Avoid common mistakes when visualizing clusters in Python for better results

Visualizing clusters is crucial in unsupervised machine learning as it helps us gain insights into data patterns. However, there are a few common mistakes that people make when visualizing clusters in Python. In this thread, we’ll explore some of the mistakes, and provide correct code snippets for each.

1. Using the wrong number of clusters:

Selecting an inappropriate number of clusters can lead to misinterpretation of the data. Using too few or too many clusters can obscure meaningful patterns. The code below is an example of choosing the correct number of clusters for the clustering algorithm and then visualizing them based on two features to verify if the correct number of clusters was chosen.

2. Attempting to visualize high-dimensional data without reduction:

One common mistake is attempting to visualize high-dimensional data directly without using dimensionality reduction techniques. This mistake can lead to ineffective and cluttered visualizations that fail to capture the underlying patterns in the data. The code below performs dimensionality reduction before visualizing the clusters.