RRepoGEO

REPOGEO REPORT · LITE

krishnaik06/Data-Science-Projects-For-Resumes

Default branch main · commit 14c419b5 · scanned 6/29/2026, 1:28:31 PM

GitHub: 1,775 stars · 493 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
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 krishnaik06/Data-Science-Projects-For-Resumes, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A curated collection of end-to-end machine learning, deep learning, and NLP projects designed to build a strong data science portfolio for job seekers.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    data-science, machine-learning, deep-learning, nlp, mlops, portfolio-projects, resume-projects, end-to-end-projects, python, ai-projects
  • mediumreadme#3
    Add a clear introductory sentence to the README

    Why:

    CURRENT
    # Data-Science-Projects-For-Resumes
    COPY-PASTE FIX
    # Data-Science-Projects-For-Resumes

This repository provides a comprehensive collection of end-to-end machine learning, deep learning, and NLP projects specifically designed to help aspiring data scientists build a strong portfolio and enhance their resumes.

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 krishnaik06/Data-Science-Projects-For-Resumes
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pandas
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pandas · recommended 1×
  2. numpy · recommended 1×
  3. matplotlib · recommended 1×
  4. seaborn · recommended 1×
  5. scikit-learn · recommended 1×
  • CATEGORY QUERY
    How can I find beginner-friendly end-to-end machine learning projects to build a strong portfolio?
    you: not recommended
    AI recommended (in order):
    1. pandas
    2. numpy
    3. matplotlib
    4. seaborn
    5. scikit-learn
    6. Streamlit
    7. Flask
    8. NLTK
    9. Gradio
    10. TensorFlow
    11. PyTorch
    12. Keras
    13. surprise
    14. XGBoost
    15. LightGBM
    16. Dash
    17. spaCy
    18. BERT

    AI recommended 18 alternatives but never named krishnaik06/Data-Science-Projects-For-Resumes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good resources for learning MLOps and deploying deep learning models for resume projects?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Extended (TFX) (tensorflow/tfx)
    2. Kubeflow (kubeflow/kubeflow)
    3. AWS SageMaker
    4. FastAPI (tiangolo/fastapi)
    5. Docker
    6. GitHub Actions
    7. MLFlow (mlflow/mlflow)
    8. Transformers library (huggingface/transformers)
    9. Accelerate (huggingface/accelerate)
    10. Hugging Face Spaces
    11. Uvicorn (encode/uvicorn)
    12. Google Cloud Vertex AI
    13. Azure Machine Learning

    AI recommended 13 alternatives but never named krishnaik06/Data-Science-Projects-For-Resumes. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 krishnaik06/Data-Science-Projects-For-Resumes?
    pass
    AI did not name krishnaik06/Data-Science-Projects-For-Resumes — 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 krishnaik06/Data-Science-Projects-For-Resumes in production, what risks or prerequisites should they evaluate first?
    pass
    AI named krishnaik06/Data-Science-Projects-For-Resumes 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 krishnaik06/Data-Science-Projects-For-Resumes solve, and who is the primary audience?
    pass
    AI did not name krishnaik06/Data-Science-Projects-For-Resumes — 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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  • Brand-free category queries5 vs 2 in Lite
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