Project Description

Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, different techniques were employed to train and evaluate models with unbalanced classes. Imbalanced-learn and scikit-learn libraries were used to build and evaluate models using resampling.

Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company, the first step was to oversample the data using the RandomOverSampler and SMOTE algorithms, and undersample the data using the ClusterCentroids algorithm. Then, used a combinatorial approach of over- and undersampling using the SMOTEENN algorithm. Next, compared two new machine learning models that reduce bias, BalancedRandomForestClassifier and EasyEnsembleClassifier, to predict credit risk.