Practical Exploratory Data Analysis for Machine Learning

Abstract

In this hands-on workshop, participants will learn how to leverage Exploratory Data Analysis (EDA) to guide feature engineering and inform Machine Learning model development. Using real Formula 1 data, we will explore patterns in lap performance, car telemetry, and race conditions, and then use those insights to design features for a clustering model. The session begins with a concise overview of EDA concepts and commonly used techniques in Python using pandas, visualizations, and statistical summaries. Participants will then work through a guided notebook exercise, performing their own data analysis, generating new features, and evaluating how those features influence model results. This workshop emphasizes the thinking process behind effective feature engineering. By the end of the session, participants will have a practical workflow for conducting EDA and translating insights into machine learning improvements. Basic familiarity with Python and pandas is recommended. The workshop is especially valuable for students seeking to strengthen their data analytics skills.

Bio

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Ananda Ribeiro is a Data Scientist who began her career as a systems engineer in the aerospace industry before transitioning into machine learning and data analytics. She has worked at Embraer, Boeing, and Coca-Cola, where she won 1st place in the company’s Global Data Analytics Challenge (2020). Her work applies data science to real-world challenges in aviation safety, supply chain, and equipment reliability, and she enjoys sharing her experience through conference talks and mentoring.