Chapter 7: K-Nearest Neighbors (KNN) introduces one of the simplest yet effective algorithms for classification and regression tasks. Learn how KNN predicts outcomes based on the similarity between data points. This chapter covers the fundamentals of the KNN algorithm, how to select the right number of neighbors (k), and how to apply this technique to solve real-world problems. Whether you’re working with small datasets or large-scale applications, this guide provides the tools and knowledge to use KNN effectively for both classification and regression.
What You’ll Learn:
- The basics of K-Nearest Neighbors (KNN) and how it works.
- How to choose the optimal number of neighbors (k) for your model.
- How to apply KNN for both classification and regression problems.
- Techniques to handle distance metrics and feature scaling for better model performance.
Why This Chapter?
- A great resource for beginners and intermediate learners.
- Includes practical examples and step-by-step instructions for implementing KNN.
- Learn to apply KNN to solve real-world classification and regression problems effectively.
