Advanced Data Science for Credit Risk Modeling: The Good, The Bad, and The Defaulted скачать в хорошем качестве

Advanced Data Science for Credit Risk Modeling: The Good, The Bad, and The Defaulted

Ever tried building a credit risk model when your data lives in Google Sheets and your loan statuses are about as reliable as weather forecasts? You'll learn practical data science lessons about surviving data quality issues, the critical importance of target variable definition, adding genetics to feature selection algorithms, and how engineered transactional features can transform your predictions from "probably fine" to "we actually know what we're doing." We’ll show how classical ML approaches like logistic regression and XGBoost remain highly effective for binary classification problems, proving that sometimes the fundamentals work better than the latest AI trends. Perfect for anyone who's ever wondered how machine learning works when your data isn't clean, your labels aren't perfect, and your stakeholders want results yesterday. Marta Barigozzi holds a PhD in Mathematics, made the leap from algebraic geometry to data science, and currently works as a Data Scientist at Mollie specializing in credit risk and probability of default models.