Synthetic Data Generation

Synthetic Data Generation refers to the process of creating artificial data that simulates real-world data, which can be used for various purposes such as training machine learning models, testing algorithms, or validating systems. This artificial data can replicate the statistical properties and patterns of real data without disclosing any actual sensitive information. Synthetic data is often generated using algorithms, simulations, or statistical methods, and it serves to facilitate research, development, and analysis in fields where real data may be scarce, expensive to obtain, or protected due to privacy regulations. The generated data can be tailored to specific requirements and scenarios, allowing for more controlled experimentation and model development.