A Bird's Eye View: Leveraging Machine Learning to Generate Nests
A full-stack machine learning application, that classifies scooters into nests in real-time based on scooter attributes and city features. It then makes new nest recommendations for the non-nest scooters which are generated via geospatial clustering.
Data Collection and Machine Learning Pipeline
The major bike and scooter providers (Bird, JUMP, Lime) don't have publicly accessible APIs. However, some folks have seemingly been able to reverse-engineer the Bird API used to populate the maps in their Android and iOS applications.
One interesting feature of this data is the nest_id, which indicates if the Bird scooter is in a "nest" - a centralized drop-off spot for charged Birds to be released back into circulation.
By predicting whether a Bird is part of a nest or not, we could automate location recommendations for newly charged Birds to be released back on the streets.
I set out to ask the following questions:
Can real-time predictions be made to determine if a scooter is currently in a nest?
For non-nest scooters, can new nest location recommendations be generated from geospatial clustering?
A walk through of the statistical analysis and machine learning model development can be found here
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