Loan Default Prediction with Machine Learning | Course Module

This case study introduces the fundamentals of machine learning through a practical, end-to-end example in Python. You’ll learn how financial professionals can use machine learning models to make predictions, with a focus on assessing loan default risk. The course starts with the core concept. Machine learning is a method of identifying patterns in data to make predictions or recommendations. In this case, the goal is to predict whether a customer is likely to default on a loan, which is critical for risk assessment and pricing decisions in finance. You’ll focus on supervised learning, the most common type used in business. This involves training a model on historical data with a defined outcome, such as whether a loan defaulted or not. The course specifically builds a binary classification model that answers a simple yes-or-no question. The case study walks through a real dataset of vehicle loans, using variables such as credit history, customer demographics, and financial characteristics to predict default risk. This provides a practical, real-world application of machine learning in a finance context. You’ll begin by loading, cleaning, and exploring the data using Python. From there, you’ll perform exploratory data analysis to identify patterns and relationships that may influence loan outcomes. A key step is feature engineering, where you transform and refine the data to improve model performance. This process helps ensure that the inputs used by the model are meaningful and predictive. You’ll then build two foundational classification models: logistic regression and random forest. These are widely used techniques that form the building blocks of more advanced machine learning applications. The course also covers model evaluation. You’ll learn how to measure accuracy, interpret results, and use tools like ROC curves to assess model performance. Techniques such as class balancing are introduced to improve prediction quality, especially when dealing with uneven datasets. Everything is taught step by step, with clear explanations of each line of code and hands-on exercises to reinforce learning. By the end, you’ll have completed a full machine learning workflow, from raw data to final model evaluation. Relevant for: Financial analysts, data analysts, credit risk professionals, and anyone looking to build practical machine learning skills for real-world business problems. Subscribe for more practical insights on data analysis, machine learning, and real-world finance applications.