I've been aching to get my feet wet with a machine learning project, and I've found one that should be relatively simple, and actually has non-negligible business value for my organization. The marketing guys have to remove bot activity from our tracking data by hand for their metrics. I wanted to pull some data from GA, and have them construct a data set (bot, not-a-bot). There are probably 5-10 (numerical) categories that we have to train the algorithm, and the data set can be made as big as the marketing guys have an appetite for.
I've done a bit of reading, and played with RapidMiner/Knime/Weka a bit. I plan to do everything in Python, with
scikits-learn, possibly working in R where I have to. My questions:
- Is this a "not actually that easy at all" problem?
- Given the number of categories, about how large should the training set be?
- Given the problem, what algorithms should I start with?
- Has anyone else done any learning around bot detection? How did it work? Am I barking up the wrong tree?
Thanks in advance community!