I am an absolute beginner in data science and I had this (possibly stupid) question on my mind, while reading a problem in Kaggle: Say I'm given IDs of some clients, IDs of products that they sell, and quantity of the product sold, and I'm asked to predict the quantity of a product, given the client ID, and the product ID.
Now, say the client IDs are in the range 10000 - 50000 and the product IDs are in the range 1-10.
Suppose, for a moment that the client IDs were random integers from 1-1000000 and the product IDs were random integers from 1000-2000. This isn't supposed to make the slightest change in the results, is it? After all, IDs are mere tags.
But thinking data-wise, I've just bloated up two entire columns in my data to a higher scale, and these are two features as well.
So how do I think about this? How do I factor in features which are IDs? How do I 'normalize' them?
I hope I'm not being vague here. I just don't know a better way of phrasing this question.