Abstract: Imbalanced classes is a surprisingly common problem in machine learning. However, many machine learning algorithms do not work very well with it and can give a wrong sense of good performance because of the high accuracy scores. we will get familiar with the class imbalance and then see various techniques to handle imbalanced classes. Anyone working with machine learning would definitely come across a scenario where the number of observations belonging to one class is significantly lower than those belonging to the other classes and this is known as imbalanced class distribution. In this scenario, the predictive model could be biased and inaccurate if not taken care of. I will describe various approaches for solving such scenarios using various techniques listed below. I will also go through the pros and cons of each technique. Speaker: Ravi Singh