RRepoGEO

REPOGEO REPORT · LITE

Diyago/Tabular-data-generation

Default branch master · commit fc970d79 · scanned 6/1/2026, 3:31:50 AM

GitHub: 569 stars · 83 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Diyago/Tabular-data-generation, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Update the repository description to reflect its purpose as a library

    Why:

    CURRENT
    We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.
    COPY-PASTE FIX
    TabGAN is a Python library providing a unified interface for generating high-quality synthetic tabular data using state-of-the-art generative approaches like GANs, Diffusion Models, and LLMs.
  • mediumreadme#2
    Add a concise, direct opening sentence to the README

    Why:

    COPY-PASTE FIX
    TabGAN is a comprehensive Python library designed for generating high-quality synthetic tabular data, offering a unified interface to state-of-the-art generative models including GANs, Diffusion Models, and Large Language Models.
  • mediumtopics#3
    Add 'synthetic-data' to the repository topics

    Why:

    CURRENT
    adversarial-filtering, deep-learning, feature-engineering, gan, gans, machine-learning, python, tabular-data, train-dataframe
    COPY-PASTE FIX
    adversarial-filtering, deep-learning, feature-engineering, gan, gans, machine-learning, python, tabular-data, train-dataframe, synthetic-data

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface Diyago/Tabular-data-generation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CTGAN
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. CTGAN · recommended 1×
  2. TVAE · recommended 1×
  3. SDV · recommended 1×
  4. Synthcity · recommended 1×
  5. DataSynthesizer · recommended 1×
  • CATEGORY QUERY
    How can I generate realistic synthetic tabular datasets for machine learning model training?
    you: not recommended
    AI recommended (in order):
    1. CTGAN
    2. TVAE
    3. SDV
    4. Synthcity
    5. DataSynthesizer
    6. Gretel.ai
    7. Copulas

    AI recommended 7 alternatives but never named Diyago/Tabular-data-generation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python tools exist for synthesizing structured data using advanced generative models?
    you: not recommended
    AI recommended (in order):
    1. SDV (Synthetic Data Vault) (sdv-dev/SDV)
    2. CTGAN (Conditional Tabular GAN) (sdv-dev/CTGAN)
    3. TVAE (Tabular Variational Autoencoder) (sdv-dev/TVAE)
    4. Gretel.ai (gretelai/gretel-client)
    5. Synthcity (vanderschaarlab/synthcity)
    6. Faker (joke2k/faker)

    AI recommended 6 alternatives but never named Diyago/Tabular-data-generation. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Diyago/Tabular-data-generation?
    pass
    AI did not name Diyago/Tabular-data-generation — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Diyago/Tabular-data-generation in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Diyago/Tabular-data-generation explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Diyago/Tabular-data-generation solve, and who is the primary audience?
    pass
    AI did not name Diyago/Tabular-data-generation — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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Diyago/Tabular-data-generation — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite