fischlerben / Machine-Learning-Credit-Risk

Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample th…
11Updated 3 years ago

Related projects

Alternatives and complementary repositories for Machine-Learning-Credit-Risk