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Synthetic data is artificially generated data that is not collected from real world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy.

Synthetic data can be used for many applications:

• Privacy
• Remove bias
• Balance datasets
• Augment datasets

This repository contains material related with Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series. It consists in a set of different GANs architectures developed ussing Tensorflow 2.0. An example Jupyter Notebook is included, to show how to use the different architectures.

pip install ydata-synthetic

Here you can find usage examples of the package and models to synthesize tabular data.

Credit Fraud dataset

Stock dataset

• Synthetic GitHub: https://github.com/ydataai/ydata-synthetic
• Synthetic Data Community Slack: click here to join

• GAN
• CGAN (Conditional GAN)
• WGAN (Wasserstein GAN)
• WGAN-GP (Wassertein GAN with Gradient Penalty)

• TimeGAN