Build practical skills in credit default prediction with Python by learning how to prepare data, develop classification models, and evaluate predictive performance for financial risk analysis. In this course, you will follow a structured workflow that begins with importing datasets and libraries, preprocessing data, handling missing values, encoding categorical features, scaling numerical variables, and performing exploratory data analysis (EDA) to uncover meaningful patterns.

As you progress, you will build and assess logistic regression models using evaluation techniques such as confusion matrices and ROC curves. You will also optimize model performance through Grid Search and Randomized Search hyperparameter tuning. The course then expands into decision tree modeling, where you will explore splitting criteria, visualize models with Graphviz, and implement them in Python. Finally, you will apply Random Forest techniques to reduce overfitting and improve predictive accuracy for credit default prediction. Designed for learners who want to strengthen their Python-based predictive modeling skills, this course emphasizes practical implementation and model evaluation using real-world credit datasets. By the end of the course, you will be able to apply, analyze, evaluate, and construct machine learning models that support more informed decision-making in financial risk management.