What would be a good machine learning model to predict the next vending machine location? I want to build a machine learning model to choose a location for the next vending machine in Boston. I want to divide the map into small neighborhood, and I came up with features that are related to the geospatial locations and user information.
One paradox that concerns me is that: we'll use past vending machines' features (X) and sales (y) to train the model; in that case, the model that predict the next vending machine will likely choose some place very close to the old vending machine locations. But at the same time, we wouldn't want to build a vending machine in exactly the same location as an older one.
Additionally, vending machines are usually built one by one as time goes by; the model used to choose the 10th vending machine in the city, and the 1300th vending machine could be very different. This almost made me think this is a time-series problem. How to build a model that can take these problems into consideration? Any insight on features, logistics, or models are all deeply appreciated!

Let's consider the inputs and outputs for a moment.
Say your model takes as inputs things like sales and geolocation.  The output is if that location is a vending machine or not.
Now...is that useful?  Your model does not tell you where to place a vending machine.  It tells you if that spot on the map is already a vending machine.  That isn't what you want.
What you likely want is to find a location on the map which is not a vending machine and optimizes some criteria (likely potential sales).  You might want:

*

*A lot of foot traffic

*Not to be too close to other vending machines

*etc.

You need to feed in all that information into some sort of function which scores the location in terms of it being a good or bad location based on the above criteria.  Then, you want to find the location which optimizes that function.
A really simple example of this might be to put a vending machine somewhere which is farthest from all other vending machines. Of course this might put the machine in the middle of a field somewhere, which is why you need to add constraints to this optimization somehow.
