Project AirLiquide

Project Description

Point of Sales columns along with location and time stamps are key features in this dataset and the first milestone of this project is to build a base model for Rush Hour prediction as a particular gas station. As the project evolves, several washboarding tools are being designed and built on AWS and GCP.
A link to the live notebook will be updated below prior to every event. Please check your emails for instructions to join and run the Notebooks.

Inspiration

Predict & Visualize the Rush Hour for a particular gas station in Anaheim, CA operated by AirLiquide.

Given the data involves time-series, live feed from camera and many other features a complete linear regression analysis of the dataset is recommended prior to building your base model.

Acknowledgements

The dataset has been recieved and analysed during a collaboration of PhillyTalent and the Machine Learning Group at AirBnB (https://medium.com/airbnb-engineering)

 

 

Time-series analysis

Dashboard design

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