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The presentation will introduce the “Stile eco-system” developed at ING Analytics to speed up the time to market of machine learning models for the instant lending domain. The main issue to solve is the duality between Spark and Pandas for feature generation. Spark is used for development while dealing with the billions of transactions stored in the data warehouse. Pandas is used in production when applications are scored one by one in a real-time situation. During the presentation, Gilles will explain how the template for model development works, with a specific focus on feature creation. Additionally, Gilles will highlight how Pandas and PySpark are integrated in common functionalities, and the user-friendly testing framework developed to ensure consistency between the two worlds, and, finally, how to easily trim the code to only produce the features required for the final model. Gilles Verbockhaven is Chapter Lead at ING Retail Banking Analytics and manages a team of five Data Scientists. He has been working at ING for 20 years now and has experience in various domains, ranging from market risk to modelling. Since 2017, he has been working in the Machine Learning area and has specialized in designing analytic solutions for collections and pricing. In his free time, he spends his energy running and biking.

This presentation introduces the Genetic Algorithms + Feature Importance Feature Selection technique, implemented in the open source Python package felimination. Genetic algorithms are a powerful optimization technique that can be effectively utilized for feature selection in machine learning models. By combining genetic algorithms with feature importance, we aim to enhance the feature selection process, leading to more robust and interpretable models. We will start by reviewing genetic algorithms, detailing the steps of pool initialization, crossover, mutation, and selection. The presentation will continue by showcasing some code snippets using felimination, a Python package containing a suite of algorithms for feature selection, including the genetic algorithm with feature importance selector. Claudio Salvatore Arcidiacono is a Senior Machine Learning Engineer at Mollie. He has been working in the fintech sector over the past 7 years with lots of experience in classical machine learning problems. He loves to contribute to data science open source libraries like feature engine, scikit-learn, and narwhals. He maintains a couple of open source libraries himself (felimination and sklearo). In his free time, he is a coffee scientist, using a data-driven approach to dial in the perfect cup of espresso.