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Mastering Predictive Analytics with R

You're reading from   Mastering Predictive Analytics with R Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts

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Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781783982806
Length 414 pages
Edition 1st Edition
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Toc

Table of Contents (13) Chapters Close

Preface 1. Gearing Up for Predictive Modeling 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Neural Networks 5. Support Vector Machines 6. Tree-based Methods 7. Ensemble Methods 8. Probabilistic Graphical Models 9. Time Series Analysis 10. Topic Modeling 11. Recommendation Systems Index

Predicting intense earthquakes


Having reviewed several time series models, we are now ready for some practical examples. Our first data set is a time series of earthquakes having magnitude that exceeds 4.0 on the Richter scale in Greece over the period between the year 2000 and the year 2008. This data set was recorded by the Observatory of Athens and is hosted on the website of the University of Athens, Faculty of Geology, Department of Geophysics & Geothermics. The data is available online at http://www.geophysics.geol.uoa.gr/catalog/catgr_20002008.epi.

We will import these data directly by using the package RCurl. From this package, we will use the functions getURL(), which retrieves the contents of a particular address on the Internet, and textConnection(), which will interpret the result as raw text. Once we have the data, we provide meaningful names for the columns using information from the website:

> library("RCurl")
> seismic_raw <- read.table(textConnection(getURL(
...
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