KNN Algorithm Explained with Distance Metrics & Python Implementation (Step-by-Step) Episode 15 b
In this episode, we combine one of the most important concepts in machine learning — Distance Metrics — with the practical working of the K-Nearest Neighbors (KNN) algorithm.
KNN relies entirely on how distance is measured between data points. Understanding distance metrics is therefore critical to building accurate models.
In this video, we cover:
What is KNN and how it works
Why distance metrics are essential in KNN
Euclidean Distance (default metric)
Manhattan Distance and when to use it
Minkowski Distance (generalized form)
Effect of distance metrics on classification
Step-by-step Python implementation of KNN
Model training, prediction, and evaluation
We also demonstrate how changing the distance metric impacts the model’s behavior and results.
This is part of our Machine Learning Fundamentals Series, designed for beginners and intermediate learners.
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