ydata-synthetic

The Streamlit app provides a guided workflow for training and sampling, making synthetic data generation accessible without deep coding, as emphasized in the features for quick, low-code experiences.

Includes multiple GAN architectures like CTGAN, WGAN, and DRAGAN, plus specialized models for tabular and time-series data, offering flexibility for various use cases, as listed in the key features.

The fast Gaussian Mixture Model enables quick synthetic data generation without requiring GPU resources, ideal for environments with limited hardware, highlighted as a key feature for quickstarting.

CTGAN is specifically designed for high-quality synthetic tabular data with conditional features, addressing challenges in tabular synthesis, as noted in the features for conditional architectures.

Supports models like TimeGAN and DoppelGANger for generating synthetic sequential data, extending utility beyond tabular datasets, with examples provided for stock and FCC MBA datasets.