Create DBSCAN Notebook for anomaly detection under Unsupervised Learning

## Description:

I would like to contribute a Jupyter Notebook that demonstrates anomaly detection using DBSCAN.

## Details:

The notebook includes:

- Generating synthetic data with clusters and injected anomalies
- Standardizing features
- Applying DBSCAN for clustering and anomaly detection
- Visualizing clusters and highlighting anomalies
- Inspecting anomaly points

This notebook can be used as a reference for anomaly detection in machine learning projects.
Future improvements can include adding support for CSV datasets, parameter tuning, and extended visualizations.

No file or test path is specified; start by locating the Unsupervised Learning notebook area and reviewing nearby notebook conventions. Done means adding a Jupyter Notebook with synthetic clustered data and injected anomalies, feature standardization, DBSCAN-based detection, visualized clusters, and inspection of anomaly points.

Written by the indexing model from the issue text.