Uncover Actionable Insights with PyCaret 3.0: How to Build a Clustering Model in Power BI
Introduction
Clustering is a technique that groups data points with similar characteristics. These groupings are useful for exploring data, identifying patterns, and analyzing a subset of data.
Organizing data into clusters helps identify underlying structures in the data and finds applications across many industries. Some common business use cases for clustering are:
- Customer segmentation for the purpose of marketing.
- Customer purchasing behavior analysis for promotions and discounts.
- Identifying geo-clusters in an epidemic outbreak such as COVID-19.
Types of Clustering
Given the subjective nature of clustering tasks, there are various algorithms that suit different types of problems. Each algorithm has its own rules and the mathematics behind how clusters are calculated.
This tutorial is about implementing a clustering analysis in Power BI using a Python library called PyCaret. Discussion of the specific algorithmic details and mathematics behind these algorithms is out-of-scope for this tutorial.
In this tutorial, we will use a K-Means algorithm which is one of the simplest and most popular unsupervised machine learning algorithms. If you would like to learn more about K-Means, you can read this paper.
Setting up Python
If you have used Python before, it is likely that you already have Anaconda Distribution installed on your computer. If not, click here to download Anaconda Distribution with Python 3.10.
Setting up the Environment
Before we start using PyCaret’s machine learning capabilities in Power BI we have to create a virtual…
