Handling Imbalanced Data with Imbalance-Learn in Python

This article was published as a part of the Data Science Blogathon. Almost every data scientist must have encountered the data for which they need to perform imbalanced binary classification. Imbalanced data means the number of rows or frequency of data points of one class is much more than the other class. In other words, the ratio of the value counts of classes is much higher. Such data set is known as an imbalanced dataset in which the class having more data points is the majority class and the other is the minority class. The imbalance makes the classification more challenging.