Build imbalanced data handling algorithm

As part of personal project I need someone to build a Python code to handle imbalanced data for a loan default dataset by experimenting 3 techniques: oversampling, under-sampling and SMOTE in different % and different order and choose the best % for each of the 3 and the best order to get the best classification accuracy (according to f1 score)