Introduction
SDV (Synthetic Data Vault) is a Python library that provides multiple algorithms for generating realistic synthetic data from real datasets. It supports various data types including tabular, relational, and time-series data.
The library offers several synthesizers that use different approaches to model and replicate data patterns:
- Gaussian Copulas: Statistical approach that models the distribution and relationships between columns
- CTGAN: Deep learning approach using conditional Generative Adversarial Networks
- CopulaGAN: Combines copulas with GANs for improved synthesis of complex data distributions
Use cases include creating training data for machine learning, testing applications without real data, and generating privacy-preserving datasets.
Available methods
This section covers several SDV methods for different use cases: