I have a basic understanding of how machine learning works, but my knowledge isn't enough in order to develop a personal project I would like to start.
I want to develop a system based on online machine learning, aimed to binary classification:
- The inputs/features I should consider are only categorical (but I may as well introduce also numerical inputs in the future, I cannot exclude the possibility as I'm still not sure about the features of the samples I will consider).
- I would like to have some decay notion for older samples, in order to enhance the responsiveness of the system to changes.
- I'm searching for something more like a compromise between complexity and correctness of the results.
This is the context. I want to write a software for writing down lists of things to do. Each list will be associated to a category by the user itself (the categories are not fixed, the user will be able to edit them). By relying on the history of previous lists, I want to predict what the user will put in the next one. Thus, basically, given a particular todo written in the past few lists, I want to classify it as "insert in list" or "do not insert in list".
Could you give me some pointers, please? I need your help in order to focus towards the most fitting algorithms and techniques for my needs. Many thanks in advance.