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Self Supervised Experiments for Model 2.0

Experiments for Model 2.0

Uses proprietary USRA flight data to train an XGBoost regressor model to predict estimated time of arrival (ETA) as a residual from the actual time of arrival. Also includes support modules for pulling data, processing data, and evaluating value of prediction task (e.g. cancellation rate improvement).

Testing JupyterHub for Cal State San Bernardino https://csusb-hub-project.nrp-nautilus.io/

Our JupyterHubs are available at https://csusb-jupyter.nrp-nautilus.io/ and https://csusb-hpc.nrp-nautilus.io/

Page for customized Jupyter-hub stacks for CSUSB