KNN( K-Nearest Neighbour)

Why overcomplicate things when you can just look around? In this video, we dive into K-Nearest Neighbors (KNN)—the most intuitive "lazy learner" in Machine Learning. Whether you're an engineering student or a data enthusiast, this guide explains how machines make predictions based on the simple idea that "birds of a feather flock together." In this tutorial, we cover: The Logic: How KNN uses distance to classify data points without needing a complex "training" phase. Choosing 'K': The art of picking the right number of neighbors—and why an even number might cause a tie! Distance Metrics: A quick look at how we measure "closeness" using Euclidean and Manhattan distance. Read the Technical Deep-Dive I’ve written a detailed technical guide on Medium that covers everything from feature scaling to handling the "Curse of Dimensionality" in KNN. Check it out for the full Python implementation:   / understanding-k-nearest-neighbors-knn-in-m...