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Practical Data Analysis Cookbook

You're reading from   Practical Data Analysis Cookbook Over 60 practical recipes on data exploration and analysis

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781783551668
Length 384 pages
Edition 1st Edition
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Author (1):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Preparing the Data 2. Exploring the Data FREE CHAPTER 3. Classification Techniques 4. Clustering Techniques 5. Reducing Dimensions 6. Regression Methods 7. Time Series Techniques 8. Graphs 9. Natural Language Processing 10. Discrete Choice Models 11. Simulations 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...
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