Testing 6 different machine learning models to determine which is best at predicting credit risk.

Analysis of different machine learning models' performance on predicting credit default

Use scikit-learn and imbalanced-learn machine learning libraries to assess credit card risk.

Performed supervised machine learning using oversampling, undersampling and combination sampling techniques to determine credit risk for bank customers.

The purpose of this analysis was to create a supervised machine learning model that could accurately predict credit risk using python's sklearn library.

Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn