Summary  
This chapter explains how to implement the k-Nearest Neighbors algorithm by calculating distances between data points, selecting the k closest neighbors, and using them for prediction.  

General domain of usage  
Machine learning classification tasks

This course provides a comprehensive introduction to supervised learning using Python. You will explore the fundamental concepts, algorithms, and evaluation techniques that underpin supervised machine learning, with practical examples using scikit-learn and visualization tools.

Explore the foundational concepts of supervised learning, including its definition, types of problems, and real-world applications.

Learn the essential steps for preparing data for supervised learning, including handling missing values, encoding categorical variables, and feature scaling.

Dive into the most widely used supervised learning algorithms, including their principles and implementation in Python.

Master the techniques for evaluating supervised learning models and ensuring their generalizability.

Learn to use visualization tools to interpret and communicate the results of supervised learning models.

k-Nearest Neighbors (k-NN)