🧠 Create synthetic data using machine learning. The SDV offers multiple models, ranging from classical statistical methods (GaussianCopula) to deep learning methods (CTGAN). Generate data for single tables, multiple connected tables or sequential tables.
📊 Evaluate and visualize data. Compare the synthetic data to the real data against a variety of measures. Diagnose problems and generate a quality report to get more insights.
🔄 Preprocess, anonymize and define constraints. Control data processing to improve the quality of synthetic data, choose from different types of anonymization and define business rules in the form of logical constraints.