# Synthetic training data
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/synth-data.html

> Learn how to generate synthetic datasets that mimic real-world data for training, validation, and testing—enabling safe data sharing and model development when access to real data is limited due to privacy or regulatory constraints.

# Synthetic training data

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/dr_synth_data/dr_synth_data.ipynb)

This notebook provides a powerful code-first accelerator to help  generate synthetic datasets in tabular format. It enables you to create synthetic data that mimics the structure and statistical properties of real-world datasets, offering a safe and efficient way to augment existing data or create entirely new datasets. The generated synthetic datasets can be uploaded directly to AI Catalog, where they can be organized, managed, and reused for various machine learning projects.

This approach is particularly useful in scenarios where access to real data is limited due to privacy, security, or regulatory constraints. By generating synthetic datasets, users can expand their training data without compromising sensitive information. These synthetic datasets can be used for model training, validation, and testing, allowing for more robust model development and better generalization on unseen data.

The notebook outlines how to create a synthetic training data set in a CSV file, with name, address, phone number, company, account number, and credit score.

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