Data Science beginners Hands on:Class Imbalanced Datasets & SMOTE

Imbalanced classes in a data sets are a common problem in ML classification algorithms where there are a disproportionate ratio of observations in each class. Class imbalance can be found in many different areas including medical diagnosis, spam filtering, and fraud detection.

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.

Balancing datasets is a very effective way to improve the performance of your classifiers. However, it should be noted that there could be a degradation of overall accuracy measures for the majority class due to balancing. What strategies to adopt in what situations should be arrived at based on the problem statement and also after rigorous experiments for those problem statements.

we try to deal with this problem, see how it affects the Accuracy & Precision with minority & majority classes issues all with practical hands on so that one can easily understand why there is need of SMOTE [Synthetic Minority Over-sampling Technique] to solve this issue of class imbalanced.

Lets just dive in with hands on, click on below link to get detail PDF:

I hope after Hands on one can definitely understand how to deal with Class imbalanced datasets with SMOTE.

Thanks !!! Happy Learning!!