DataCebo Synthetic Data Vault (SDV)
The Synthetic Data Vault (SDV) is a versatile Python library that generates synthetic data across single tables, relational datasets, and time series models. It leverages various machine learning techniques, from classical statistical methods to deep learning, enabling users to augment, test software, and enhance data privacy while maintaining realistic patterns.
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Top DataCebo Synthetic Data Vault (SDV) Features
- Single table synthesis
- Multi-table relational modeling
- Sequential data generation
- Privacy protection through synthesis
- Data augmentation capabilities
- Scenario planning support
- Testing software functionality
- Customizable data processing
- Model-agnostic evaluation tools
- Quality reporting diagnostics
- Anonymization options available
- Business rule integration
- Ecosystem of synthetic models
- Benchmarking synthetic data outputs
- Use of statistical copulas
- Deep learning integration
- Compatibility with public libraries
- Quickstart coding framework
- Global developer community engagement
- Continuous feature updates.
