Analysts often seek to classify or categorize items, for example, to predict whether a given person is a potential buyer or not. Other examples include classifying—a product as defective or not, a tax return as fraudulent or not, a customer as likely to default on a payment or not, and a credit card transaction as genuine or fraudulent. This chapter covers recipes to use R to apply several classification techniques.
R Data Analysis Cookbook
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R Data Analysis Cookbook
By:
Overview of this book
<p>Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data.</p>
<p>This book empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. The book also teaches you to quickly adapt the example code for your own needs and save yourself the time needed to construct code from scratch.</p>
Table of Contents (18 chapters)
R Data Analysis Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Acquire and Prepare the Ingredients – Your Data
What's in There? – Exploratory Data Analysis
Where Does It Belong? – Classification
Give Me a Number – Regression
Can You Simplify That? – Data Reduction Techniques
Lessons from History – Time Series Analysis
It's All About Your Connections – Social Network Analysis
Put Your Best Foot Forward – Document and Present Your Analysis
Work Smarter, Not Harder – Efficient and Elegant R Code
Where in the World? – Geospatial Analysis
Playing Nice – Connecting to Other Systems
Index
Customer Reviews