RRepoGEO

REPOGEO REPORT · LITE

LearnDataSci/articles

Default branch master · commit cf779e45 · scanned 6/6/2026, 4:32:41 PM

GitHub: 589 stars · 1,068 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
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 LearnDataSci/articles, 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
    Clarify the README's opening statement to emphasize educational code examples

    Why:

    CURRENT
    A repository for the code, files, and other assets used in LearnDataSci articles.
    COPY-PASTE FIX
    This repository serves as a comprehensive collection of practical, runnable Python code examples, Jupyter notebooks, and datasets directly accompanying the educational articles on LearnDataSci, designed for hands-on learning in data science and machine learning.
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, or a custom license if applicable) in the root of the repository to clearly state the terms of use for the code and assets.
  • mediumtopics#3
    Expand repository topics to include educational and example-specific keywords

    Why:

    CURRENT
    data-analysis, data-science, data-visualization, machine-learning, machine-learning-algorithms, machinelearning, python
    COPY-PASTE FIX
    data-analysis, data-science, data-visualization, machine-learning, machine-learning-algorithms, machinelearning, python, tutorial, code-examples, educational, learning-resources, jupyter-notebooks

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 LearnDataSci/articles
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle · recommended 1×
  2. Towards Data Science · recommended 1×
  3. scikit-learn · recommended 1×
  4. TensorFlow · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    Where can I find practical Python code examples for various data science and machine learning topics?
    you: not recommended
    AI recommended (in order):
    1. Kaggle
    2. Towards Data Science
    3. scikit-learn
    4. TensorFlow
    5. PyTorch
    6. Keras
    7. Pandas
    8. DataCamp
    9. Coursera
    10. edX
    11. Real Python

    AI recommended 11 alternatives but never named LearnDataSci/articles. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good Python resources for learning web scraping, data streaming, or database interaction?
    you: not recommended
    AI recommended (in order):
    1. Beautiful Soup 4 (BeautifulSoup/bs4)
    2. Requests (psf/requests)
    3. Scrapy (scrapy/scrapy)
    4. Selenium (SeleniumHQ/selenium)
    5. Apache Kafka
    6. confluent-kafka-python (confluentinc/confluent-kafka-python)
    7. kafka-python (dpkp/kafka-python)
    8. RabbitMQ
    9. pika (pika/pika)
    10. Redis
    11. redis-py (redis/redis-py)
    12. SQLAlchemy (sqlalchemy/sqlalchemy)
    13. Psycopg2 (psycopg/psycopg2)
    14. MySQL Connector/Python (mysql/mysql-connector-python)
    15. sqlite3
    16. PyMongo (mongodb/mongo-python-driver)

    AI recommended 16 alternatives but never named LearnDataSci/articles. This is the gap to close.

    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 LearnDataSci/articles?
    pass
    AI named LearnDataSci/articles explicitly

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

  • If a team adopts LearnDataSci/articles in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LearnDataSci/articles 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 LearnDataSci/articles solve, and who is the primary audience?
    pass
    AI named LearnDataSci/articles 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 LearnDataSci/articles. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite