I am trying to assign manually scores to identify bad accounts (not in Stacks community!) based on some specific conditions, for example if they have account names containing only numbers and a limited number of review, a past history poor, poor score, banned, and so on.
To do it, as I mentioned, I am doing the following:
-if number of reviews is < 2 then assign -1; or -if past history is poor then assign -1; or -if account score is less than 3 then assign -1;
and so on.
Since I have variables such as number of reviews, past history, account score, for all the users in my dataset, I am just wondering it I should assign a score in a different way, maybe using a more statistical approach based on average. My goal is to determine an algorithm which can predict if an account is bad or not thought time based on the above conditions.
I would appreciate it if you could let me know what you think.