K-Means Clustering with Python

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In a previous video I told you about
one of the unsupervised machine learning
technique called hierarchical clustering
which is useful when you have less than 10,000 individuals or elements to analyze
In this video I will show you another
clustering technique called the K-Means method
One of the advantages that this method has,
compared to hierarchical clustering, is that it has the ability to analyze databases with more than 10,000 individuals.
However, in order to carry out the K-means method
it is necessary to know in advance the number of clusters
in which we want to divide the elements contained in the
database
If you don't know this information, in the video I show you a technique called "Jambu Elbow", which will help you finding the optimal number of clusters to get