I have a Machine Learning course this semester and the professor asked us to find a real-world problem and solve it by one of machine learning methods introduced in the class, as:
- Decision Trees
- Artificial Neural Networks
- Support Vector Machines
- Instance-based Learning (kNN, LWL)
- Bayesian Networks
- Reinforcement learning
I am one of fans of stackoverflow and stackexchange and know database dumps of these websites are provided to the public because they are awesome! I hope I could find a good machine learning challenge about these databases and solve it.
One idea came to my mind is predicting tags for questions based on the entered words in question body. I think the Bayesian network is the right tool for learning tags for a question but need more research. Anyway, after learning phase when user finishes entering the question some tags should be suggested to him.
Please tell me:
I want to ask the stats community as experienced people about ML two questions:
Do you think tag suggestion is at least a problem which has any chance to solve? Do you have any advice about it? I am a little worried because stackexchange does not implement such feature yet.
Do you have any other/better idea for the ML project that is based on stackexchange database? I find it really hard to find something to learn from stackexchange databases.
Consideration about database errors: I would like to point that although the databases are huge and have many instances, they are not perfect and are prune to error. The obvious one is the age of users that is unreliable. Even selected tags for the question are not 100% correct. Anyway, we should consider the percent of correctness of data in selecting a problem.
Consideration about the problem itself: My project should not be about
data-mining or something like this. It just should be an application of ML methods in real-world.