K-Means SMOTE - Machine Learning with Imbalanced Data

In this video, we explore the foundations of K-Means SMOTE, a powerful technique for addressing imbalanced datasets using the Imbalanced-learn library in Python. Learn the key concepts behind K-Means clustering, and how this method can be applied to create balanced datasets. We break down how KMeans SMOTE works, discussing cluster selection, density thresholds, and interpolation strategies. Let us know your thoughts in the comments, and don't forget to like and share! Want to learn more? Check out our course: https://www.trainindata.com/p/machine-learning-with-imbalanced-data