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

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

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

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