Book Image

Practical Data Analysis Cookbook

By : Tomasz Drabas
Book Image

Practical Data Analysis Cookbook

By: Tomasz Drabas

Overview of this book

Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors. This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more. First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews. By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.
Table of Contents (19 chapters)
Practical Data Analysis Cookbook
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Reducing the dimensions using the kernel version of PCA


Principal Components Analysis (PCA) transforms a correlated set of variables into a set of principal components: variables that are linearly uncorrelated (orthogonal). PCA can produce as many principal components as there are variables but normally it would reduce the dimensionality of your data. The first principal component accounts for the highest amount of variability in the data, with the following principal components accounting for decreasingly less variance explained and the restriction of orthogonality (uncorrelated) to the other principal components.

Getting ready

To execute this recipe, you will need pandas, NumPy, and MLPY. For the plotting, you will need Matplotlib with MPL Toolkits. No other prerequisites are required.

How to do it…

In a fashion similar to the previous recipes, we wrap our model building efforts within a method so that we can time it using the timeit decorator (the reduce_pca.py file):

@hlp.timeit
def reduce_PCA...