In this chapter, we talk about a few more data mining and machine learning techniques. We will talk about a really simple technique called k-nearest neighbors (KNN). We'll then use KNN to predict a rating for a movie. After that, we'll go on to talk about dimensionality reduction and principal component analysis. We'll also look at an example of PCA where we will reduce 4D data to two dimensions while still preserving its variance.

We'll then walk through the concept of data warehousing and see the advantages of the newer ELT process over the ETL process. We'll learn the fun concept of reinforcement learning and see the technique used behind the intelligent Pac-Man agent of the Pac-Man game. Lastly, we'll see some fancy terminology used for reinforcement learning.

We'll cover the following topics:

• The concept of k-nearest neighbors
• Implementation of KNN to predict the rating of a movie
• Dimensionality reduction and principal component analysis
• Example of PCA with the Iris dataset
• Data warehousing and ETL versus ELT
• What is reinforcement learning
• The working behind the intelligent Pac-Man game
• Some fancy words used for reinforcement learning