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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Master machine learning techniques with R to deliver insights for complex projects

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783984527
Length 400 pages
Edition 1st Edition
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (15) Chapters Close

Preface 1. A Process for Success FREE CHAPTER 2. Linear Regression – The Blocking and Tackling of Machine Learning 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis and Recommendation Engines 11. Time Series and Causality 12. Text Mining A. R Fundamentals Index

Chapter 12. Text Mining

 

"I think it's much more interesting to live not knowing than to have answers which might be wrong."

 
 --Richard Feynman

The world is awash in textual data. If you Google, Bing, or Yahoo how much of the data is unstructured, that is, in a textual format, estimates would range from 80 to 90 percent. The real number doesn't matter. What does matter is that a large proportion of the data is in a text format. The implication is that anyone seeking to find insights in the data must develop the capability to process and analyze text.

When I first started out as a market researcher, I used to manually pore through page after page of moderator-led focus groups and interviews with the hope of capturing some qualitative insight—an Aha! moment if you will—and then haggle with fellow team members over whether they had the same insight or not. Then, you would always have that one individual in a project who would swoop in...

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