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.

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

21. Lesson 2

22. Crap data = crap results

23. Check your assumptions:
Don’t behave like a History
major trying to analyze 4 million
records using machine learning.

24. Just enough to be dangerous

25. Lesson 3

26. These are not causal
These are correlations

27. @portentint
portent.co/playpenguin
That’s it