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

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

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

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

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

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