The study of machine learning has revealed that it can unleash new applications in a variety of disciplines. Many limitations limit their expressiveness

and researchers are working to overcome them to fully exploit the power of data-driven machine learning (ML) and deep learning (DL) techniques. The data imbalance presents major hurdles for classification and prediction problems in machine learning

restricting data analytics and acquiring relevant insights in practically all real-world research domains. In visual learning

network information security

failure prediction

digital marketing

healthcare

and a variety of other domains

raw data suffers from a biased data distribution of one class over the other. This article aims to present a taxonomy of the approaches for handling imbalanced data problems and their comparative study on the classification metrics and their application areas. We have explored very recent trends of techniques employed for solutions to class imbalance problems in datasets and have also discussed their limitations. This article has also identified open challenges for further research in the direction of class data imbalance.