A multi-factor data mining and transformer-based predictive modeling approach for career success using educational and behavioral traits

Artificial Intelligence (AI) and automation are increasingly transforming the job market, necessitating advanced methods to enhance job opportunities and career satisfaction for students. In this context, data mining plays a crucial role by uncovering hidden patterns and relationships within large-scale educational and behavioral datasets, enabling more accurate and data-driven insights. This study investigates the use of data mining predictive to analyze career satisfaction based on students' academic achievements and behavioral traits. Specifically, we explore the efficacy of a transformer-based Bidirectional Encoder Representations from Transformers (BERT) model, which incorporates embedding layers and feed-forward networks within its multi-layer transformer blocks to capture complex, non-linear relationships among diverse educational and behavioral factors. For comparative purposes, traditional machine learning models, and deep learning architectures are also applied to the same Education & Career Success data set. For comparative purposes, traditional machine learning models such as support vector machines, logistic regression, and random forest, as well as a deep learning baseline using gated recurrent units, were also implemented on the same dataset. The empirical analysis demonstrates that the BERT model significantly outperforms these baseline methods, achieving a highest classification accuracy of 98%, compared to 80-85% for traditional and deep learning approaches. This superior performance highlights the proposed model's ability to effectively integrate and contextualize multifaceted input features, making it a powerful tool for predicting career satisfaction outcomes.