Predicting Loan Defaults with Machine Learning

Financial institutions rely heavily on accurate risk assessment to make informed lending decisions and minimize losses. Understanding how to leverage data to predict whether a borrower will default is one of the most valuable skills in modern finance and data science. In this text-based course, you will learn how to transform raw financial data into reliable predictive models. You will gain a solid foundation in credit risk concepts, data preprocessing, and machine learning algorithms, enabling you to build, evaluate, and interpret models that forecast loan defaults. What you'll learn: - Understand key credit risk terminology, foundational concepts, and the mechanics of loan default prediction. - Clean and preprocess financial datasets using modern data manipulation libraries in Python. - Handle highly imbalanced credit data using industry-standard techniques to ensure robust predictions. - Train and tune machine learning classifiers like logistic regression and tree-based models. - Evaluate model performance using realistic financial metrics such as precision, recall, and ROC-AUC. - Analyze feature importance to understand which borrower characteristics drive default risk. This course begins with essential financial and data definitions before guiding you step-by-step through exploratory analysis, data preparation, model training, and final evaluation. Designed for beginners, aspiring data scientists, and finance professionals, this program requires no prior machine learning experience. Start building your financial data science skills today.