RRepoGEO

REPOGEO REPORT · LITE

AutoViML/AutoViz

Default branch master · commit 63f4b3c6 · scanned 5/11/2026, 10:02:54 AM

GitHub: 1,902 stars · 214 forks

AI VISIBILITY SCORE
63 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 AutoViML/AutoViz, 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
  • highreadme#1
    Reposition the README's opening to highlight automated EDA for large datasets

    Why:

    CURRENT
    Unlock the power of **AutoViz** to visualize any dataset, any size, with just a single line of code! Plus, now you can get a quick assessment of your dataset's quality and fix DQ issues through the FixDQ() function.
    COPY-PASTE FIX
    Unlock the power of **AutoViz** to visualize any dataset, including **large datasets**, with just a single line of code! Beyond just plotting, AutoViz provides **automated exploratory data analysis (EDA)** and a quick assessment of your dataset's quality, allowing you to fix data quality (DQ) issues through the `FixDQ()` function.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://pypi.org/project/autoviz
  • lowtopics#3
    Expand repository topics to include specific automated EDA and data quality terms

    Why:

    CURRENT
    auto-sklearn, automated-machine-learning, automl, automl-algorithms, machine-learning, python, python3, scikit-learn, tableau, tpot, visualization, xgboost
    COPY-PASTE FIX
    auto-sklearn, automated-machine-learning, automl, automl-algorithms, machine-learning, python, python3, scikit-learn, tableau, tpot, visualization, xgboost, data-profiling, eda, exploratory-data-analysis, data-quality

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
1 / 2
50% of queries surface AutoViML/AutoViz
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
Datashader
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Datashader · recommended 1×
  2. Altair · recommended 1×
  3. Plotly Express · recommended 1×
  4. HoloViews · recommended 1×
  5. Matplotlib · recommended 1×
  • CATEGORY QUERY
    How to quickly generate data visualizations for large datasets in Python?
    you: not recommended
    AI recommended (in order):
    1. Datashader
    2. Altair
    3. Plotly Express
    4. HoloViews
    5. Matplotlib
    6. Seaborn

    AI recommended 6 alternatives but never named AutoViML/AutoViz. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for automated data visualization and quality assessment with minimal code?
    you: #3
    AI recommended (in order):
    1. Sweetviz (fbdesignpro/sweetviz)
    2. Pandas Profiling (ydataai/ydata-profiling)
    3. Autoviz (AutoViML/AutoViz) ← you
    4. DataPrep.EDA (sfu-db/DataPrep)
    5. Tableau Public / Tableau Desktop
    6. Power BI
    7. D-Tale (man-group/dtale)
    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 AutoViML/AutoViz?
    pass
    AI named AutoViML/AutoViz explicitly

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

  • If a team adopts AutoViML/AutoViz in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AutoViML/AutoViz 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 AutoViML/AutoViz solve, and who is the primary audience?
    pass
    AI named AutoViML/AutoViz explicitly

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

Embed your GEO score

Drop this badge into the README of AutoViML/AutoViz. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/AutoViML/AutoViz.svg)](https://repogeo.com/en/r/AutoViML/AutoViz)
HTML
<a href="https://repogeo.com/en/r/AutoViML/AutoViz"><img src="https://repogeo.com/badge/AutoViML/AutoViz.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

AutoViML/AutoViz — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite