# hitsz-ids/synthetic-data-generator

SDG is a specialized framework designed to generate high-quality structured tabular data.

Repository: https://github.com/hitsz-ids/synthetic-data-generator
Canonical: https://ross.abutalabs.com/products/synthetic-data-generator
Language: Python
License: Apache-2.0
License Family: permissive
Topics: deep-learning, gan, generative-ai, machine-learning, privacy, synthetic-data, tabular-data, agent, data-generator, llm
Last push: 2026-08-17T18:56:21+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 40, longevity 79
- inputs: {"age_days": 1119, "days_push": 16, "days_rel": 638, "gap_med": 14, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2434, forks 391 (observed 2026-08-28T04:06:51.305466+00:00)

## What it is
SDG (Synthetic Data Generator) is a Python framework for generating high-quality synthetic structured tabular data using deep learning, GANs, and LLMs. It supports privacy-preserving data synthesis and off-table inference for machine learning workflows.

## Use cases
- generate synthetic tabular data for machine learning
- create privacy-safe fake datasets from real tables
- augment small datasets with synthetic rows
- use LLMs to synthesize structured data
- train models without exposing sensitive data

## When to choose
- you need realistic synthetic tabular data for training or testing
- privacy constraints prevent sharing real datasets
- you want GAN- or LLM-based tabular data generation in Python

## When to avoid
- you need synthetic images, audio, or text rather than tabular data
- you need a simple no-ML fake data faker tool

## Facets
- artifact type: library
- maturity: active
- function: data-generation, machine-learning, llm-inference, privacy
- domain: machine-learning, data-science, privacy, artificial-intelligence
- platform: python
- tags: synthetic-data, tabular-data, gan, generative-ai, llm, data-privacy

## Member repositories
- hitsz-ids/synthetic-data-generator (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:51.305466+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:31:08.682875+00:00, confidence not recorded.
  - readme: https://github.com/hitsz-ids/synthetic-data-generator (fetched 2026-08-28T04:06:51.305466+00:00, sha b1993ddf8326)
- Data as of 2026-08-30T08:39:29.467469+00:00.