Charaf ZGUIOUAR
Quantitative Finance and Econometrics Gradutate from Sorbonne's University. Currently working as Data Scientist at BNP Paribas & as lecturer at Sorbonne's University.
Session
In many regulated industries—finance, healthcare, insurance—logistic regression remains the model of choice for its interpretability and regulatory acceptability. Yet capturing non-linear effects and interactions often requires variable binning, and naive approaches (equal-width or quantile cuts) can either wash out signal or invite overfitting. In this 30-minute session, data scientists and risk analysts with a working knowledge of logistic regression and Python will learn to:
-Diagnose the weaknesses of basic binning strategies.
-Select and apply optimal-binning algorithms for different use cases.
-Assess bin stability and guard against model overfit.
All code, data samples, and a turnkey notebook will be available on GitHub, so you can start experimenting immediately.