Practical Exercises with Clustering Models

In this final section, you will engage in practical exercises that involve building, tuning, and evaluating clustering models on real-world datasets. These exercises are designed to reinforce the concepts learned throughout the chapter and demonstrate how to effectively apply clustering techniques in various scenarios. By the end of this section, you will have hands-on experience that they can leverage in their own ML projects.

Exercise 1: Clustering with K-means on the Iris Dataset

In this example, we’ll apply K-means clustering to the well-known Iris dataset and evaluate the results using multiple metrics.

Implementation steps:

1. Load libraries:
2. Load the dataset:
3. Create and train the KMeans Model:
4. Evaluate the Clustering:
5. Visualize the Cluster Assignments (PCA Projection):

Exercise 2: Comparing DBSCAN and K-means on Moons Data

This exercise demonstrates how DBSCAN can outperform K-means on data with non-convex shapes.

Implementation...