This is the sort of thing I'm personally very interested in, and I have some pretty novel ideas for how I'd approach it. That said, I wouldn't participate in this because it clearly devalues the industry. You should really rethink your approach.
Developers who are considering participation in this, I'd suggest you build something for yourself with data acquired elsewhere.
> I wouldn't participate in this because it clearly devalues the industry.
People this may be aimed at:
* Experienced devs in boring day-jobs who are seeking some kind of off-time challenge.
* People just getting into ML and want to solve something real.
* CS students with spare time.
You know more about ML than me, but it doesn't sound like they're looking for a cancer cure; just fishing around for a one-off challenge. Or maybe they're taking names for future interview candidates.
> Developers who are considering participation in this, I'd suggest you build something for yourself with data acquired elsewhere.
Relax, dude. If people think this an interesting problem to solve, what's that to you?
Honestly, I think this is a very cool challenge. As someone who just went on a Grouper last night in Boston and had a great time, I think I just might participate and submit something. Do you have any limitations on how many people can form a team? Personally, I would pair on this with my roommate. He's the big data guy, and I'm the coder.
1. The data is collected from the user's FB profile or comes from our internal ratings
2. The platinum_albums header is just a joke, we anonymized the data
3. Thanks for pointing that out. There was a bug with a few rows that is now fixed.
I just noticed in the FAQ it states, "...several fields have been renamed of course." If I'm understanding this correctly, any real-world conclusions you draw will be completely meaningless, as we're essentially working from a mislabeled dataset.
That's true, but to have the best chance of designing a good method/analysis, I need to know what the variables in my analysis mean. Otherwise, it is tougher to make decisions about what variables it makes sense to include in a model, what sorts of transformations make sense, what sort of approaches might work best, etc.
I would echo this sentiment. Not only are the columns intentionally mis-labeled but they also appear to be computed, meaning some of the variance inherent to the original sample will have been lost.
Developers who are considering participation in this, I'd suggest you build something for yourself with data acquired elsewhere.