In this video, we have discussed DBSCAN Implementation in Machine learning. 

What is DBSCAN?
DBSCAN stands for Density-Based spatial clustering of Application. It is an unsupervised learning technique used in machine learning algorithms and model building. It is a clustering technique used to distinguish between high- and low-density clusters. In this video, we are going to see the DBSCAN clustering algorithm in Machine Learning. This algorithm is based on the intuitive notion of "clusters" and "Noise". 

DBSCAN algorithm requires two parameters: 
1) eps 
2) MinPts 

Following steps is going to cover in this videos:

1) Importing packages.
2) Loading Datasets.
3) Slicing Columns.
4) Performing DBSCAN clustering using DBSCAN() Methods
5) Predicting number cluster and creating the new columns.
6) Performing the metrics operation with silhouette_score().

Implementing DBSCAN algorithm using Sklearn:
https://www.geeksforgeeks.org/implementing-dbscan-algorithm-using-sklearn/