K-Nearest Neighbors (KNN)

Lesson in the Machine Learning Roadmap · Stage 18: Classification

Last updated 14 September 2026

What is K-Nearest Neighbors (KNN)?

K-Nearest Neighbors (KNN) is a simple algorithm used for classifying data points based on their proximity to other data points. Given a new, unclassified data point, KNN identifies the 'K' closest data points (neighbors) from the training dataset. The class that appears most frequently among these 'K' neighbors is then assigned as the class of the new data point.

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Free resources to learn K-Nearest Neighbors (KNN)

K-Nearest Neighbors (KNN): common questions

Can I learn K-Nearest Neighbors (KNN) in Hindi?

This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain K-Nearest Neighbors (KNN) in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.

Is this K-Nearest Neighbors (KNN) tutorial for beginners?

It is lesson 74 of 150 in the free Machine Learning roadmap, in stage 18, Classification. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.