Clustering Método KMeans en Python ENGLISH SUBTITLES

>> YOUR LINK HERE: ___ http://youtube.com/watch?v=s6PSSzeUMFk

If you find the video valuable for you and you want to support my channel, you can do it in my paypal account: • https://www.paypal.com/paypalme/rocio... • Download the files I use in the video on • https://github.com/rociochavezmx/Roci... • Buy me a coffee • https://www.buymeacoffee.com/i2hxzeo • Blog Aprende Ciencia de Datos con Rocío Chávez : https://rociochavezml.com/ • 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

#############################

New on site