Perdict credit risk with Machine Learning Models in Python
Project Background
Using Python to build and evaluate several supervised machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions better predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.
Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. More preprocessing steps need to be considered (imbalanced-learn). Employ different techniques to train and evaluate models with unbalanced classes.