DBSCAN Clustering | Your First Machine Learning Model

In this video, we build our first density-based clustering model using DBSCAN. Starting with the Mall Customers dataset, we walk through the process step by step: data exploration, preprocessing and scaling, picking a starting eps with the k-distance plot, tuning min_samples, running DBSCAN, and visualizing clusters and noise. This tutorial is designed for beginners in machine learning and data science who want a clear, practical introduction to clustering without guessing the number of clusters. All of the code, Jupyter notebook, and instructions are available in the GitHub repository here: https://github.com/KelvinLinBU/Your_F... By the end of this video, you will understand how DBSCAN works, what eps and min_samples mean, how to choose eps using the k-distance plot, how to balance clusters and noise, and how to turn raw data into meaningful insights. If you found this helpful, please like, comment, share, and subscribe for more tutorials on machine learning, data science, and software engineering. Shoutout to ‪@dontmakelies‬ for her editing work! Check out my book Modern Data: From Ingestion to Production available on Amazon, Apple Books, and Barnes & Nobles: 🔗 Amazon 🚚 : https://www.amazon.com/dp/B0GH8J71SC 🔗 Barnes & Noble 📚: https://www.barnesandnoble.com/w/mode... 🔗 Apple Books 🍎: https://books.apple.com/us/book/moder...

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