Projects

This chapter focuses on hands-on projects that apply key machine learning and data analysis concepts. The two projects in this section will help solidify your understanding of algorithms and data transformation techniques.

Projects Covered in This Chapter

• K-Nearest Neighbors (KNN) Implementation from Scratch: In this project, you will build the KNN algorithm from scratch. You will learn how KNN works, how to calculate distances between points, and how to classify new data points based on their nearest neighbors.
• Principal Component Analysis (PCA) Implementation: This project guides you through the implementation of PCA, a dimensionality reduction technique. You will learn how PCA works, how to reduce the number of features in a dataset, and how to extract the most important components of the data.

Key Takeaways:

• Gain practical experience by implementing machine learning algorithms and data transformation techniques from scratch.
• Learn how to manipulate and preprocess data for more efficient analysis.
• Understand the underlying mechanics of popular algorithms, which will enhance your ability to tune and optimize models.

These projects will help you apply theoretical concepts in real-world scenarios, making you more confident in using machine learning techniques in your own projects.