Summary  
This chapter demonstrates how to preprocess numeric features, apply the DBSCAN density-based clustering algorithm with hyperparameter tuning (eps and min_samples), and visualize resulting clusters and outliers on a real dataset.  

General domain of usage  
Customer segmentation in retail marketing

You'll use the **mall customers** dataset, which contains the following columns:

You should also follow these steps before clustering:
     
1.  **Load the data:** you'll use `pandas` to load the CSV file;
2.  **Select relevant features:** you'll focus on `'Annual Income (k$)'` and `'Spending Score (1-100)'` columns;
3.  **Data scaling (important for DBSCAN):** since DBSCAN uses distance calculations, it's crucial to scale features to have similar ranges. You can use `StandardScaler` for this purpose.

## Interpretation 

The code creates **5 clusters** in this case. It's important to analyze the resulting clusters to gain insights into **customer segmentation**. For example, you might find clusters representing: 

- High-income, high-spending customers;     
- High-income, low-spending customers;    
- Low-income, high-spending customers;     
- Low-income, low-spending customers; 
- Middle-income, middle-spending customers. 

Which statement best describes a key advantage of using DBSCAN for clustering the mall customers dataset?

Explore the power of hidden patterns with unsupervised learning. Master the most influential clustering algorithms, including K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models. Learn to evaluate cluster quality using WSS and Silhouette scores, handle diverse distance measures, and implement robust solutions on real-world datasets. Build the skills to segment customers and discover structures in unlabeled data using Scikit-learn.

Implementing on Real Dataset

Interpretation

Concluding Remarks

Implementing on Real Dataset

Interpretation

Concluding Remarks