SDV supports modeling single table datasets. It provides unique
features for making it easy for the user to learn models and synthesize
datasets. Some important features of sdv.tabular include:
Support for tables with primary key
Support to anonymize certain fields like addresses, emails, phone
numbers, names and other PII information.
Support for a number of different data types - categorical,
numerical, discrete-ordinal and datetimes.
Support multiple types of statistical and deep learning models:
GaussianCopula Model: A tool to model multivariate distributions using
CTGAN Model: A GAN-based Deep Learning data synthesizer that can generate
synthetic tabular data with high fidelity.