I am quite new to machine learning with small experience and I did some projects.
Now I have a project relates to insurance. So I have databases about clients that I will merge to get all possible information about the clients and I have one database for the claims. I need to build a model to identify how risky the client based on ranks.
My question: I need to build my target variable that ranks the clients based on how risky they are, counting on the claims. I could have different strategies to do that, but I am confused about how I will deal with the following: - Shall I do a specific type of analysis before building the ranks such as clustering, or I need to have a strong theoretical assumption matching with the project provider vision. - If I use some variables in the claims database to build up the ranks, how shall I deal with them later. In other words, shall I remove them from the final data set for training, to avoid correlation with target variable, or I can treat them in a different way and keep them. - If I will keep them, is there a special treatment for them depending on whether they are categorical or continuous variables.
Every machine learning project's starting point is EDA. First create some feature, like how often do they get bad claims or how many do they get. Then do some EDA to find which features are more useful. Secondly, the problem looks like classification. Clustering is usually harder to evaluate.