I am using Azure Machine Learning to build a model which will predict if a project will be approved (1) or not (0).
My dataset is composed of a list of projects. Each line represents a project and its details - starting day, theme, author, place, people involved, stage, date of last stage and approved.
There are 15 possible crescent stages a project can pass through before being approved. On the other hand, in some special cases, a project can be approved mid-way, that is, before getting to the last stage which is the most commom
I will be receiving daily updates on some projects, as well as, new projects that are coming in. I am trying to build a model which will predict the probability of it being approved or not based on my inputs (which will inclue stage).
I want to use stage as an input, but if I use it with a two-class boosted decision tree it will indirectly give the answer to my model.
I've read a little bit about HMM and tried to learn how to apply to my ML model but did not understand how to. Could anyone guide me to the right path, please? Should I really use HMM?
