Data Science, Artificial Intelligence, K Means, Audience Engagement, Python.
benefits for clustering analysis. Our data set has 16 features and is therefore 16-dimensional, which is not ideal for performing K-means clustering. The more features we have, the more likely we are to encounter the “curse of dimensionality” when we attempt K-means clustering. This is a phenomenon