In this subject, we formed teams of 4 and were asked to choose a challenge from one of the five companies involved. Estrella Damm at the moment has a lot of data of their huge barrels given to the most exclusive clients called BeerDrives. This excel data remains yet unused by the company and wants some fresh ideas on how using this data could improve Damms headquarters so loss of beer and logistic hell is avoided altogether thanks to a improved monitorization or any idea that could improve that.
This is our solution to the DAMM challenge. BeerLogic is a web application that uses all the data given by damm to show predictions of dates for the next refills of Beerdrives and possible route scheduling that could be done. Also includes tools of data visualization, that consist of relevant graphics and statistics for the company to improve their logistics.
Alex HerreroPol FornerLluc ClaveraWalter Troiani
In order to execute this project that consists in 2 well-differentiated parts: The data pipeline (Including all the data transformations and the predictions made by the beerlogic machine learning model) and the front-end website. To run the data pipeline, which may take several minutes just run the jupyter notebook:
pip install -r data_pipeline/requirements.txt
jupyter notebook
enter data_pipeline/ETL_run_pipeline.ipynb
run allTo run the frontend website just open the web_prototype/index.html file and enjoy!
This project is licensed under the Apache License 2.0. See the LICENSE file for more details.