Python for Credit Risk Analysis and Default Prediction

Understanding and managing credit risk is a vital skill in modern finance and data analytics. This text-based course guides you through the core concepts of predicting loan defaults using Python. You will transition from understanding basic financial terminology to building and evaluating your own predictive models. Through structured written explanations and step-by-step code walkthroughs, you will gain the confidence to analyze credit data and make data-driven risk assessments. What you'll learn: Understand foundational credit risk concepts and default definitions; Prepare and clean financial datasets using modern Python data libraries; Address class imbalance using industry-standard preprocessing techniques; Build predictive models using logistic regression and scikit-learn; Evaluate model performance using precision, recall, and ROC-AUC metrics; Apply clean coding practices and type hints to financial modeling workflows. The course begins with essential credit risk terminology and data preparation techniques. You will then progress through model building, evaluation, and modern Python best practices for financial risk analysis. This course is designed for aspiring data analysts, finance professionals, and beginners looking to apply Python to real-world financial problems with no prior machine learning experience required. Start reading today to master the fundamentals of credit default prediction.