Hi! We are JNA and in this year's edition of the FME Datathon we presented a solution to Accenture's challenge.
Given a database of product shipements, we were tasked to train a model to predict whether the shipment would be late or not, as well as present business insights regarding supply chain cost and emission reduction, as well as on the creation of the new Hubs. Each shipment comes from a port and has a city as its destination, and must necessarily pass by a Hub. We not only provided the best places to create a Hub in terms of a greediness parameters (that quantifies the priority given to cost reduction as opposed to CO2 emission reduction), but also recommended the best route given the existing Hubs. We used Machine Learning with the scikit-learn framework, and Graph Optimization with the networkx library given a theoretical model that we've created.
About us: We are:
- Joel (Second year Data science student)
- Jan (Second year Maths + Computer science student)
- Nathaniel (Second year Maths + Data science student)
- Alex (Second year Maths + Data science)
Emails:
- Joel: joel.sole.casale@gmail.com
- Jan: tarrats.jan@gmail.com
- Nathaniel: nathanielmitrani@gmail.com
- Alex: alexstedev@gmail.com