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Welcome to hell Penalties and Big Data
@portentint ask me questions
Kinda cool
and a cautionary tale
How hard could machine learning be?
I was a history major
Used: Links we disavowed and/ or Google pointed at and said “these are bad.”
Trained: Based on domains that were clear disavows
Trained: Based on links Google pointed out
Total: 250,000 domains
Then: Hand-review results (random, 1,000 links)
Lesson 1
Excel doesn’t like 250,000 row spreadsheets
Work with text files, or use SQLLite
Here are a few rules of thumb for detecting bad links. These are not causal They are correlations
BigML: Upload source
BigML: Dataset
BigML: Model
Lesson 2
Crap data = crap results
Check your assumptions: Don’t behave like a History major trying to analyze 4 million records using machine learning.
Just enough to be dangerous
Lesson 3
These are not causal These are correlations
@portentint portent.co/playpenguin That’s it
Penguin, Penalties and 'Big Data' - How I analyzed 250,000 domains
Penguin, Penalties and 'Big Data' - How I analyzed 250,000 domains
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Penguin, Penalties and 'Big Data' - How I analyzed 250,000 domains

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In an attempt to find a way to review thousands of links for link manipulation penalty clients, I analyzed 250,000 domains for signals.

I had mixed results. This deck talks about how I pulled the data together, the tools I used, what worked, and what really didn't.

Published in: Marketing, Data & Analytics
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Penguin, Penalties and 'Big Data' - How I analyzed 250,000 domains

  1. Welcome to hell Penalties and Big Data
  2. @portentint ask me questions
  3. Kinda cool
  4. and a cautionary tale
  5. How hard could machine learning be?
  6. I was a history major
  7. Used: Links we disavowed and/ or Google pointed at and said “these are bad.”
  8. Trained: Based on domains that were clear disavows
  9. Trained: Based on links Google pointed out
  10. Total: 250,000 domains
  11. Then: Hand-review results (random, 1,000 links)
  12. Lesson 1
  13. Excel doesn’t like 250,000 row spreadsheets
  14. Work with text files, or use SQLLite
  15. Here are a few rules of thumb for detecting bad links. These are not causal They are correlations
  16. BigML: Upload source
  17. BigML: Dataset
  18. BigML: Model
  19. Lesson 2
  20. Crap data = crap results
  21. Check your assumptions: Don’t behave like a History major trying to analyze 4 million records using machine learning.
  22. Just enough to be dangerous
  23. Lesson 3
  24. These are not causal These are correlations
  25. @portentint portent.co/playpenguin That’s it

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