possibilities of GAT in employee attrition prediction and emphasizes its capacity to model complex interactions inside employee data, therefore providing a fresh approach for strategic workforce management and human resource analytics.