Phase 1: Classification Algorithms · ~90 minutes · Python

K-Nearest Neighbors and Distances

Store everything. Predict by looking at your neighbors. The simplest algorithm that actually works.

What you will learn

Implement KNN classification and regression from scratch with configurable K and distance-weighted voting
Compare L1, L2, cosine, and Minkowski distance metrics and select the appropriate one for a given data type
Explain the curse of dimensionality and demonstrate why KNN degrades in high-dimensional spaces
Build a KD-tree for efficient nearest neighbor search and analyze when it outperforms brute-force

Introduction

Type: Build
Language: Python
Prerequisites: Earlier lessons in this course
Time: ~90 minutes

Objective

Learning objectives

Implement KNN classification and regression from scratch with configurable K and distance-weighted voting
Compare L1, L2, cosine, and Minkowski distance metrics and select the appropriate one for a given data type
Explain the curse of dimensionality and demonstrate why KNN degrades in high-dimensional spaces
Build a KD-tree for efficient nearest neighbor search and analyze when it outperforms brute-force

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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