More specifically, it contains information about 2,000 individuals and has their IDs, as well as geodemographic features, such as Age, Occupation, etc. The data set we’ve chosen for this tutorial comprises 2,000 observations and 7 features. We’ll use customer data, which we load in the form of a pandas’ data frame. The second step is to acquire the data which we’ll later be segmenting. We start as we do with any programming task: by importing the relevant Python libraries. In the next part of this tutorial, we’ll begin working on our PCA and K-means methods using Python.
In case you’re not a fan of the heavy theory, keep reading. This paper discusses the exact relationship between the techniques and why a combination of both techniques could be beneficial. In the case of PCA and K-means in particular, there appears to be an even closer relationship between the two. On top of that, by decreasing the number of features the noise is also reduced. Chief among them? By reducing the number of features, we’re improving the performance of our algorithm. There are varying reasons for using a dimensionality reduction step such as PCA prior to data segmentation. In this tutorial, we’ll see a practical example of a mixture of PCA and K-means for clustering data using Python. Did you know that you can combine Principal Components Analysis (PCA) and K-means Clustering to improve segmentation results?
