This workflow applies the lean restocking system for bycicles in Washington DC stations, as trained in another workflow. Data is a subset of the bike data set. Model will predict one of three classes: Add bikes; Remove bikes; No Action. 1. Data is read and prepared as in the training workflow, that is: Bike ratios are claculated as total # bikes/available spots by station; 10 past ratios in input vector 2. Model is read and applied 3. Prediction class (besides No Action) is accepted only if confidence exceeds a given threshold 4. Stations in need of bike reshuffling are shown in a web dashboard
This is a companion discussion topic for the original entry at https://kni.me/w/NSLUQFTptcYKbVAM