Add Comparison Notebook for Clustering Algorithms

[Cgarg9]

Description:

To help users understand when to use each clustering algorithm, add a comparison notebook that applies multiple clustering algorithms on the same dataset and compares results. This will help users choose suitable methods based on dataset characteristics.

Tasks:

• Create a notebook to compare K-Means, DBSCAN, Agglomerative Clustering, and other clustering methods (e.g., K-Medoids, Spectral).
• Provide visual comparisons and discuss the strengths and weaknesses of each algorithm.
• Summarize key takeaways for each method.
• Name the notebook clustering_comparison.ipynb.
• Update the README file with links to any resources or references used.