Suppose I have a large data set with lots of features(attributes). And I'm tasked to build some kind of scoring model to rank certain objects with all these features. How do I go about doing this?
From my understanding so far, I like to think of this as a supervised learning problem. But the problem is there is NO labeled classes (or at least it's not apparent). How can I rank order these objects? The closest thing I can think of is credit scores, but in credit scoring models, one supposedly has labeled classes as to who historically was good and bad.
Should I invent/create some metric based on the list of attributes and use them as labeled cases? Like if attribute$_1 > x$ and attribute$_2< y$ etc., then it's considered "good"
I believe they want a numerical ranking (i.e., scoring all the objects have numerical scores assigned to the objects like credit scores). If that's the case, then do I even need machine learning/data mining? Can't I just rank it by these attributes once they agree what the ordering means?