ydata-synthetic 0.3.1

Synthetic data generation methods with different synthetization methods.

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What is Synthetic Data?

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.

Why Synthetic Data?

Synthetic data can be used for many applications:

• Privacy
• Remove bias
• Balance datasets
• Augment datasets

ydata-synthetic

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.

Quickstart

pip install ydata-synthetic

Examples

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

Credit Fraud dataset

Stock dataset

Project Resources

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

In this repo you can find the following GAN architectures:

Tabular data

• GAN
• CGAN (Conditional GAN)
• WGAN (Wasserstein GAN)
• WGAN-GP (Wassertein GAN with Gradient Penalty)
• DRAGAN (On Convergence and stability of GANS)
• Cramer GAN (The Cramer Distance as a Solution to Biased Wasserstein Gradients)

Sequential data

• TimeGAN