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Using Classification / Machine Learning to Determine Credit Risk

In this project I will build and analyze different machine-learning models to predict credit risk.

In the Resampling_Credit_Risk notebook I will perform the following tests and compare the results:

  • Oversample data using Naive Random Oversampler / SMOTE algorithms.

  • Undersample data using Cluster Centroids algorithm.

  • Over / undersample using a combination SMOTEENN algorithm.

In the Ensemble_Learning_Credit_Risk notebook I will run two different ensemble classifiers (Balanced Random Forest / Easy Ensemble) and compare the results.