Melon analytics pricing

This package provides one class: ImmoNeighbors. You should train one such instance per floor(number_of_rooms) from 1 to 6+. Example code:

''' no_of_rooms = 3

training_data = immo[np.floor(immo["rooms"]) == no_of_rooms] neighbors = ImmoNeighbors(training_data.copy())

apartments = apartments_to_benchmark[['lon','lat','area']].to_numpy() dists,inds,rents = neighbors.k_neighbors(apartments)

rent_percentiles = neighbors.weighted_percentiles(dists,rents,percentiles=[10,50,90]) '''

Usually, problematic inputs will be ignored and have a corresponding row of NaNs in the output. More details about that can be found in the documentation of the respective function.