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Synthetic Data Generator is an open-source framework designed to generate high-quality synthetic tabular datasets that replicate the statistical characteristics of real data while avoiding privacy risks. The platform enables developers and data scientists to create artificial datasets that preserve important relationships between variables without containing sensitive personal information. This makes the generated data suitable for tasks such as machine learning model training, testing software systems, sharing datasets across organizations, and conducting research without violating privacy regulations. The system supports multiple generation methods including statistical models, generative adversarial networks, and large language model–based synthesis. It also includes a data processing module capable of handling different data types, preprocessing columns, managing missing values, and converting formats automatically before model training.
Features
- Framework for generating high-quality synthetic tabular datasets
- Support for statistical, GAN-based, and LLM-based data synthesis models
- Automatic preprocessing and post-processing for different data types
- Privacy-preserving synthetic data suitable for sharing and research
- Plugin architecture for extending models and data connectors
- Tools for generating large datasets for machine learning training and testing
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