Credit Default Prediction with Python: Apply & Analyze

Overview

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 curves, ensuring accuracy and reliability in predicting defaults. They will optimize logistic regression models through hyperparameter tuning methods like Grid Search and Randomized Search. Expanding further, the course introduces decision tree theory and practical coding steps, enhanced with visualization using Graphviz for interpretability. Finally, learners will construct Random Forest models to reduce overfitting and improve predictive performance, applying ensemble learning techniques to real-world credit datasets. By the end of this course, learners will be able to apply, analyze, evaluate, and construct predictive models that enhance decision-making in financial risk management, using industry-standard tools and Python libraries.

Syllabus

Data Preparation & Model Foundations

In this module, learners gain a strong foundation in building a credit default prediction model using Python. The module introduces the project’s scope, outlines the workflow, and emphasizes the importance of structured data handling. Learners will explore data preprocessing techniques such as handling missing values, encoding categorical features, and scaling numerical variables. In addition, they will perform exploratory data analysis (EDA) to identify patterns, visualize distributions, and uncover key relationships within the dataset. Finally, learners will split the dataset into training and testing sets to ensure reliable evaluation of logistic regression models for predicting credit default risk.

Model Building & Advanced Techniques

In this module, learners advance beyond data preparation into the core of predictive modeling. The module introduces evaluation metrics such as the confusion matrix and ROC curve to assess classification performance in credit default prediction. Learners will then explore hyperparameter tuning methods like Grid Search and Randomized Search to optimize logistic regression models. The module further builds knowledge with decision tree theory, covering splitting criteria, visualization using Graphviz, and practical implementation in Python. Finally, learners will apply ensemble techniques with Random Forest to reduce overfitting and improve model accuracy for robust credit risk prediction.