I have some frequency data that I have gathered from the web (sourced from a video game) and stored in a database.
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' and its properties along with a 'user' that the item belongs to and I've been thinking about how to
rank rate/value these items into value bands.
The items carry a monetary/market value but I cannot gather this data en-masse. Therefore I would like to attach my own estimated points system that approximates market value based on frequency
alone, and other properties that the item has. To clarify, the higher rated an item is the more rare and valuable it should be. (The data I have so far is the rarity/frequency). I'm thinking that some analysis techniques would be required.
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'm looking for analysis techniques that might be able to bring together these properties to calculate a point value for any given item. (If this can be done)
(Also if nothing else I'll just use low frequency = high rating)