PyData Paris - April 2025 Meetup

Mark your calendar for the next session of the PyData Paris Meetup, on April 8th 2025. This Meetup will be hosted by Artefact, a global leader in data and AI consulting services, in their offices, 19 rue Richer 75009 Paris.
The speakers for this session will be Martin Renou, Abdoulaye Sakho and Vincent Auriau.

Schedule
6:45pm - 7:00pm: Arrival
7:00pm - 7:15pm: Community announcements & short address by Emmanuel Malherbe, Director of Artefact Research Center
7:15pm - 8:00pm: Real-time collaborative editors in JupyterLab, Martin Renou
8:00 - 8:15pm: Handling imbalance data for client scoring, Abdoulaye Sakho
8:15 - 8:30pm: Choice modeling with the Python package choice-learn, Vincent Auriau
8:30pm - 10:00pm: Networking & drinks

Speakers
Martin Renou
Martin Renou is a Technical Director at QuantStack and a maintainer of Project Jupyter. Among other projects Martin is a core team member of the ipywidgets project and maintains many Jupyter widget packages such as ipyleaflet, ipydatagrid, ipygany, ipycanvas, and bqplot. He is a co-creator of the Voilà dashboarding system, and the xeus-python kernel.

Abdoulaye Sakho
Abdoulaye Sakho is a graduate of ENSIIE and the M2 Random Modeling program at Université Paris Cité. He is currently doing a CIFRE thesis at LPSM (Sorbonne University) under the supervision of Erwan SCORNET and Emmanuel MALHERBE. He is a member of the Artefact Research Center.

Vincent Auriau
Vincent is a PhD student at Artefact and CentraleSupélec, under the supervision of Vincent Mousseau. His work focuses on modeling customer preferences for assortment optimization. Previously he was a Data Scientist at L'Oréal R&I where he worked on different computer vision projects.

Abstracts
Real-time collaborative editors in JupyterLab
Jupyter has long been a powerful tool for interactive computing, but real-time collaboration has been a missing piece. In this talk, I’ll present our work on bringing true collaborative editing to JupyterLab, allowing multiple users to work on the same notebook simultaneously. The highlight of the talk will be a live demo showcasing JupyterGIS for collaborative GIS editing and JupyterCAD for real-time 3D CAD collaboration. I’ll also cover the technical foundations and challenges we faced in making these advanced editors work seamlessly in a collaborative environment.

Handling imbalance data for client scoring
In data science, we talk about unbalanced data in a binary classification context when one class is under-represented compared with the other. Typically, it is the class of interest that is under-represented (cheaters, customers who bought a product on a site, sick patients...). In such a context, it is hard to train ML models. To tackle this challenge, various rebalancing strategies have been introduced in the literature.
Throughout our presentation, we will focus on the oversampling strategies, which aim to generate synthetic data within the minority class to rebalance the data. SMOTE is the most common oversampling strategy, and many variants are based on it. We will also propose an alternative based on kernel estimation and generalized random forests.
Finally, we will take a look at these techniques and observe their interest in a concrete use case from the banking industry.

Choice modeling with the Python package choice-learn
Discrete choice models aim at predicting choices made by individuals from a set of options, called an assortment. Such models are used as inputs to operational problems such as assortment optimization, pricing or product recommendation. We present Choice-Learn, a Python package for choice modeling practitioners and researchers. The package enables processing choice data, and then formulating, estimating, and operationalizing choice models based on the TensorFlow library. We provide a unified implementation of classical choice models as well as neural network-based methods.