I'm playing with regression models in scikit-learn. The goal is to predict how much inventory we should purchase for the next 90 days. My data set has hundred of product categories. Each category has many unique features that do not apply to every category.
For Example: Shirt category could have "size" and "color" features where as the category Razors could have a "number of blades" feature.
Should I split my data up by category and make a different model for each? Or is it suffient to have one model in which I keep the products category as one of the features?