Describe your change:

Implementation of Density-based spatial clustering of applications with noise (DBSCAN) Algorithm using Python from Scratch

How it works :
Take every point and see how many other points are close to it by e (epsilon) radius distance. If that point has covered minimum of number of points (minPts), then it will be considered as Core else Noise. An Noise can also be classified as Border if it falls into the area covered by the core point.

Reference Website : https://en.wikipedia.org/wiki/DBSCAN
Reference YouTube Video : https://youtu.be/-p354tQsKrs?si=TkI9nrnAiqDHyjR-

• Add an algorithm?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".