Credit Risk Defaulting Classification
Classification of credit risk defaults using k-means clustering to identify high-risk profiles.
Problem
Financial institutions need to accurately identify customers at risk of defaulting on credit obligations to minimize losses and optimize lending decisions.
Approach
Applied k-means clustering algorithm to historical credit data, segmenting customers into risk categories based on financial behavior patterns.
Outcome
Developed a clustering model that effectively groups customers by default risk, enabling more targeted risk management strategies.
Technologies
PythonScikit-LearnJupyter Notebook