Practical Data Analysis Cookbook

Chapter 5. Reducing Dimensions

In this chapter, we will cover various techniques to reduce dimensions of your data. You will learn the following recipes:

• Creating three-dimensional scatter plots to present principal components
• Reducing the dimensions using the kernel version of PCA
• Using Principal Component Analysis to find things that matter
• Finding the principal components in your data using randomized PCA
• Extracting the useful dimensions using Linear Discriminant Analysis
• Using various dimension reduction techniques to classify calls using the k-Nearest Neighbors classification model

Introduction

The abundance of data available nowadays can be mind-boggling; the datasets grow not only in terms of the number of observations, but also get richer in terms ...