I'm working on a project for fun using data (items from a persistent video game) I've gathered from the web.
At the moment, the data consists of around 180,000 rows which will probably grow quite quickly. The data (each row) itself consists of an 'item' along with a 'user' that the item belongs to and I've been thinking about how to rank/value these items.
The items carry a monetary value but I cannot gather this data en-masse so would like to attach my own estimated ranking/points system based on frequency alone that will change as the data grows/changes. (I could use the monetary value data to check whether the model is reasonably accurate though).
At the moment there are roughly 4000 distinct items with frequencies ranging from 1 (very rare) to 1500 (very common) amongst this collection of 180,000 (pulled from ~1200 users). (I've calculated the mean of these frequencies which is 48.1 with a SD of 63 - but my stats knowledge is low so I'm looking for tips).
I'm wondering if I could get some pointers in the right direction to transform this data into a '1-100'-type of rating system and also perhaps useful techniques to get insight on the data. (I'm a stats rookie, please go easy).