Lean Restocking Alert Signal: Deployment

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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://hub.knime.com/knime/workflows/Examples/50_Applications/55_Bike_Restocking_Alert/02_Bike_Restocking_Alert_Deploy*NSLUQFTptcYKbVAM
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