This study integrates TabTransformer and CTGAN for predicting job satisfaction among South Korean college graduates. TabTransformer handles complex tabular data relationships with self-attention, while CTGAN generates high-quality synthetic samples. The combined approach achieves an accuracy of 0.85, precision of 0.83, recall of 0.82, F1-score of 0.82, and an AUC of 0.88. Cross-validation confirms the model's robustness and generalizability with a mean accuracy of 0.85 and a standard deviation of 0.008. The integration of TabTransformer and CTGAN enhances predictive accuracy and model generalizability, providing valuable insights for employment policy and research.

Abstract
1. Introduction
2. Related works
3. Materials and Methods
    3.1. Data Preparation
    3.2. Feature Extraction using TabTransformer
    3.3. Data Augmentation using CTGAN
    3.4. Model Training with Augmented Data
    3.5. Final Classification Model Constructionand Evaluation
    3.6. Dataset
4. Results and PerformanceEvaluation
    4.1. Experimental Setup
    4.2 Evaluation Metrics
    4.3. Experimental Results
    4.4. Accuracy
    4.5. Precision
    4.6. Recall
    4.7. F1-score
    4.8. Area Under the ROC Curve (AUC)
    4.9. Cross-Validation
5. Discussion
6. Conclusions
References

• 변해원(Department of AI-Software, Inje University, South Korea) | Haewon Byeon Corresponding author