Code and instructions for techniques to creating, visualizing, and interpreting customer segments.
In this repo, you will find the code and instructions for this article. It is advised to read through the article whilst coding along using the Customer Segmentation.ipynb notebook.
This repo and the corresponding article decribe several techniques for clustering, visualizing and interpreting clustering algorithms and output. I explored k-Means and DBSCAN as clustering algorithms, t-SNE and PCA for dimensionality reduction, and applied a variance technique and feature importance to select variables that uniquely represent clusters.