We will explore the concept of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and learn about how it can be used to identify patterns and clusters in various types of data. DBSCAN is an unsupervised machine learning algorithm known for its ability to discover clusters of arbitrary shapes and sizes, making it a valuable tool in a wide range of applications, from anomaly detection to customer segmentation.

Throughout this project, we will cover the fundamental concepts behind DBSCAN and walk you through the step-by-step implementation process. By the end of the project, you will gain a comprehensive understanding of how DBSCAN works and how to apply it to real-world datasets.

This guided project is suitable for data enthusiasts, machine learning practitioners, and anyone interested in uncovering patterns and clusters in data. Participants should have a basic understanding of Python programming concepts. No prior experience with DBSCAN is required, as we will cover the necessary theoretical foundations before diving into the practical implementations.