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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Toc

Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem 2. Data Acquisition FREE CHAPTER 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

GDELT and oil

The premise of this chapter is that we can manipulate GDELT data to determine, to a greater or lesser extent, the price of oil based on historic events. The accuracy of our predictor will depend on many variables including the detail of our events, the number used and our hypotheses surrounding the nature of the relationship between oil and these events.

The oil industry is very complex and is driven by many factors. It has been found however, that most major oil price fluctuations are largely explained by shifts in the demand of crude oil. The price also increases during times of greater demand for stock, and historically has been high in times of geopolitical tension in the Middle East. In particular, political events have a strong influence on the oil price and it is this aspect that we will concentrate on.

Crude oil is produced by many countries around the world; there are however, three main benchmarks that are used by producers for pricing:

  • Brent: Produced by various entities...
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