Fostering Creative sports talents with transformer models for inclusive financial

The study introduces a novel sports analytics approach and, for the first time, applies the TabTransformer model to predict attendance at fitness classes. The main objective is to uncover potential sports talent and create in-depth financial planning based on attendance patterns. Compared to the deep model and traditional model, the TabTransformer performs better with an accuracy of 0.710, precision of 0.738, recall of 0.707, F1 score of 0.722, and an AUC-ROC of 0.818. This is because the model can make use of textual embeddings to handle categorical features and linear transformations for numerical features, which are able to capture complex interactions between data. The results depict the high ability of the model to identify committed members in well-attended groups (e.g., Aqua and HIIT), but the model’s moderate recovery in poor-attendance groups (e.g., Strength and Cycling) directs us towards further investigation of barriers to access. These insights pave the way for designing targeted interventions and inclusive financial strategies, including membership subsidies and flexible schedules. Despite limitations such as the moderate size of the dataset and the lack of financial features, this research lays a strong foundation for the application of Transformer models in sports analytics. Ultimately, this study emphasizes the importance of using Transformer-based analytics to generate creative and equitable outcomes in fitness programs and is a step forward in identifying talent and promoting inclusion in sports.