The purpose of this analysis is to apply machine learning to solve the challenge of credit risk. Credit risk is an inherently unbalanced classification problem, because good loans easily outnumber risky loans. Therefore it is necessary to employ different techniques to train and evaluate models with unbalanced classes. In this challenge, I have used the imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. Using the credit card dataset from LendingClub, I have oversampled the data using the RandomOverSampler and SMOTE algorithms, and undersampled the data using the ClusterCentroids algorithm. Then I used a combination approach of over- and undersampling using the SMOTEEN algorithm. After that, I compared two new machine learning models that reduce bias, BalancedRandomForestClassifier and EasyEnsembleClassifier, to predict credit risk.

None of these results were very accurate in predicting high-risk loans. The EasyEnsemble AdaBoost Classifier model was slightly better at detecting high-risk credit, but it also falsely predicted 979 high-risk loans. Therefore, I do not recommend any of these models for predicting credit risk.