StephenDini/Credit_Risk_Analysis

Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company, an oversample of the data will be done using the RandomOverSampler and SMOTE algorithms, and undersample the data using the ClusterCentroids algorithm. Then, a combinatorial approach of over- and undersampling using the SMOTEENN algorithm wil be done. Next, a comparison of the two new machine learning models that reduce bias, BalancedRandomForestClassifier and EasyEnsembleClassifier, to predict credit risk. Finally, the performance of these models will be evaluated and a written recommendation on whether they should be used to predict credit risk.