Credit Default Prediction with Python: Apply & Analyze

This course provides a hands-on journey into credit risk prediction using Python with a focus on logistic regression, decision trees, and ensemble methods. Learners will begin by outlining project workflows, importing data, and applying data preprocessing techniques such as handling missing values, encoding categorical features, and scaling numerical variables. Through exploratory data analysis (EDA), they will interpret data patterns and relationships to build stronger foundations for modeling. Moving into advanced modeling, learners will evaluate models using confusion matrices and ROC curv…