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Data Challenge : Predict the level of hygiene of food production

We're excited to launch a new data challenge that aims to use machine learning to figure out the quality of food places in France. By using a dataset from the "Alim' confiance" system, which is available for everyone to use thanks to, data scientists taking part of this challenge will try to come up with new ways to make eating out safer and help everyone know more about the quality of establishment food.

This challenge is all about sharing knowledge and working together.

The Data: A Closer Look at Food Cleanliness

This data comes from official checks on places where food is made or sold, like markets, restaurants, and factories, all over France. It shows how clean each place is, ranking them from "very clean" to "needs urgent improvement." This information helps customers make better choices and encourages places to keep their standards high.

Why This Matters

Sharing the results of these checks is part of a bigger effort to make what the government does more open. It helps shoppers know more about their food, and it pushes the food industry to do better. This challenge supports the idea of sharing knowledge freely.

What We're Looking For

We want you to create a machine learning model that can guess how clean a food place is. This could change the way we think about food safety, helping both people who eat the food and those who check on the places. It's also a chance to work together and share ideas freely.

Getting Started

We've got everything you need to begin, including a basic machine learning model example and extra materials to help you understand the data better. There's even a Jupyter notebook example provided to help you start coding your own model.

Rules of the Challenge

You'll need to use Python and Jupyter Notebook to create your model. Our team at will check your Jupyter notebook to make sure it follows the rules and only uses the data provided (train and test set).

Sign in / up today

Join our challenge to find out more about food cleanliness through data. By working together, we can make a real difference in providing visibility on food quality.

For more information and to start, visit:

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