Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.
Repository from Github https://github.commdabbous88/Credit_Risk_Analysis
