This repository provides a demonstration of synthetic data generation using a UK retail transactional dataset. It is targeted at professionals in retail, e-commerce, finance, and supply chain sectors, offering a privacy-preserving and realistic dataset for testing, analysis, and machine learning, particularly for LLM training.

How It Works

The project leverages Syncora.ai's platform to generate high-fidelity synthetic data that mimics real-world patterns without exposing sensitive information. This approach allows for the creation of privacy-safe datasets, accelerating AI and LLM development by augmenting limited data, reducing bias, and improving model performance. The synthetic data is designed to be ready-to-use for LLM training, enabling faster prototyping and fine-tuning.

Quick Start & Requirements

• The repository contains a synthetic retail dataset in CSV format and a Jupyter Notebook for exploration and usage.
• No specific installation commands are provided, but the presence of a Jupyter Notebook suggests a Python environment is required.

Highlighted Details

• The synthetic data is generated using Syncora.ai, a platform focused on privacy-safe, high-quality synthetic data creation.
• The dataset is suitable for LLM training and AI development, offering realistic and privacy-safe data for modeling.
• It enables safe data sharing and collaboration without compliance risks.

Maintenance & Community

• Information regarding maintenance, community, or specific contributors is not detailed in the provided README.
• Links to Syncora.ai are provided for generating custom synthetic datasets.

Licensing & Compatibility

• The licensing information is not specified in the provided README.
• Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The README does not specify any limitations or caveats regarding the synthetic data generation process or the dataset itself. Further investigation into the Syncora.ai platform may be required to understand potential limitations.