Preventing Food Waste by Building a Forecasting Model to Predict Sales of Fresh Food
  • The Results

Preventing Food Waste by Building a Forecasting Model to Predict Sales of Fresh Food

Challenge Completed!

In this two-month AI challenge, join a global team of 50 changemakers to help a foodtech startup further build its AI solution to prevent food waste.

 

The Problem

Today, the world wastes or loses around a third of the food it produces, while almost 690 million people are hungry. If we keep throwing away 1/3 of the produced food, we’ll need 2 planets to feed 10 billion people in 2050. Reusing and recycling food waste is only a part of the solution. It’s the prevention that we need most to build a sustainable food future. According to Project Drawdown, solving food waste is the best solution for climate change.  

Sustainable Development Goal 12: Ensure sustainable consumption and production patterns, contains a wide range of targets, one of which is closely related to halving global food waste at the retail and consumer levels.

To feed the world sustainably, global initiatives, activities, and projects are held on food losses and waste reduction including this project.

 

The project goals

Build an ML model to forecast sales of fresh food in retail. If the forecast is more accurate, ordering is done better too. Resulting in less overstock and thus food going to waste due to the short shelf-life. 

  • Build a scraper that collects promotion data from retailers in Belgium (both fresh and non-fresh products)
  • Create an ontology that unifies the data from different sources through semantic matching,  but also keeping track of original product and promotion names.
  • Build a model that links the promotion data from the web to the sales data. 

Optionally: a similar model can be built for the Netherlands and Italy, where the effect of language versus country can be further researched.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works