REPOGEO REPORT · LITE

kennethleungty/Failed-ML

Default branch main · commit 1aead7f1 · scanned 6/9/2026, 5:37:52 PM

GitHub: 752 stars · 51 forks

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 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 kennethleungty/Failed-ML, 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 README opening to emphasize "curated collection"

Why:

CURRENT

If you are looking for examples of how ML can fail despite all its incredible potential, you have come to the right place. Beyond the wonderful success stories of applied machine learning, here is a list of failed projects which we can learn a lot from.

COPY-PASTE FIX

If you are looking for a curated collection of real-world examples of how ML can fail despite all its incredible potential, you have come to the right place. Beyond the wonderful success stories of applied machine learning, this repository provides a comprehensive list of failed projects from which we can learn a lot.

mediumtopics#2

Add more specific topics to signal "collection of examples"

Why:

CURRENT

ai, artificial-intelligence, classification, computer-vision, data-engineering, data-quality, data-science, deep-learning, failed-data-science, failed-machine-learning, failed-ml, fml, forecasting, machine-learning, ml, natural-language-processing, production, recsys, regression

COPY-PASTE FIX

ai, artificial-intelligence, classification, computer-vision, data-engineering, data-quality, data-science, deep-learning, failed-data-science, failed-machine-learning, failed-ml, fml, forecasting, machine-learning, ml, natural-language-processing, production, recsys, regression, ml-case-studies, lessons-learned, failure-analysis, ml-failures-database

mediumabout#3

Refine repository description to emphasize "curated collection"

Why:

CURRENT

Compilation of high-profile real-world examples of failed machine learning projects

COPY-PASTE FIX

A curated compilation of high-profile real-world examples of failed machine learning projects, serving as a centralized resource for lessons learned.

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 kennethleungty/Failed-ML

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Kaggle

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Kaggle · recommended 1×
Papers with Code · recommended 1×
Medium · recommended 1×
Towards Data Science · recommended 1×
LinkedIn · recommended 1×

CATEGORY QUERY
Where can I find real-world examples of common machine learning project failures?
you: not recommended
AI recommended (in order):
1. Kaggle
2. Papers with Code
3. Medium
4. Towards Data Science
5. LinkedIn
6. NeurIPS
7. ICML
8. KDD
9. Strata Data & AI Conference
10. Designing Machine Learning Systems
11. Machine Learning Engineering
12. Building Machine Learning Powered Applications
13. Practical AI
14. TWIML AI Podcast
15. Data Skeptic
16. Google Cloud
17. AWS
18. Netflix TechBlog
19. Uber Engineering Blog
20. Google AI Blog
AI recommended 20 alternatives but never named kennethleungty/Failed-ML. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the biggest challenges and risks in deploying artificial intelligence systems?
you: not recommended
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 kennethleungty/Failed-ML?
pass
AI named kennethleungty/Failed-ML explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts kennethleungty/Failed-ML in production, what risks or prerequisites should they evaluate first?
pass
AI named kennethleungty/Failed-ML 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 kennethleungty/Failed-ML solve, and who is the primary audience?
pass
AI did not name kennethleungty/Failed-ML — 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

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

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/kennethleungty/Failed-ML.svg)](https://repogeo.com/en/r/kennethleungty/Failed-ML)

HTML

<a href="https://repogeo.com/en/r/kennethleungty/Failed-ML"><img src="https://repogeo.com/badge/kennethleungty/Failed-ML.svg" alt="RepoGEO" /></a>

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Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite