Summary  
This chapter demonstrates how to implement and tune the DBSCAN density-based clustering algorithm—showing how the eps and min_samples hyperparameters affect cluster formation and how core and border points are identified.  

General domain of usage  
Unsupervised machine learning (density-based clustering)

You'll create two datasets to demonstrate DBSCAN's strengths: 
 
- **Moons:** two interleaving half circles; 
- **Circles:** a small circle inside a larger circle.

The algorithm is as follows:

1.  You instantiate the `DBSCAN` object, setting `eps` and `min_samples`;      
2.  You fit the model to your data; 
3.  You visualize the results by plotting the **data points** and coloring them according to their assigned **cluster labels**.

## Tuning Hyperparameters 

The choice of `eps` and `min_samples` significantly impacts the clustering outcome. Experiment with different values to find what works best for your data. For instance, if `eps` is too large, all points might end up in a **single cluster**. If `eps` is too small, many points might be classified as **noise**. You can also scale the features. 

Which statement best describes DBSCAN and its application to the dummy datasets in this chapter?

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Implementing on Dummy Dataset

Tuning Hyperparameters