My steps:

• Import the modules
• Read the Dataset
• Check for relative proportions of Fraudulant Cases
• Plot the named features
• Reorder the columns Amount, Time then the rest
• Plot the distributions of the features
• Check for null values
• Separate response and features (Undersampling before cross validation leads to overfitting)
• Use SkLearn for splitting
• Create the cross validation framework
• Import the imbalance learn module, classifiers, metrics
• Import Random Forest
• Used metrics such as accuracy, precision, recall, F1 score
• Define get_model_best_estimator_and_metrics
• Cumulatively create a table for the ROC curve