RRepoGEO

REPOGEO REPORT · LITE

CodeCutTech/Data-science

Default branch master · commit 4babeef8 · scanned 5/13/2026, 4:47:45 AM

GitHub: 4,199 stars · 1,064 forks

AI VISIBILITY SCORE
22 /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
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 CodeCutTech/Data-science, 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
    Refine repository description to emphasize curated learning content

    Why:

    CURRENT
    Collection of useful data science topics along with articles, videos, and code
    COPY-PASTE FIX
    Curated collection of practical data science articles, tutorials, and code examples for MLOps, data management, and visualization, designed to help data scientists learn modern tools.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of an appropriate open-source license (e.g., MIT License).
  • mediumtopics#3
    Update repository topics to include learning-focused keywords

    Why:

    CURRENT
    articles, artificial-intelligence, data-analysis, data-science, data-visualization, machine-learning, natural-language-processing, python, scraping, time-series
    COPY-PASTE FIX
    articles, artificial-intelligence, data-analysis, data-science, data-visualization, machine-learning, natural-language-processing, python, scraping, time-series, tutorials, learning-resources, code-examples, mlops-tutorials

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 CodeCutTech/Data-science
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
mlflow/mlflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. mlflow/mlflow · recommended 2×
  2. Kaggle · recommended 1×
  3. pandas-dev/pandas · recommended 1×
  4. numpy/numpy · recommended 1×
  5. scikit-learn/scikit-learn · recommended 1×
  • CATEGORY QUERY
    Where can I find practical code examples and tutorials for modern data science tools?
    you: not recommended
    AI recommended (in order):
    1. Kaggle
    2. Pandas (pandas-dev/pandas)
    3. NumPy (numpy/numpy)
    4. Scikit-learn (scikit-learn/scikit-learn)
    5. TensorFlow (tensorflow/tensorflow)
    6. PyTorch (pytorch/pytorch)
    7. Matplotlib (matplotlib/matplotlib)
    8. Seaborn (mwaskom/seaborn)
    9. Towards Data Science (Medium)
    10. MLflow (mlflow/mlflow)
    11. Datacamp Tutorials/Courses
    12. Python
    13. R
    14. SQL
    15. NLTK (nltk/nltk)
    16. spaCy (explosion/spaCy)
    17. Google Colaboratory (Colab) Notebooks
    18. Real Python
    19. YouTube Channels
    20. freeCodeCamp.org (freeCodeCamp/freeCodeCamp)
    21. Krish Naik
    22. Data School

    AI recommended 22 alternatives but never named CodeCutTech/Data-science. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good resources to learn MLOps best practices with real-world code?
    you: not recommended
    AI recommended (in order):
    1. Vertex AI
    2. Dataflow
    3. Kubeflow (kubeflow/kubeflow)
    4. Azure Machine Learning
    5. Azure DevOps
    6. MLflow (mlflow/mlflow)
    7. Delta Lake (delta-io/delta)
    8. Databricks Workflows
    9. Apache Spark (apache/spark)
    10. Databricks

    AI recommended 10 alternatives but never named CodeCutTech/Data-science. 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 CodeCutTech/Data-science?
    pass
    AI did not name CodeCutTech/Data-science — 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 CodeCutTech/Data-science in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CodeCutTech/Data-science 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 CodeCutTech/Data-science solve, and who is the primary audience?
    pass
    AI did not name CodeCutTech/Data-science — 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?

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