How to Handle Imbalanced Data in Machine Learning

One of the most common problems when working with classification tasks is imbalanced data where one class is dominating over the other. For example, in the Credit Card fraud detection task, there will be very few fraud transactions (positive class) when compared with non-fraud transactions (negative class). Sometimes, it is even possible that 99.99% of transactions will be non-fraud and only 0.01% of transactions will be fraud transactions. You can have a class imbalance problem on binary classification tasks as well as multi-class classification tasks. However, the techniques we are going to learn here can be applied to both.