Every other algorithm we've covered so far actually learns something during training. It builds a model, adjusts weights, grows a tree. KNN does none of that. It stores the entire training dataset. When a new example comes in, it finds the K most similar training examples and lets them vote. Majority wins. That's it. No training. No model. Just memory and distance. And yet it works surprisingly well on many problems. Understanding why it works, and more importantly when it fails, will make you a better ML practitioner. What You'll Learn Here How KNN classifies using distance and voting The three most common distance metrics and when to use each How K affects the bias-variance tradeoff Why scaling is critical for KNN KNN for regression The curse of dimensionality and why KNN struggles with many features Weighted KNN and when it helps How KNN Actually Works You have a new point. You want to classify it.…