K-Means Clustering in Python | Your First Machine Learning Model

In this video, we build our first unsupervised learning model using K-Means clustering. Starting with the Mall Customers dataset, we walk through the entire process step by step: data exploration, preprocessing, choosing the right number of clusters with the Elbow Method and Silhouette Score, and finally visualizing and interpreting the results. This tutorial is designed for beginners in machine learning and data science who want a clear, practical introduction to clustering. All of the code, Jupyter notebook, and instructions are available in the GitHub repository here: https://github.com/KelvinLinBU/Your_First_K-Means_Model By the end of this video, you’ll understand how K-Means works, how to evaluate the quality of your clusters, 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/modern-data-kelvin-lin/1149201590? 🔗 Apple Books 🍎: https://books.apple.com/us/book/modern-data/id6757802062