From the course: Machine Learning with Python: k-Means Clustering

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How to interpret the results of k-means clustering in Python

How to interpret the results of k-means clustering in Python - Python Tutorial

From the course: Machine Learning with Python: k-Means Clustering

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How to interpret the results of k-means clustering in Python

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- [Instructor] Before we get started, note that this video is the fourth in a full video sequence that explains how to segment data using k-means clustering, evaluate and visualize clusters, choose the right number of clusters, and interpret the results of k-means clustering. So if you have not done so, watch the previous three videos for a detailed explanation of the prior code. Now that we've clustered our data, evaluated the clusters, visualize the clusters, and chosen an appropriate value for k, let's segment the data again with k set to five and interpret the results. We start by instantiating a cluster object called km from the k-means class with n clusters, which is k set to five. We then cluster the customer scale dataset using the new cluster object. Next, we create a data frame for the cluster centroids called cluster centers. This data frame makes it easier to interpret the cluster centers and is also…

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