Synthetic Data Generation with SDV

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:

• Synthetic data with SDV and Gaussian copulas
• Synthetic data with SDV and CTGAN
• Synthetic data with SDV and CopulaGAN