I am trying to decide which particular algorithm would be most appropriate for my use-case.
I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.
I define similarity as weighted comparison of features. I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.
Here's what my data looks like:
I'm wondering what similarity measure would make sense? I work in python, so prefer a pythonic/sci-kit learn way of doing this.