Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
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
Upcoming SlideShare
Loading in …5
×
Marketing, Data & Analytics
6,230 views
Feb. 27, 2015

Penguin, Penalties and 'Big Data' - How I analyzed 250,000 domains

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.

no profile picture user

  • Login to see the comments

Show More

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

×