I am analyzing the avocado dataset to predict the future prices of avocado depending on the region and type (organic/conventional).
I've trained my model which seems to be working. The test results look good.
Here is what my data looks like after processing: the date (which I turned into the index column), total volume, type (label encoded to 1s and 0s), and 54 columns for the 54 regions that I one-hot encoded.
However, I'm not sure how to generate data for future prediction given specific X feature values. Basically I want the end user to provide a (future) date, a type, a region, and my model would predict the price of the avocado. For example, I want to predict the price of organic avocado for Albany in 3 months. I know that my model could forecast for a certain date, based on the prices of the past date. But how would it work when I assign the region and type feature to a specific value? Is it even feasible? Should I be using another model? I know that I could train a LSTM model for each of the 54 regions, but I'm sure there is a better way...
Sorry if this is a trivial question, I'm a beginner and I've been stuck on this for days and would really appreciate any help/guidance!