RRepoGEO

REPOGEO REPORT · LITE

AutoViML/AutoViz

Default branch master · commit 63f4b3c6 · scanned 6/21/2026, 2:28:23 PM

GitHub: 1,905 stars · 214 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
80 /100
Healthy
Category recall
2 / 2
Avg rank #3.5 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
  • highhomepage#1
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://pypi.org/project/autoviz/
  • mediumtopics#2
    Refine and expand repository topics

    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, automated-visualization, data-quality, exploratory-data-analysis, eda, data-profiling
  • lowabout#3
    Enhance repository description with key terms

    Why:

    CURRENT
    Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
    COPY-PASTE FIX
    Automatically visualize any dataset for quick exploratory data analysis and data quality assessment with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

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
2 / 2
100% of queries surface AutoViML/AutoViz
Avg rank
#3.5
Lower is better. #1 = top recommendation.
Share of voice
18%
Of all named tools, what % are you?
Top rival
fbdesignpro/sweetviz
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. fbdesignpro/sweetviz · recommended 1×
  2. ydataai/ydata-profiling · recommended 1×
  3. sfu-db/dataprep · recommended 1×
  4. lux-org/lux · recommended 1×
  5. ydata-profiling · recommended 1×
  • CATEGORY QUERY
    What Python library provides automated data visualization for quick exploratory analysis?
    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. Lux (lux-org/lux)
    Show full AI answer
  • CATEGORY QUERY
    How can I quickly visualize and assess data quality with minimal Python code?
    you: #4
    AI recommended (in order):
    1. ydata-profiling
    2. Sweetviz
    3. DataPrep
    4. Autoviz ← you
    5. missingno
    6. Great Expectations
    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?

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MARKDOWN (README)
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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