TabDDPM implements a denoising diffusion probabilistic model specifically designed for tabular data synthesis. The approach trains a neural network to reverse a gradual noising process, enabling high-quality generation of synthetic tabular datasets. The implementation includes training pipelines, hyperparameter tuning scripts, and evaluation tools for comparing synthetic data quality against real data.