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 you can leverage in your 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.
Here are the implementation steps:
- Load the libraries.
- Load the dataset.
- Create and train the KMeans model.
- Evaluate the clustering.
- Visualize the cluster assignments (PCA projection).
Exercise 2: Comparing DBSCAN and K-means on moon data
This exercise demonstrates how DBSCAN can outperform...
