How to deal with many levels for independent variable while doing modelling?

Question

Hi,

I have a variable called 'Brand' in my dataset which essentially is the brand name of the product. There are about 2300 levels for this variable and I 'm looking to use this as one of the independent variables for predictive modelling, where price is the target variable. Can someone pls let me know how to deal with so many levels in the independent variable?

Phil_Kay · Answer

Another simple way to reduce the number of levels would be with a partition model. Price as the Y and Brand as the X. You can try different numbers of splits and save the leaf numbers and/or labels to create new variables.

ron_horne · Answer

I would like to suggest two very different methods I have used in the past to deal with many categories. In my case they would be chronological geographical categories, but they should work in your case of brands.

* If you have any exogenous knowledge about the brands perhaps you can cluster them to a much more meaningful classification. For example, by country or by premium vs. main street vs. generic.
* Using some method (i.e. Anova) rank the brands by price and look for any clusters and discontinuity points. Perhaps they cluster by price ranges.

Once you have these clusters by any of the methods mentioned you can add this new variable to the model. this could be done instead of using the original detailed brand names or in addition in a nested manner.

Let us know what worked for you,

Ron

Peter_Bartell · Answer

Still yet another thought for you akin to @dale_lehman's idea, is to try Multiple Correspondence Analysis as a dimensionality reduction method as well?