Develop k-Nearest Neighbors in Python From Scratch

Last Updated on February 24, 2020

In this tutorial you are going to learn about the k-Nearest Neighbors algorithm including how it works and how to implement it from scratch in Python (without libraries).

A simple but powerful approach for making predictions is to use the most similar historical examples to the new data. This is the principle behind the k-Nearest Neighbors algorithm.

After completing this tutorial you will know:

• How to code the k-Nearest Neighbors algorithm step-by-step.
• How to evaluate k-Nearest Neighbors on a real dataset.
• How to use k-Nearest Neighbors to make a prediction for new data.

Kick-start your project with my new book Machine Learning Algorithms From Scratch, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

• Updated Sep/2014: Original version of the tutorial.
• Updated Oct/2019: Complete rewritten from the ground up.

Tutorial Overview

This section will provide a brief background on the k-Nearest Neighbors algorithm
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