Creating a K-Nearest Neighbours (KNN) Algorithm from Scratch in Python
Introduction
In this blog post, we’ll walk through the process of creating a K-Nearest Neighbours (KNN) algorithm from scratch using Python. KNN is a simple, yet powerful supervised machine learning algorithm used for classification and regression tasks. We’ll cover the basic concepts of KNN and then dive into the implementation. By the end of this tutorial, you’ll have a solid understanding of how KNN works and how to build it from the ground up.
Understanding K-Nearest Neighbors
KNN is a non-parametric, lazy learning algorithm. It doesn’t make any assumptions about the underlying data distribution and it doesn’t learn a discriminative function from the training data. Instead, it memorizes the training dataset and makes predictions based on the similarity between the input and the stored data points.
The basic idea behind KNN is simple:
- Calculate the distance between the input point and all the points in the training dataset.
- Select the K closest points (neighbors) to the input point.
- Perform a majority vote (for classification) or take the average (for regression) of the K neighbors to determine the prediction.
