Imbalanced classes are a common problem in classification due to disproportionate ratio of observations in each class. Class imbalance can be found in many different areas including medical diagnosis, spam filtering, and fraud detection. Most machine learning algorithms work best when the number of samples in each class are about equal. This is because most algorithms are designed to maximize accuracy and reduce error. In this problem we look at a few ways to handle imbalanced class problem using Credit Card Data.
Objective: To correctly classify the minority class of fraudulent transactions.