13. Imbalanced Datasets

Overview

By the end of this chapter, you will be able to identify use cases where datasets are likely to be imbalanced; formulate strategies for dealing with imbalanced datasets; build classification models, such as logistic regression models, after balancing datasets; and analyze classification metrics to validate whether adopted strategies are yielding the desired results.

In this chapter, you will be dealing with imbalanced datasets, which are very prevalent in real-life scenarios. You will be using techniques such as SMOTE, MSMOTE, and random undersampling to address imbalanced datasets.